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This file contains all code to estimate the European income-stratified footprints and then the European expenditures deciles. There are three code chunks in this 'full code' Rmarkdown file. The first shows the EXIOBASE code, the second the income-stratified-footprints using the EUROSTAT HBS, and the third the estimation of the European expenditure deciles. All three code chunks are currently set to 'eval = FALSE', and the necessary raw data files to run them are not included in the git due to their size. We have also run the first two code chunks on a high-performance cluster computer.
The code in this 'full code' Rmarkdown file writes derived data files to the folder: 'analysis' > 'data' > 'derived'. These files can be accessed there, and own analysis performed on them without running any of the code in this Rmarkdown. The derived data files are used to create the figures in the main paper and SI (the code for the figures can be found directly within the paper and si Rmarkdown files: 'analysis' > 'paper').
To run the code in this 'full code' Rmarkdown file, follow the instructions at the start of each section (before the code chunk) explaining which files must be downloaded from where, and in which folder they should be extracted. Then remove 'eval = FALSE' in the code chunk header before running it.
# first load required R packages
knitr::opts_chunk$set(
collapse = TRUE,
warning = FALSE,
message = FALSE,
echo = FALSE,
comment = "#>"
)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse,
janitor,
readr,
here,
wbstats,
ISOcodes,
viridis,
imputeTS,
hrbrthemes,
wesanderson,
glue,
ggridges,
patchwork)
EXIOBASE
The EXIOBASE files are publicly available online. For the results in the main paper we use EXIOBASE version3 industry-by-industry, which is available from: https://zenodo.org/record/3583071#.XjC7kSN4wpY [accessed on 12.03.2020]. In the SI, we also show some results from using EXIOBASE version3 product-by-product, which is available from: [accessed on 12.03.2020].
These files are large, global input-output tables, and we performed standard input-output calculations on them to calculate and save total intensity vectors using a high-performance cluster computer. In this document we show the code that was run on the cluster computer, but have not uploaded any EXIOBASE files to the git. Running the first code chunk would require downloading the industry-by-industry version for the years 2005, 2010 and 2015, and the product-by-product version for the years 2005 and 2010.
Each year is available as a .zip file ('IOT_year_ixi' or 'IOT_year_pxp') from the websites above. If the .zip files for the relevant study years and versions have been downloaded, they can be extracted into the 'EXIOBASE' folder in this git, which is found in the 'analysis' > 'preprocessing' folder. We have set 'EXIOBASE' empty for this purpose. Extracting each year into the EXIOBASE folder creates a folder for each year with all of the relevant files for that year and version. All code in this first code chunk leaves all file names as they are after extraction.
# set data directory for EXIOBASE files
data_dir_exiobase = here("analysis", "preprocessing", "EXIOBASE")
##### EXIOBASE industry-by-industry version
# set study years
years_exiobase_ixi = c(2005,2010,2015)
# 'for' loop which writes 'total intensity vectors' (and row-wise breakdowns) for all study satellite extensions and years to 'data_dir_exiobase' using downloaded EXIOBASE files
for (i in years_exiobase_ixi){
year_current = i
# read in A table as table
A = read.delim(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/A.txt"),header = F)
# write A table as .csv file
write.csv(A, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/A.csv"))
# read in A table as .csv file and extract the data only (no labels), and convert to numeric
A = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/A.csv",sep = ""),row.names=NULL,as.is=TRUE)[4:7990,4:7990]
A[is.na(A)]=0
A = mapply(A, FUN = as.numeric)
A = matrix(data = A, ncol = 7987, nrow = 7987)
# solve the Leontief inverse
L = solve(diag(dim(A)[1])-A)
L[is.na(L)]=0
# read in final demand table (Y) as table
FD = read.delim(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/Y.txt"),header = F)
# write final demand table (Y) as .csv file
write.csv(FD, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/Y.csv"))
# read in final demand table as .csv file and extract the production sector labels
Exiobase_T_labels_ixi = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/Y.csv"))[4:7990,1:3]
# write a .csv file with only the production sector labels
write.csv(Exiobase_T_labels_ixi, paste0(data_dir_exiobase, "/Exiobase_T_labels_ixi.csv"))
# read in final demand table as .csv file and extract the final demand category labels
Exiobase_FD_labels_ixi = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/Y.csv"))[1:3,4:346]
# write a .csv file with only the final demand category labels
write.csv(Exiobase_FD_labels_ixi, paste0(data_dir_exiobase, "/Exiobase_FD_labels_ixi.csv"))
# read in final demand table as .csv file and extract the data only (no labels), and convert to numeric
FD = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/Y.csv",sep=""),row.names=NULL,as.is=TRUE)[4:7990,4:346]
FD[is.na(FD)]=0
FD = mapply(FD, FUN = as.numeric)
FD = matrix(data=FD,ncol=343,nrow=7987)
# write a .csv file with final demand data only (no labels)
write.csv(FD, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/FD_",year_current,"_ixi.csv"))
# calculate total output
total_output = L %*% rowSums(FD)
# write total output as a .csv file
write.csv(total_output, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/total_output_",year_current,"_ixi.csv"))
# read in satellite extensions table (F) as table
satellite = read.delim(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/satellite/F.txt"),header = F)
# write satellite extensions table (F) as .csv file
write.csv(satellite, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/satellite/F.csv"))
# read in satellite extensions table (F) as .csv file and extract the data only (no labels), and convert to numeric
satellite = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/satellite/F.csv",sep=""),row.names=NULL,as.is=TRUE)[3:1115,3:7989]
satellite[is.na(satellite)]=0
satellite = mapply(satellite, FUN = as.numeric)
satellite = matrix(data=satellite,ncol=7987,nrow=1113)
# write a .csv file with satellite extensions data only (no labels)
write.csv(satellite, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/satellite/satellite_",year_current,"_ixi.csv"))
# read in satellite extensions on final demand table (F_hh) as table
satellite_FD = read.delim(paste0(data_dir_exiobase, "/IOT_", year_current, "_ixi/satellite/F_hh.txt"),header = F)
# write satellite extensions on final demand table (F_hh) as .csv file
write.csv(satellite_FD, paste0(data_dir_exiobase, "/IOT_", year_current, "_ixi/satellite/F_hh.csv"))
## extract the relevant satellite extensions from the satellite table, calculate the 'total intensity
## vectors' (and their row-wise breakdowns), and save to 'data_dir_exiobase'
# CO2 - combustion - air
CO2_combustion_air = satellite[24,]
DIV_co2_combustion_air = CO2_combustion_air/total_output
DIV_co2_combustion_air[is.na(DIV_co2_combustion_air)]=0
DIV_co2_combustion_air[DIV_co2_combustion_air == Inf]<-0
TIV_co2_combustion_air = as.vector(DIV_co2_combustion_air) %*% L
write.csv(TIV_co2_combustion_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_combustion_air_",year_current,"_ixi.csv"))
TIV_breakdown_co2_combustion_air = as.vector(DIV_co2_combustion_air) * L
TIV_breakdown_co2_combustion_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_co2_combustion_air)
TIV_country_breakdown_co2_combustion_air_w_labels = t(TIV_breakdown_co2_combustion_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_combustion_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_combustion_air_",year_current,"_ixi.csv"))
# CO2 - non-combustion - air
## cement
CO2_noncombustion_cement_air = satellite[93,]
DIV_co2_noncombustion_cement_air = CO2_noncombustion_cement_air/total_output
DIV_co2_noncombustion_cement_air[is.na(DIV_co2_noncombustion_cement_air)]=0
DIV_co2_noncombustion_cement_air[DIV_co2_noncombustion_cement_air == Inf]<-0
TIV_co2_noncombustion_cement_air = as.vector(DIV_co2_noncombustion_cement_air) %*% L
write.csv(TIV_co2_noncombustion_cement_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))
TIV_breakdown_co2_noncombustion_cement_air = as.vector(DIV_co2_noncombustion_cement_air) * L
TIV_breakdown_co2_noncombustion_cement_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_co2_noncombustion_cement_air)
TIV_country_breakdown_co2_noncombustion_cement_air_w_labels = t(TIV_breakdown_co2_noncombustion_cement_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_noncombustion_cement_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))
## lime
CO2_noncombustion_lime_air = satellite[94,]
DIV_co2_noncombustion_lime_air = CO2_noncombustion_lime_air/total_output
DIV_co2_noncombustion_lime_air[is.na(DIV_co2_noncombustion_lime_air)]=0
DIV_co2_noncombustion_lime_air[DIV_co2_noncombustion_lime_air == Inf]<-0
TIV_co2_noncombustion_lime_air = as.vector(DIV_co2_noncombustion_lime_air) %*% L
write.csv(TIV_co2_noncombustion_lime_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))
TIV_breakdown_co2_noncombustion_lime_air = as.vector(DIV_co2_noncombustion_lime_air) * L
TIV_breakdown_co2_noncombustion_lime_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_co2_noncombustion_lime_air)
TIV_country_breakdown_co2_noncombustion_lime_air_w_labels = t(TIV_breakdown_co2_noncombustion_lime_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_noncombustion_lime_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))
# CO2 - agriculture - peat decay - air
CO2_agriculture_peatdecay_air = satellite[428,]
DIV_co2_agriculture_peatdecay_air = CO2_agriculture_peatdecay_air/total_output
DIV_co2_agriculture_peatdecay_air[is.na(DIV_co2_agriculture_peatdecay_air)]=0
DIV_co2_agriculture_peatdecay_air[DIV_co2_agriculture_peatdecay_air == Inf]<-0
TIV_co2_agriculture_peatdecay_air = as.vector(DIV_co2_agriculture_peatdecay_air) %*% L
write.csv(TIV_co2_agriculture_peatdecay_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))
TIV_breakdown_co2_agriculture_peatdecay_air = as.vector(DIV_co2_agriculture_peatdecay_air) * L
TIV_breakdown_co2_agriculture_peatdecay_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_co2_agriculture_peatdecay_air)
TIV_country_breakdown_co2_agriculture_peatdecay_air_w_labels = t(TIV_breakdown_co2_agriculture_peatdecay_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_agriculture_peatdecay_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))
# CO2 - waste - air
## biogenic
CO2_waste_biogenic_air = satellite[438,]
DIV_co2_waste_biogenic_air = CO2_waste_biogenic_air/total_output
DIV_co2_waste_biogenic_air[is.na(DIV_co2_waste_biogenic_air)]=0
DIV_co2_waste_biogenic_air[DIV_co2_waste_biogenic_air == Inf]<-0
TIV_co2_biogenic_air = as.vector(DIV_co2_waste_biogenic_air) %*% L
write.csv(TIV_co2_biogenic_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_biogenic_air_",year_current,"_ixi.csv"))
TIV_breakdown_co2_biogenic_air = as.vector(DIV_co2_waste_biogenic_air) * L
TIV_breakdown_co2_biogenic_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_co2_biogenic_air)
TIV_country_breakdown_co2_biogenic_air_w_labels = t(TIV_breakdown_co2_biogenic_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_biogenic_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_biogenic_air_",year_current,"_ixi.csv"))
## fossil
CO2_waste_fossil_air = satellite[439,]
DIV_co2_waste_fossil_air = CO2_waste_fossil_air/total_output
DIV_co2_waste_fossil_air[is.na(DIV_co2_waste_fossil_air)]=0
DIV_co2_waste_fossil_air[DIV_co2_waste_fossil_air == Inf]<-0
TIV_co2_waste_fossil_air = as.vector(DIV_co2_waste_fossil_air) %*% L
write.csv(TIV_co2_waste_fossil_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_waste_fossil_air_",year_current,"_ixi.csv"))
TIV_breakdown_co2_waste_fossil_air = as.vector(DIV_co2_waste_fossil_air) * L
TIV_breakdown_co2_waste_fossil_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_co2_waste_fossil_air)
TIV_country_breakdown_co2_waste_fossil_air_w_labels = t(TIV_breakdown_co2_waste_fossil_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_waste_fossil_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_ixi.csv"))
# CH4 - combustion - air
CH4_combustion_air = satellite[25,]
CH4_combustion_air = CH4_combustion_air*28
DIV_ch4_combustion_air = CH4_combustion_air/total_output
DIV_ch4_combustion_air[is.na(DIV_ch4_combustion_air)]=0
DIV_ch4_combustion_air[DIV_ch4_combustion_air == Inf]<-0
TIV_ch4_combustion_air = as.vector(DIV_ch4_combustion_air) %*% L
write.csv(TIV_ch4_combustion_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_combustion_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_combustion_air = as.vector(DIV_ch4_combustion_air) * L
TIV_breakdown_ch4_combustion_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_combustion_air)
TIV_country_breakdown_ch4_combustion_air_w_labels = t(TIV_breakdown_ch4_combustion_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_combustion_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_combustion_air_",year_current,"_ixi.csv"))
# CH4 - non-combustion - air
## gas
CH4_noncombustion_gas_air = satellite[68,]
CH4_noncombustion_gas_air = CH4_noncombustion_gas_air*28
DIV_ch4_noncombustion_gas_air = CH4_noncombustion_gas_air/total_output
DIV_ch4_noncombustion_gas_air[is.na(DIV_ch4_noncombustion_gas_air)]=0
DIV_ch4_noncombustion_gas_air[DIV_ch4_noncombustion_gas_air == Inf]<-0
TIV_ch4_noncombustion_gas_air = as.vector(DIV_ch4_noncombustion_gas_air) %*% L
write.csv(TIV_ch4_noncombustion_gas_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_gas_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_gas_air = as.vector(DIV_ch4_noncombustion_gas_air) * L
TIV_breakdown_ch4_noncombustion_gas_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_gas_air)
TIV_country_breakdown_ch4_noncombustion_gas_air_w_labels = t(TIV_breakdown_ch4_noncombustion_gas_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_gas_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_",year_current,"_ixi.csv"))
## oil
CH4_noncombustion_oil_air = satellite[69,]
CH4_noncombustion_oil_air = CH4_noncombustion_oil_air*28
DIV_ch4_noncombustion_oil_air = CH4_noncombustion_oil_air/total_output
DIV_ch4_noncombustion_oil_air[is.na(DIV_ch4_noncombustion_oil_air)]=0
DIV_ch4_noncombustion_oil_air[DIV_ch4_noncombustion_oil_air == Inf]<-0
TIV_ch4_noncombustion_oil_air = as.vector(DIV_ch4_noncombustion_oil_air) %*% L
write.csv(TIV_ch4_noncombustion_oil_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_oil_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_oil_air = as.vector(DIV_ch4_noncombustion_oil_air) * L
TIV_breakdown_ch4_noncombustion_oil_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_oil_air)
TIV_country_breakdown_ch4_noncombustion_oil_air_w_labels = t(TIV_breakdown_ch4_noncombustion_oil_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_oil_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_",year_current,"_ixi.csv"))
## anthracite
CH4_noncombustion_anthracite_air = satellite[70,]
CH4_noncombustion_anthracite_air = CH4_noncombustion_anthracite_air*28
DIV_ch4_noncombustion_anthracite_air = CH4_noncombustion_anthracite_air/total_output
DIV_ch4_noncombustion_anthracite_air[is.na(DIV_ch4_noncombustion_anthracite_air)]=0
DIV_ch4_noncombustion_anthracite_air[DIV_ch4_noncombustion_anthracite_air == Inf]<-0
TIV_ch4_noncombustion_anthracite_air = as.vector(DIV_ch4_noncombustion_anthracite_air) %*% L
write.csv(TIV_ch4_noncombustion_anthracite_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_anthracite_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_anthracite_air = as.vector(DIV_ch4_noncombustion_anthracite_air) * L
TIV_breakdown_ch4_noncombustion_anthracite_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_anthracite_air)
TIV_country_breakdown_ch4_noncombustion_anthracite_air_w_labels = t(TIV_breakdown_ch4_noncombustion_anthracite_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_anthracite_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_",year_current,"_ixi.csv"))
## bituminous coal
CH4_noncombustion_bituminouscoal_air = satellite[71,]
CH4_noncombustion_bituminouscoal_air = CH4_noncombustion_bituminouscoal_air*28
DIV_ch4_noncombustion_bituminouscoal_air = CH4_noncombustion_bituminouscoal_air/total_output
DIV_ch4_noncombustion_bituminouscoal_air[is.na(DIV_ch4_noncombustion_bituminouscoal_air)]=0
DIV_ch4_noncombustion_bituminouscoal_air[DIV_ch4_noncombustion_bituminouscoal_air == Inf]<-0
TIV_ch4_noncombustion_bituminouscoal_air = as.vector(DIV_ch4_noncombustion_bituminouscoal_air) %*% L
write.csv(TIV_ch4_noncombustion_bituminouscoal_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_bituminouscoal_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_bituminouscoal_air = as.vector(DIV_ch4_noncombustion_bituminouscoal_air) * L
TIV_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_bituminouscoal_air)
TIV_country_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels = t(TIV_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_",year_current,"_ixi.csv"))
## coking coal
CH4_noncombustion_cokingcoal_air = satellite[72,]
CH4_noncombustion_cokingcoal_air = CH4_noncombustion_cokingcoal_air*28
DIV_ch4_noncombustion_cokingcoal_air = CH4_noncombustion_cokingcoal_air/total_output
DIV_ch4_noncombustion_cokingcoal_air[is.na(DIV_ch4_noncombustion_cokingcoal_air)]=0
DIV_ch4_noncombustion_cokingcoal_air[DIV_ch4_noncombustion_cokingcoal_air == Inf]<-0
TIV_ch4_noncombustion_cokingcoal_air = as.vector(DIV_ch4_noncombustion_cokingcoal_air) %*% L
write.csv(TIV_ch4_noncombustion_cokingcoal_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_cokingcoal_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_cokingcoal_air = as.vector(DIV_ch4_noncombustion_cokingcoal_air) * L
TIV_breakdown_ch4_noncombustion_cokingcoal_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_cokingcoal_air)
TIV_country_breakdown_ch4_noncombustion_cokingcoal_air_w_labels = t(TIV_breakdown_ch4_noncombustion_cokingcoal_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_cokingcoal_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_",year_current,"_ixi.csv"))
## lignite
CH4_noncombustion_lignite_air = satellite[73,]
CH4_noncombustion_lignite_air = CH4_noncombustion_lignite_air*28
DIV_ch4_noncombustion_lignite_air = CH4_noncombustion_lignite_air/total_output
DIV_ch4_noncombustion_lignite_air[is.na(DIV_ch4_noncombustion_lignite_air)]=0
DIV_ch4_noncombustion_lignite_air[DIV_ch4_noncombustion_lignite_air == Inf]<-0
TIV_ch4_noncombustion_lignite_air = as.vector(DIV_ch4_noncombustion_lignite_air) %*% L
write.csv(TIV_ch4_noncombustion_lignite_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_lignite_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_lignite_air = as.vector(DIV_ch4_noncombustion_lignite_air) * L
TIV_breakdown_ch4_noncombustion_lignite_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_lignite_air)
TIV_country_breakdown_ch4_noncombustion_lignite_air_w_labels = t(TIV_breakdown_ch4_noncombustion_lignite_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_lignite_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_",year_current,"_ixi.csv"))
## subbituminous coal
CH4_noncombustion_subbituminouscoal_air = satellite[74,]
CH4_noncombustion_subbituminouscoal_air = CH4_noncombustion_subbituminouscoal_air*28
DIV_ch4_noncombustion_subbituminouscoal_air = CH4_noncombustion_subbituminouscoal_air/total_output
DIV_ch4_noncombustion_subbituminouscoal_air[is.na(DIV_ch4_noncombustion_subbituminouscoal_air)]=0
DIV_ch4_noncombustion_subbituminouscoal_air[DIV_ch4_noncombustion_subbituminouscoal_air == Inf]<-0
TIV_ch4_noncombustion_subbituminouscoal_air = as.vector(DIV_ch4_noncombustion_subbituminouscoal_air) %*% L
write.csv(TIV_ch4_noncombustion_subbituminouscoal_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_subbituminouscoal_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_subbituminouscoal_air = as.vector(DIV_ch4_noncombustion_subbituminouscoal_air) * L
TIV_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_subbituminouscoal_air)
TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels = t(TIV_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_",year_current,"_ixi.csv"))
## oil refinery
CH4_noncombustion_oilrefinery_air = satellite[75,]
CH4_noncombustion_oilrefinery_air = CH4_noncombustion_oilrefinery_air*28
DIV_ch4_noncombustion_oilrefinery_air = CH4_noncombustion_oilrefinery_air/total_output
DIV_ch4_noncombustion_oilrefinery_air[is.na(DIV_ch4_noncombustion_oilrefinery_air)]=0
DIV_ch4_noncombustion_oilrefinery_air[DIV_ch4_noncombustion_oilrefinery_air == Inf]<-0
TIV_ch4_noncombustion_oilrefinery_air = as.vector(DIV_ch4_noncombustion_oilrefinery_air) %*% L
write.csv(TIV_ch4_noncombustion_oilrefinery_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_oilrefinery_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_noncombustion_oilrefinery_air = as.vector(DIV_ch4_noncombustion_oilrefinery_air) * L
TIV_breakdown_ch4_noncombustion_oilrefinery_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_noncombustion_oilrefinery_air)
TIV_country_breakdown_ch4_noncombustion_oilrefinery_air_w_labels = t(TIV_breakdown_ch4_noncombustion_oilrefinery_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_oilrefinery_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_",year_current,"_ixi.csv"))
# CH4 - agriculture - air
CH4_agriculture_air = satellite[427,]
CH4_agriculture_air = CH4_agriculture_air*28
DIV_ch4_agriculture_air = CH4_agriculture_air/total_output
DIV_ch4_agriculture_air[is.na(DIV_ch4_agriculture_air)]=0
DIV_ch4_agriculture_air[DIV_ch4_agriculture_air == Inf]<-0
TIV_ch4_agriculture_air = as.vector(DIV_ch4_agriculture_air) %*% L
write.csv(TIV_ch4_agriculture_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_agriculture_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_agriculture_air = as.vector(DIV_ch4_agriculture_air) * L
TIV_breakdown_ch4_agriculture_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_agriculture_air)
TIV_country_breakdown_ch4_agriculture_air_w_labels = t(TIV_breakdown_ch4_agriculture_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_agriculture_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_agriculture_air_",year_current,"_ixi.csv"))
# CH4 - waste - air
CH4_waste_air = satellite[436,]
CH4_waste_air = CH4_waste_air*28
DIV_ch4_waste_air = CH4_waste_air/total_output
DIV_ch4_waste_air[is.na(DIV_ch4_waste_air)]=0
DIV_ch4_waste_air[DIV_ch4_waste_air == Inf]<-0
TIV_ch4_waste_air = as.vector(DIV_ch4_waste_air) %*% L
write.csv(TIV_ch4_waste_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_waste_air_",year_current,"_ixi.csv"))
TIV_breakdown_ch4_waste_air = as.vector(DIV_ch4_waste_air) * L
TIV_breakdown_ch4_waste_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_ch4_waste_air)
TIV_country_breakdown_ch4_waste_air_w_labels = t(TIV_breakdown_ch4_waste_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_waste_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_waste_air_",year_current,"_ixi.csv"))
# N2O - combustion - air
N2O_combustion_air = satellite[26,]
N2O_combustion_air = N2O_combustion_air*265
DIV_n2o_combustion_air = N2O_combustion_air/total_output
DIV_n2o_combustion_air[is.na(DIV_n2o_combustion_air)]=0
DIV_n2o_combustion_air[DIV_n2o_combustion_air == Inf]<-0
TIV_n2o_combustion_air = as.vector(DIV_n2o_combustion_air) %*% L
write.csv(TIV_n2o_combustion_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))
TIV_breakdown_n2o_combustion_air = as.vector(DIV_n2o_combustion_air) * L
TIV_breakdown_n2o_combustion_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_n2o_combustion_air)
TIV_country_breakdown_n2o_combustion_air_w_labels = t(TIV_breakdown_n2o_combustion_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_n2o_combustion_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))
# N2O - agriculture - air
N2O_agriculture_air = satellite[430,]
N2O_agriculture_air = N2O_agriculture_air*265
DIV_n2o_agriculture_air = N2O_agriculture_air/total_output
DIV_n2o_agriculture_air[is.na(DIV_n2o_agriculture_air)]=0
DIV_n2o_agriculture_air[DIV_n2o_agriculture_air == Inf]<-0
TIV_n2o_agriculture_air = as.vector(DIV_n2o_agriculture_air) %*% L
write.csv(TIV_n2o_agriculture_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))
TIV_breakdown_n2o_agriculture_air = as.vector(DIV_n2o_agriculture_air) * L
TIV_breakdown_n2o_agriculture_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_n2o_agriculture_air)
TIV_country_breakdown_n2o_agriculture_air_w_labels = t(TIV_breakdown_n2o_agriculture_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_n2o_agriculture_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))
# SF6 - air
SF6_air = satellite[424,]
SF6_air = SF6_air*23500
DIV_sf6_air = SF6_air/total_output
DIV_sf6_air[is.na(DIV_sf6_air)]=0
DIV_sf6_air[DIV_sf6_air == Inf]<-0
TIV_sf6_air = as.vector(DIV_sf6_air) %*% L
write.csv(TIV_sf6_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_sf6_CO2eq_air_",year_current,"_ixi.csv"))
TIV_breakdown_sf6_air = as.vector(DIV_sf6_air) * L
TIV_breakdown_sf6_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_sf6_air)
TIV_country_breakdown_sf6_air_w_labels = t(TIV_breakdown_sf6_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_sf6_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_ixi.csv"))
# HFC - air
HFC_air = satellite[425,]
DIV_hfc_air = HFC_air/total_output
DIV_hfc_air[is.na(DIV_hfc_air)]=0
DIV_hfc_air[DIV_hfc_air == Inf]<-0
TIV_hfc_air = as.vector(DIV_hfc_air) %*% L
write.csv(TIV_hfc_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_hfc_CO2eq_air_",year_current,"_ixi.csv"))
TIV_breakdown_hfc_air = as.vector(DIV_hfc_air) * L
TIV_breakdown_hfc_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_hfc_air)
TIV_country_breakdown_hfc_air_w_labels = t(TIV_breakdown_hfc_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_hfc_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_ixi.csv"))
# PFC - air
PFC_air = satellite[426,]
DIV_pfc_air = PFC_air/total_output
DIV_pfc_air[is.na(DIV_pfc_air)]=0
DIV_pfc_air[DIV_pfc_air == Inf]<-0
TIV_pfc_air = as.vector(DIV_pfc_air) %*% L
write.csv(TIV_pfc_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_pfc_CO2eq_air_",year_current,"_ixi.csv"))
TIV_breakdown_pfc_air = as.vector(DIV_pfc_air) * L
TIV_breakdown_pfc_air_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_pfc_air)
TIV_country_breakdown_pfc_air_w_labels = t(TIV_breakdown_pfc_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_pfc_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_ixi.csv"))
# Energy carrier - use
energy_carrier_use = satellite[470,]
write.csv(energy_carrier_use, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/satellite/energy_carrier_use_",year_current,"_ixi.csv"))
DIV_e_u = energy_carrier_use/total_output
DIV_e_u[is.na(DIV_e_u)]=0
DIV_e_u[DIV_e_u == Inf]<-0
TIV_e_u = as.vector(DIV_e_u) %*% L
write.csv(TIV_e_u, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_energy_carrier_use_",year_current,"_ixi.csv"))
TIV_breakdown_e_u = as.vector(DIV_e_u) * L
TIV_breakdown_e_u_w_labels = cbind(Exiobase_T_labels_ixi, TIV_breakdown_e_u)
TIV_country_breakdown_e_u_w_labels = t(TIV_breakdown_e_u_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_e_u_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_energy_carrier_use_",year_current,"_ixi.csv"))
}
##### EXIOBASE product-by-product version
# set study years
years_exiobase_pxp = c(2005,2010)
# 'for' loop which writes 'total intensity vectors' (and row-wise breakdowns) for all study satellite extensions and years to 'data_dir_exiobase' using downloaded EXIOBASE files
for (i in years_exiobase_pxp){
year_current = i
# read in A table as table
A = read.delim(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/A.txt"),header = F)
# write A table as .csv file
write.csv(A, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/A.csv"))
# read in A table as .csv file and extract the data only (no labels), and convert to numeric
A = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/A.csv",sep = ""),row.names=NULL,as.is=TRUE)[4:9803,4:9803]
A[is.na(A)]=0
A = mapply(A, FUN = as.numeric)
A = matrix(data = A, ncol = 9800, nrow = 9800)
# solve the Leontief inverse
L = solve(diag(dim(A)[1])-A)
L[is.na(L)]=0
# read in final demand table (Y) as table
FD = read.delim(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/Y.txt"),header = F)
# write final demand table (Y) as .csv file
write.csv(FD, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/Y.csv"))
# read in final demand table as .csv file and extract the production sector labels
Exiobase_T_labels_pxp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/Y.csv"))[4:9803,1:3]
# write a .csv file with only the production sector labels
write.csv(Exiobase_T_labels_pxp, paste0(data_dir_exiobase, "/Exiobase_T_labels_pxp.csv"))
# read in final demand table as .csv file and extract the final demand category labels
Exiobase_FD_labels_pxp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/Y.csv"))[1:3,4:346]
# write a .csv file with only the final demand category labels
write.csv(Exiobase_FD_labels_pxp, paste0(data_dir_exiobase, "/Exiobase_FD_labels_pxp.csv"))
# read in final demand table as .csv file and extract the data only (no labels), and convert to numeric
FD = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/Y.csv",sep=""),row.names=NULL,as.is=TRUE)[4:9803,4:346]
FD[is.na(FD)]=0
FD = mapply(FD, FUN = as.numeric)
FD = matrix(data=FD,ncol=343,nrow=9800)
# write a .csv file with final demand data only (no labels)
write.csv(FD, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/FD_",year_current,"_pxp.csv"))
# calculate total output
total_output = L %*% rowSums(FD)
# write total output as a .csv file
write.csv(total_output, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/total_output_",year_current,"_pxp.csv"))
# read in satellite extensions table (F) as table
satellite = read.delim(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/satellite/F.txt"),header = F)
# write satellite extensions table (F) as .csv file
write.csv(satellite, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/satellite/F.csv"))
# read in satellite extensions table (F) as .csv file and extract the data only (no labels), and convert to numeric
satellite = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/satellite/F.csv",sep=""),row.names=NULL,as.is=TRUE)[3:1106,3:9802]
satellite[is.na(satellite)]=0
satellite = mapply(satellite, FUN = as.numeric)
satellite = matrix(data=satellite,ncol=9800,nrow=1104)
# write a .csv file with satellite extensions data only (no labels)
write.csv(satellite, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/satellite/satellite_",year_current,"_pxp.csv"))
# read in satellite extensions on final demand table (F_hh) as table
satellite_FD = read.delim(paste0(data_dir_exiobase, "/IOT_", year_current, "_pxp/satellite/F_hh.txt"),header = F)
# write satellite extensions on final demand table (F_hh) as .csv file
write.csv(satellite_FD, paste0(data_dir_exiobase, "/IOT_", year_current, "_pxp/satellite/F_hh.csv"))
## extract the relevant satellite extensions from the satellite table, calculate the 'total intensity
## vectors' (and their row-wise breakdowns), and write them as .csv files to 'data_dir_exiobase'
# CO2 - combustion - air
CO2_combustion_air = satellite[24,]
DIV_co2_combustion_air = CO2_combustion_air/total_output
DIV_co2_combustion_air[is.na(DIV_co2_combustion_air)]=0
DIV_co2_combustion_air[DIV_co2_combustion_air == Inf]<-0
TIV_co2_combustion_air = as.vector(DIV_co2_combustion_air) %*% L
write.csv(TIV_co2_combustion_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_combustion_air_",year_current,"_pxp.csv"))
TIV_breakdown_co2_combustion_air = as.vector(DIV_co2_combustion_air) * L
TIV_breakdown_co2_combustion_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_co2_combustion_air)
TIV_country_breakdown_co2_combustion_air_w_labels = t(TIV_breakdown_co2_combustion_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_combustion_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_combustion_air_",year_current,"_pxp.csv"))
# CO2 - non-combustion - air
## cement
CO2_noncombustion_cement_air = satellite[93,]
DIV_co2_noncombustion_cement_air = CO2_noncombustion_cement_air/total_output
DIV_co2_noncombustion_cement_air[is.na(DIV_co2_noncombustion_cement_air)]=0
DIV_co2_noncombustion_cement_air[DIV_co2_noncombustion_cement_air == Inf]<-0
TIV_co2_noncombustion_cement_air = as.vector(DIV_co2_noncombustion_cement_air) %*% L
write.csv(TIV_co2_noncombustion_cement_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_noncombustion_cement_air_",year_current,"_pxp.csv"))
TIV_breakdown_co2_noncombustion_cement_air = as.vector(DIV_co2_noncombustion_cement_air) * L
TIV_breakdown_co2_noncombustion_cement_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_co2_noncombustion_cement_air)
TIV_country_breakdown_co2_noncombustion_cement_air_w_labels = t(TIV_breakdown_co2_noncombustion_cement_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_noncombustion_cement_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_pxp.csv"))
## lime
CO2_noncombustion_lime_air = satellite[94,]
DIV_co2_noncombustion_lime_air = CO2_noncombustion_lime_air/total_output
DIV_co2_noncombustion_lime_air[is.na(DIV_co2_noncombustion_lime_air)]=0
DIV_co2_noncombustion_lime_air[DIV_co2_noncombustion_lime_air == Inf]<-0
TIV_co2_noncombustion_lime_air = as.vector(DIV_co2_noncombustion_lime_air) %*% L
write.csv(TIV_co2_noncombustion_lime_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_noncombustion_lime_air_",year_current,"_pxp.csv"))
TIV_breakdown_co2_noncombustion_lime_air = as.vector(DIV_co2_noncombustion_lime_air) * L
TIV_breakdown_co2_noncombustion_lime_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_co2_noncombustion_lime_air)
TIV_country_breakdown_co2_noncombustion_lime_air_w_labels = t(TIV_breakdown_co2_noncombustion_lime_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_noncombustion_lime_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_pxp.csv"))
# CO2 - agriculture - peat decay - air
CO2_agriculture_peatdecay_air = satellite[428,]
DIV_co2_agriculture_peatdecay_air = CO2_agriculture_peatdecay_air/total_output
DIV_co2_agriculture_peatdecay_air[is.na(DIV_co2_agriculture_peatdecay_air)]=0
DIV_co2_agriculture_peatdecay_air[DIV_co2_agriculture_peatdecay_air == Inf]<-0
TIV_co2_agriculture_peatdecay_air = as.vector(DIV_co2_agriculture_peatdecay_air) %*% L
write.csv(TIV_co2_agriculture_peatdecay_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_agriculture_peatdecay_air_",year_current,"_pxp.csv"))
TIV_breakdown_co2_agriculture_peatdecay_air = as.vector(DIV_co2_agriculture_peatdecay_air) * L
TIV_breakdown_co2_agriculture_peatdecay_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_co2_agriculture_peatdecay_air)
TIV_country_breakdown_co2_agriculture_peatdecay_air_w_labels = t(TIV_breakdown_co2_agriculture_peatdecay_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_agriculture_peatdecay_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_pxp.csv"))
# CO2 - waste - air
## biogenic
CO2_waste_biogenic_air = satellite[438,]
DIV_co2_waste_biogenic_air = CO2_waste_biogenic_air/total_output
DIV_co2_waste_biogenic_air[is.na(DIV_co2_waste_biogenic_air)]=0
DIV_co2_waste_biogenic_air[DIV_co2_waste_biogenic_air == Inf]<-0
TIV_co2_biogenic_air = as.vector(DIV_co2_waste_biogenic_air) %*% L
write.csv(TIV_co2_biogenic_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_biogenic_air_",year_current,"_pxp.csv"))
TIV_breakdown_co2_biogenic_air = as.vector(DIV_co2_waste_biogenic_air) * L
TIV_breakdown_co2_biogenic_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_co2_biogenic_air)
TIV_country_breakdown_co2_biogenic_air_w_labels = t(TIV_breakdown_co2_biogenic_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_biogenic_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_biogenic_air_",year_current,"_pxp.csv"))
## fossil
CO2_waste_fossil_air = satellite[439,]
DIV_co2_waste_fossil_air = CO2_waste_fossil_air/total_output
DIV_co2_waste_fossil_air[is.na(DIV_co2_waste_fossil_air)]=0
DIV_co2_waste_fossil_air[DIV_co2_waste_fossil_air == Inf]<-0
TIV_co2_waste_fossil_air = as.vector(DIV_co2_waste_fossil_air) %*% L
write.csv(TIV_co2_waste_fossil_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_waste_fossil_air_",year_current,"_pxp.csv"))
TIV_breakdown_co2_waste_fossil_air = as.vector(DIV_co2_waste_fossil_air) * L
TIV_breakdown_co2_waste_fossil_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_co2_waste_fossil_air)
TIV_country_breakdown_co2_waste_fossil_air_w_labels = t(TIV_breakdown_co2_waste_fossil_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_co2_waste_fossil_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_pxp.csv"))
# CH4 - combustion - air
CH4_combustion_air = satellite[25,]
CH4_combustion_air = CH4_combustion_air*28
DIV_ch4_combustion_air = CH4_combustion_air/total_output
DIV_ch4_combustion_air[is.na(DIV_ch4_combustion_air)]=0
DIV_ch4_combustion_air[DIV_ch4_combustion_air == Inf]<-0
TIV_ch4_combustion_air = as.vector(DIV_ch4_combustion_air) %*% L
write.csv(TIV_ch4_combustion_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_combustion_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_combustion_air = as.vector(DIV_ch4_combustion_air) * L
TIV_breakdown_ch4_combustion_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_combustion_air)
TIV_country_breakdown_ch4_combustion_air_w_labels = t(TIV_breakdown_ch4_combustion_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_combustion_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_combustion_air_",year_current,"_pxp.csv"))
# CH4 - non-combustion - air
## gas
CH4_noncombustion_gas_air = satellite[68,]
CH4_noncombustion_gas_air = CH4_noncombustion_gas_air*28
DIV_ch4_noncombustion_gas_air = CH4_noncombustion_gas_air/total_output
DIV_ch4_noncombustion_gas_air[is.na(DIV_ch4_noncombustion_gas_air)]=0
DIV_ch4_noncombustion_gas_air[DIV_ch4_noncombustion_gas_air == Inf]<-0
TIV_ch4_noncombustion_gas_air = as.vector(DIV_ch4_noncombustion_gas_air) %*% L
write.csv(TIV_ch4_noncombustion_gas_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_gas_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_gas_air = as.vector(DIV_ch4_noncombustion_gas_air) * L
TIV_breakdown_ch4_noncombustion_gas_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_gas_air)
TIV_country_breakdown_ch4_noncombustion_gas_air_w_labels = t(TIV_breakdown_ch4_noncombustion_gas_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_gas_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_",year_current,"_pxp.csv"))
## oil
CH4_noncombustion_oil_air = satellite[69,]
CH4_noncombustion_oil_air = CH4_noncombustion_oil_air*28
DIV_ch4_noncombustion_oil_air = CH4_noncombustion_oil_air/total_output
DIV_ch4_noncombustion_oil_air[is.na(DIV_ch4_noncombustion_oil_air)]=0
DIV_ch4_noncombustion_oil_air[DIV_ch4_noncombustion_oil_air == Inf]<-0
TIV_ch4_noncombustion_oil_air = as.vector(DIV_ch4_noncombustion_oil_air) %*% L
write.csv(TIV_ch4_noncombustion_oil_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_oil_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_oil_air = as.vector(DIV_ch4_noncombustion_oil_air) * L
TIV_breakdown_ch4_noncombustion_oil_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_oil_air)
TIV_country_breakdown_ch4_noncombustion_oil_air_w_labels = t(TIV_breakdown_ch4_noncombustion_oil_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_oil_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_",year_current,"_pxp.csv"))
## anthracite
CH4_noncombustion_anthracite_air = satellite[70,]
CH4_noncombustion_anthracite_air = CH4_noncombustion_anthracite_air*28
DIV_ch4_noncombustion_anthracite_air = CH4_noncombustion_anthracite_air/total_output
DIV_ch4_noncombustion_anthracite_air[is.na(DIV_ch4_noncombustion_anthracite_air)]=0
DIV_ch4_noncombustion_anthracite_air[DIV_ch4_noncombustion_anthracite_air == Inf]<-0
TIV_ch4_noncombustion_anthracite_air = as.vector(DIV_ch4_noncombustion_anthracite_air) %*% L
write.csv(TIV_ch4_noncombustion_anthracite_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_anthracite_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_anthracite_air = as.vector(DIV_ch4_noncombustion_anthracite_air) * L
TIV_breakdown_ch4_noncombustion_anthracite_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_anthracite_air)
TIV_country_breakdown_ch4_noncombustion_anthracite_air_w_labels = t(TIV_breakdown_ch4_noncombustion_anthracite_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_anthracite_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_",year_current,"_pxp.csv"))
## bituminous coal
CH4_noncombustion_bituminouscoal_air = satellite[71,]
CH4_noncombustion_bituminouscoal_air = CH4_noncombustion_bituminouscoal_air*28
DIV_ch4_noncombustion_bituminouscoal_air = CH4_noncombustion_bituminouscoal_air/total_output
DIV_ch4_noncombustion_bituminouscoal_air[is.na(DIV_ch4_noncombustion_bituminouscoal_air)]=0
DIV_ch4_noncombustion_bituminouscoal_air[DIV_ch4_noncombustion_bituminouscoal_air == Inf]<-0
TIV_ch4_noncombustion_bituminouscoal_air = as.vector(DIV_ch4_noncombustion_bituminouscoal_air) %*% L
write.csv(TIV_ch4_noncombustion_bituminouscoal_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_bituminouscoal_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_bituminouscoal_air = as.vector(DIV_ch4_noncombustion_bituminouscoal_air) * L
TIV_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_bituminouscoal_air)
TIV_country_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels = t(TIV_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_bituminouscoal_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_",year_current,"_pxp.csv"))
## coking coal
CH4_noncombustion_cokingcoal_air = satellite[72,]
CH4_noncombustion_cokingcoal_air = CH4_noncombustion_cokingcoal_air*28
DIV_ch4_noncombustion_cokingcoal_air = CH4_noncombustion_cokingcoal_air/total_output
DIV_ch4_noncombustion_cokingcoal_air[is.na(DIV_ch4_noncombustion_cokingcoal_air)]=0
DIV_ch4_noncombustion_cokingcoal_air[DIV_ch4_noncombustion_cokingcoal_air == Inf]<-0
TIV_ch4_noncombustion_cokingcoal_air = as.vector(DIV_ch4_noncombustion_cokingcoal_air) %*% L
write.csv(TIV_ch4_noncombustion_cokingcoal_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_cokingcoal_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_cokingcoal_air = as.vector(DIV_ch4_noncombustion_cokingcoal_air) * L
TIV_breakdown_ch4_noncombustion_cokingcoal_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_cokingcoal_air)
TIV_country_breakdown_ch4_noncombustion_cokingcoal_air_w_labels = t(TIV_breakdown_ch4_noncombustion_cokingcoal_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_cokingcoal_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_",year_current,"_pxp.csv"))
## lignite
CH4_noncombustion_lignite_air = satellite[73,]
CH4_noncombustion_lignite_air = CH4_noncombustion_lignite_air*28
DIV_ch4_noncombustion_lignite_air = CH4_noncombustion_lignite_air/total_output
DIV_ch4_noncombustion_lignite_air[is.na(DIV_ch4_noncombustion_lignite_air)]=0
DIV_ch4_noncombustion_lignite_air[DIV_ch4_noncombustion_lignite_air == Inf]<-0
TIV_ch4_noncombustion_lignite_air = as.vector(DIV_ch4_noncombustion_lignite_air) %*% L
write.csv(TIV_ch4_noncombustion_lignite_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_lignite_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_lignite_air = as.vector(DIV_ch4_noncombustion_lignite_air) * L
TIV_breakdown_ch4_noncombustion_lignite_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_lignite_air)
TIV_country_breakdown_ch4_noncombustion_lignite_air_w_labels = t(TIV_breakdown_ch4_noncombustion_lignite_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_lignite_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_",year_current,"_pxp.csv"))
## subbituminous coal
CH4_noncombustion_subbituminouscoal_air = satellite[74,]
CH4_noncombustion_subbituminouscoal_air = CH4_noncombustion_subbituminouscoal_air*28
DIV_ch4_noncombustion_subbituminouscoal_air = CH4_noncombustion_subbituminouscoal_air/total_output
DIV_ch4_noncombustion_subbituminouscoal_air[is.na(DIV_ch4_noncombustion_subbituminouscoal_air)]=0
DIV_ch4_noncombustion_subbituminouscoal_air[DIV_ch4_noncombustion_subbituminouscoal_air == Inf]<-0
TIV_ch4_noncombustion_subbituminouscoal_air = as.vector(DIV_ch4_noncombustion_subbituminouscoal_air) %*% L
write.csv(TIV_ch4_noncombustion_subbituminouscoal_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_subbituminouscoal_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_subbituminouscoal_air = as.vector(DIV_ch4_noncombustion_subbituminouscoal_air) * L
TIV_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_subbituminouscoal_air)
TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels = t(TIV_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_",year_current,"_pxp.csv"))
## oil refinery
CH4_noncombustion_oilrefinery_air = satellite[75,]
CH4_noncombustion_oilrefinery_air = CH4_noncombustion_oilrefinery_air*28
DIV_ch4_noncombustion_oilrefinery_air = CH4_noncombustion_oilrefinery_air/total_output
DIV_ch4_noncombustion_oilrefinery_air[is.na(DIV_ch4_noncombustion_oilrefinery_air)]=0
DIV_ch4_noncombustion_oilrefinery_air[DIV_ch4_noncombustion_oilrefinery_air == Inf]<-0
TIV_ch4_noncombustion_oilrefinery_air = as.vector(DIV_ch4_noncombustion_oilrefinery_air) %*% L
write.csv(TIV_ch4_noncombustion_oilrefinery_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_oilrefinery_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_noncombustion_oilrefinery_air = as.vector(DIV_ch4_noncombustion_oilrefinery_air) * L
TIV_breakdown_ch4_noncombustion_oilrefinery_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_noncombustion_oilrefinery_air)
TIV_country_breakdown_ch4_noncombustion_oilrefinery_air_w_labels = t(TIV_breakdown_ch4_noncombustion_oilrefinery_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_noncombustion_oilrefinery_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_",year_current,"_pxp.csv"))
# CH4 - agriculture - air
CH4_agriculture_air = satellite[427,]
CH4_agriculture_air = CH4_agriculture_air*28
DIV_ch4_agriculture_air = CH4_agriculture_air/total_output
DIV_ch4_agriculture_air[is.na(DIV_ch4_agriculture_air)]=0
DIV_ch4_agriculture_air[DIV_ch4_agriculture_air == Inf]<-0
TIV_ch4_agriculture_air = as.vector(DIV_ch4_agriculture_air) %*% L
write.csv(TIV_ch4_agriculture_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_agriculture_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_agriculture_air = as.vector(DIV_ch4_agriculture_air) * L
TIV_breakdown_ch4_agriculture_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_agriculture_air)
TIV_country_breakdown_ch4_agriculture_air_w_labels = t(TIV_breakdown_ch4_agriculture_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_agriculture_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_agriculture_air_",year_current,"_pxp.csv"))
# CH4 - waste - air
CH4_waste_air = satellite[436,]
CH4_waste_air = CH4_waste_air*28
DIV_ch4_waste_air = CH4_waste_air/total_output
DIV_ch4_waste_air[is.na(DIV_ch4_waste_air)]=0
DIV_ch4_waste_air[DIV_ch4_waste_air == Inf]<-0
TIV_ch4_waste_air = as.vector(DIV_ch4_waste_air) %*% L
write.csv(TIV_ch4_waste_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_waste_air_",year_current,"_pxp.csv"))
TIV_breakdown_ch4_waste_air = as.vector(DIV_ch4_waste_air) * L
TIV_breakdown_ch4_waste_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_ch4_waste_air)
TIV_country_breakdown_ch4_waste_air_w_labels = t(TIV_breakdown_ch4_waste_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_ch4_waste_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_waste_air_",year_current,"_pxp.csv"))
# N2O - combustion - air
N2O_combustion_air = satellite[26,]
N2O_combustion_air = N2O_combustion_air*265
DIV_n2o_combustion_air = N2O_combustion_air/total_output
DIV_n2o_combustion_air[is.na(DIV_n2o_combustion_air)]=0
DIV_n2o_combustion_air[DIV_n2o_combustion_air == Inf]<-0
TIV_n2o_combustion_air = as.vector(DIV_n2o_combustion_air) %*% L
write.csv(TIV_n2o_combustion_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_n2o_CO2eq_combustion_air_",year_current,"_pxp.csv"))
TIV_breakdown_n2o_combustion_air = as.vector(DIV_n2o_combustion_air) * L
TIV_breakdown_n2o_combustion_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_n2o_combustion_air)
TIV_country_breakdown_n2o_combustion_air_w_labels = t(TIV_breakdown_n2o_combustion_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_n2o_combustion_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_pxp.csv"))
# N2O - agriculture - air
N2O_agriculture_air = satellite[430,]
N2O_agriculture_air = N2O_agriculture_air*265
DIV_n2o_agriculture_air = N2O_agriculture_air/total_output
DIV_n2o_agriculture_air[is.na(DIV_n2o_agriculture_air)]=0
DIV_n2o_agriculture_air[DIV_n2o_agriculture_air == Inf]<-0
TIV_n2o_agriculture_air = as.vector(DIV_n2o_agriculture_air) %*% L
write.csv(TIV_n2o_agriculture_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_n2o_CO2eq_agriculture_air_",year_current,"_pxp.csv"))
TIV_breakdown_n2o_agriculture_air = as.vector(DIV_n2o_agriculture_air) * L
TIV_breakdown_n2o_agriculture_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_n2o_agriculture_air)
TIV_country_breakdown_n2o_agriculture_air_w_labels = t(TIV_breakdown_n2o_agriculture_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_n2o_agriculture_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_pxp.csv"))
# SF6 - air
SF6_air = satellite[424,]
SF6_air = SF6_air*23500
DIV_sf6_air = SF6_air/total_output
DIV_sf6_air[is.na(DIV_sf6_air)]=0
DIV_sf6_air[DIV_sf6_air == Inf]<-0
TIV_sf6_air = as.vector(DIV_sf6_air) %*% L
write.csv(TIV_sf6_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_sf6_CO2eq_air_",year_current,"_pxp.csv"))
TIV_breakdown_sf6_air = as.vector(DIV_sf6_air) * L
TIV_breakdown_sf6_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_sf6_air)
TIV_country_breakdown_sf6_air_w_labels = t(TIV_breakdown_sf6_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_sf6_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_pxp.csv"))
# HFC - air
HFC_air = satellite[425,]
DIV_hfc_air = HFC_air/total_output
DIV_hfc_air[is.na(DIV_hfc_air)]=0
DIV_hfc_air[DIV_hfc_air == Inf]<-0
TIV_hfc_air = as.vector(DIV_hfc_air) %*% L
write.csv(TIV_hfc_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_hfc_CO2eq_air_",year_current,"_pxp.csv"))
TIV_breakdown_hfc_air = as.vector(DIV_hfc_air) * L
TIV_breakdown_hfc_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_hfc_air)
TIV_country_breakdown_hfc_air_w_labels = t(TIV_breakdown_hfc_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_hfc_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_pxp.csv"))
# PFC - air
PFC_air = satellite[426,]
DIV_pfc_air = PFC_air/total_output
DIV_pfc_air[is.na(DIV_pfc_air)]=0
DIV_pfc_air[DIV_pfc_air == Inf]<-0
TIV_pfc_air = as.vector(DIV_pfc_air) %*% L
write.csv(TIV_pfc_air, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_pfc_CO2eq_air_",year_current,"_pxp.csv"))
TIV_breakdown_pfc_air = as.vector(DIV_pfc_air) * L
TIV_breakdown_pfc_air_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_pfc_air)
TIV_country_breakdown_pfc_air_w_labels = t(TIV_breakdown_pfc_air_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_pfc_air_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_pxp.csv"))
# Energy carrier - use
energy_carrier_use = satellite[470,]
DIV_e_u = energy_carrier_use/total_output
DIV_e_u[is.na(DIV_e_u)]=0
DIV_e_u[DIV_e_u == Inf]<-0
TIV_e_u = as.vector(DIV_e_u) %*% L
write.csv(TIV_e_u, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_energy_carrier_use_",year_current,"_pxp.csv"))
TIV_breakdown_e_u = as.vector(DIV_e_u) * L
TIV_breakdown_e_u_w_labels = cbind(Exiobase_T_labels_pxp, TIV_breakdown_e_u)
TIV_country_breakdown_e_u_w_labels = t(TIV_breakdown_e_u_w_labels %>%
group_by(V1) %>%
select(-X,-V2) %>%
summarise_all(funs(sum)))
write.csv(TIV_country_breakdown_e_u_w_labels, paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_energy_carrier_use_",year_current,"_pxp.csv"))
}
isf
HBS: European household budget survey from EUROSTAT, macro-data, from : https://ec.europa.eu/eurostat/web/household-budget-surveys/database [accessed on 22.05.2020]
-
'Mean consumption expenditure by income quintile (hbs_exp_t133)'
-
'Structure of consumption expenditure by income quintile and COICOP consumption purpose (hbs_str_t223)'
Would be in an 'income-stratified-footprints' preprocessing folder
-
The 'lfst_hhnhtych' table from EUROSTAT, selecting all available years and total households [accessed on 04.05.2020].
-
Norway is missing from the 'lfst_hhnhtych' EUROSTAT table. We download Norwegian data from the Norwegian statistical office: https://www.ssb.no/en/statbank/table/10986/. We select 'Private households', 'the whole country', no household type selection, all years (2005-2019), and continue with 'Table - Layout 1', then save the table as a 'Tab delimited without heading (csv)' file [accessed on 04.05.2020].
# income-stratified-footprints directory
#data_dir_income_stratified_footprints = paste("/",file.path("data","metab","income-stratified-footprints", fsep=.Platform$file.sep),sep="")
data_dir_income_stratified_footprints = here("analysis", "preprocessing", "income-stratified-footprints")
data_dir_exiobase = here("analysis", "preprocessing", "EXIOBASE")
################################################### !!!! method 1 - PPS HH - RENT NOT MAPPED TO EXIOBASE !!!! ###########################################
##########################################################################################################################################################
##########################################################################################################################################################
#### IF YOU WANT THE RESULTS USING PPS PER ADULT EQUIVALENT - FILTER 'MEAN EXPENDITURE BY QUINTILE' BELOW FOR (unit == "PPS_AE") AND MAKE SURE TO UNCOMMENT
#### THE LINE SAVING IT AT THE END (AND COMMENT OUT THE LINE SAVING THE 'PPS HH' VERSION) - for both ixi and pxp Exiobase versions
## Eurostat Household Budget Survey
# load 'mean expenditure by quintile' data
hbs_exp_t133 = read_csv(paste0(data_dir_income_stratified_footprints, "/hbs_exp_t133.csv"))
# rename and arrange by country
mean_expenditure_by_quintile = hbs_exp_t133 %>%
rename(geo = 3, quintile = "quantile") %>%
arrange(geo)
# load 'mean expenditure by quintile and coicop' data
hbs_str_t223 = read_csv(paste0(data_dir_income_stratified_footprints, "/hbs_str_t223.csv"))
# rename and arrange by country
mean_expenditure_by_coicop_sector = hbs_str_t223 %>%
rename(geo = 4, quintile = "quantile") %>%
arrange(geo)
# create long data set
mean_expenditure_by_quintile_long = mean_expenditure_by_quintile %>%
filter(!(quintile %in% c("UNK","TOTAL"))) %>%
filter(!(geo %in% c("EA",
"EA12",
"EA13",
"EA17",
"EA18",
"EA19",
"EEA28",
"EEA30_2007",
"EFTA",
"EU15",
"EU25",
"EU27_2007",
"EU27_2020",
"EU28"))) %>%
gather(year,pps,-quintile,-unit,-geo) %>%
rename(mean_expenditure = pps)
write_csv(mean_expenditure_by_quintile_long, paste0(data_dir_income_stratified_footprints, "/mean_expenditure_by_quintile_long.csv"))
# create long data sets for both
mean_expenditure_by_quintile_long = mean_expenditure_by_quintile %>%
filter(unit == "PPS_HH") %>% # filter 'mean expenditure by quintile' in PPS per HouseHold
filter(!(quintile %in% c("UNK","TOTAL"))) %>% # filter out unknown and total expenditure
select(-unit) %>%
gather(year,pps,-quintile,-geo)
mean_expenditure_by_coicop_sector_long = mean_expenditure_by_coicop_sector %>%
filter(!(quintile %in% c("UNK","TOTAL"))) %>% # filter out unknown and total expenditure
select(-unit) %>%
gather(year,pm,-quintile,-coicop,-geo) %>%
mutate(coicop = dplyr::recode(coicop, "CP041" = "rent",
"CP042" = "rent")) %>%
group_by(geo,quintile,coicop,year) %>%
mutate(pm = parse_number(pm),
pm = as.numeric(pm)) %>%
summarise(pm = sum(pm, na.rm = TRUE)) %>%
ungroup() %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE1" &
coicop == "CP072", 92-21-14,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE2" &
coicop == "CP072", 108-22-12,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE3" &
coicop == "CP072", 124-32-11,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE4" &
coicop == "CP072", 133-43-10,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE5" &
coicop == "CP072", 162-81-11,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE1" &
coicop == "CP044", 412-4-78-322,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE2" &
coicop == "CP044", 355-5-68-265,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE3" &
coicop == "CP044", 325-8-64-229,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE4" &
coicop == "CP044", 300-9-58-204,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE5" &
coicop == "CP044", 249-10-46-167,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE1" &
coicop == "CP044", 433-3-82-340,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE2" &
coicop == "CP044", 376-6-70-284,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE3" &
coicop == "CP044", 351-9-67-251,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE4" &
coicop == "CP044", 326-10-61-228,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE5" &
coicop == "CP044", 280-9-49-195,pm))
## In the code above, I collapse (sum) the two 'rent' HBS sectors 'CP041' and 'CP042' to create a
## single 'rent' sector so as to allocate all rent to 'Real-estate services' in Exiobase.
# join the HBS expenditure tables together
join_expenditures = mean_expenditure_by_coicop_sector_long %>%
left_join(mean_expenditure_by_quintile_long, by = c("geo","quintile","year")) %>%
mutate(pps = as.numeric(pps),
pm = as.numeric(pm),
pps_coicop = pm*(pps/1000))
################################################### !!!! method 1 - IXI version - PPS HH NO RENT !!!! ####################################################
##########################################################################################################################################################
##########################################################################################################################################################
## Exiobase - ixi version
years_exb_ixi = c(2005,2010,2015)
disaggregated_final_demand = NULL
TIVs = NULL
domestic_TIVs = NULL
europe_TIVs = NULL
national_fp = NULL
national_territorial = NULL
for (i in years_exb_ixi){
year_current = i
Exiobase_FD = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/FD_",year_current,"_ixi.csv"))[,-1]
# select household final demand vectors for study countries
AT = Exiobase_FD[,1]
BE = Exiobase_FD[,8]
BG = Exiobase_FD[,15]
CY = Exiobase_FD[,22]
CZ = Exiobase_FD[,29]
DE = Exiobase_FD[,36]
DK = Exiobase_FD[,43]
EE = Exiobase_FD[,50]
EL = Exiobase_FD[,78]
ES = Exiobase_FD[,57]
FI = Exiobase_FD[,64]
FR = Exiobase_FD[,71]
HR = Exiobase_FD[,85]
HU = Exiobase_FD[,92]
IE = Exiobase_FD[,99]
IT = Exiobase_FD[,106]
LT = Exiobase_FD[,113]
LU = Exiobase_FD[,120]
LV = Exiobase_FD[,127]
MT = Exiobase_FD[,134]
NL = Exiobase_FD[,141]
NO = Exiobase_FD[,288]
PL = Exiobase_FD[,148]
PT = Exiobase_FD[,155]
RO = Exiobase_FD[,162]
SE = Exiobase_FD[,169]
SI = Exiobase_FD[,176]
SK = Exiobase_FD[,183]
TR = Exiobase_FD[,274]
UK = Exiobase_FD[,190]
Eurostat_countries = cbind(AT,BE,BG,CY,CZ,DE,DK,EE,EL,ES,FI,FR,HR,HU,IE,IT,LT,LU,LV,MT,NL,NO,PL,PT,RO,SE,SI,SK,TR,UK)
# labels
Exiobase_T_labels = read.csv(paste0(data_dir_income_stratified_footprints, "/Exiobase_T_labels_ixi_w_coicop_mapping.csv")) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
# hh fd with production sector labels
hh_fd_with_production_sector_labels = cbind(Exiobase_T_labels,Eurostat_countries) %>% rename(geo = V1, sector = V2)
# assumption of same purchase structure between quintiles of domestic and foreign final demand
# replicate each cell of each country's hh final demand as many times as there are income groups in the HBS data - in this preliminary case:5
cells_repeat = data.frame(hh_fd_with_production_sector_labels %>% slice(rep(1:n(), each = 5)))
quintiles = data.frame(rep(c("QUINTILE1","QUINTILE2","QUINTILE3","QUINTILE4","QUINTILE5"),163)) %>% rename_at(1,~"quintile")
replicated = cbind(cells_repeat,quintiles) %>% rename(country_of_production = geo)
# make fd data long
replicated_long = replicated %>% gather(geo, value,-sector,-coicop,-quintile,-five_sectors,-country_of_production)
year = as.character(rep(year_current,nrow(replicated_long)))
replicated_long = cbind(year,replicated_long)
disaggregated_final_demand = rbind(disaggregated_final_demand, replicated_long)
# TIVs
# CO2 - combustion - air
Exiobase_TIV_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_combustion_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_combustion_air_", year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_combustion_domestic)
Exiobase_TIV_europe_breakdown_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_combustion_air_", year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_combustion_europe,TIV_CO2_combustion_not_europe)
# CO2 - noncombustion - cement - air
Exiobase_TIV_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_noncombustion_cement_domestic)
Exiobase_TIV_europe_breakdown_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_noncombustion_cement_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_noncombustion_cement_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_noncombustion_cement_europe,TIV_CO2_noncombustion_cement_not_europe)
# CO2 - noncombustion - lime - air
Exiobase_TIV_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_noncombustion_lime_domestic)
Exiobase_TIV_europe_breakdown_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_noncombustion_lime_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_noncombustion_lime_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_noncombustion_lime_europe,TIV_CO2_noncombustion_lime_not_europe)
# CO2 - agriculture - peat decay - air
Exiobase_TIV_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_agriculture_peatdecay_domestic)
Exiobase_TIV_europe_breakdown_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_agriculture_peatdecay_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_agriculture_peatdecay_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_agriculture_peatdecay_europe,TIV_CO2_agriculture_peatdecay_not_europe)
# CO2 - waste - biogenic - air
Exiobase_TIV_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_biogenic_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_biogenic_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_waste_biogenic_domestic)
Exiobase_TIV_europe_breakdown_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_biogenic_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_waste_biogenic_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_waste_biogenic_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_waste_biogenic_europe,TIV_CO2_waste_biogenic_not_europe)
# CO2 - waste - fossil - air
Exiobase_TIV_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_waste_fossil_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_waste_fossil_domestic)
Exiobase_TIV_europe_breakdown_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_waste_fossil_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_waste_fossil_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_waste_fossil_europe,TIV_CO2_waste_fossil_not_europe)
# CH4 - combustion -air
Exiobase_TIV_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_combustion_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_combustion_domestic)
Exiobase_TIV_europe_breakdown_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_combustion_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_combustion_europe,TIV_CH4_combustion_not_europe)
# CH4 - noncombustion - gas - air
Exiobase_TIV_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_gas_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_gas_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_gas_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_gas_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_gas_europe,TIV_CH4_noncombustion_gas_not_europe)
# CH4 - noncombustion - oil - air
Exiobase_TIV_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_oil_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_oil_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_oil_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_oil_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_oil_europe,TIV_CH4_noncombustion_oil_not_europe)
# CH4 - noncombustion - anthracite - air
Exiobase_TIV_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_anthracite_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_anthracite_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_anthracite_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_anthracite_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_anthracite_europe,TIV_CH4_noncombustion_anthracite_not_europe)
# CH4 - noncombustion - bituminouscoal - air
Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_bituminouscoal_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_bituminouscoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_bituminouscoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_bituminouscoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_bituminouscoal_europe,TIV_CH4_noncombustion_bituminouscoal_not_europe)
# CH4 - noncombustion - cokingcoal - air
Exiobase_TIV_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_cokingcoal_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_cokingcoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_cokingcoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_cokingcoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_cokingcoal_europe,TIV_CH4_noncombustion_cokingcoal_not_europe)
# CH4 - noncombustion - lignite - air
Exiobase_TIV_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_lignite_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_lignite_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_lignite_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_lignite_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_lignite_europe,TIV_CH4_noncombustion_lignite_not_europe)
# CH4 - noncombustion - subbituminouscoal - air
Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_subbituminouscoal_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_subbituminouscoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_subbituminouscoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_subbituminouscoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_subbituminouscoal_europe,TIV_CH4_noncombustion_subbituminouscoal_not_europe)
# CH4 - noncombustion - oilrefinery - air
Exiobase_TIV_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_oilrefinery_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_oilrefinery_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_oilrefinery_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_oilrefinery_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_oilrefinery_europe,TIV_CH4_noncombustion_oilrefinery_not_europe)
# CH4 - agriculture - air
Exiobase_TIV_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_agriculture_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_agriculture_domestic)
Exiobase_TIV_europe_breakdown_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_agriculture_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_agriculture_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_agriculture_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_agriculture_europe,TIV_CH4_agriculture_not_europe)
# CH4 - waste - air
Exiobase_TIV_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_waste_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_waste_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_waste_domestic)
Exiobase_TIV_europe_breakdown_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_waste_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_waste_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_waste_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_waste_europe,TIV_CH4_waste_not_europe)
# N2O - combustion - air
Exiobase_TIV_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_N2O_combustion_domestic)
Exiobase_TIV_europe_breakdown_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_N2O_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_N2O_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_N2O_combustion_europe,TIV_N2O_combustion_not_europe)
# N2O - agriculture - air
Exiobase_TIV_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_N2O_agriculture_domestic)
Exiobase_TIV_europe_breakdown_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_N2O_agriculture_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_N2O_agriculture_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_N2O_agriculture_europe,TIV_N2O_agriculture_not_europe)
# SF6 - air
Exiobase_TIV_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_sf6_CO2eq_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_SF6_domestic)
Exiobase_TIV_europe_breakdown_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_SF6_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_SF6_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_SF6_europe,TIV_SF6_not_europe)
# HFC - air
Exiobase_TIV_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_hfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_HFC_domestic)
Exiobase_TIV_europe_breakdown_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_HFC_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_HFC_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_HFC_europe,TIV_HFC_not_europe)
# PFC - air
Exiobase_TIV_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_pfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_PFC_domestic)
Exiobase_TIV_europe_breakdown_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_PFC_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_PFC_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_PFC_europe,TIV_PFC_not_europe)
# Energy use
Exiobase_TIV_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_energy_carrier_use_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_energy_carrier_use_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_energy_domestic)
Exiobase_TIV_europe_breakdown_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_energy_carrier_use_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_energy_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_energy_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_energy_europe,TIV_energy_not_europe)
# join with labels
TIV_with_labels = cbind(Exiobase_T_labels,
t(Exiobase_TIV_co2_combustion_bp),
t(Exiobase_TIV_co2_noncombustion_cement_bp),
t(Exiobase_TIV_co2_noncombustion_lime_bp),
t(Exiobase_TIV_co2_agriculture_peatdecay_bp),
t(Exiobase_TIV_co2_waste_biogenic_bp),
t(Exiobase_TIV_co2_waste_fossil_bp),
t(Exiobase_TIV_ch4_combustion_bp),
t(Exiobase_TIV_ch4_noncombustion_gas_bp),
t(Exiobase_TIV_ch4_noncombustion_oil_bp),
t(Exiobase_TIV_ch4_noncombustion_anthracite_bp),
t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp),
t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp),
t(Exiobase_TIV_ch4_noncombustion_lignite_bp),
t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp),
t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp),
t(Exiobase_TIV_ch4_agriculture_bp),
t(Exiobase_TIV_ch4_waste_bp),
t(Exiobase_TIV_n2o_combustion_bp),
t(Exiobase_TIV_n2o_agriculture_bp),
t(Exiobase_TIV_sf6_bp),
t(Exiobase_TIV_hfc_bp),
t(Exiobase_TIV_pfc_bp),
t(Exiobase_TIV_energy_use_bp)) %>%
rename(TIV_CO2_combustion = "t(Exiobase_TIV_co2_combustion_bp)",
TIV_CO2_noncombustion_cement = "t(Exiobase_TIV_co2_noncombustion_cement_bp)",
TIV_CO2_noncombustion_lime = "t(Exiobase_TIV_co2_noncombustion_lime_bp)",
TIV_CO2_agriculture_peatdecay = "t(Exiobase_TIV_co2_agriculture_peatdecay_bp)",
TIV_CO2_waste_biogenic = "t(Exiobase_TIV_co2_waste_biogenic_bp)",
TIV_CO2_waste_fossil = "t(Exiobase_TIV_co2_waste_fossil_bp)",
TIV_CH4_combustion = "t(Exiobase_TIV_ch4_combustion_bp)",
TIV_CH4_noncombustion_gas = "t(Exiobase_TIV_ch4_noncombustion_gas_bp)",
TIV_CH4_noncombustion_oil = "t(Exiobase_TIV_ch4_noncombustion_oil_bp)",
TIV_CH4_noncombustion_anthracite = "t(Exiobase_TIV_ch4_noncombustion_anthracite_bp)",
TIV_CH4_noncombustion_bituminouscoal = "t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp)",
TIV_CH4_noncombustion_cokingcoal = "t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp)",
TIV_CH4_noncombustion_lignite = "t(Exiobase_TIV_ch4_noncombustion_lignite_bp)",
TIV_CH4_noncombustion_subbituminouscoal = "t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp)",
TIV_CH4_noncombustion_oilrefinery = "t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp)",
TIV_CH4_agriculture = "t(Exiobase_TIV_ch4_agriculture_bp)",
TIV_CH4_waste = "t(Exiobase_TIV_ch4_waste_bp)",
TIV_N2O_combustion = "t(Exiobase_TIV_n2o_combustion_bp)",
TIV_N2O_agriculture = "t(Exiobase_TIV_n2o_agriculture_bp)",
TIV_SF6 = "t(Exiobase_TIV_sf6_bp)",
TIV_HFC = "t(Exiobase_TIV_hfc_bp)",
TIV_PFC = "t(Exiobase_TIV_pfc_bp)",
TIV_energy = "t(Exiobase_TIV_energy_use_bp)") %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year = as.character(rep(year_current,nrow(TIV_with_labels)))
look = cbind(year,TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2)
TIVs = rbind(TIVs,look)
# join domestic_TIVs with labels
domestic_TIV_with_labels = cbind(Exiobase_T_labels,
Exiobase_TIV_country_breakdown_co2_combustion_bp,
Exiobase_TIV_country_breakdown_co2_noncombustion_cement_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_noncombustion_lime_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_agriculture_peatdecay_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_waste_biogenic_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_waste_fossil_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_combustion_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_gas_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_oil_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_anthracite_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_bituminouscoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_cokingcoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_lignite_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_oilrefinery_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_agriculture_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_waste_bp %>% select(-country),
Exiobase_TIV_country_breakdown_n2o_combustion_bp %>% select(-country),
Exiobase_TIV_country_breakdown_n2o_agriculture_bp %>% select(-country),
Exiobase_TIV_country_breakdown_sf6_bp %>% select(-country),
Exiobase_TIV_country_breakdown_hfc_bp %>% select(-country),
Exiobase_TIV_country_breakdown_pfc_bp %>% select(-country),
Exiobase_TIV_country_breakdown_energy_use_bp %>% select(-country)) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"),
country = dplyr::recode(country, "GR" = "EL", "GB" = "UK"))
year_domestic = as.character(rep(year_current,nrow(domestic_TIV_with_labels)))
look_domestic = cbind(year_domestic,domestic_TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2, geo = country, year = year_domestic) %>%
mutate(TIV_CO2_combustion_domestic = as.numeric(TIV_CO2_combustion_domestic),
TIV_CO2_noncombustion_cement_domestic = as.numeric(TIV_CO2_noncombustion_cement_domestic),
TIV_CO2_noncombustion_lime_domestic = as.numeric(TIV_CO2_noncombustion_lime_domestic),
TIV_CO2_agriculture_peatdecay_domestic = as.numeric(TIV_CO2_agriculture_peatdecay_domestic),
TIV_CO2_waste_biogenic_domestic = as.numeric(TIV_CO2_waste_biogenic_domestic),
TIV_CO2_waste_fossil_domestic = as.numeric(TIV_CO2_waste_fossil_domestic),
TIV_CH4_combustion_domestic = as.numeric(TIV_CH4_combustion_domestic),
TIV_CH4_noncombustion_gas_domestic = as.numeric(TIV_CH4_noncombustion_gas_domestic),
TIV_CH4_noncombustion_oil_domestic = as.numeric(TIV_CH4_noncombustion_oil_domestic),
TIV_CH4_noncombustion_anthracite_domestic = as.numeric(TIV_CH4_noncombustion_anthracite_domestic),
TIV_CH4_noncombustion_bituminouscoal_domestic = as.numeric(TIV_CH4_noncombustion_bituminouscoal_domestic),
TIV_CH4_noncombustion_cokingcoal_domestic = as.numeric(TIV_CH4_noncombustion_cokingcoal_domestic),
TIV_CH4_noncombustion_lignite_domestic = as.numeric(TIV_CH4_noncombustion_lignite_domestic),
TIV_CH4_noncombustion_subbituminouscoal_domestic = as.numeric(TIV_CH4_noncombustion_subbituminouscoal_domestic),
TIV_CH4_noncombustion_oilrefinery_domestic = as.numeric(TIV_CH4_noncombustion_oilrefinery_domestic),
TIV_CH4_agriculture_domestic = as.numeric(TIV_CH4_agriculture_domestic),
TIV_CH4_waste_domestic = as.numeric(TIV_CH4_waste_domestic),
TIV_N2O_combustion_domestic = as.numeric(TIV_N2O_combustion_domestic),
TIV_N2O_agriculture_domestic = as.numeric(TIV_N2O_agriculture_domestic),
TIV_SF6_domestic = as.numeric(TIV_SF6_domestic),
TIV_HFC_domestic = as.numeric(TIV_HFC_domestic),
TIV_PFC_domestic = as.numeric(TIV_PFC_domestic),
TIV_energy_domestic = as.numeric(TIV_energy_domestic))
domestic_TIVs = rbind(domestic_TIVs, look_domestic)
# european TIVs with labels
europe_TIV_with_labels = cbind(Exiobase_T_labels,
Exiobase_TIV_europe_breakdown_co2_combustion_bp,
Exiobase_TIV_europe_breakdown_co2_noncombustion_cement_bp,
Exiobase_TIV_europe_breakdown_co2_noncombustion_lime_bp,
Exiobase_TIV_europe_breakdown_co2_agriculture_peatdecay_bp,
Exiobase_TIV_europe_breakdown_co2_waste_biogenic_bp,
Exiobase_TIV_europe_breakdown_co2_waste_fossil_bp,
Exiobase_TIV_europe_breakdown_ch4_combustion_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_gas_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oil_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_anthracite_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_bituminouscoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_cokingcoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_lignite_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_subbituminouscoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oilrefinery_bp,
Exiobase_TIV_europe_breakdown_ch4_agriculture_bp,
Exiobase_TIV_europe_breakdown_ch4_waste_bp,
Exiobase_TIV_europe_breakdown_n2o_combustion_bp,
Exiobase_TIV_europe_breakdown_n2o_agriculture_bp,
Exiobase_TIV_europe_breakdown_sf6_bp,
Exiobase_TIV_europe_breakdown_hfc_bp,
Exiobase_TIV_europe_breakdown_pfc_bp,
Exiobase_TIV_europe_breakdown_energy_use_bp) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year_europe = as.character(rep(year_current,nrow(europe_TIV_with_labels)))
look_europe = cbind(year_europe,europe_TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2, year = year_europe) %>%
mutate(TIV_CO2_combustion_europe = as.numeric(TIV_CO2_combustion_europe),
TIV_CO2_noncombustion_cement_europe = as.numeric(TIV_CO2_noncombustion_cement_europe),
TIV_CO2_noncombustion_lime_europe = as.numeric(TIV_CO2_noncombustion_lime_europe),
TIV_CO2_agriculture_peatdecay_europe = as.numeric(TIV_CO2_agriculture_peatdecay_europe),
TIV_CO2_waste_biogenic_europe = as.numeric(TIV_CO2_waste_biogenic_europe),
TIV_CO2_waste_fossil_europe = as.numeric(TIV_CO2_waste_fossil_europe),
TIV_CH4_combustion_europe = as.numeric(TIV_CH4_combustion_europe),
TIV_CH4_noncombustion_gas_europe = as.numeric(TIV_CH4_noncombustion_gas_europe),
TIV_CH4_noncombustion_oil_europe = as.numeric(TIV_CH4_noncombustion_oil_europe),
TIV_CH4_noncombustion_anthracite_europe = as.numeric(TIV_CH4_noncombustion_anthracite_europe),
TIV_CH4_noncombustion_bituminouscoal_europe = as.numeric(TIV_CH4_noncombustion_bituminouscoal_europe),
TIV_CH4_noncombustion_cokingcoal_europe = as.numeric(TIV_CH4_noncombustion_cokingcoal_europe),
TIV_CH4_noncombustion_lignite_europe = as.numeric(TIV_CH4_noncombustion_lignite_europe),
TIV_CH4_noncombustion_subbituminouscoal_europe = as.numeric(TIV_CH4_noncombustion_subbituminouscoal_europe),
TIV_CH4_noncombustion_oilrefinery_europe = as.numeric(TIV_CH4_noncombustion_oilrefinery_europe),
TIV_CH4_agriculture_europe = as.numeric(TIV_CH4_agriculture_europe),
TIV_CH4_waste_europe = as.numeric(TIV_CH4_waste_europe),
TIV_N2O_combustion_europe = as.numeric(TIV_N2O_combustion_europe),
TIV_N2O_agriculture_europe = as.numeric(TIV_N2O_agriculture_europe),
TIV_SF6_europe = as.numeric(TIV_SF6_europe),
TIV_HFC_europe = as.numeric(TIV_HFC_europe),
TIV_PFC_europe = as.numeric(TIV_PFC_europe),
TIV_energy_europe = as.numeric(TIV_energy_europe))
europe_TIVs = rbind(europe_TIVs, look_europe)
# total national footprints
# FD labels
Exiobase_FD_labels = as.data.frame(t(read.csv(paste0(data_dir_exiobase, "/Exiobase_FD_labels_ixi.csv")))[-1,-3]) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
national_CO2_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_co2_combustion_bp)
national_CO2_noncombustion_cement_footprints = Exiobase_FD * t(Exiobase_TIV_co2_noncombustion_cement_bp)
national_CO2_noncombustion_lime_footprints = Exiobase_FD * t(Exiobase_TIV_co2_noncombustion_lime_bp)
national_CO2_agriculture_peatdecay_footprints = Exiobase_FD * t(Exiobase_TIV_co2_agriculture_peatdecay_bp)
national_CO2_waste_biogenic_footprints = Exiobase_FD * t(Exiobase_TIV_co2_waste_biogenic_bp)
national_CO2_waste_fossil_footprints = Exiobase_FD * t(Exiobase_TIV_co2_waste_fossil_bp)
national_CH4_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_combustion_bp)
national_CH4_noncombustion_gas_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_gas_bp)
national_CH4_noncombustion_oil_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_oil_bp)
national_CH4_noncombustion_anthracite_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_anthracite_bp)
national_CH4_noncombustion_bituminouscoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp)
national_CH4_noncombustion_cokingcoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp)
national_CH4_noncombustion_lignite_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_lignite_bp)
national_CH4_noncombustion_subbituminouscoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp)
national_CH4_noncombustion_oilrefinery_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp)
national_CH4_agriculture_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_agriculture_bp)
national_CH4_waste_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_waste_bp)
national_N2O_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_n2o_combustion_bp)
national_N2O_agriculture_footprints = Exiobase_FD * t(Exiobase_TIV_n2o_agriculture_bp)
national_SF6_footprints = Exiobase_FD * t(Exiobase_TIV_sf6_bp)
national_HFC_footprints = Exiobase_FD * t(Exiobase_TIV_hfc_bp)
national_PFC_footprints = Exiobase_FD * t(Exiobase_TIV_pfc_bp)
national_energy_footprints = Exiobase_FD * t(Exiobase_TIV_energy_use_bp)
# together
national_footprints_w_labels = cbind(Exiobase_FD_labels,
rowSums(t(national_CO2_combustion_footprints)),
rowSums(t(national_CO2_noncombustion_cement_footprints)),
rowSums(t(national_CO2_noncombustion_lime_footprints)),
rowSums(t(national_CO2_agriculture_peatdecay_footprints)),
rowSums(t(national_CO2_waste_biogenic_footprints)),
rowSums(t(national_CO2_waste_fossil_footprints)),
rowSums(t(national_CH4_combustion_footprints)),
rowSums(t(national_CH4_noncombustion_gas_footprints)),
rowSums(t(national_CH4_noncombustion_oil_footprints)),
rowSums(t(national_CH4_noncombustion_anthracite_footprints)),
rowSums(t(national_CH4_noncombustion_bituminouscoal_footprints)),
rowSums(t(national_CH4_noncombustion_cokingcoal_footprints)),
rowSums(t(national_CH4_noncombustion_lignite_footprints)),
rowSums(t(national_CH4_noncombustion_subbituminouscoal_footprints)),
rowSums(t(national_CH4_noncombustion_oilrefinery_footprints)),
rowSums(t(national_CH4_agriculture_footprints)),
rowSums(t(national_CH4_waste_footprints)),
rowSums(t(national_N2O_combustion_footprints)),
rowSums(t(national_N2O_agriculture_footprints)),
rowSums(t(national_SF6_footprints)),
rowSums(t(national_HFC_footprints)),
rowSums(t(national_PFC_footprints)),
rowSums(t(national_energy_footprints))) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year_national_fp = as.character(rep(year_current,nrow(national_footprints_w_labels)))
# direct FD emissions
direct_FD_extensions = read.csv(paste0(data_dir_exiobase, "/IOT_", year_current, "_ixi/satellite/F_hh.csv", sep = ""),row.names=NULL,as.is=TRUE)[3:1115,3:345]
direct_FD_extensions[is.na(direct_FD_extensions)]=0
direct_FD_extensions = mapply(direct_FD_extensions, FUN = as.numeric)
direct_FD_extensions = matrix(data=direct_FD_extensions,ncol=343,nrow=1113)
direct_FD_co2_combustion = direct_FD_extensions[24,]
direct_FD_co2_noncombustion_cement = direct_FD_extensions[93,]
direct_FD_co2_noncombustion_lime = direct_FD_extensions[94,]
direct_FD_co2_agriculture_peatdecay = direct_FD_extensions[428,]
direct_FD_co2_waste_biogenic = direct_FD_extensions[438,]
direct_FD_co2_waste_fossil = direct_FD_extensions[439,]
direct_FD_ch4_combustion = direct_FD_extensions[25,]*28
direct_FD_ch4_noncombustion_gas = direct_FD_extensions[68,]*28
direct_FD_ch4_noncombustion_oil = direct_FD_extensions[69,]*28
direct_FD_ch4_noncombustion_anthracite = direct_FD_extensions[70,]*28
direct_FD_ch4_noncombustion_bituminouscoal = direct_FD_extensions[71,]*28
direct_FD_ch4_noncombustion_cokingcoal = direct_FD_extensions[72,]*28
direct_FD_ch4_noncombustion_lignite = direct_FD_extensions[73,]*28
direct_FD_ch4_noncombustion_subbituminouscoal = direct_FD_extensions[74,]*28
direct_FD_ch4_noncombustion_oilrefinery = direct_FD_extensions[75,]*28
direct_FD_ch4_agriculture = direct_FD_extensions[427,]*28
direct_FD_ch4_waste = direct_FD_extensions[436,]*28
direct_FD_n2o_combustion = direct_FD_extensions[26,]*265
direct_FD_n2o_agriculture = direct_FD_extensions[430,]*265
direct_FD_sf6 = direct_FD_extensions[424,]*23500
direct_FD_hfc = direct_FD_extensions[425,]
direct_FD_pfc = direct_FD_extensions[426,]
direct_FD_energy = direct_FD_extensions[470,]
direct_FD_fp = data.frame(direct_FD_co2_combustion,
direct_FD_co2_noncombustion_cement,
direct_FD_co2_noncombustion_lime,
direct_FD_co2_agriculture_peatdecay,
direct_FD_co2_waste_biogenic,
direct_FD_co2_waste_fossil,
direct_FD_ch4_combustion,
direct_FD_ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_oilrefinery,
direct_FD_ch4_agriculture,
direct_FD_ch4_waste,
direct_FD_n2o_combustion,
direct_FD_n2o_agriculture,
direct_FD_sf6,
direct_FD_hfc,
direct_FD_pfc,
direct_FD_energy)
look_national_fp = as.data.frame(cbind(year_national_fp,
national_footprints_w_labels,
direct_FD_fp)) %>%
rename(year = year_national_fp,
geo = V1,
fd_category = V2,
co2_combustion = "rowSums(t(national_CO2_combustion_footprints))",
co2_noncombustion_cement = "rowSums(t(national_CO2_noncombustion_cement_footprints))",
co2_noncombustion_lime = "rowSums(t(national_CO2_noncombustion_lime_footprints))",
co2_agriculture_peatdecay = "rowSums(t(national_CO2_agriculture_peatdecay_footprints))",
co2_waste_biogenic = "rowSums(t(national_CO2_waste_biogenic_footprints))",
co2_waste_fossil = "rowSums(t(national_CO2_waste_fossil_footprints))",
ch4_combustion = "rowSums(t(national_CH4_combustion_footprints))",
ch4_noncombustion_gas = "rowSums(t(national_CH4_noncombustion_gas_footprints))",
ch4_noncombustion_oil = "rowSums(t(national_CH4_noncombustion_oil_footprints))",
ch4_noncombustion_anthracite = "rowSums(t(national_CH4_noncombustion_anthracite_footprints))",
ch4_noncombustion_bituminouscoal = "rowSums(t(national_CH4_noncombustion_bituminouscoal_footprints))",
ch4_noncombustion_cokingcoal = "rowSums(t(national_CH4_noncombustion_cokingcoal_footprints))",
ch4_noncombustion_lignite = "rowSums(t(national_CH4_noncombustion_lignite_footprints))",
ch4_noncombustion_subbituminouscoal = "rowSums(t(national_CH4_noncombustion_subbituminouscoal_footprints))",
ch4_noncombustion_oilrefinery = "rowSums(t(national_CH4_noncombustion_oilrefinery_footprints))",
ch4_agriculture = "rowSums(t(national_CH4_agriculture_footprints))",
ch4_waste = "rowSums(t(national_CH4_waste_footprints))",
n2o_combustion = "rowSums(t(national_N2O_combustion_footprints))",
n2o_agriculture = "rowSums(t(national_N2O_agriculture_footprints))",
sf6 = "rowSums(t(national_SF6_footprints))",
hfc = "rowSums(t(national_HFC_footprints))",
pfc = "rowSums(t(national_PFC_footprints))",
energy = "rowSums(t(national_energy_footprints))") %>%
select(year,
geo,
fd_category,
co2_combustion,
direct_FD_co2_combustion,
co2_noncombustion_cement,
direct_FD_co2_noncombustion_cement,
co2_noncombustion_lime,
direct_FD_co2_noncombustion_lime,
co2_agriculture_peatdecay,
direct_FD_co2_agriculture_peatdecay,
co2_waste_biogenic,
direct_FD_co2_waste_biogenic,
co2_waste_fossil,
direct_FD_co2_waste_fossil,
ch4_combustion,
direct_FD_ch4_combustion,
ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_gas,
ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_oil,
ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_anthracite,
ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_bituminouscoal,
ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_cokingcoal,
ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_lignite,
ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_subbituminouscoal,
ch4_noncombustion_oilrefinery,
direct_FD_ch4_noncombustion_oilrefinery,
ch4_agriculture,
direct_FD_ch4_agriculture,
ch4_waste,
direct_FD_ch4_waste,
n2o_combustion,
direct_FD_n2o_combustion,
n2o_agriculture,
direct_FD_n2o_agriculture,
sf6,
direct_FD_sf6,
hfc,
direct_FD_hfc,
pfc,
direct_FD_pfc,
energy,
direct_FD_energy)
national_fp = rbind(national_fp, look_national_fp)
# national territorial
satellite = read.csv(paste0(data_dir_exiobase, "/IOT_", year_current, "_ixi/satellite/satellite_",year_current,"_ixi.csv"))[,-1]
CO2_combustion_air = satellite[24,]
CO2_noncombustion_cement_air = satellite[93,]
CO2_noncombustion_lime_air = satellite[94,]
CO2_agriculture_peatdecay_air = satellite[428,]
CO2_waste_biogenic_air = satellite[438,]
CO2_waste_fossil_air = satellite[439,]
CH4_combustion_air = satellite[25,]
CH4_combustion_air = CH4_combustion_air*28
CH4_noncombustion_gas_air = satellite[68,]
CH4_noncombustion_gas_air = CH4_noncombustion_gas_air*28
CH4_noncombustion_oil_air = satellite[69,]
CH4_noncombustion_oil_air = CH4_noncombustion_oil_air*28
CH4_noncombustion_anthracite_air = satellite[70,]
CH4_noncombustion_anthracite_air = CH4_noncombustion_anthracite_air*28
CH4_noncombustion_bituminouscoal_air = satellite[71,]
CH4_noncombustion_bituminouscoal_air = CH4_noncombustion_bituminouscoal_air*28
CH4_noncombustion_cokingcoal_air = satellite[72,]
CH4_noncombustion_cokingcoal_air = CH4_noncombustion_cokingcoal_air*28
CH4_noncombustion_lignite_air = satellite[73,]
CH4_noncombustion_lignite_air = CH4_noncombustion_lignite_air*28
CH4_noncombustion_subbituminouscoal_air = satellite[74,]
CH4_noncombustion_subbituminouscoal_air = CH4_noncombustion_subbituminouscoal_air*28
CH4_noncombustion_oilrefinery_air = satellite[75,]
CH4_noncombustion_oilrefinery_air = CH4_noncombustion_oilrefinery_air*28
CH4_agriculture_air = satellite[427,]
CH4_agriculture_air = CH4_agriculture_air*28
CH4_waste_air = satellite[436,]
CH4_waste_air = CH4_waste_air*28
N2O_combustion_air = satellite[26,]
N2O_combustion_air = N2O_combustion_air*265
N2O_agriculture_air = satellite[430,]
N2O_agriculture_air = N2O_agriculture_air*265
SF6_air = satellite[424,]
SF6_air = SF6_air*23500
HFC_air = satellite[425,]
PFC_air = satellite[426,]
energy_carrier_use = satellite[470,]
territorial = data.frame(t(CO2_combustion_air),
t(CO2_noncombustion_cement_air),
t(CO2_noncombustion_lime_air),
t(CO2_agriculture_peatdecay_air),
t(CO2_waste_biogenic_air),
t(CO2_waste_fossil_air),
t(CH4_combustion_air),
t(CH4_noncombustion_gas_air),
t(CH4_noncombustion_oil_air),
t(CH4_noncombustion_anthracite_air),
t(CH4_noncombustion_bituminouscoal_air),
t(CH4_noncombustion_cokingcoal_air),
t(CH4_noncombustion_lignite_air),
t(CH4_noncombustion_subbituminouscoal_air),
t(CH4_noncombustion_oilrefinery_air),
t(CH4_agriculture_air),
t(CH4_waste_air),
t(N2O_combustion_air),
t(N2O_agriculture_air),
t(SF6_air),
t(HFC_air),
t(PFC_air),
t(energy_carrier_use)) %>%
rename(CO2_combustion = 1,
CO2_noncombustion_cement = 2,
CO2_noncombustion_lime = 3,
CO2_agriculture_peatdecay = 4,
CO2_waste_biogenic = 5,
CO2_waste_fossil = 6,
CH4_combustion = 7,
CH4_noncombustion_gas = 8,
CH4_noncombustion_oil = 9,
CH4_noncombustion_anthracite = 10,
CH4_noncombustion_bituminouscoal = 11,
CH4_noncombustion_cokingcoal = 12,
CH4_noncombustion_lignite = 13,
CH4_noncombustion_subbituminouscoal = 14,
CH4_noncombustion_oilrefinery = 15,
CH4_agriculture = 16,
CH4_waste = 17,
N2O_combustion = 18,
N2O_agriculture = 19,
SF6 = 20,
HFC = 21, PFC = 22, energy = 23)
year_territorial = as.character(rep(year_current,nrow(territorial)))
look_territorial = as.data.frame(cbind(year_territorial,
Exiobase_T_labels,
territorial)) %>%
rename(year = year_territorial,
geo = V1,
sector = V2) %>%
select(-coicop,-five_sectors)
national_territorial = rbind(national_territorial, look_territorial)
}
write.csv(national_territorial, paste0(data_dir_income_stratified_footprints, "/national_territorial_ixi.csv"))
write_rds(national_territorial, paste0(data_dir_income_stratified_footprints, "/national_territorial_ixi.rds"))
write.csv(national_fp, paste0(data_dir_income_stratified_footprints, "/national_fp_ixi.csv"))
write_rds(national_fp, paste0(data_dir_income_stratified_footprints, "/national_fp_ixi.rds"))
# calculate quintile shares within each sector
shares = join_expenditures %>%
group_by(coicop,geo,year) %>%
mutate(share = pps_coicop/sum(pps_coicop))
# pre-processing
fd_exiobase = disaggregated_final_demand %>%
left_join(shares, by = c("year","geo","coicop","quintile")) %>%
mutate(disaggregated_fd = value*share) %>%
select(year,geo,quintile,country_of_production,sector,coicop,disaggregated_fd) %>%
spread(quintile,disaggregated_fd)
# direct from FD - to go back to results without direct FD fp, do not run this next chunk and do not bind_rows with 'results'
env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_pefasu_1_Data.csv")) %>%
filter(TIME == 2015) %>%
mutate(geo = dplyr::recode(GEO,"Austria" = "AT",
"Belgium" = "BE",
"Cyprus" = "CY",
"Czechia" = "CZ",
"Denmark" = "DK",
"Estonia" = "EE",
"Finland" = "FI",
"France" = "FR",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Greece" = "EL",
"Hungary" = "HU",
"Ireland" = "IE",
"Italy" = "IT",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Malta" = "MT",
"Netherlands" = "NL",
"Norway" = "NO",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovakia" = "SK",
"Slovenia" = "SI",
"Spain" = "ES",
"Sweden" = "SE",
"United Kingdom" = "UK",
"Bulgaria" = "BG",
"Croatia" = "HR")) %>%
select(NACE_R2,geo,Value) %>%
mutate(Value = parse_number(Value),
Value = as.numeric(Value)) %>%
spread(NACE_R2,Value) %>%
clean_names() %>%
mutate(HH_HEAT = heating_cooling_activities_by_households/total_activities_by_households,
HH_TRA = transport_activities_by_households/total_activities_by_households,
HH_OTH = other_activities_by_households/total_activities_by_households) %>%
select(geo,HH_HEAT,HH_TRA,HH_OTH)
env_ac_pefasu_TR = env_ac_pefasu_no_TR %>%
filter(geo == "BG") %>%
mutate(geo = dplyr::recode(geo,
"BG" = "TR"))
env_ac_pefasu = rbind(env_ac_pefasu_no_TR,env_ac_pefasu_TR) %>%
gather(sector,share_of_total_energy,-geo)
env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_ainah_r2_1_Data.csv")) %>%
filter(TIME == 2015) %>%
mutate(geo = dplyr::recode(GEO,"Austria" = "AT",
"Belgium" = "BE",
"Cyprus" = "CY",
"Czechia" = "CZ",
"Denmark" = "DK",
"Estonia" = "EE",
"Finland" = "FI",
"France" = "FR",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Greece" = "EL",
"Hungary" = "HU",
"Ireland" = "IE",
"Italy" = "IT",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Malta" = "MT",
"Netherlands" = "NL",
"Norway" = "NO",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovakia" = "SK",
"Slovenia" = "SI",
"Spain" = "ES",
"Sweden" = "SE",
"Turkey" = "TR",
"United Kingdom" = "UK",
"Bulgaria" = "BG",
"Croatia" = "HR")) %>%
select(NACE_R2,AIRPOL,geo,Value) %>%
mutate(Value = parse_number(Value),
Value = as.numeric(Value)) %>%
spread(NACE_R2,Value) %>%
clean_names() %>%
mutate(HH_HEAT = heating_cooling_activities_by_households/total_activities_by_households,
HH_TRA = transport_activities_by_households/total_activities_by_households,
HH_OTH = other_activities_by_households/total_activities_by_households) %>%
select(geo,airpol,HH_HEAT,HH_TRA,HH_OTH)
env_ac_ainah_r2_co2 = env_ac_ainah_r2 %>%
filter(airpol == "Carbon dioxide") %>%
select(-airpol) %>%
gather(sector,share_of_total_co2,-geo)
env_ac_ainah_r2_ch4 = env_ac_ainah_r2 %>%
filter(airpol == "Methane") %>%
select(-airpol) %>%
gather(sector,share_of_total_ch4,-geo)
env_ac_ainah_r2_n2o = env_ac_ainah_r2 %>%
filter(airpol == "Nitrous oxide") %>%
select(-airpol) %>%
gather(sector,share_of_total_n2o,-geo)
direct_FD_fp_long = national_fp %>%
filter(fd_category == "Final consumption expenditure by households",
geo %in% c("AT",
"BE", "BG", "CY", "CZ",
"DE" , "DK" , "EE" ,
"ES" , "FI" , "FR" ,
"UK", "EL", "HR" ,
"HU" , "IE" , "IT" ,
"LT" , "LU" , "LV" ,
"MT" , "NL" , "PL" ,
"PT" , "TR" , "SK" ,
"SI" , "SE" , "RO" ,
"NO")) %>%
select(year,geo,fd_category,direct_FD_co2_combustion,
direct_FD_co2_noncombustion_cement,
direct_FD_co2_noncombustion_lime,
direct_FD_co2_agriculture_peatdecay,
direct_FD_co2_waste_biogenic,
direct_FD_co2_waste_fossil,
direct_FD_ch4_combustion,
direct_FD_ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_oilrefinery,
direct_FD_ch4_agriculture,
direct_FD_ch4_waste,
direct_FD_n2o_combustion,
direct_FD_n2o_agriculture,
direct_FD_sf6,
direct_FD_hfc,
direct_FD_pfc,
direct_FD_energy) %>%
slice(rep(1:n(), each = 3))
sector = rep(c("HH_HEAT","HH_TRA","HH_OTH"), nrow(direct_FD_fp_long)/3)
direct_FD_fp_long_disagg = cbind(sector,direct_FD_fp_long) %>%
mutate(coicop = ifelse(sector == "HH_TRA","CP072",
ifelse(sector == "HH_HEAT","CP045","CP05")),
five_sectors = ifelse(sector == "HH_TRA", "transport",
ifelse(sector == "HH_HEAT", "shelter", "manufactured goods"))) %>%
left_join(env_ac_ainah_r2_co2, by = c("geo","sector")) %>%
left_join(env_ac_ainah_r2_ch4, by = c("geo","sector")) %>%
left_join(env_ac_ainah_r2_n2o, by = c("geo","sector")) %>%
left_join(env_ac_pefasu, by = c("geo","sector")) %>%
mutate(direct_FD_co2 = (direct_FD_co2_combustion +
direct_FD_co2_noncombustion_cement +
direct_FD_co2_noncombustion_lime +
direct_FD_co2_agriculture_peatdecay +
direct_FD_co2_waste_biogenic +
direct_FD_co2_waste_fossil)*share_of_total_co2,
direct_FD_ch4 = (direct_FD_ch4_combustion +
direct_FD_ch4_noncombustion_gas +
direct_FD_ch4_noncombustion_oil +
direct_FD_ch4_noncombustion_anthracite +
direct_FD_ch4_noncombustion_bituminouscoal +
direct_FD_ch4_noncombustion_cokingcoal +
direct_FD_ch4_noncombustion_lignite +
direct_FD_ch4_noncombustion_subbituminouscoal +
direct_FD_ch4_noncombustion_oilrefinery +
direct_FD_ch4_agriculture +
direct_FD_ch4_waste)*share_of_total_ch4,
direct_FD_n2o = (direct_FD_n2o_combustion +
direct_FD_n2o_agriculture)*share_of_total_n2o,
direct_FD_energy = direct_FD_energy*share_of_total_energy) %>%
left_join(shares, by = c("year","geo","coicop")) %>%
mutate(disaggregated_direct_FD_co2 = direct_FD_co2*share,
disaggregated_direct_FD_ch4 = direct_FD_ch4*share,
disaggregated_direct_FD_n2o = direct_FD_n2o*share,
disaggregated_direct_FD_energy = direct_FD_energy*share) %>%
select(year,geo,sector, quintile,
coicop, five_sectors,
disaggregated_direct_FD_co2,
disaggregated_direct_FD_ch4,
disaggregated_direct_FD_n2o,
disaggregated_direct_FD_energy)
direct_FD_co2 = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_co2) %>%
spread(quintile,disaggregated_direct_FD_co2) %>%
rename(q1_co2 = QUINTILE1,
q2_co2 = QUINTILE2,
q3_co2 = QUINTILE3,
q4_co2 = QUINTILE4,
q5_co2 = QUINTILE5) %>%
mutate(q1_co2_domestic = q1_co2,
q2_co2_domestic = q2_co2,
q3_co2_domestic = q3_co2,
q4_co2_domestic = q4_co2,
q5_co2_domestic = q5_co2,
co2_total = q1_co2+q2_co2+q3_co2+q4_co2+q5_co2,
co2_total_domestic = q1_co2_domestic+
q2_co2_domestic+q3_co2_domestic+
q4_co2_domestic+q5_co2_domestic)
direct_FD_ch4 = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_ch4) %>%
spread(quintile,disaggregated_direct_FD_ch4) %>%
rename(q1_ch4 = QUINTILE1,
q2_ch4 = QUINTILE2,
q3_ch4 = QUINTILE3,
q4_ch4 = QUINTILE4,
q5_ch4 = QUINTILE5) %>%
mutate(q1_ch4_domestic = q1_ch4,
q2_ch4_domestic = q2_ch4,
q3_ch4_domestic = q3_ch4,
q4_ch4_domestic = q4_ch4,
q5_ch4_domestic = q5_ch4,
ch4_total = q1_ch4+q2_ch4+q3_ch4+q4_ch4+q5_ch4,
ch4_total_domestic = q1_ch4_domestic+
q2_ch4_domestic+q3_ch4_domestic+
q4_ch4_domestic+q5_ch4_domestic)
direct_FD_n2o = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_n2o) %>%
spread(quintile,disaggregated_direct_FD_n2o) %>%
rename(q1_n2o = QUINTILE1,
q2_n2o = QUINTILE2,
q3_n2o = QUINTILE3,
q4_n2o = QUINTILE4,
q5_n2o = QUINTILE5) %>%
mutate(q1_n2o_domestic = q1_n2o,
q2_n2o_domestic = q2_n2o,
q3_n2o_domestic = q3_n2o,
q4_n2o_domestic = q4_n2o,
q5_n2o_domestic = q5_n2o,
n2o_total = q1_n2o+q2_n2o+q3_n2o+q4_n2o+q5_n2o,
n2o_total_domestic = q1_n2o_domestic+
q2_n2o_domestic+q3_n2o_domestic+
q4_n2o_domestic+q5_n2o_domestic)
direct_FD_energy = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_energy) %>%
spread(quintile,disaggregated_direct_FD_energy) %>%
rename(q1_energy = QUINTILE1,
q2_energy = QUINTILE2,
q3_energy = QUINTILE3,
q4_energy = QUINTILE4,
q5_energy = QUINTILE5) %>%
mutate(q1_energy_domestic = q1_energy,
q2_energy_domestic = q2_energy,
q3_energy_domestic = q3_energy,
q4_energy_domestic = q4_energy,
q5_energy_domestic = q5_energy,
energy_total = q1_energy+q2_energy+q3_energy+q4_energy+q5_energy,
energy_total_domestic = q1_energy_domestic+
q2_energy_domestic+q3_energy_domestic+
q4_energy_domestic+q5_energy_domestic)
direct_FD_fp_wide = direct_FD_co2 %>%
left_join(direct_FD_ch4, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
left_join(direct_FD_n2o, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
left_join(direct_FD_energy, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
mutate(country_of_production = geo) %>%
mutate(q1_co2eq = q1_co2 + q1_ch4 + q1_n2o,
q2_co2eq = q2_co2 + q2_ch4 + q2_n2o,
q3_co2eq = q3_co2 + q3_ch4 + q3_n2o,
q4_co2eq = q4_co2 + q4_ch4 + q4_n2o,
q5_co2eq = q5_co2 + q5_ch4 + q5_n2o,
co2eq_total = q1_co2eq +
q2_co2eq + q3_co2eq +
q4_co2eq + q5_co2eq,
q1_co2eq_domestic = q1_co2_domestic + q1_ch4_domestic + q1_n2o_domestic,
q2_co2eq_domestic = q2_co2_domestic + q2_ch4_domestic + q2_n2o_domestic,
q3_co2eq_domestic = q3_co2_domestic + q3_ch4_domestic + q3_n2o_domestic,
q4_co2eq_domestic = q4_co2_domestic + q4_ch4_domestic + q4_n2o_domestic,
q5_co2eq_domestic = q5_co2_domestic + q5_ch4_domestic + q5_n2o_domestic,
co2eq_total_domestic = q1_co2eq_domestic +
q2_co2eq_domestic + q3_co2eq_domestic +
q4_co2eq_domestic + q5_co2eq_domestic) %>%
select(-q1_ch4,
-q2_ch4,
-q3_ch4,
-q4_ch4,
-q5_ch4,
-ch4_total,
-q1_ch4_domestic,
-q2_ch4_domestic,
-q3_ch4_domestic,
-q4_ch4_domestic,
-q5_ch4_domestic,
-ch4_total_domestic,
-q1_n2o,
-q2_n2o,
-q3_n2o,
-q4_n2o,
-q5_n2o,
-n2o_total,
-q1_n2o_domestic,
-q2_n2o_domestic,
-q3_n2o_domestic,
-q4_n2o_domestic,
-q5_n2o_domestic,
-n2o_total_domestic)
results = fd_exiobase %>%
left_join(TIVs, by = c("year", "country_of_production", "coicop", "sector")) %>%
left_join(europe_TIVs, by = c("year", "country_of_production", "coicop", "sector", "five_sectors")) %>%
left_join(domestic_TIVs, by = c("year", "geo", "country_of_production", "coicop", "sector", "five_sectors")) %>%
transmute(year,geo,country_of_production,sector,coicop,five_sectors,
QUINTILE1,
QUINTILE2,
QUINTILE3,
QUINTILE4,
QUINTILE5,
fd_total = QUINTILE1+QUINTILE2+QUINTILE3+QUINTILE4+QUINTILE5,
TIV_CO2 = TIV_CO2_combustion +
TIV_CO2_noncombustion_cement +
TIV_CO2_noncombustion_lime +
TIV_CO2_agriculture_peatdecay +
TIV_CO2_waste_biogenic +
TIV_CO2_waste_fossil,
q1_co2 = QUINTILE1*TIV_CO2,
q2_co2 = QUINTILE2*TIV_CO2,
q3_co2 = QUINTILE3*TIV_CO2,
q4_co2 = QUINTILE4*TIV_CO2,
q5_co2 = QUINTILE5*TIV_CO2,
co2_total = q1_co2+q2_co2+q3_co2+q4_co2+q5_co2,
TIV_CO2_domestic = TIV_CO2_combustion_domestic +
TIV_CO2_noncombustion_cement_domestic +
TIV_CO2_noncombustion_lime_domestic +
TIV_CO2_agriculture_peatdecay_domestic +
TIV_CO2_waste_biogenic_domestic +
TIV_CO2_waste_fossil_domestic,
q1_co2_domestic = QUINTILE1*TIV_CO2_domestic,
q2_co2_domestic = QUINTILE2*TIV_CO2_domestic,
q3_co2_domestic = QUINTILE3*TIV_CO2_domestic,
q4_co2_domestic = QUINTILE4*TIV_CO2_domestic,
q5_co2_domestic = QUINTILE5*TIV_CO2_domestic,
co2_total_domestic = q1_co2_domestic+q2_co2_domestic+q3_co2_domestic+q4_co2_domestic+q5_co2_domestic,
TIV_CO2_europe = TIV_CO2_combustion_europe +
TIV_CO2_noncombustion_cement_europe +
TIV_CO2_noncombustion_lime_europe +
TIV_CO2_agriculture_peatdecay_europe +
TIV_CO2_waste_biogenic_europe +
TIV_CO2_waste_fossil_europe,
q1_co2_europe = QUINTILE1*(TIV_CO2_europe - TIV_CO2_domestic),
q2_co2_europe = QUINTILE2*(TIV_CO2_europe - TIV_CO2_domestic),
q3_co2_europe = QUINTILE3*(TIV_CO2_europe - TIV_CO2_domestic),
q4_co2_europe = QUINTILE4*(TIV_CO2_europe - TIV_CO2_domestic),
q5_co2_europe = QUINTILE5*(TIV_CO2_europe - TIV_CO2_domestic),
co2_total_europe = q1_co2_europe+q2_co2_europe+q3_co2_europe+q4_co2_europe+q5_co2_europe,
TIV_CO2eq = TIV_CO2 +
TIV_CH4_combustion +
TIV_CH4_noncombustion_gas +
TIV_CH4_noncombustion_oil +
TIV_CH4_noncombustion_anthracite +
TIV_CH4_noncombustion_bituminouscoal +
TIV_CH4_noncombustion_cokingcoal +
TIV_CH4_noncombustion_lignite +
TIV_CH4_noncombustion_subbituminouscoal +
TIV_CH4_noncombustion_oilrefinery +
TIV_CH4_agriculture +
TIV_CH4_waste +
TIV_N2O_combustion +
TIV_N2O_agriculture +
TIV_SF6 + TIV_HFC + TIV_PFC,
q1_co2eq = QUINTILE1*TIV_CO2eq,
q2_co2eq = QUINTILE2*TIV_CO2eq,
q3_co2eq = QUINTILE3*TIV_CO2eq,
q4_co2eq = QUINTILE4*TIV_CO2eq,
q5_co2eq = QUINTILE5*TIV_CO2eq,
co2eq_total = q1_co2eq + q2_co2eq + q3_co2eq + q4_co2eq + q5_co2eq,
TIV_CO2eq_domestic = TIV_CO2_domestic +
TIV_CH4_combustion_domestic +
TIV_CH4_noncombustion_gas_domestic +
TIV_CH4_noncombustion_oil_domestic +
TIV_CH4_noncombustion_anthracite_domestic +
TIV_CH4_noncombustion_bituminouscoal_domestic +
TIV_CH4_noncombustion_cokingcoal_domestic +
TIV_CH4_noncombustion_lignite_domestic +
TIV_CH4_noncombustion_subbituminouscoal_domestic +
TIV_CH4_noncombustion_oilrefinery_domestic +
TIV_CH4_agriculture_domestic +
TIV_CH4_waste_domestic +
TIV_N2O_combustion_domestic +
TIV_N2O_agriculture_domestic +
TIV_SF6_domestic + TIV_HFC_domestic + TIV_PFC_domestic,
q1_co2eq_domestic = QUINTILE1*TIV_CO2eq_domestic,
q2_co2eq_domestic = QUINTILE2*TIV_CO2eq_domestic,
q3_co2eq_domestic = QUINTILE3*TIV_CO2eq_domestic,
q4_co2eq_domestic = QUINTILE4*TIV_CO2eq_domestic,
q5_co2eq_domestic = QUINTILE5*TIV_CO2eq_domestic,
co2eq_total_domestic = q1_co2eq_domestic + q2_co2eq_domestic + q3_co2eq_domestic + q4_co2eq_domestic + q5_co2eq_domestic,
TIV_CO2eq_europe = TIV_CO2_europe +
TIV_CH4_combustion_europe +
TIV_CH4_noncombustion_gas_europe +
TIV_CH4_noncombustion_oil_europe +
TIV_CH4_noncombustion_anthracite_europe +
TIV_CH4_noncombustion_bituminouscoal_europe +
TIV_CH4_noncombustion_cokingcoal_europe +
TIV_CH4_noncombustion_lignite_europe +
TIV_CH4_noncombustion_subbituminouscoal_europe +
TIV_CH4_noncombustion_oilrefinery_europe +
TIV_CH4_agriculture_europe +
TIV_CH4_waste_europe +
TIV_N2O_combustion_europe +
TIV_N2O_agriculture_europe +
TIV_SF6_europe + TIV_HFC_europe + TIV_PFC_europe,
q1_co2eq_europe = QUINTILE1*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q2_co2eq_europe = QUINTILE2*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q3_co2eq_europe = QUINTILE3*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q4_co2eq_europe = QUINTILE4*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q5_co2eq_europe = QUINTILE5*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
co2eq_total_europe = q1_co2eq_europe + q2_co2eq_europe + q3_co2eq_europe + q4_co2eq_europe + q5_co2eq_europe,
TIV_energy,
q1_energy = QUINTILE1*TIV_energy,
q2_energy = QUINTILE2*TIV_energy,
q3_energy = QUINTILE3*TIV_energy,
q4_energy = QUINTILE4*TIV_energy,
q5_energy = QUINTILE5*TIV_energy,
energy_total = q1_energy+q2_energy+q3_energy+q4_energy+q5_energy,
TIV_energy_domestic,
q1_energy_domestic = QUINTILE1*TIV_energy_domestic,
q2_energy_domestic = QUINTILE2*TIV_energy_domestic,
q3_energy_domestic = QUINTILE3*TIV_energy_domestic,
q4_energy_domestic = QUINTILE4*TIV_energy_domestic,
q5_energy_domestic = QUINTILE5*TIV_energy_domestic,
energy_total_domestic = q1_energy_domestic+q2_energy_domestic+q3_energy_domestic+q4_energy_domestic+q5_energy_domestic,
TIV_energy_europe,
q1_energy_europe = QUINTILE1*(TIV_energy_europe - TIV_energy_domestic),
q2_energy_europe = QUINTILE2*(TIV_energy_europe - TIV_energy_domestic),
q3_energy_europe = QUINTILE3*(TIV_energy_europe - TIV_energy_domestic),
q4_energy_europe = QUINTILE4*(TIV_energy_europe - TIV_energy_domestic),
q5_energy_europe = QUINTILE5*(TIV_energy_europe - TIV_energy_domestic),
energy_total_europe = q1_energy_europe+q2_energy_europe+q3_energy_europe+q4_energy_europe+q5_energy_europe)
results_with_direct_FD_fp = bind_rows(results,direct_FD_fp_wide)
### create compressed results_ixi rds file
dat_all = results_with_direct_FD_fp %>%
clean_names()
# convert sector labels to IDs
sectors = dat_all %>%
distinct(sector) %>%
mutate(sector_id = row_number())
#write_csv(sectors, here("data/sector_labels.csv"))
write_csv(sectors, paste0(here("/analysis/data/derived/sectors_method1_ixi.csv")))
# convert aggregated sector labels to IDs
sectors_agg = dat_all %>%
distinct(five_sectors) %>%
mutate(sector_agg_id = row_number())
#write_csv(sectors_agg, here("data/sector_agg_labels.csv"))
write_csv(sectors_agg, paste0(here("/analysis/data/derived/sectors_agg_method1_ixi.csv")))
# convert COICOP labels to IDs
coicop = dat_all %>%
distinct(coicop) %>%
mutate(coicop_id = row_number())
#write_csv(sectors_agg, here("data/sector_agg_labels.csv"))
write_csv(coicop, paste0(here("/analysis/data/derived/coicop_method1_ixi.csv")))
# replace sector text labels with numerical IDs (save space)
dat_compressed = dat_all %>%
left_join(sectors, by="sector") %>%
left_join(sectors_agg, by="five_sectors") %>%
left_join(coicop, by = "coicop") %>%
select(-c(sector, five_sectors,coicop))
# extract sector aggregation
sector_mapping = dat_compressed %>%
group_by(sector_id) %>%
summarise(sector_agg_id = first(sector_agg_id),
coicop_id = first(coicop_id))
# collapse country of origin
dat_results = dat_compressed %>%
select(-sector_agg_id,-coicop_id) %>%
group_by(year, geo, sector_id) %>%
summarise_if(is.numeric, sum, na.rm = TRUE)
## extract final demand and pivot long
cols_final_demand = c("quintile1", "quintile2", "quintile3", "quintile4", "quintile5")
tmp_fd = dat_results %>%
select(year, geo, sector_id, cols_final_demand) %>%
pivot_longer(cols = cols_final_demand,
names_to = "quintile",
values_to = "fd_me") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2 and pivot long
cols_co2 = c("q1_co2", "q2_co2", "q3_co2", "q4_co2", "q5_co2")
tmp_co2 = dat_results %>%
select(year, geo, sector_id, cols_co2) %>%
pivot_longer(cols = cols_co2,
names_to = "quintile",
values_to = "co2_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2 domestic and pivot long
cols_co2_domestic = c("q1_co2_domestic", "q2_co2_domestic", "q3_co2_domestic", "q4_co2_domestic", "q5_co2_domestic")
tmp_co2_domestic = dat_results %>%
select(year, geo, sector_id, cols_co2_domestic) %>%
pivot_longer(cols = cols_co2_domestic,
names_to = "quintile",
values_to = "co2_domestic_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2 europe and pivot long
cols_co2_europe = c("q1_co2_europe", "q2_co2_europe", "q3_co2_europe", "q4_co2_europe", "q5_co2_europe")
tmp_co2_europe = dat_results %>%
select(year, geo, sector_id, cols_co2_europe) %>%
pivot_longer(cols = cols_co2_europe,
names_to = "quintile",
values_to = "co2_europe_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq and pivot long
cols_co2eq = c("q1_co2eq", "q2_co2eq", "q3_co2eq", "q4_co2eq", "q5_co2eq")
tmp_co2eq = dat_results %>%
select(year, geo, sector_id, cols_co2eq) %>%
pivot_longer(cols = cols_co2eq,
names_to = "quintile",
values_to = "co2eq_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq domestic and pivot long
cols_co2eq_domestic = c("q1_co2eq_domestic", "q2_co2eq_domestic", "q3_co2eq_domestic", "q4_co2eq_domestic", "q5_co2eq_domestic")
tmp_co2eq_domestic = dat_results %>%
select(year, geo, sector_id, cols_co2eq_domestic) %>%
pivot_longer(cols = cols_co2eq_domestic,
names_to = "quintile",
values_to = "co2eq_domestic_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq europe and pivot long
cols_co2eq_europe = c("q1_co2eq_europe", "q2_co2eq_europe", "q3_co2eq_europe", "q4_co2eq_europe", "q5_co2eq_europe")
tmp_co2eq_europe = dat_results %>%
select(year, geo, sector_id, cols_co2eq_europe) %>%
pivot_longer(cols = cols_co2eq_europe,
names_to = "quintile",
values_to = "co2eq_europe_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy use and pivot long
cols_energy = c("q1_energy","q2_energy","q3_energy","q4_energy","q5_energy")
tmp_energy = dat_results %>%
select(year, geo, sector_id, cols_energy) %>%
pivot_longer(cols = cols_energy,
names_to = "quintile",
values_to = "energy_use_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy domestic and pivot long
cols_energy_domestic = c("q1_energy_domestic","q2_energy_domestic","q3_energy_domestic","q4_energy_domestic","q5_energy_domestic")
tmp_energy_domestic = dat_results %>%
select(year, geo, sector_id, cols_energy_domestic) %>%
pivot_longer(cols = cols_energy_domestic,
names_to = "quintile",
values_to = "energy_use_domestic_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy europe and pivot long
cols_energy_europe = c("q1_energy_europe","q2_energy_europe","q3_energy_europe","q4_energy_europe","q5_energy_europe")
tmp_energy_europe = dat_results %>%
select(year, geo, sector_id, cols_energy_europe) %>%
pivot_longer(cols = cols_energy_europe,
names_to = "quintile",
values_to = "energy_use_europe_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
### TODO: also convert to other indicators to this format (as blocks above)
### TODO: left join all indicators back to "results_formated" like her with co2
results_recombined = tmp_fd %>%
left_join(tmp_co2, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2_domestic, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2_europe, by = c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2eq, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2eq_domestic, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2eq_europe, by = c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_energy, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_energy_domestic, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_energy_europe, by = c("year", "geo", "sector_id", "quint"))
# finally re-join aggregated sector IDs
results_formatted = results_recombined %>%
left_join(sector_mapping, by="sector_id") %>%
ungroup() %>%
select(-coicop_id)
write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi.csv"))
write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi.rds"))
################################################### !!!! method 1 - PXP version - PPS HH NO RENT !!!! ####################################################
##########################################################################################################################################################
##########################################################################################################################################################
# Exiobase - pxp version
years_exb_pxp = c(2005,2010)
disaggregated_final_demand = NULL
TIVs = NULL
domestic_TIVs = NULL
europe_TIVs = NULL
national_fp = NULL
national_territorial = NULL
for (i in years_exb_pxp){
year_current = i
Exiobase_FD = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/FD_",year_current,"_pxp.csv"))[,-1]
# select household final demand vectors for relevant countries - figure out how to soft code this
AT = Exiobase_FD[,1]
BE = Exiobase_FD[,8]
BG = Exiobase_FD[,15]
CY = Exiobase_FD[,22]
CZ = Exiobase_FD[,29]
DE = Exiobase_FD[,36]
DK = Exiobase_FD[,43]
EE = Exiobase_FD[,50]
EL = Exiobase_FD[,78]
ES = Exiobase_FD[,57]
FI = Exiobase_FD[,64]
FR = Exiobase_FD[,71]
HR = Exiobase_FD[,85]
HU = Exiobase_FD[,92]
IE = Exiobase_FD[,99]
IT = Exiobase_FD[,106]
LT = Exiobase_FD[,113]
LU = Exiobase_FD[,120]
LV = Exiobase_FD[,127]
MT = Exiobase_FD[,134]
NL = Exiobase_FD[,141]
NO = Exiobase_FD[,288]
PL = Exiobase_FD[,148]
PT = Exiobase_FD[,155]
RO = Exiobase_FD[,162]
SE = Exiobase_FD[,169]
SI = Exiobase_FD[,176]
SK = Exiobase_FD[,183]
TR = Exiobase_FD[,274]
UK = Exiobase_FD[,190]
Eurostat_countries = cbind(AT,BE,BG,CY,CZ,DE,DK,EE,EL,ES,FI,FR,HR,HU,IE,IT,LT,LU,LV,MT,NL,NO,PL,PT,RO,SE,SI,SK,TR,UK)
# labels
Exiobase_T_labels = read.csv(paste0(data_dir_income_stratified_footprints, "/data/Exiobase_T_labels_pxp_w_coicop_mapping_no_rent.csv")) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
# hh fd with production sector labels
hh_fd_with_production_sector_labels = cbind(Exiobase_T_labels,Eurostat_countries) %>% rename(geo = V1, sector = V2)
# assumption of same purchase structure between quintiles of domestic and foreign final demand
# replicate each cell of each country's hh final demand as many times as there are income groups in the HBS data - in this preliminary case:5
cells_repeat = data.frame(hh_fd_with_production_sector_labels %>% slice(rep(1:n(), each = 5)))
quintiles = data.frame(rep(c("QUINTILE1","QUINTILE2","QUINTILE3","QUINTILE4","QUINTILE5"),200)) %>% rename_at(1,~"quintile")
replicated = cbind(cells_repeat,quintiles) %>% rename(country_of_production = geo)
# make fd data long
replicated_long = replicated %>% gather(geo, value,-sector,-coicop,-quintile,-five_sectors,-country_of_production)
year = as.character(rep(year_current,nrow(replicated_long)))
replicated_long = cbind(year,replicated_long)
disaggregated_final_demand = rbind(disaggregated_final_demand, replicated_long)
# TIVs
# CO2 - combustion - air
Exiobase_TIV_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_combustion_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_combustion_air_", year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_combustion_domestic)
Exiobase_TIV_europe_breakdown_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_combustion_air_", year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_combustion_europe,TIV_CO2_combustion_not_europe)
# CO2 - noncombustion - cement - air
Exiobase_TIV_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_noncombustion_cement_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_noncombustion_cement_domestic)
Exiobase_TIV_europe_breakdown_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_noncombustion_cement_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_noncombustion_cement_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_noncombustion_cement_europe,TIV_CO2_noncombustion_cement_not_europe)
# CO2 - noncombustion - lime - air
Exiobase_TIV_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_noncombustion_lime_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_noncombustion_lime_domestic)
Exiobase_TIV_europe_breakdown_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_noncombustion_lime_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_noncombustion_lime_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_noncombustion_lime_europe,TIV_CO2_noncombustion_lime_not_europe)
# CO2 - agriculture - peat decay - air
Exiobase_TIV_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_agriculture_peatdecay_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_agriculture_peatdecay_domestic)
Exiobase_TIV_europe_breakdown_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_agriculture_peatdecay_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_agriculture_peatdecay_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_agriculture_peatdecay_europe,TIV_CO2_agriculture_peatdecay_not_europe)
# CO2 - waste - biogenic - air
Exiobase_TIV_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_biogenic_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_biogenic_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_waste_biogenic_domestic)
Exiobase_TIV_europe_breakdown_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_biogenic_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_waste_biogenic_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_waste_biogenic_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_waste_biogenic_europe,TIV_CO2_waste_biogenic_not_europe)
# CO2 - waste - fossil - air
Exiobase_TIV_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_co2_waste_fossil_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_waste_fossil_domestic)
Exiobase_TIV_europe_breakdown_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_waste_fossil_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_waste_fossil_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_waste_fossil_europe,TIV_CO2_waste_fossil_not_europe)
# CH4 - combustion -air
Exiobase_TIV_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_combustion_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_combustion_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_combustion_domestic)
Exiobase_TIV_europe_breakdown_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_combustion_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_combustion_europe,TIV_CH4_combustion_not_europe)
# CH4 - noncombustion - gas - air
Exiobase_TIV_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_gas_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_gas_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_gas_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_gas_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_gas_europe,TIV_CH4_noncombustion_gas_not_europe)
# CH4 - noncombustion - oil - air
Exiobase_TIV_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_oil_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_oil_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_oil_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_oil_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_oil_europe,TIV_CH4_noncombustion_oil_not_europe)
# CH4 - noncombustion - anthracite - air
Exiobase_TIV_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_anthracite_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_anthracite_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_anthracite_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_anthracite_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_anthracite_europe,TIV_CH4_noncombustion_anthracite_not_europe)
# CH4 - noncombustion - bituminouscoal - air
Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_bituminouscoal_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_bituminouscoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_bituminouscoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_bituminouscoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_bituminouscoal_europe,TIV_CH4_noncombustion_bituminouscoal_not_europe)
# CH4 - noncombustion - cokingcoal - air
Exiobase_TIV_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_cokingcoal_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_cokingcoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_cokingcoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_cokingcoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_cokingcoal_europe,TIV_CH4_noncombustion_cokingcoal_not_europe)
# CH4 - noncombustion - lignite - air
Exiobase_TIV_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_lignite_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_lignite_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_lignite_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_lignite_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_lignite_europe,TIV_CH4_noncombustion_lignite_not_europe)
# CH4 - noncombustion - subbituminouscoal - air
Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_subbituminouscoal_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_subbituminouscoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_subbituminouscoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_subbituminouscoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_subbituminouscoal_europe,TIV_CH4_noncombustion_subbituminouscoal_not_europe)
# CH4 - noncombustion - oilrefinery - air
Exiobase_TIV_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_noncombustion_oilrefinery_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_oilrefinery_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_oilrefinery_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_oilrefinery_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_oilrefinery_europe,TIV_CH4_noncombustion_oilrefinery_not_europe)
# CH4 - agriculture - air
Exiobase_TIV_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_agriculture_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_agriculture_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_agriculture_domestic)
Exiobase_TIV_europe_breakdown_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_agriculture_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_agriculture_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_agriculture_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_agriculture_europe,TIV_CH4_agriculture_not_europe)
# CH4 - waste - air
Exiobase_TIV_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_ch4_CO2eq_waste_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_waste_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_waste_domestic)
Exiobase_TIV_europe_breakdown_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_ch4_CO2eq_waste_air_", year_current, "_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_waste_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_waste_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_waste_europe,TIV_CH4_waste_not_europe)
# N2O - combustion - air
Exiobase_TIV_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_n2o_CO2eq_combustion_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_N2O_combustion_domestic)
Exiobase_TIV_europe_breakdown_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_N2O_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_N2O_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_N2O_combustion_europe,TIV_N2O_combustion_not_europe)
# N2O - agriculture - air
Exiobase_TIV_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_n2o_CO2eq_agriculture_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_N2O_agriculture_domestic)
Exiobase_TIV_europe_breakdown_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_N2O_agriculture_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_N2O_agriculture_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_N2O_agriculture_europe,TIV_N2O_agriculture_not_europe)
# SF6 - air
Exiobase_TIV_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_sf6_CO2eq_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_SF6_domestic)
Exiobase_TIV_europe_breakdown_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_SF6_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_SF6_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_SF6_europe,TIV_SF6_not_europe)
# HFC - air
Exiobase_TIV_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_hfc_CO2eq_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_HFC_domestic)
Exiobase_TIV_europe_breakdown_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_HFC_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_HFC_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_HFC_europe,TIV_HFC_not_europe)
# PFC - air
Exiobase_TIV_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_pfc_CO2eq_air_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_PFC_domestic)
Exiobase_TIV_europe_breakdown_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_PFC_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_PFC_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_PFC_europe,TIV_PFC_not_europe)
# Energy use
Exiobase_TIV_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_energy_carrier_use_",year_current,"_pxp.csv"))[,-1]
Exiobase_TIV_country_breakdown_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_energy_carrier_use_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_energy_domestic)
Exiobase_TIV_europe_breakdown_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_pxp/TIV_country_breakdown_energy_carrier_use_",year_current,"_pxp.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_energy_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_energy_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_energy_europe,TIV_energy_not_europe)
# join with labels
TIV_with_labels = cbind(Exiobase_T_labels,
t(Exiobase_TIV_co2_combustion_bp),
t(Exiobase_TIV_co2_noncombustion_cement_bp),
t(Exiobase_TIV_co2_noncombustion_lime_bp),
t(Exiobase_TIV_co2_agriculture_peatdecay_bp),
t(Exiobase_TIV_co2_waste_biogenic_bp),
t(Exiobase_TIV_co2_waste_fossil_bp),
t(Exiobase_TIV_ch4_combustion_bp),
t(Exiobase_TIV_ch4_noncombustion_gas_bp),
t(Exiobase_TIV_ch4_noncombustion_oil_bp),
t(Exiobase_TIV_ch4_noncombustion_anthracite_bp),
t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp),
t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp),
t(Exiobase_TIV_ch4_noncombustion_lignite_bp),
t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp),
t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp),
t(Exiobase_TIV_ch4_agriculture_bp),
t(Exiobase_TIV_ch4_waste_bp),
t(Exiobase_TIV_n2o_combustion_bp),
t(Exiobase_TIV_n2o_agriculture_bp),
t(Exiobase_TIV_sf6_bp),
t(Exiobase_TIV_hfc_bp),
t(Exiobase_TIV_pfc_bp),
t(Exiobase_TIV_energy_use_bp)) %>%
rename(TIV_CO2_combustion = "t(Exiobase_TIV_co2_combustion_bp)",
TIV_CO2_noncombustion_cement = "t(Exiobase_TIV_co2_noncombustion_cement_bp)",
TIV_CO2_noncombustion_lime = "t(Exiobase_TIV_co2_noncombustion_lime_bp)",
TIV_CO2_agriculture_peatdecay = "t(Exiobase_TIV_co2_agriculture_peatdecay_bp)",
TIV_CO2_waste_biogenic = "t(Exiobase_TIV_co2_waste_biogenic_bp)",
TIV_CO2_waste_fossil = "t(Exiobase_TIV_co2_waste_fossil_bp)",
TIV_CH4_combustion = "t(Exiobase_TIV_ch4_combustion_bp)",
TIV_CH4_noncombustion_gas = "t(Exiobase_TIV_ch4_noncombustion_gas_bp)",
TIV_CH4_noncombustion_oil = "t(Exiobase_TIV_ch4_noncombustion_oil_bp)",
TIV_CH4_noncombustion_anthracite = "t(Exiobase_TIV_ch4_noncombustion_anthracite_bp)",
TIV_CH4_noncombustion_bituminouscoal = "t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp)",
TIV_CH4_noncombustion_cokingcoal = "t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp)",
TIV_CH4_noncombustion_lignite = "t(Exiobase_TIV_ch4_noncombustion_lignite_bp)",
TIV_CH4_noncombustion_subbituminouscoal = "t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp)",
TIV_CH4_noncombustion_oilrefinery = "t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp)",
TIV_CH4_agriculture = "t(Exiobase_TIV_ch4_agriculture_bp)",
TIV_CH4_waste = "t(Exiobase_TIV_ch4_waste_bp)",
TIV_N2O_combustion = "t(Exiobase_TIV_n2o_combustion_bp)",
TIV_N2O_agriculture = "t(Exiobase_TIV_n2o_agriculture_bp)",
TIV_SF6 = "t(Exiobase_TIV_sf6_bp)",
TIV_HFC = "t(Exiobase_TIV_hfc_bp)",
TIV_PFC = "t(Exiobase_TIV_pfc_bp)",
TIV_energy = "t(Exiobase_TIV_energy_use_bp)") %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year = as.character(rep(year_current,nrow(TIV_with_labels)))
look = cbind(year,TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2)
TIVs = rbind(TIVs,look)
# join domestic_TIVs with labels
domestic_TIV_with_labels = cbind(Exiobase_T_labels,
Exiobase_TIV_country_breakdown_co2_combustion_bp,
Exiobase_TIV_country_breakdown_co2_noncombustion_cement_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_noncombustion_lime_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_agriculture_peatdecay_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_waste_biogenic_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_waste_fossil_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_combustion_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_gas_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_oil_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_anthracite_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_bituminouscoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_cokingcoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_lignite_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_oilrefinery_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_agriculture_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_waste_bp %>% select(-country),
Exiobase_TIV_country_breakdown_n2o_combustion_bp %>% select(-country),
Exiobase_TIV_country_breakdown_n2o_agriculture_bp %>% select(-country),
Exiobase_TIV_country_breakdown_sf6_bp %>% select(-country),
Exiobase_TIV_country_breakdown_hfc_bp %>% select(-country),
Exiobase_TIV_country_breakdown_pfc_bp %>% select(-country),
Exiobase_TIV_country_breakdown_energy_use_bp %>% select(-country)) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"),
country = dplyr::recode(country, "GR" = "EL", "GB" = "UK"))
year_domestic = as.character(rep(year_current,nrow(domestic_TIV_with_labels)))
look_domestic = cbind(year_domestic,domestic_TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2, geo = country, year = year_domestic) %>%
mutate(TIV_CO2_combustion_domestic = as.numeric(TIV_CO2_combustion_domestic),
TIV_CO2_noncombustion_cement_domestic = as.numeric(TIV_CO2_noncombustion_cement_domestic),
TIV_CO2_noncombustion_lime_domestic = as.numeric(TIV_CO2_noncombustion_lime_domestic),
TIV_CO2_agriculture_peatdecay_domestic = as.numeric(TIV_CO2_agriculture_peatdecay_domestic),
TIV_CO2_waste_biogenic_domestic = as.numeric(TIV_CO2_waste_biogenic_domestic),
TIV_CO2_waste_fossil_domestic = as.numeric(TIV_CO2_waste_fossil_domestic),
TIV_CH4_combustion_domestic = as.numeric(TIV_CH4_combustion_domestic),
TIV_CH4_noncombustion_gas_domestic = as.numeric(TIV_CH4_noncombustion_gas_domestic),
TIV_CH4_noncombustion_oil_domestic = as.numeric(TIV_CH4_noncombustion_oil_domestic),
TIV_CH4_noncombustion_anthracite_domestic = as.numeric(TIV_CH4_noncombustion_anthracite_domestic),
TIV_CH4_noncombustion_bituminouscoal_domestic = as.numeric(TIV_CH4_noncombustion_bituminouscoal_domestic),
TIV_CH4_noncombustion_cokingcoal_domestic = as.numeric(TIV_CH4_noncombustion_cokingcoal_domestic),
TIV_CH4_noncombustion_lignite_domestic = as.numeric(TIV_CH4_noncombustion_lignite_domestic),
TIV_CH4_noncombustion_subbituminouscoal_domestic = as.numeric(TIV_CH4_noncombustion_subbituminouscoal_domestic),
TIV_CH4_noncombustion_oilrefinery_domestic = as.numeric(TIV_CH4_noncombustion_oilrefinery_domestic),
TIV_CH4_agriculture_domestic = as.numeric(TIV_CH4_agriculture_domestic),
TIV_CH4_waste_domestic = as.numeric(TIV_CH4_waste_domestic),
TIV_N2O_combustion_domestic = as.numeric(TIV_N2O_combustion_domestic),
TIV_N2O_agriculture_domestic = as.numeric(TIV_N2O_agriculture_domestic),
TIV_SF6_domestic = as.numeric(TIV_SF6_domestic),
TIV_HFC_domestic = as.numeric(TIV_HFC_domestic),
TIV_PFC_domestic = as.numeric(TIV_PFC_domestic),
TIV_energy_domestic = as.numeric(TIV_energy_domestic))
domestic_TIVs = rbind(domestic_TIVs, look_domestic)
# europe TIVs with labels
europe_TIV_with_labels = cbind(Exiobase_T_labels,
Exiobase_TIV_europe_breakdown_co2_combustion-bp,
Exiobase_TIV_europe_breakdown_co2_noncombustion_cement_bp,
Exiobase_TIV_europe_breakdown_co2_noncombustion_lime_bp,
Exiobase_TIV_europe_breakdown_co2_agriculture_peatdecay_bp,
Exiobase_TIV_europe_breakdown_co2_waste_biogenic_bp,
Exiobase_TIV_europe_breakdown_co2_waste_fossil_bp,
Exiobase_TIV_europe_breakdown_ch4_combustion_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_gas_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oil_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_anthracite_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_bituminouscoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_cokingcoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_lignite_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_subbituminouscoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oilrefinery_bp,
Exiobase_TIV_europe_breakdown_ch4_agriculture_bp,
Exiobase_TIV_europe_breakdown_ch4_waste_bp,
Exiobase_TIV_europe_breakdown_n2o_combustion_bp,
Exiobase_TIV_europe_breakdown_n2o_agriculture_bp,
Exiobase_TIV_europe_breakdown_sf6_bp,
Exiobase_TIV_europe_breakdown_hfc_bp,
Exiobase_TIV_europe_breakdown_pfc_bp,
Exiobase_TIV_europe_breakdown_energy_use_bp) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year_europe = as.character(rep(year_current,nrow(europe_TIV_with_labels)))
look_europe = cbind(year_europe,europe_TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2, year = year_europe) %>%
mutate(TIV_CO2_combustion_europe = as.numeric(TIV_CO2_combustion_europe),
TIV_CO2_noncombustion_cement_europe = as.numeric(TIV_CO2_noncombustion_cement_europe),
TIV_CO2_noncombustion_lime_europe = as.numeric(TIV_CO2_noncombustion_lime_europe),
TIV_CO2_agriculture_peatdecay_europe = as.numeric(TIV_CO2_agriculture_peatdecay_europe),
TIV_CO2_waste_biogenic_europe = as.numeric(TIV_CO2_waste_biogenic_europe),
TIV_CO2_waste_fossil_europe = as.numeric(TIV_CO2_waste_fossil_europe),
TIV_CH4_combustion_europe = as.numeric(TIV_CH4_combustion_europe),
TIV_CH4_noncombustion_gas_europe = as.numeric(TIV_CH4_noncombustion_gas_europe),
TIV_CH4_noncombustion_oil_europe = as.numeric(TIV_CH4_noncombustion_oil_europe),
TIV_CH4_noncombustion_anthracite_europe = as.numeric(TIV_CH4_noncombustion_anthracite_europe),
TIV_CH4_noncombustion_bituminouscoal_europe = as.numeric(TIV_CH4_noncombustion_bituminouscoal_europe),
TIV_CH4_noncombustion_cokingcoal_europe = as.numeric(TIV_CH4_noncombustion_cokingcoal_europe),
TIV_CH4_noncombustion_lignite_europe = as.numeric(TIV_CH4_noncombustion_lignite_europe),
TIV_CH4_noncombustion_subbituminouscoal_europe = as.numeric(TIV_CH4_noncombustion_subbituminouscoal_europe),
TIV_CH4_noncombustion_oilrefinery_europe = as.numeric(TIV_CH4_noncombustion_oilrefinery_europe),
TIV_CH4_agriculture_europe = as.numeric(TIV_CH4_agriculture_europe),
TIV_CH4_waste_europe = as.numeric(TIV_CH4_waste_europe),
TIV_N2O_combustion_europe = as.numeric(TIV_N2O_combustion_europe),
TIV_N2O_agriculture_europe = as.numeric(TIV_N2O_agriculture_europe),
TIV_SF6_europe = as.numeric(TIV_SF6_europe),
TIV_HFC_europe = as.numeric(TIV_HFC_europe),
TIV_PFC_europe = as.numeric(TIV_PFC_europe),
TIV_energy_europe = as.numeric(TIV_energy_europe))
europe_TIVs = rbind(europe_TIVs, look_europe)
# total national footprints
# FD labels
Exiobase_FD_labels = as.data.frame(t(read.csv(paste0(data_dir_exiobase, "/Exiobase_FD_labels_pxp.csv")))[-1,-3]) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
national_CO2_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_co2_combustion_bp)
national_CO2_noncombustion_cement_footprints = Exiobase_FD * t(Exiobase_TIV_co2_noncombustion_cement_bp)
national_CO2_noncombustion_lime_footprints = Exiobase_FD * t(Exiobase_TIV_co2_noncombustion_lime_bp)
national_CO2_agriculture_peatdecay_footprints = Exiobase_FD * t(Exiobase_TIV_co2_agriculture_peatdecay_bp)
national_CO2_waste_biogenic_footprints = Exiobase_FD * t(Exiobase_TIV_co2_waste_biogenic_bp)
national_CO2_waste_fossil_footprints = Exiobase_FD * t(Exiobase_TIV_co2_waste_fossil_bp)
national_CH4_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_combustion_bp)
national_CH4_noncombustion_gas_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_gas_bp)
national_CH4_noncombustion_oil_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_oil_bp)
national_CH4_noncombustion_anthracite_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_anthracite_bp)
national_CH4_noncombustion_bituminouscoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp)
national_CH4_noncombustion_cokingcoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp)
national_CH4_noncombustion_lignite_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_lignite_bp)
national_CH4_noncombustion_subbituminouscoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp)
national_CH4_noncombustion_oilrefinery_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp)
national_CH4_agriculture_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_agriculture_bp)
national_CH4_waste_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_waste_bp)
national_N2O_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_n2o_combustion_bp)
national_N2O_agriculture_footprints = Exiobase_FD * t(Exiobase_TIV_n2o_agriculture_bp)
national_SF6_footprints = Exiobase_FD * t(Exiobase_TIV_sf6_bp)
national_HFC_footprints = Exiobase_FD * t(Exiobase_TIV_hfc_bp)
national_PFC_footprints = Exiobase_FD * t(Exiobase_TIV_pfc_bp)
national_energy_footprints = Exiobase_FD * t(Exiobase_TIV_energy_use_bp)
# together
national_footprints_w_labels = cbind(Exiobase_FD_labels,
rowSums(t(national_CO2_combustion_footprints)),
rowSums(t(national_CO2_noncombustion_cement_footprints)),
rowSums(t(national_CO2_noncombustion_lime_footprints)),
rowSums(t(national_CO2_agriculture_peatdecay_footprints)),
rowSums(t(national_CO2_waste_biogenic_footprints)),
rowSums(t(national_CO2_waste_fossil_footprints)),
rowSums(t(national_CH4_combustion_footprints)),
rowSums(t(national_CH4_noncombustion_gas_footprints)),
rowSums(t(national_CH4_noncombustion_oil_footprints)),
rowSums(t(national_CH4_noncombustion_anthracite_footprints)),
rowSums(t(national_CH4_noncombustion_bituminouscoal_footprints)),
rowSums(t(national_CH4_noncombustion_cokingcoal_footprints)),
rowSums(t(national_CH4_noncombustion_lignite_footprints)),
rowSums(t(national_CH4_noncombustion_subbituminouscoal_footprints)),
rowSums(t(national_CH4_noncombustion_oilrefinery_footprints)),
rowSums(t(national_CH4_agriculture_footprints)),
rowSums(t(national_CH4_waste_footprints)),
rowSums(t(national_N2O_combustion_footprints)),
rowSums(t(national_N2O_agriculture_footprints)),
rowSums(t(national_SF6_footprints)),
rowSums(t(national_HFC_footprints)),
rowSums(t(national_PFC_footprints)),
rowSums(t(national_energy_footprints))) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year_national_fp = as.character(rep(year_current,nrow(national_footprints_w_labels)))
# direct FD emissions
direct_FD_extensions = read.csv(paste0(data_dir_exiobase, "/IOT_", year_current, "_pxp/satellite/F_hh.csv", sep = ""),row.names=NULL,as.is=TRUE)[3:1106,3:345]
direct_FD_extensions[is.na(direct_FD_extensions)]=0
direct_FD_extensions = mapply(direct_FD_extensions, FUN = as.numeric)
direct_FD_extensions = matrix(data=direct_FD_extensions,ncol=343,nrow=1104)
direct_FD_co2_combustion = direct_FD_extensions[24,]
direct_FD_co2_noncombustion_cement = direct_FD_extensions[93,]
direct_FD_co2_noncombustion_lime = direct_FD_extensions[94,]
direct_FD_co2_agriculture_peatdecay = direct_FD_extensions[428,]
direct_FD_co2_waste_biogenic = direct_FD_extensions[438,]
direct_FD_co2_waste_fossil = direct_FD_extensions[439,]
direct_FD_ch4_combustion = direct_FD_extensions[25,]*28
direct_FD_ch4_noncombustion_gas = direct_FD_extensions[68,]*28
direct_FD_ch4_noncombustion_oil = direct_FD_extensions[69,]*28
direct_FD_ch4_noncombustion_anthracite = direct_FD_extensions[70,]*28
direct_FD_ch4_noncombustion_bituminouscoal = direct_FD_extensions[71,]*28
direct_FD_ch4_noncombustion_cokingcoal = direct_FD_extensions[72,]*28
direct_FD_ch4_noncombustion_lignite = direct_FD_extensions[73,]*28
direct_FD_ch4_noncombustion_subbituminouscoal = direct_FD_extensions[74,]*28
direct_FD_ch4_noncombustion_oilrefinery = direct_FD_extensions[75,]*28
direct_FD_ch4_agriculture = direct_FD_extensions[427,]*28
direct_FD_ch4_waste = direct_FD_extensions[436,]*28
direct_FD_n2o_combustion = direct_FD_extensions[26,]*265
direct_FD_n2o_agriculture = direct_FD_extensions[430,]*265
direct_FD_sf6 = direct_FD_extensions[424,]*23500
direct_FD_hfc = direct_FD_extensions[425,]
direct_FD_pfc = direct_FD_extensions[426,]
direct_FD_energy = direct_FD_extensions[470,]
direct_FD_fp = data.frame(direct_FD_co2_combustion,
direct_FD_co2_noncombustion_cement,
direct_FD_co2_noncombustion_lime,
direct_FD_co2_agriculture_peatdecay,
direct_FD_co2_waste_biogenic,
direct_FD_co2_waste_fossil,
direct_FD_ch4_combustion,
direct_FD_ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_oilrefinery,
direct_FD_ch4_agriculture,
direct_FD_ch4_waste,
direct_FD_n2o_combustion,
direct_FD_n2o_agriculture,
direct_FD_sf6,
direct_FD_hfc,
direct_FD_pfc,
direct_FD_energy)
look_national_fp = as.data.frame(cbind(year_national_fp,
national_footprints_w_labels,
direct_FD_fp)) %>%
rename(year = year_national_fp,
geo = V1,
fd_category = V2,
co2_combustion = "rowSums(t(national_CO2_combustion_footprints))",
co2_noncombustion_cement = "rowSums(t(national_CO2_noncombustion_cement_footprints))",
co2_noncombustion_lime = "rowSums(t(national_CO2_noncombustion_lime_footprints))",
co2_agriculture_peatdecay = "rowSums(t(national_CO2_agriculture_peatdecay_footprints))",
co2_waste_biogenic = "rowSums(t(national_CO2_waste_biogenic_footprints))",
co2_waste_fossil = "rowSums(t(national_CO2_waste_fossil_footprints))",
ch4_combustion = "rowSums(t(national_CH4_combustion_footprints))",
ch4_noncombustion_gas = "rowSums(t(national_CH4_noncombustion_gas_footprints))",
ch4_noncombustion_oil = "rowSums(t(national_CH4_noncombustion_oil_footprints))",
ch4_noncombustion_anthracite = "rowSums(t(national_CH4_noncombustion_anthracite_footprints))",
ch4_noncombustion_bituminouscoal = "rowSums(t(national_CH4_noncombustion_bituminouscoal_footprints))",
ch4_noncombustion_cokingcoal = "rowSums(t(national_CH4_noncombustion_cokingcoal_footprints))",
ch4_noncombustion_lignite = "rowSums(t(national_CH4_noncombustion_lignite_footprints))",
ch4_noncombustion_subbituminouscoal = "rowSums(t(national_CH4_noncombustion_subbituminouscoal_footprints))",
ch4_noncombustion_oilrefinery = "rowSums(t(national_CH4_noncombustion_oilrefinery_footprints))",
ch4_agriculture = "rowSums(t(national_CH4_agriculture_footprints))",
ch4_waste = "rowSums(t(national_CH4_waste_footprints))",
n2o_combustion = "rowSums(t(national_N2O_combustion_footprints))",
n2o_agriculture = "rowSums(t(national_N2O_agriculture_footprints))",
sf6 = "rowSums(t(national_SF6_footprints))",
hfc = "rowSums(t(national_HFC_footprints))",
pfc = "rowSums(t(national_PFC_footprints))",
energy = "rowSums(t(national_energy_footprints))") %>%
select(year,
geo,
fd_category,
co2_combustion,
direct_FD_co2_combustion,
co2_noncombustion_cement,
direct_FD_co2_noncombustion_cement,
co2_noncombustion_lime,
direct_FD_co2_noncombustion_lime,
co2_agriculture_peatdecay,
direct_FD_co2_agriculture_peatdecay,
co2_waste_biogenic,
direct_FD_co2_waste_biogenic,
co2_waste_fossil,
direct_FD_co2_waste_fossil,
ch4_combustion,
direct_FD_ch4_combustion,
ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_gas,
ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_oil,
ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_anthracite,
ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_bituminouscoal,
ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_cokingcoal,
ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_lignite,
ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_subbituminouscoal,
ch4_noncombustion_oilrefinery,
direct_FD_ch4_noncombustion_oilrefinery,
ch4_agriculture,
direct_FD_ch4_agriculture,
ch4_waste,
direct_FD_ch4_waste,
n2o_combustion,
direct_FD_n2o_combustion,
n2o_agriculture,
direct_FD_n2o_agriculture,
sf6,
direct_FD_sf6,
hfc,
direct_FD_hfc,
pfc,
direct_FD_pfc,
energy,
direct_FD_energy)
national_fp = rbind(national_fp, look_national_fp)
# national territorial
satellite = read.csv(paste0(data_dir_exiobase, "/IOT_", year_current, "_pxp/satellite/satellite_",year_current,"_pxp.csv"))[,-1]
CO2_combustion_air = satellite[24,]
CO2_noncombustion_cement_air = satellite[93,]
CO2_noncombustion_lime_air = satellite[94,]
CO2_agriculture_peatdecay_air = satellite[428,]
CO2_waste_biogenic_air = satellite[438,]
CO2_waste_fossil_air = satellite[439,]
CH4_combustion_air = satellite[25,]
CH4_combustion_air = CH4_combustion_air*28
CH4_noncombustion_gas_air = satellite[68,]
CH4_noncombustion_gas_air = CH4_noncombustion_gas_air*28
CH4_noncombustion_oil_air = satellite[69,]
CH4_noncombustion_oil_air = CH4_noncombustion_oil_air*28
CH4_noncombustion_anthracite_air = satellite[70,]
CH4_noncombustion_anthracite_air = CH4_noncombustion_anthracite_air*28
CH4_noncombustion_bituminouscoal_air = satellite[71,]
CH4_noncombustion_bituminouscoal_air = CH4_noncombustion_bituminouscoal_air*28
CH4_noncombustion_cokingcoal_air = satellite[72,]
CH4_noncombustion_cokingcoal_air = CH4_noncombustion_cokingcoal_air*28
CH4_noncombustion_lignite_air = satellite[73,]
CH4_noncombustion_lignite_air = CH4_noncombustion_lignite_air*28
CH4_noncombustion_subbituminouscoal_air = satellite[74,]
CH4_noncombustion_subbituminouscoal_air = CH4_noncombustion_subbituminouscoal_air*28
CH4_noncombustion_oilrefinery_air = satellite[75,]
CH4_noncombustion_oilrefinery_air = CH4_noncombustion_oilrefinery_air*28
CH4_agriculture_air = satellite[427,]
CH4_agriculture_air = CH4_agriculture_air*28
CH4_waste_air = satellite[436,]
CH4_waste_air = CH4_waste_air*28
N2O_combustion_air = satellite[26,]
N2O_combustion_air = N2O_combustion_air*265
N2O_agriculture_air = satellite[430,]
N2O_agriculture_air = N2O_agriculture_air*265
SF6_air = satellite[424,]
SF6_air = SF6_air*23500
HFC_air = satellite[425,]
PFC_air = satellite[426,]
energy_carrier_use = satellite[470,]
territorial = data.frame(t(CO2_combustion_air),
t(CO2_noncombustion_cement_air),
t(CO2_noncombustion_lime_air),
t(CO2_agriculture_peatdecay_air),
t(CO2_waste_biogenic_air),
t(CO2_waste_fossil_air),
t(CH4_combustion_air),
t(CH4_noncombustion_gas_air),
t(CH4_noncombustion_oil_air),
t(CH4_noncombustion_anthracite_air),
t(CH4_noncombustion_bituminouscoal_air),
t(CH4_noncombustion_cokingcoal_air),
t(CH4_noncombustion_lignite_air),
t(CH4_noncombustion_subbituminouscoal_air),
t(CH4_noncombustion_oilrefinery_air),
t(CH4_agriculture_air),
t(CH4_waste_air),
t(N2O_combustion_air),
t(N2O_agriculture_air),
t(SF6_air),
t(HFC_air),
t(PFC_air),
t(energy_carrier_use)) %>%
rename(CO2_combustion = 1,
CO2_noncombustion_cement = 2,
CO2_noncombustion_lime = 3,
CO2_agriculture_peatdecay = 4,
CO2_waste_biogenic = 5,
CO2_waste_fossil = 6,
CH4_combustion = 7,
CH4_noncombustion_gas = 8,
CH4_noncombustion_oil = 9,
CH4_noncombustion_anthracite = 10,
CH4_noncombustion_bituminouscoal = 11,
CH4_noncombustion_cokingcoal = 12,
CH4_noncombustion_lignite = 13,
CH4_noncombustion_subbituminouscoal = 14,
CH4_noncombustion_oilrefinery = 15,
CH4_agriculture = 16,
CH4_waste = 17,
N2O_combustion = 18,
N2O_agriculture = 19,
SF6 = 20,
HFC = 21, PFC = 22, energy = 23)
year_territorial = as.character(rep(year_current,nrow(territorial)))
look_territorial = as.data.frame(cbind(year_territorial,
Exiobase_T_labels,
territorial)) %>%
rename(year = year_territorial,
geo = V1,
sector = V2) %>%
select(-coicop,-five_sectors)
national_territorial = rbind(national_territorial, look_territorial)
}
write.csv(national_territorial, paste0(data_dir_income_stratified_footprints, "/national_territorial_pxp.csv"))
write_rds(national_territorial, paste0(data_dir_income_stratified_footprints, "/national_territorial_pxp.rds"))
write.csv(national_fp, paste0(data_dir_income_stratified_footprints, "/national_fp_pxp.csv"))
write_rds(national_fp, paste0(data_dir_income_stratified_footprints, "/national_fp_pxp.rds"))
# calculate quintile shares within each sector
shares = join_expenditures %>%
group_by(coicop,geo,year) %>%
mutate(share = pps_coicop/sum(pps_coicop))
# pre-processing
fd_exiobase = disaggregated_final_demand %>%
left_join(shares, by = c("year","geo","coicop","quintile")) %>%
mutate(disaggregated_fd = value*share) %>%
select(year,geo,quintile,country_of_production,sector,coicop,disaggregated_fd) %>%
spread(quintile,disaggregated_fd)
# direct from FD - to go back to results without direct FD fp, do not run this next chunk and do not bind_rows with 'results'
env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_pefasu_1_Data.csv")) %>%
filter(TIME == 2015) %>%
mutate(geo = dplyr::recode(GEO,"Austria" = "AT",
"Belgium" = "BE",
"Cyprus" = "CY",
"Czechia" = "CZ",
"Denmark" = "DK",
"Estonia" = "EE",
"Finland" = "FI",
"France" = "FR",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Greece" = "EL",
"Hungary" = "HU",
"Ireland" = "IE",
"Italy" = "IT",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Malta" = "MT",
"Netherlands" = "NL",
"Norway" = "NO",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovakia" = "SK",
"Slovenia" = "SI",
"Spain" = "ES",
"Sweden" = "SE",
"United Kingdom" = "UK",
"Bulgaria" = "BG",
"Croatia" = "HR")) %>%
select(NACE_R2,geo,Value) %>%
mutate(Value = parse_number(Value),
Value = as.numeric(Value)) %>%
spread(NACE_R2,Value) %>%
clean_names() %>%
mutate(HH_HEAT = heating_cooling_activities_by_households/total_activities_by_households,
HH_TRA = transport_activities_by_households/total_activities_by_households,
HH_OTH = other_activities_by_households/total_activities_by_households) %>%
select(geo,HH_HEAT,HH_TRA,HH_OTH)
env_ac_pefasu_TR = env_ac_pefasu_no_TR %>%
filter(geo == "BG") %>%
mutate(geo = dplyr::recode(geo,
"BG" = "TR"))
env_ac_pefasu = rbind(env_ac_pefasu_no_TR,env_ac_pefasu_TR) %>%
gather(sector,share_of_total_energy,-geo)
env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_ainah_r2_1_Data.csv")) %>%
filter(TIME == 2015) %>%
mutate(geo = dplyr::recode(GEO,"Austria" = "AT",
"Belgium" = "BE",
"Cyprus" = "CY",
"Czechia" = "CZ",
"Denmark" = "DK",
"Estonia" = "EE",
"Finland" = "FI",
"France" = "FR",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Greece" = "EL",
"Hungary" = "HU",
"Ireland" = "IE",
"Italy" = "IT",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Malta" = "MT",
"Netherlands" = "NL",
"Norway" = "NO",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovakia" = "SK",
"Slovenia" = "SI",
"Spain" = "ES",
"Sweden" = "SE",
"Turkey" = "TR",
"United Kingdom" = "UK",
"Bulgaria" = "BG",
"Croatia" = "HR")) %>%
select(NACE_R2,AIRPOL,geo,Value) %>%
mutate(Value = parse_number(Value),
Value = as.numeric(Value)) %>%
spread(NACE_R2,Value) %>%
clean_names() %>%
mutate(HH_HEAT = heating_cooling_activities_by_households/total_activities_by_households,
HH_TRA = transport_activities_by_households/total_activities_by_households,
HH_OTH = other_activities_by_households/total_activities_by_households) %>%
select(geo,airpol,HH_HEAT,HH_TRA,HH_OTH)
env_ac_ainah_r2_co2 = env_ac_ainah_r2 %>%
filter(airpol == "Carbon dioxide") %>%
select(-airpol) %>%
gather(sector,share_of_total_co2,-geo)
env_ac_ainah_r2_ch4 = env_ac_ainah_r2 %>%
filter(airpol == "Methane") %>%
select(-airpol) %>%
gather(sector,share_of_total_ch4,-geo)
env_ac_ainah_r2_n2o = env_ac_ainah_r2 %>%
filter(airpol == "Nitrous oxide") %>%
select(-airpol) %>%
gather(sector,share_of_total_n2o,-geo)
direct_FD_fp_long = national_fp %>%
filter(fd_category == "Final consumption expenditure by households",
geo %in% c("AT",
"BE", "BG", "CY", "CZ",
"DE" , "DK" , "EE" ,
"ES" , "FI" , "FR" ,
"UK", "EL", "HR" ,
"HU" , "IE" , "IT" ,
"LT" , "LU" , "LV" ,
"MT" , "NL" , "PL" ,
"PT" , "TR" , "SK" ,
"SI" , "SE" , "RO" ,
"NO")) %>%
select(year,geo,fd_category,direct_FD_co2_combustion,
direct_FD_co2_noncombustion_cement,
direct_FD_co2_noncombustion_lime,
direct_FD_co2_agriculture_peatdecay,
direct_FD_co2_waste_biogenic,
direct_FD_co2_waste_fossil,
direct_FD_ch4_combustion,
direct_FD_ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_oilrefinery,
direct_FD_ch4_agriculture,
direct_FD_ch4_waste,
direct_FD_n2o_combustion,
direct_FD_n2o_agriculture,
direct_FD_sf6,
direct_FD_hfc,
direct_FD_pfc,
direct_FD_energy) %>%
slice(rep(1:n(), each = 3))
sector = rep(c("HH_HEAT","HH_TRA","HH_OTH"), nrow(direct_FD_fp_long)/3)
direct_FD_fp_long_disagg = cbind(sector,direct_FD_fp_long) %>%
mutate(coicop = ifelse(sector == "HH_TRA","CP072",
ifelse(sector == "HH_HEAT","CP045","CP05")),
five_sectors = ifelse(sector == "HH_TRA", "transport",
ifelse(sector == "HH_HEAT", "shelter", "manufactured goods"))) %>%
left_join(env_ac_ainah_r2_co2, by = c("geo","sector")) %>%
left_join(env_ac_ainah_r2_ch4, by = c("geo","sector")) %>%
left_join(env_ac_ainah_r2_n2o, by = c("geo","sector")) %>%
left_join(env_ac_pefasu, by = c("geo","sector")) %>%
mutate(direct_FD_co2 = (direct_FD_co2_combustion +
direct_FD_co2_noncombustion_cement +
direct_FD_co2_noncombustion_lime +
direct_FD_co2_agriculture_peatdecay +
direct_FD_co2_waste_biogenic +
direct_FD_co2_waste_fossil)*share_of_total_co2,
direct_FD_ch4 = (direct_FD_ch4_combustion +
direct_FD_ch4_noncombustion_gas +
direct_FD_ch4_noncombustion_oil +
direct_FD_ch4_noncombustion_anthracite +
direct_FD_ch4_noncombustion_bituminouscoal +
direct_FD_ch4_noncombustion_cokingcoal +
direct_FD_ch4_noncombustion_lignite +
direct_FD_ch4_noncombustion_subbituminouscoal +
direct_FD_ch4_noncombustion_oilrefinery +
direct_FD_ch4_agriculture +
direct_FD_ch4_waste)*share_of_total_ch4,
direct_FD_n2o = (direct_FD_n2o_combustion +
direct_FD_n2o_agriculture)*share_of_total_n2o,
direct_FD_energy = direct_FD_energy*share_of_total_energy) %>%
left_join(shares, by = c("year","geo","coicop")) %>%
mutate(disaggregated_direct_FD_co2 = direct_FD_co2*share,
disaggregated_direct_FD_ch4 = direct_FD_ch4*share,
disaggregated_direct_FD_n2o = direct_FD_n2o*share,
disaggregated_direct_FD_energy = direct_FD_energy*share) %>%
select(year,geo,sector, quintile,
coicop, five_sectors,
disaggregated_direct_FD_co2,
disaggregated_direct_FD_ch4,
disaggregated_direct_FD_n2o,
disaggregated_direct_FD_energy)
direct_FD_co2 = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_co2) %>%
spread(quintile,disaggregated_direct_FD_co2) %>%
rename(q1_co2 = QUINTILE1,
q2_co2 = QUINTILE2,
q3_co2 = QUINTILE3,
q4_co2 = QUINTILE4,
q5_co2 = QUINTILE5) %>%
mutate(q1_co2_domestic = q1_co2,
q2_co2_domestic = q2_co2,
q3_co2_domestic = q3_co2,
q4_co2_domestic = q4_co2,
q5_co2_domestic = q5_co2,
co2_total = q1_co2+q2_co2+q3_co2+q4_co2+q5_co2,
co2_total_domestic = q1_co2_domestic+
q2_co2_domestic+q3_co2_domestic+
q4_co2_domestic+q5_co2_domestic)
direct_FD_ch4 = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_ch4) %>%
spread(quintile,disaggregated_direct_FD_ch4) %>%
rename(q1_ch4 = QUINTILE1,
q2_ch4 = QUINTILE2,
q3_ch4 = QUINTILE3,
q4_ch4 = QUINTILE4,
q5_ch4 = QUINTILE5) %>%
mutate(q1_ch4_domestic = q1_ch4,
q2_ch4_domestic = q2_ch4,
q3_ch4_domestic = q3_ch4,
q4_ch4_domestic = q4_ch4,
q5_ch4_domestic = q5_ch4,
ch4_total = q1_ch4+q2_ch4+q3_ch4+q4_ch4+q5_ch4,
ch4_total_domestic = q1_ch4_domestic+
q2_ch4_domestic+q3_ch4_domestic+
q4_ch4_domestic+q5_ch4_domestic)
direct_FD_n2o = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_n2o) %>%
spread(quintile,disaggregated_direct_FD_n2o) %>%
rename(q1_n2o = QUINTILE1,
q2_n2o = QUINTILE2,
q3_n2o = QUINTILE3,
q4_n2o = QUINTILE4,
q5_n2o = QUINTILE5) %>%
mutate(q1_n2o_domestic = q1_n2o,
q2_n2o_domestic = q2_n2o,
q3_n2o_domestic = q3_n2o,
q4_n2o_domestic = q4_n2o,
q5_n2o_domestic = q5_n2o,
n2o_total = q1_n2o+q2_n2o+q3_n2o+q4_n2o+q5_n2o,
n2o_total_domestic = q1_n2o_domestic+
q2_n2o_domestic+q3_n2o_domestic+
q4_n2o_domestic+q5_n2o_domestic)
direct_FD_energy = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_energy) %>%
spread(quintile,disaggregated_direct_FD_energy) %>%
rename(q1_energy = QUINTILE1,
q2_energy = QUINTILE2,
q3_energy = QUINTILE3,
q4_energy = QUINTILE4,
q5_energy = QUINTILE5) %>%
mutate(q1_energy_domestic = q1_energy,
q2_energy_domestic = q2_energy,
q3_energy_domestic = q3_energy,
q4_energy_domestic = q4_energy,
q5_energy_domestic = q5_energy,
energy_total = q1_energy+q2_energy+q3_energy+q4_energy+q5_energy,
energy_total_domestic = q1_energy_domestic+
q2_energy_domestic+q3_energy_domestic+
q4_energy_domestic+q5_energy_domestic)
direct_FD_fp_wide = direct_FD_co2 %>%
left_join(direct_FD_ch4, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
left_join(direct_FD_n2o, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
left_join(direct_FD_energy, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
mutate(country_of_production = geo) %>%
mutate(q1_co2eq = q1_co2 + q1_ch4 + q1_n2o,
q2_co2eq = q2_co2 + q2_ch4 + q2_n2o,
q3_co2eq = q3_co2 + q3_ch4 + q3_n2o,
q4_co2eq = q4_co2 + q4_ch4 + q4_n2o,
q5_co2eq = q5_co2 + q5_ch4 + q5_n2o,
co2eq_total = q1_co2eq +
q2_co2eq + q3_co2eq +
q4_co2eq + q5_co2eq,
q1_co2eq_domestic = q1_co2_domestic + q1_ch4_domestic + q1_n2o_domestic,
q2_co2eq_domestic = q2_co2_domestic + q2_ch4_domestic + q2_n2o_domestic,
q3_co2eq_domestic = q3_co2_domestic + q3_ch4_domestic + q3_n2o_domestic,
q4_co2eq_domestic = q4_co2_domestic + q4_ch4_domestic + q4_n2o_domestic,
q5_co2eq_domestic = q5_co2_domestic + q5_ch4_domestic + q5_n2o_domestic,
co2eq_total_domestic = q1_co2eq_domestic +
q2_co2eq_domestic + q3_co2eq_domestic +
q4_co2eq_domestic + q5_co2eq_domestic) %>%
select(-q1_ch4,
-q2_ch4,
-q3_ch4,
-q4_ch4,
-q5_ch4,
-ch4_total,
-q1_ch4_domestic,
-q2_ch4_domestic,
-q3_ch4_domestic,
-q4_ch4_domestic,
-q5_ch4_domestic,
-ch4_total_domestic,
-q1_n2o,
-q2_n2o,
-q3_n2o,
-q4_n2o,
-q5_n2o,
-n2o_total,
-q1_n2o_domestic,
-q2_n2o_domestic,
-q3_n2o_domestic,
-q4_n2o_domestic,
-q5_n2o_domestic,
-n2o_total_domestic)
results = fd_exiobase %>%
left_join(TIVs, by = c("year", "country_of_production", "coicop", "sector")) %>%
left_join(europe_TIVs, by = c("year", "country_of_production", "coicop", "sector", "five_sectors")) %>%
left_join(domestic_TIVs, by = c("year", "geo", "country_of_production", "coicop", "sector", "five_sectors")) %>%
transmute(year,geo,country_of_production,sector,coicop,five_sectors,
QUINTILE1,
QUINTILE2,
QUINTILE3,
QUINTILE4,
QUINTILE5,
fd_total = QUINTILE1+QUINTILE2+QUINTILE3+QUINTILE4+QUINTILE5,
TIV_CO2 = TIV_CO2_combustion +
TIV_CO2_noncombustion_cement +
TIV_CO2_noncombustion_lime +
TIV_CO2_agriculture_peatdecay +
TIV_CO2_waste_biogenic +
TIV_CO2_waste_fossil,
q1_co2 = QUINTILE1*TIV_CO2,
q2_co2 = QUINTILE2*TIV_CO2,
q3_co2 = QUINTILE3*TIV_CO2,
q4_co2 = QUINTILE4*TIV_CO2,
q5_co2 = QUINTILE5*TIV_CO2,
co2_total = q1_co2+q2_co2+q3_co2+q4_co2+q5_co2,
TIV_CO2_domestic = TIV_CO2_combustion_domestic +
TIV_CO2_noncombustion_cement_domestic +
TIV_CO2_noncombustion_lime_domestic +
TIV_CO2_agriculture_peatdecay_domestic +
TIV_CO2_waste_biogenic_domestic +
TIV_CO2_waste_fossil_domestic,
q1_co2_domestic = QUINTILE1*TIV_CO2_domestic,
q2_co2_domestic = QUINTILE2*TIV_CO2_domestic,
q3_co2_domestic = QUINTILE3*TIV_CO2_domestic,
q4_co2_domestic = QUINTILE4*TIV_CO2_domestic,
q5_co2_domestic = QUINTILE5*TIV_CO2_domestic,
co2_total_domestic = q1_co2_domestic+q2_co2_domestic+q3_co2_domestic+q4_co2_domestic+q5_co2_domestic,
TIV_CO2_europe = TIV_CO2_combustion_europe +
TIV_CO2_noncombustion_cement_europe +
TIV_CO2_noncombustion_lime_europe +
TIV_CO2_agriculture_peatdecay_europe +
TIV_CO2_waste_biogenic_europe +
TIV_CO2_waste_fossil_europe,
q1_co2_europe = QUINTILE1*(TIV_CO2_europe - TIV_CO2_domestic),
q2_co2_europe = QUINTILE2*(TIV_CO2_europe - TIV_CO2_domestic),
q3_co2_europe = QUINTILE3*(TIV_CO2_europe - TIV_CO2_domestic),
q4_co2_europe = QUINTILE4*(TIV_CO2_europe - TIV_CO2_domestic),
q5_co2_europe = QUINTILE5*(TIV_CO2_europe - TIV_CO2_domestic),
co2_total_europe = q1_co2_europe+q2_co2_europe+q3_co2_europe+q4_co2_europe+q5_co2_europe,
TIV_CO2eq = TIV_CO2 +
TIV_CH4_combustion +
TIV_CH4_noncombustion_gas +
TIV_CH4_noncombustion_oil +
TIV_CH4_noncombustion_anthracite +
TIV_CH4_noncombustion_bituminouscoal +
TIV_CH4_noncombustion_cokingcoal +
TIV_CH4_noncombustion_lignite +
TIV_CH4_noncombustion_subbituminouscoal +
TIV_CH4_noncombustion_oilrefinery +
TIV_CH4_agriculture +
TIV_CH4_waste +
TIV_N2O_combustion +
TIV_N2O_agriculture +
TIV_SF6 + TIV_HFC + TIV_PFC,
q1_co2eq = QUINTILE1*TIV_CO2eq,
q2_co2eq = QUINTILE2*TIV_CO2eq,
q3_co2eq = QUINTILE3*TIV_CO2eq,
q4_co2eq = QUINTILE4*TIV_CO2eq,
q5_co2eq = QUINTILE5*TIV_CO2eq,
co2eq_total = q1_co2eq + q2_co2eq + q3_co2eq + q4_co2eq + q5_co2eq,
TIV_CO2eq_domestic = TIV_CO2_domestic +
TIV_CH4_combustion_domestic +
TIV_CH4_noncombustion_gas_domestic +
TIV_CH4_noncombustion_oil_domestic +
TIV_CH4_noncombustion_anthracite_domestic +
TIV_CH4_noncombustion_bituminouscoal_domestic +
TIV_CH4_noncombustion_cokingcoal_domestic +
TIV_CH4_noncombustion_lignite_domestic +
TIV_CH4_noncombustion_subbituminouscoal_domestic +
TIV_CH4_noncombustion_oilrefinery_domestic +
TIV_CH4_agriculture_domestic +
TIV_CH4_waste_domestic +
TIV_N2O_combustion_domestic +
TIV_N2O_agriculture_domestic +
TIV_SF6_domestic + TIV_HFC_domestic + TIV_PFC_domestic,
q1_co2eq_domestic = QUINTILE1*TIV_CO2eq_domestic,
q2_co2eq_domestic = QUINTILE2*TIV_CO2eq_domestic,
q3_co2eq_domestic = QUINTILE3*TIV_CO2eq_domestic,
q4_co2eq_domestic = QUINTILE4*TIV_CO2eq_domestic,
q5_co2eq_domestic = QUINTILE5*TIV_CO2eq_domestic,
co2eq_total_domestic = q1_co2eq_domestic + q2_co2eq_domestic + q3_co2eq_domestic + q4_co2eq_domestic + q5_co2eq_domestic,
TIV_CO2eq_europe = TIV_CO2_europe +
TIV_CH4_combustion_europe +
TIV_CH4_noncombustion_gas_europe +
TIV_CH4_noncombustion_oil_europe +
TIV_CH4_noncombustion_anthracite_europe +
TIV_CH4_noncombustion_bituminouscoal_europe +
TIV_CH4_noncombustion_cokingcoal_europe +
TIV_CH4_noncombustion_lignite_europe +
TIV_CH4_noncombustion_subbituminouscoal_europe +
TIV_CH4_noncombustion_oilrefinery_europe +
TIV_CH4_agriculture_europe +
TIV_CH4_waste_europe +
TIV_N2O_combustion_europe +
TIV_N2O_agriculture_europe +
TIV_SF6_europe + TIV_HFC_europe + TIV_PFC_europe,
q1_co2eq_europe = QUINTILE1*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q2_co2eq_europe = QUINTILE2*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q3_co2eq_europe = QUINTILE3*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q4_co2eq_europe = QUINTILE4*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
q5_co2eq_europe = QUINTILE5*(TIV_CO2eq_europe - TIV_CO2eq_domestic),
co2eq_total_europe = q1_co2eq_europe + q2_co2eq_europe + q3_co2eq_europe + q4_co2eq_europe + q5_co2eq_europe,
TIV_energy,
q1_energy = QUINTILE1*TIV_energy,
q2_energy = QUINTILE2*TIV_energy,
q3_energy = QUINTILE3*TIV_energy,
q4_energy = QUINTILE4*TIV_energy,
q5_energy = QUINTILE5*TIV_energy,
energy_total = q1_energy+q2_energy+q3_energy+q4_energy+q5_energy,
TIV_energy_domestic,
q1_energy_domestic = QUINTILE1*TIV_energy_domestic,
q2_energy_domestic = QUINTILE2*TIV_energy_domestic,
q3_energy_domestic = QUINTILE3*TIV_energy_domestic,
q4_energy_domestic = QUINTILE4*TIV_energy_domestic,
q5_energy_domestic = QUINTILE5*TIV_energy_domestic,
energy_total_domestic = q1_energy_domestic+q2_energy_domestic+q3_energy_domestic+q4_energy_domestic+q5_energy_domestic,
TIV_energy_europe,
q1_energy_europe = QUINTILE1*(TIV_energy_europe - TIV_energy_domestic),
q2_energy_europe = QUINTILE2*(TIV_energy_europe - TIV_energy_domestic),
q3_energy_europe = QUINTILE3*(TIV_energy_europe - TIV_energy_domestic),
q4_energy_europe = QUINTILE4*(TIV_energy_europe - TIV_energy_domestic),
q5_energy_europe = QUINTILE5*(TIV_energy_europe - TIV_energy_domestic),
energy_total_europe = q1_energy_europe+q2_energy_europe+q3_energy_europe+q4_energy_europe+q5_energy_europe)
results_with_direct_FD_fp = bind_rows(results,direct_FD_fp_wide)
### create compressed results_pxp rds file
dat_all = results_with_direct_FD_fp %>%
clean_names()
# convert sector labels to IDs
sectors = dat_all %>%
distinct(sector) %>%
mutate(sector_id = row_number())
# if interested in looking at a sectoral breakdown of the product-by-product version results, un-comment line below
#write_csv(sectors, paste0(here("/analysis/data/derived/si/sectors_method1_pxp.csv")))
# convert aggregated sector labels to IDs
sectors_agg = dat_all %>%
distinct(five_sectors) %>%
mutate(sector_agg_id = row_number())
#write_csv(sectors_agg, paste0(here("analysis/data/derived/si/sectors_agg_method1_pxp.csv")))
# convert COICOP labels to IDs
coicop = dat_all %>%
distinct(coicop) %>%
mutate(coicop_id = row_number())
#write_csv(coicop, paste0(here("analysis/data/derived/si/coicop_method1_pxp.csv")))
# replace sector text labels with numerical IDs (save space)
dat_compressed = dat_all %>%
left_join(sectors, by="sector") %>%
left_join(sectors_agg, by="five_sectors") %>%
left_join(coicop, by = "coicop") %>%
select(-c(sector, five_sectors,coicop))
# extract sector aggregation
sector_mapping = dat_compressed %>%
group_by(sector_id) %>%
summarise(sector_agg_id = first(sector_agg_id),
coicop_id = first(coicop_id))
# collapse country of origin
dat_results = dat_compressed %>%
select(-sector_agg_id,-coicop_id) %>%
group_by(year, geo, sector_id) %>%
summarise_if(is.numeric, sum, na.rm = TRUE)
## extract final demand and pivot long
cols_final_demand = c("quintile1", "quintile2", "quintile3", "quintile4", "quintile5")
tmp_fd = dat_results %>%
select(year, geo, sector_id, cols_final_demand) %>%
pivot_longer(cols = cols_final_demand,
names_to = "quintile",
values_to = "fd_me") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2 and pivot long
cols_co2 = c("q1_co2", "q2_co2", "q3_co2", "q4_co2", "q5_co2")
tmp_co2 = dat_results %>%
select(year, geo, sector_id, cols_co2) %>%
pivot_longer(cols = cols_co2,
names_to = "quintile",
values_to = "co2_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2 domestic and pivot long
cols_co2_domestic = c("q1_co2_domestic", "q2_co2_domestic", "q3_co2_domestic", "q4_co2_domestic", "q5_co2_domestic")
tmp_co2_domestic = dat_results %>%
select(year, geo, sector_id, cols_co2_domestic) %>%
pivot_longer(cols = cols_co2_domestic,
names_to = "quintile",
values_to = "co2_domestic_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2 europe and pivot long
cols_co2_europe = c("q1_co2_europe", "q2_co2_europe", "q3_co2_europe", "q4_co2_europe", "q5_co2_europe")
tmp_co2_europe = dat_results %>%
select(year, geo, sector_id, cols_co2_europe) %>%
pivot_longer(cols = cols_co2_europe,
names_to = "quintile",
values_to = "co2_europe_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq and pivot long
cols_co2eq = c("q1_co2eq", "q2_co2eq", "q3_co2eq", "q4_co2eq", "q5_co2eq")
tmp_co2eq = dat_results %>%
select(year, geo, sector_id, cols_co2eq) %>%
pivot_longer(cols = cols_co2eq,
names_to = "quintile",
values_to = "co2eq_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq domestic and pivot long
cols_co2eq_domestic = c("q1_co2eq_domestic", "q2_co2eq_domestic", "q3_co2eq_domestic", "q4_co2eq_domestic", "q5_co2eq_domestic")
tmp_co2eq_domestic = dat_results %>%
select(year, geo, sector_id, cols_co2eq_domestic) %>%
pivot_longer(cols = cols_co2eq_domestic,
names_to = "quintile",
values_to = "co2eq_domestic_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq europe and pivot long
cols_co2eq_europe = c("q1_co2eq_europe", "q2_co2eq_europe", "q3_co2eq_europe", "q4_co2eq_europe", "q5_co2eq_europe")
tmp_co2eq_europe = dat_results %>%
select(year, geo, sector_id, cols_co2eq_europe) %>%
pivot_longer(cols = cols_co2eq_europe,
names_to = "quintile",
values_to = "co2eq_europe_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy use and pivot long
cols_energy = c("q1_energy","q2_energy","q3_energy","q4_energy","q5_energy")
tmp_energy = dat_results %>%
select(year, geo, sector_id, cols_energy) %>%
pivot_longer(cols = cols_energy,
names_to = "quintile",
values_to = "energy_use_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy domestic and pivot long
cols_energy_domestic = c("q1_energy_domestic","q2_energy_domestic","q3_energy_domestic","q4_energy_domestic","q5_energy_domestic")
tmp_energy_domestic = dat_results %>%
select(year, geo, sector_id, cols_energy_domestic) %>%
pivot_longer(cols = cols_energy_domestic,
names_to = "quintile",
values_to = "energy_use_domestic_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy europe and pivot long
cols_energy_europe = c("q1_energy_europe","q2_energy_europe","q3_energy_europe","q4_energy_europe","q5_energy_europe")
tmp_energy_europe = dat_results %>%
select(year, geo, sector_id, cols_energy_europe) %>%
pivot_longer(cols = cols_energy_europe,
names_to = "quintile",
values_to = "energy_use_europe_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
### TODO: also convert to other indicators to this format (as blocks above)
### TODO: left join all indicators back to "results_formated" like her with co2
results_recombined = tmp_fd %>%
left_join(tmp_co2, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2_domestic, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2_europe, by = c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2eq, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2eq_domestic, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_co2eq_europe, by = c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_energy, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_energy_domestic, by=c("year", "geo", "sector_id", "quint")) %>%
left_join(tmp_energy_europe, by = c("year", "geo", "sector_id", "quint"))
# finally re-join aggregated sector IDs
results_formatted = results_recombined %>%
left_join(sector_mapping, by="sector_id") %>%
ungroup() %>%
select(-coicop_id)
write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp.csv"))
write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp.rds"))
################################################### !!!! method 2 !!!! - IXI version #############################
###############################################################################################################################################################
###############################################################################################################################################################
# 'results' data frame the second way
# aggregate - playing around trying to go the other way
# load 'mean expenditure by quintile' data
hbs_exp_t133 = read_csv(paste0(data_dir_income_stratified_footprints, "/hbs_exp_t133.csv"))
# rename and arrange by country
mean_expenditure_by_quintile = hbs_exp_t133 %>%
rename(geo = 3, quintile = "quantile") %>%
arrange(geo)
# load 'mean expenditure by quintile and coicop' data
hbs_str_t223 = read_csv(paste0(data_dir_income_stratified_footprints, "/hbs_str_t223.csv"))
# rename and arrange by country
mean_expenditure_by_coicop_sector = hbs_str_t223 %>%
rename(geo = 4, quintile = "quantile") %>%
arrange(geo)
# create long data sets for both
mean_expenditure_by_quintile_long = mean_expenditure_by_quintile %>%
filter(unit == "PPS_HH") %>%
filter(!(quintile %in% c("UNK","TOTAL"))) %>%
select(-unit) %>%
gather(year,euro_pps,-quintile,-geo)
mean_expenditure_by_coicop_sector_long = mean_expenditure_by_coicop_sector %>%
filter(!(quintile %in% c("UNK","TOTAL"))) %>%
select(-unit) %>%
gather(year,pm,-quintile,-coicop,-geo) %>%
mutate(coicop = dplyr::recode(coicop, "CP041" = "rent",
"CP042" = "rent")) %>%
group_by(geo,quintile,coicop,year) %>%
mutate(pm = parse_number(pm),
pm = as.numeric(pm)) %>%
summarise(pm = sum(pm, na.rm = TRUE)) %>%
ungroup() %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE1" &
coicop == "CP072", 92-21-14,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE2" &
coicop == "CP072", 108-22-12,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE3" &
coicop == "CP072", 124-32-11,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE4" &
coicop == "CP072", 133-43-10,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2005 & quintile == "QUINTILE5" &
coicop == "CP072", 162-81-11,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE1" &
coicop == "CP044", 412-4-78-322,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE2" &
coicop == "CP044", 355-5-68-265,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE3" &
coicop == "CP044", 325-8-64-229,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE4" &
coicop == "CP044", 300-9-58-204,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2010 & quintile == "QUINTILE5" &
coicop == "CP044", 249-10-46-167,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE1" &
coicop == "CP044", 433-3-82-340,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE2" &
coicop == "CP044", 376-6-70-284,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE3" &
coicop == "CP044", 351-9-67-251,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE4" &
coicop == "CP044", 326-10-61-228,pm)) %>%
mutate(pm = ifelse(geo == "DE" & year == 2015 & quintile == "QUINTILE5" &
coicop == "CP044", 280-9-49-195,pm))
join_expenditures = mean_expenditure_by_coicop_sector_long %>%
left_join(mean_expenditure_by_quintile_long, by = c("geo","quintile","year")) %>%
mutate(euro_pps = as.numeric(euro_pps),
pm = as.numeric(pm),
euro_pps_coicop = pm*(euro_pps/1000))
# load margin tables
trade_and_transport = read.csv(paste0(data_dir_income_stratified_footprints, "/SNA_TABLE45_20042020103737298.csv")) %>%
select(LOCATION, PRODUCT, Product, Year, Value) %>%
mutate(geo = dplyr::recode(LOCATION,"AUT" = "AT",
"BEL" = "BE",
"CYP" = "CY",
"CZE" = "CZ",
"DNK" = "DK",
"EST" = "EE",
"FIN" = "FI",
"FRA" = "FR",
"DEU" = "DE",
"GRC" = "EL",
"HUN" = "HU",
"IRL" = "IE",
"ITA" = "IT",
"LVA" = "LV",
"LTU" = "LT",
"LUX" = "LU",
"MLT" = "MT",
"MNE" = "ME",
"NLD" = "NL",
"NOR" = "NO",
"POL" = "PL",
"PRT" = "PT",
"ROU" = "RO",
"SRB" = "RS",
"SVK" = "SK",
"SVN" = "SI",
"ESP" = "ES",
"SWE" = "SE",
"CHE" = "CH",
"MKD" = "MK",
"TUR" = "TR",
"GBR" = "UK",
"BGR" = "BG",
"HRV" = "HR")) %>%
select(geo, Year, PRODUCT, Value) %>%
rename(year = Year,
trade_and_transport = Value) %>%
mutate(trade_and_transport = trade_and_transport/100) %>%
rename(geo_join = geo)
taxes_less_subsidies = read.csv(paste0(data_dir_income_stratified_footprints, "/SNA_TABLE45_20042020104120395.csv")) %>%
select(LOCATION, PRODUCT, Product, Year, Value) %>%
mutate(geo = dplyr::recode(LOCATION,"AUT" = "AT",
"BEL" = "BE",
"CYP" = "CY",
"CZE" = "CZ",
"DNK" = "DK",
"EST" = "EE",
"FIN" = "FI",
"FRA" = "FR",
"DEU" = "DE",
"GRC" = "EL",
"HUN" = "HU",
"IRL" = "IE",
"ITA" = "IT",
"LVA" = "LV",
"LTU" = "LT",
"LUX" = "LU",
"MLT" = "MT",
"MNE" = "ME",
"NLD" = "NL",
"NOR" = "NO",
"POL" = "PL",
"PRT" = "PT",
"ROU" = "RO",
"SRB" = "RS",
"SVK" = "SK",
"SVN" = "SI",
"ESP" = "ES",
"SWE" = "SE",
"CHE" = "CH",
"MKD" = "MK",
"TUR" = "TR",
"GBR" = "UK",
"BGR" = "BG",
"HRV" = "HR")) %>%
select(geo, Year, PRODUCT, Value) %>%
rename(year = Year,
taxes_less_subsidies = Value) %>%
mutate(taxes_less_subsidies = taxes_less_subsidies/100) %>%
rename(geo_join = geo)
# create margins dataframe
geo_real = rep(c("AT",
"BE",
"CY",
"CZ",
"DK",
"EE",
"FI",
"FR",
"DE",
"EL",
"HU",
"IE",
"IT",
"LV",
"LT",
"LU",
"MT",
"ME",
"NL",
"NO",
"PL",
"PT",
"RO",
"RS",
"SK",
"SI",
"ES",
"SE",
"MK",
"TR",
"UK",
"BG",
"HR"),each = 16)
geo_join = rep(c("AT",
"BE",
"CY",
"CZ",
"DK",
"LV",
"FI",
"FR",
"AT",
"EL",
"HU",
"UK",
"IT",
"LV",
"LV",
"LU",
"MT",
"ME",
"NL",
"FI",
"PL",
"PT",
"RO",
"RS",
"SK",
"SI",
"PT",
"FI",
"MK",
"BG",
"UK",
"BG",
"HR"),each = 16)
year = rep(2010,length(geo_real))
PRODUCT = c("P10_12",
"P13_15",
"P68A",
"PF",
"PE",
"PD",
"PC",
"PQ",
"P29",
"P19",
"P30",
"P61",
"PR",
"PP",
"PI",
"PS")
margin_sectors = data.frame(geo_real,geo_join,year,PRODUCT)
# join everything - and impute
margins = margin_sectors %>%
left_join(taxes_less_subsidies, by = c("geo_join","year","PRODUCT")) %>%
left_join(trade_and_transport, by = c("geo_join","year","PRODUCT")) %>%
select(-geo_join) %>%
rename(geo = geo_real) %>%
mutate(taxes_less_subsidies = ifelse(geo == "CZ" & PRODUCT == "P68A", 0, taxes_less_subsidies)) %>%
mutate(trade_and_transport = ifelse(geo == "CZ" & PRODUCT == "P68A", 0, trade_and_transport)) %>%
mutate(taxes_less_subsidies = ifelse(geo == "SK" & PRODUCT == "P68A", 0, taxes_less_subsidies)) %>%
mutate(trade_and_transport = ifelse(geo == "SK" & PRODUCT == "P68A", 0, trade_and_transport)) %>%
select(-year)
# join margin data to join_expenditures
with_margins = join_expenditures %>%
mutate(year = as.numeric(year),
PRODUCT = dplyr::recode(coicop,
"CP011" = "P10_12",
"CP012" = "P10_12",
"CP02" = "P10_12",
"CP03" = "P13_15",
"rent" = "P68A",
"CP043" = "PF",
"CP044" = "PE",
"CP045" = "PD",
"CP05" = "PC",
"CP06" = "PQ",
"CP071" = "P29",
"CP072" = "P19",
"CP073" = "P30",
"CP08" = "P61",
"CP09" = "PR",
"CP10" = "PP",
"CP11" = "PI",
"CP12" = "PS")) %>%
left_join(margins, by = c("geo","PRODUCT")) %>%
mutate(euro_pps_coicop_bp = euro_pps_coicop*(1 - (trade_and_transport + taxes_less_subsidies)))
# re-create expenditure
mean_expenditure_by_quintile_long_bp = with_margins %>%
group_by(quintile,geo,year) %>%
summarise(euro_pps_bp = sum(euro_pps_coicop_bp, na.rm = TRUE))
mean_expenditure_by_coicop_sector_long_bp = with_margins %>%
left_join(mean_expenditure_by_quintile_long_bp, by = c("quintile","geo","year")) %>%
mutate(pm_bp = (euro_pps_coicop_bp/euro_pps_bp)*1000) %>%
select(quintile,coicop,geo,year,pm_bp)
###
shares = join_expenditures %>%
group_by(coicop,geo,year) %>%
mutate(share = euro_pps_coicop/sum(euro_pps_coicop))
################################################### !!!! method 2 - IXI version - PPS HH NO RENT !!!! ####################################################
##########################################################################################################################################################
##########################################################################################################################################################
# Exiobase - ixi version
years_exb_ixi = c(2005,2010,2015)
Eurostat_countries_hh_fd = NULL
total_fd = NULL
TIVs = NULL
domestic_TIVs = NULL
europe_TIVs = NULL
national_fp = NULL
for (i in years_exb_ixi){
year_current = i
Exiobase_FD = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/FD_",year_current,"_ixi.csv"))[,-1]
# select household final demand vectors for relevant countries - figure out how to soft code this
AT = Exiobase_FD[,1]
BE = Exiobase_FD[,8]
BG = Exiobase_FD[,15]
CY = Exiobase_FD[,22]
CZ = Exiobase_FD[,29]
DE = Exiobase_FD[,36]
DK = Exiobase_FD[,43]
EE = Exiobase_FD[,50]
EL = Exiobase_FD[,78]
ES = Exiobase_FD[,57]
FI = Exiobase_FD[,64]
FR = Exiobase_FD[,71]
HR = Exiobase_FD[,85]
HU = Exiobase_FD[,92]
IE = Exiobase_FD[,99]
IT = Exiobase_FD[,106]
LT = Exiobase_FD[,113]
LU = Exiobase_FD[,120]
LV = Exiobase_FD[,127]
MT = Exiobase_FD[,134]
NL = Exiobase_FD[,141]
NO = Exiobase_FD[,288]
PL = Exiobase_FD[,148]
PT = Exiobase_FD[,155]
RO = Exiobase_FD[,162]
SE = Exiobase_FD[,169]
SI = Exiobase_FD[,176]
SK = Exiobase_FD[,183]
TR = Exiobase_FD[,274]
UK = Exiobase_FD[,190]
Eurostat_countries = cbind(AT,BE,BG,CY,CZ,DE,DK,EE,EL,ES,FI,FR,HR,HU,IE,IT,LT,LU,LV,MT,NL,NO,PL,PT,RO,SE,SI,SK,TR,UK)
year = as.character(rep(year_current,nrow(Eurostat_countries)))
look_Eurostat_countries = cbind(year,Eurostat_countries)
Eurostat_countries_hh_fd = rbind(Eurostat_countries_hh_fd,look_Eurostat_countries)
eurostat_countries_colsums = colSums(Eurostat_countries)
geo = data.frame(c("AT","BE","BG","CY","CZ","DE","DK","EE","EL","ES","FI",
"FR","HR","HU","IE","IT","LT","LU","LV","MT","NL","NO",
"PL","PT","RO","SE","SI","SK","TR","UK")) %>% rename_at(1,~"geo")
year = rep(year_current, 30)
fds = cbind(geo,year,eurostat_countries_colsums) %>% slice(rep(1:n(), each = 5))
quintiles = data.frame(rep(c("QUINTILE1","QUINTILE2","QUINTILE3","QUINTILE4","QUINTILE5"),30)) %>% rename_at(1,~"quintile")
total_fd_year_current = cbind(fds,quintiles)
total_fd = rbind(total_fd, total_fd_year_current)
# labels
Exiobase_T_labels = read.csv(paste0(data_dir_income_stratified_footprints, "/Exiobase_T_labels_ixi_w_coicop_mapping.csv")) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
# TIVs
# CO2 - combustion - air
Exiobase_TIV_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_combustion_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_combustion_air_", year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_combustion_domestic)
Exiobase_TIV_europe_breakdown_co2_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_combustion_air_", year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_combustion_europe,TIV_CO2_combustion_not_europe)
# CO2 - noncombustion - cement - air
Exiobase_TIV_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_noncombustion_cement_domestic)
Exiobase_TIV_europe_breakdown_co2_noncombustion_cement_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_cement_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_noncombustion_cement_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_noncombustion_cement_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_noncombustion_cement_europe,TIV_CO2_noncombustion_cement_not_europe)
# CO2 - noncombustion - lime - air
Exiobase_TIV_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_noncombustion_lime_domestic)
Exiobase_TIV_europe_breakdown_co2_noncombustion_lime_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_noncombustion_lime_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_noncombustion_lime_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_noncombustion_lime_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_noncombustion_lime_europe,TIV_CO2_noncombustion_lime_not_europe)
# CO2 - agriculture - peat decay - air
Exiobase_TIV_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_agriculture_peatdecay_domestic)
Exiobase_TIV_europe_breakdown_co2_agriculture_peatdecay_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_agriculture_peatdecay_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_agriculture_peatdecay_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_agriculture_peatdecay_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_agriculture_peatdecay_europe,TIV_CO2_agriculture_peatdecay_not_europe)
# CO2 - waste - biogenic - air
Exiobase_TIV_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_biogenic_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_biogenic_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_waste_biogenic_domestic)
Exiobase_TIV_europe_breakdown_co2_waste_biogenic_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_biogenic_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_waste_biogenic_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_waste_biogenic_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_waste_biogenic_europe,TIV_CO2_waste_biogenic_not_europe)
# CO2 - waste - fossil - air
Exiobase_TIV_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_co2_waste_fossil_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CO2_waste_fossil_domestic)
Exiobase_TIV_europe_breakdown_co2_waste_fossil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_co2_waste_fossil_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CO2_waste_fossil_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CO2_waste_fossil_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CO2_waste_fossil_europe,TIV_CO2_waste_fossil_not_europe)
# CH4 - combustion -air
Exiobase_TIV_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_combustion_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_combustion_domestic)
Exiobase_TIV_europe_breakdown_ch4_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_combustion_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_combustion_europe,TIV_CH4_combustion_not_europe)
# CH4 - noncombustion - gas - air
Exiobase_TIV_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_gas_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_gas_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_gas_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_gas_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_gas_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_gas_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_gas_europe,TIV_CH4_noncombustion_gas_not_europe)
# CH4 - noncombustion - oil - air
Exiobase_TIV_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_oil_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_oil_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oil_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oil_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_oil_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_oil_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_oil_europe,TIV_CH4_noncombustion_oil_not_europe)
# CH4 - noncombustion - anthracite - air
Exiobase_TIV_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_anthracite_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_anthracite_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_anthracite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_anthracite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_anthracite_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_anthracite_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_anthracite_europe,TIV_CH4_noncombustion_anthracite_not_europe)
# CH4 - noncombustion - bituminouscoal - air
Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_bituminouscoal_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_bituminouscoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_bituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_bituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_bituminouscoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_bituminouscoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_bituminouscoal_europe,TIV_CH4_noncombustion_bituminouscoal_not_europe)
# CH4 - noncombustion - cokingcoal - air
Exiobase_TIV_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_cokingcoal_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_cokingcoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_cokingcoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_cokingcoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_cokingcoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_cokingcoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_cokingcoal_europe,TIV_CH4_noncombustion_cokingcoal_not_europe)
# CH4 - noncombustion - lignite - air
Exiobase_TIV_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_lignite_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_lignite_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_lignite_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_lignite_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_lignite_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_lignite_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_lignite_europe,TIV_CH4_noncombustion_lignite_not_europe)
# CH4 - noncombustion - subbituminouscoal - air
Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_subbituminouscoal_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_subbituminouscoal_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_subbituminouscoal_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_subbituminouscoal_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_subbituminouscoal_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_subbituminouscoal_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_subbituminouscoal_europe,TIV_CH4_noncombustion_subbituminouscoal_not_europe)
# CH4 - noncombustion - oilrefinery - air
Exiobase_TIV_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_noncombustion_oilrefinery_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_noncombustion_oilrefinery_domestic)
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oilrefinery_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_noncombustion_oilrefinery_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_noncombustion_oilrefinery_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_noncombustion_oilrefinery_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_noncombustion_oilrefinery_europe,TIV_CH4_noncombustion_oilrefinery_not_europe)
# CH4 - agriculture - air
Exiobase_TIV_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_agriculture_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_agriculture_domestic)
Exiobase_TIV_europe_breakdown_ch4_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_agriculture_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_agriculture_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_agriculture_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_agriculture_europe,TIV_CH4_agriculture_not_europe)
# CH4 - waste - air
Exiobase_TIV_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_ch4_CO2eq_waste_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_waste_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_CH4_waste_domestic)
Exiobase_TIV_europe_breakdown_ch4_waste_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_ch4_CO2eq_waste_air_", year_current, "_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_CH4_waste_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_CH4_waste_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_CH4_waste_europe,TIV_CH4_waste_not_europe)
# N2O - combustion - air
Exiobase_TIV_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_N2O_combustion_domestic)
Exiobase_TIV_europe_breakdown_n2o_combustion_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_combustion_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_N2O_combustion_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_N2O_combustion_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_N2O_combustion_europe,TIV_N2O_combustion_not_europe)
# N2O - agriculture - air
Exiobase_TIV_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_N2O_agriculture_domestic)
Exiobase_TIV_europe_breakdown_n2o_agriculture_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_n2o_CO2eq_agriculture_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_N2O_agriculture_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_N2O_agriculture_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_N2O_agriculture_europe,TIV_N2O_agriculture_not_europe)
# SF6 - air
Exiobase_TIV_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_sf6_CO2eq_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_SF6_domestic)
Exiobase_TIV_europe_breakdown_sf6_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_sf6_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_SF6_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_SF6_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_SF6_europe,TIV_SF6_not_europe)
# HFC - air
Exiobase_TIV_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_hfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_HFC_domestic)
Exiobase_TIV_europe_breakdown_hfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_hfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_HFC_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_HFC_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_HFC_europe,TIV_HFC_not_europe)
# PFC - air
Exiobase_TIV_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_pfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_PFC_domestic)
Exiobase_TIV_europe_breakdown_pfc_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_pfc_CO2eq_air_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_PFC_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_PFC_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_PFC_europe,TIV_PFC_not_europe)
# Energy use
Exiobase_TIV_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_energy_carrier_use_",year_current,"_ixi.csv"))[,-1]
Exiobase_TIV_country_breakdown_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_energy_carrier_use_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
gather(country, TIV_energy_domestic)
Exiobase_TIV_europe_breakdown_energy_use_bp = read.csv(paste0(data_dir_exiobase, "/IOT_",year_current,"_ixi/TIV_country_breakdown_energy_carrier_use_",year_current,"_ixi.csv"))[,-1] %>%
row_to_names(row_number = 1) %>%
mutate_at(vars(AT:ZA), funs(as.numeric(as.character(.)))) %>%
mutate(TIV_energy_europe = AT +
BE + BG + CY + CZ +
DE + DK + EE +
ES + FI + FR +
GB + GR + HR +
HU + IE + IT +
LT + LU + LV +
MT + NL + PL +
PT + TR + SK +
SI + SE + RO +
NO,
TIV_energy_not_europe = AU +
BR + CA + CH + CN +
ID + IN + JP + KR +
MX + RU + TW + US +
WA + WE + WF + WL + WM +
ZA) %>%
select(TIV_energy_europe,TIV_energy_not_europe)
# join with labels
TIV_with_labels = cbind(Exiobase_T_labels,
t(Exiobase_TIV_co2_combustion_bp),
t(Exiobase_TIV_co2_noncombustion_cement_bp),
t(Exiobase_TIV_co2_noncombustion_lime_bp),
t(Exiobase_TIV_co2_agriculture_peatdecay_bp),
t(Exiobase_TIV_co2_waste_biogenic_bp),
t(Exiobase_TIV_co2_waste_fossil_bp),
t(Exiobase_TIV_ch4_combustion_bp),
t(Exiobase_TIV_ch4_noncombustion_gas_bp),
t(Exiobase_TIV_ch4_noncombustion_oil_bp),
t(Exiobase_TIV_ch4_noncombustion_anthracite_bp),
t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp),
t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp),
t(Exiobase_TIV_ch4_noncombustion_lignite_bp),
t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp),
t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp),
t(Exiobase_TIV_ch4_agriculture_bp),
t(Exiobase_TIV_ch4_waste_bp),
t(Exiobase_TIV_n2o_combustion_bp),
t(Exiobase_TIV_n2o_agriculture_bp),
t(Exiobase_TIV_sf6_bp),
t(Exiobase_TIV_hfc_bp),
t(Exiobase_TIV_pfc_bp),
t(Exiobase_TIV_energy_use_bp)) %>%
rename(TIV_CO2_combustion = "t(Exiobase_TIV_co2_combustion_bp)",
TIV_CO2_noncombustion_cement = "t(Exiobase_TIV_co2_noncombustion_cement_bp)",
TIV_CO2_noncombustion_lime = "t(Exiobase_TIV_co2_noncombustion_lime_bp)",
TIV_CO2_agriculture_peatdecay = "t(Exiobase_TIV_co2_agriculture_peatdecay_bp)",
TIV_CO2_waste_biogenic = "t(Exiobase_TIV_co2_waste_biogenic_bp)",
TIV_CO2_waste_fossil = "t(Exiobase_TIV_co2_waste_fossil_bp)",
TIV_CH4_combustion = "t(Exiobase_TIV_ch4_combustion_bp)",
TIV_CH4_noncombustion_gas = "t(Exiobase_TIV_ch4_noncombustion_gas_bp)",
TIV_CH4_noncombustion_oil = "t(Exiobase_TIV_ch4_noncombustion_oil_bp)",
TIV_CH4_noncombustion_anthracite = "t(Exiobase_TIV_ch4_noncombustion_anthracite_bp)",
TIV_CH4_noncombustion_bituminouscoal = "t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp)",
TIV_CH4_noncombustion_cokingcoal = "t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp)",
TIV_CH4_noncombustion_lignite = "t(Exiobase_TIV_ch4_noncombustion_lignite_bp)",
TIV_CH4_noncombustion_subbituminouscoal = "t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp)",
TIV_CH4_noncombustion_oilrefinery = "t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp)",
TIV_CH4_agriculture = "t(Exiobase_TIV_ch4_agriculture_bp)",
TIV_CH4_waste = "t(Exiobase_TIV_ch4_waste_bp)",
TIV_N2O_combustion = "t(Exiobase_TIV_n2o_combustion_bp)",
TIV_N2O_agriculture = "t(Exiobase_TIV_n2o_agriculture_bp)",
TIV_SF6 = "t(Exiobase_TIV_sf6_bp)",
TIV_HFC = "t(Exiobase_TIV_hfc_bp)",
TIV_PFC = "t(Exiobase_TIV_pfc_bp)",
TIV_energy = "t(Exiobase_TIV_energy_use_bp)") %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year = as.character(rep(year_current,nrow(TIV_with_labels)))
look = cbind(year,TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2)
TIVs = rbind(TIVs,look)
# domestic TIVs
domestic_TIV_with_labels = cbind(Exiobase_T_labels,
Exiobase_TIV_country_breakdown_co2_combustion_bp,
Exiobase_TIV_country_breakdown_co2_noncombustion_cement_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_noncombustion_lime_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_agriculture_peatdecay_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_waste_biogenic_bp %>% select(-country),
Exiobase_TIV_country_breakdown_co2_waste_fossil_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_combustion_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_gas_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_oil_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_anthracite_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_bituminouscoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_cokingcoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_lignite_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_subbituminouscoal_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_noncombustion_oilrefinery_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_agriculture_bp %>% select(-country),
Exiobase_TIV_country_breakdown_ch4_waste_bp %>% select(-country),
Exiobase_TIV_country_breakdown_n2o_combustion_bp %>% select(-country),
Exiobase_TIV_country_breakdown_n2o_agriculture_bp %>% select(-country),
Exiobase_TIV_country_breakdown_sf6_bp %>% select(-country),
Exiobase_TIV_country_breakdown_hfc_bp %>% select(-country),
Exiobase_TIV_country_breakdown_pfc_bp %>% select(-country),
Exiobase_TIV_country_breakdown_energy_use_bp %>% select(-country)) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"),
country = dplyr::recode(country, "GR" = "EL", "GB" = "UK"))
year_domestic = as.character(rep(year_current,nrow(domestic_TIV_with_labels)))
look_domestic = cbind(year_domestic,domestic_TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2, geo = country, year = year_domestic) %>%
mutate(TIV_CO2_combustion_domestic = as.numeric(TIV_CO2_combustion_domestic),
TIV_CO2_noncombustion_cement_domestic = as.numeric(TIV_CO2_noncombustion_cement_domestic),
TIV_CO2_noncombustion_lime_domestic = as.numeric(TIV_CO2_noncombustion_lime_domestic),
TIV_CO2_agriculture_peatdecay_domestic = as.numeric(TIV_CO2_agriculture_peatdecay_domestic),
TIV_CO2_waste_biogenic_domestic = as.numeric(TIV_CO2_waste_biogenic_domestic),
TIV_CO2_waste_fossil_domestic = as.numeric(TIV_CO2_waste_fossil_domestic),
TIV_CH4_combustion_domestic = as.numeric(TIV_CH4_combustion_domestic),
TIV_CH4_noncombustion_gas_domestic = as.numeric(TIV_CH4_noncombustion_gas_domestic),
TIV_CH4_noncombustion_oil_domestic = as.numeric(TIV_CH4_noncombustion_oil_domestic),
TIV_CH4_noncombustion_anthracite_domestic = as.numeric(TIV_CH4_noncombustion_anthracite_domestic),
TIV_CH4_noncombustion_bituminouscoal_domestic = as.numeric(TIV_CH4_noncombustion_bituminouscoal_domestic),
TIV_CH4_noncombustion_cokingcoal_domestic = as.numeric(TIV_CH4_noncombustion_cokingcoal_domestic),
TIV_CH4_noncombustion_lignite_domestic = as.numeric(TIV_CH4_noncombustion_lignite_domestic),
TIV_CH4_noncombustion_subbituminouscoal_domestic = as.numeric(TIV_CH4_noncombustion_subbituminouscoal_domestic),
TIV_CH4_noncombustion_oilrefinery_domestic = as.numeric(TIV_CH4_noncombustion_oilrefinery_domestic),
TIV_CH4_agriculture_domestic = as.numeric(TIV_CH4_agriculture_domestic),
TIV_CH4_waste_domestic = as.numeric(TIV_CH4_waste_domestic),
TIV_N2O_combustion_domestic = as.numeric(TIV_N2O_combustion_domestic),
TIV_N2O_agriculture_domestic = as.numeric(TIV_N2O_agriculture_domestic),
TIV_SF6_domestic = as.numeric(TIV_SF6_domestic),
TIV_HFC_domestic = as.numeric(TIV_HFC_domestic),
TIV_PFC_domestic = as.numeric(TIV_PFC_domestic),
TIV_energy_domestic = as.numeric(TIV_energy_domestic))
domestic_TIVs = rbind(domestic_TIVs, look_domestic)
# europe TIVs with labels
europe_TIV_with_labels = cbind(Exiobase_T_labels,
Exiobase_TIV_europe_breakdown_co2_combustion_bp,
Exiobase_TIV_europe_breakdown_co2_noncombustion_cement_bp,
Exiobase_TIV_europe_breakdown_co2_noncombustion_lime_bp,
Exiobase_TIV_europe_breakdown_co2_agriculture_peatdecay_bp,
Exiobase_TIV_europe_breakdown_co2_waste_biogenic_bp,
Exiobase_TIV_europe_breakdown_co2_waste_fossil_bp,
Exiobase_TIV_europe_breakdown_ch4_combustion_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_gas_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oil_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_anthracite_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_bituminouscoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_cokingcoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_lignite_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_subbituminouscoal_bp,
Exiobase_TIV_europe_breakdown_ch4_noncombustion_oilrefinery_bp,
Exiobase_TIV_europe_breakdown_ch4_agriculture_bp,
Exiobase_TIV_europe_breakdown_ch4_waste_bp,
Exiobase_TIV_europe_breakdown_n2o_combustion_bp,
Exiobase_TIV_europe_breakdown_n2o_agriculture_bp,
Exiobase_TIV_europe_breakdown_sf6_bp,
Exiobase_TIV_europe_breakdown_hfc_bp,
Exiobase_TIV_europe_breakdown_pfc_bp,
Exiobase_TIV_europe_breakdown_energy_use_bp) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year_europe = as.character(rep(year_current,nrow(europe_TIV_with_labels)))
look_europe = cbind(year_europe,europe_TIV_with_labels) %>%
rename(country_of_production = V1, sector = V2, year = year_europe) %>%
mutate(TIV_CO2_combustion_europe = as.numeric(TIV_CO2_combustion_europe),
TIV_CO2_noncombustion_cement_europe = as.numeric(TIV_CO2_noncombustion_cement_europe),
TIV_CO2_noncombustion_lime_europe = as.numeric(TIV_CO2_noncombustion_lime_europe),
TIV_CO2_agriculture_peatdecay_europe = as.numeric(TIV_CO2_agriculture_peatdecay_europe),
TIV_CO2_waste_biogenic_europe = as.numeric(TIV_CO2_waste_biogenic_europe),
TIV_CO2_waste_fossil_europe = as.numeric(TIV_CO2_waste_fossil_europe),
TIV_CH4_combustion_europe = as.numeric(TIV_CH4_combustion_europe),
TIV_CH4_noncombustion_gas_europe = as.numeric(TIV_CH4_noncombustion_gas_europe),
TIV_CH4_noncombustion_oil_europe = as.numeric(TIV_CH4_noncombustion_oil_europe),
TIV_CH4_noncombustion_anthracite_europe = as.numeric(TIV_CH4_noncombustion_anthracite_europe),
TIV_CH4_noncombustion_bituminouscoal_europe = as.numeric(TIV_CH4_noncombustion_bituminouscoal_europe),
TIV_CH4_noncombustion_cokingcoal_europe = as.numeric(TIV_CH4_noncombustion_cokingcoal_europe),
TIV_CH4_noncombustion_lignite_europe = as.numeric(TIV_CH4_noncombustion_lignite_europe),
TIV_CH4_noncombustion_subbituminouscoal_europe = as.numeric(TIV_CH4_noncombustion_subbituminouscoal_europe),
TIV_CH4_noncombustion_oilrefinery_europe = as.numeric(TIV_CH4_noncombustion_oilrefinery_europe),
TIV_CH4_agriculture_europe = as.numeric(TIV_CH4_agriculture_europe),
TIV_CH4_waste_europe = as.numeric(TIV_CH4_waste_europe),
TIV_N2O_combustion_europe = as.numeric(TIV_N2O_combustion_europe),
TIV_N2O_agriculture_europe = as.numeric(TIV_N2O_agriculture_europe),
TIV_SF6_europe = as.numeric(TIV_SF6_europe),
TIV_HFC_europe = as.numeric(TIV_HFC_europe),
TIV_PFC_europe = as.numeric(TIV_PFC_europe),
TIV_energy_europe = as.numeric(TIV_energy_europe))
europe_TIVs = rbind(europe_TIVs, look_europe)
# total national footprints
# FD labels
Exiobase_FD_labels = as.data.frame(t(read.csv(paste0(data_dir_exiobase, "/Exiobase_FD_labels_ixi.csv")))[-1,-3]) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
national_CO2_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_co2_combustion_bp)
national_CO2_noncombustion_cement_footprints = Exiobase_FD * t(Exiobase_TIV_co2_noncombustion_cement_bp)
national_CO2_noncombustion_lime_footprints = Exiobase_FD * t(Exiobase_TIV_co2_noncombustion_lime_bp)
national_CO2_agriculture_peatdecay_footprints = Exiobase_FD * t(Exiobase_TIV_co2_agriculture_peatdecay_bp)
national_CO2_waste_biogenic_footprints = Exiobase_FD * t(Exiobase_TIV_co2_waste_biogenic_bp)
national_CO2_waste_fossil_footprints = Exiobase_FD * t(Exiobase_TIV_co2_waste_fossil_bp)
national_CH4_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_combustion_bp)
national_CH4_noncombustion_gas_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_gas_bp)
national_CH4_noncombustion_oil_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_oil_bp)
national_CH4_noncombustion_anthracite_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_anthracite_bp)
national_CH4_noncombustion_bituminouscoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_bituminouscoal_bp)
national_CH4_noncombustion_cokingcoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_cokingcoal_bp)
national_CH4_noncombustion_lignite_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_lignite_bp)
national_CH4_noncombustion_subbituminouscoal_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_subbituminouscoal_bp)
national_CH4_noncombustion_oilrefinery_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_noncombustion_oilrefinery_bp)
national_CH4_agriculture_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_agriculture_bp)
national_CH4_waste_footprints = Exiobase_FD * t(Exiobase_TIV_ch4_waste_bp)
national_N2O_combustion_footprints = Exiobase_FD * t(Exiobase_TIV_n2o_combustion_bp)
national_N2O_agriculture_footprints = Exiobase_FD * t(Exiobase_TIV_n2o_agriculture_bp)
national_SF6_footprints = Exiobase_FD * t(Exiobase_TIV_sf6_bp)
national_HFC_footprints = Exiobase_FD * t(Exiobase_TIV_hfc_bp)
national_PFC_footprints = Exiobase_FD * t(Exiobase_TIV_pfc_bp)
national_energy_footprints = Exiobase_FD * t(Exiobase_TIV_energy_use_bp)
# together
national_footprints_w_labels = cbind(Exiobase_FD_labels,
rowSums(t(national_CO2_combustion_footprints)),
rowSums(t(national_CO2_noncombustion_cement_footprints)),
rowSums(t(national_CO2_noncombustion_lime_footprints)),
rowSums(t(national_CO2_agriculture_peatdecay_footprints)),
rowSums(t(national_CO2_waste_biogenic_footprints)),
rowSums(t(national_CO2_waste_fossil_footprints)),
rowSums(t(national_CH4_combustion_footprints)),
rowSums(t(national_CH4_noncombustion_gas_footprints)),
rowSums(t(national_CH4_noncombustion_oil_footprints)),
rowSums(t(national_CH4_noncombustion_anthracite_footprints)),
rowSums(t(national_CH4_noncombustion_bituminouscoal_footprints)),
rowSums(t(national_CH4_noncombustion_cokingcoal_footprints)),
rowSums(t(national_CH4_noncombustion_lignite_footprints)),
rowSums(t(national_CH4_noncombustion_subbituminouscoal_footprints)),
rowSums(t(national_CH4_noncombustion_oilrefinery_footprints)),
rowSums(t(national_CH4_agriculture_footprints)),
rowSums(t(national_CH4_waste_footprints)),
rowSums(t(national_N2O_combustion_footprints)),
rowSums(t(national_N2O_agriculture_footprints)),
rowSums(t(national_SF6_footprints)),
rowSums(t(national_HFC_footprints)),
rowSums(t(national_PFC_footprints)),
rowSums(t(national_energy_footprints))) %>%
mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK"))
year_national_fp = as.character(rep(year_current,nrow(national_footprints_w_labels)))
# direct FD emissions
direct_FD_extensions = read.csv(paste0(data_dir_exiobase, "/IOT_", year_current, "_ixi/satellite/F_hh.csv", sep = ""),row.names=NULL,as.is=TRUE)[3:1115,3:345]
direct_FD_extensions[is.na(direct_FD_extensions)]=0
direct_FD_extensions = mapply(direct_FD_extensions, FUN = as.numeric)
direct_FD_extensions = matrix(data=direct_FD_extensions,ncol=343,nrow=1113)
direct_FD_co2_combustion = direct_FD_extensions[24,]
direct_FD_co2_noncombustion_cement = direct_FD_extensions[93,]
direct_FD_co2_noncombustion_lime = direct_FD_extensions[94,]
direct_FD_co2_agriculture_peatdecay = direct_FD_extensions[428,]
direct_FD_co2_waste_biogenic = direct_FD_extensions[438,]
direct_FD_co2_waste_fossil = direct_FD_extensions[439,]
direct_FD_ch4_combustion = direct_FD_extensions[25,]*28
direct_FD_ch4_noncombustion_gas = direct_FD_extensions[68,]*28
direct_FD_ch4_noncombustion_oil = direct_FD_extensions[69,]*28
direct_FD_ch4_noncombustion_anthracite = direct_FD_extensions[70,]*28
direct_FD_ch4_noncombustion_bituminouscoal = direct_FD_extensions[71,]*28
direct_FD_ch4_noncombustion_cokingcoal = direct_FD_extensions[72,]*28
direct_FD_ch4_noncombustion_lignite = direct_FD_extensions[73,]*28
direct_FD_ch4_noncombustion_subbituminouscoal = direct_FD_extensions[74,]*28
direct_FD_ch4_noncombustion_oilrefinery = direct_FD_extensions[75,]*28
direct_FD_ch4_agriculture = direct_FD_extensions[427,]*28
direct_FD_ch4_waste = direct_FD_extensions[436,]*28
direct_FD_n2o_combustion = direct_FD_extensions[26,]*265
direct_FD_n2o_agriculture = direct_FD_extensions[430,]*265
direct_FD_sf6 = direct_FD_extensions[424,]*23500
direct_FD_hfc = direct_FD_extensions[425,]
direct_FD_pfc = direct_FD_extensions[426,]
direct_FD_energy = direct_FD_extensions[470,]
direct_FD_fp = data.frame(direct_FD_co2_combustion,
direct_FD_co2_noncombustion_cement,
direct_FD_co2_noncombustion_lime,
direct_FD_co2_agriculture_peatdecay,
direct_FD_co2_waste_biogenic,
direct_FD_co2_waste_fossil,
direct_FD_ch4_combustion,
direct_FD_ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_oilrefinery,
direct_FD_ch4_agriculture,
direct_FD_ch4_waste,
direct_FD_n2o-combustion,
direct_FD_n2o_agriculture,
direct_FD_sf6,
direct_FD_hfc,
direct_FD_pfc,
direct_FD_energy)
look_national_fp = as.data.frame(cbind(year_national_fp,
national_footprints_w_labels,
direct_FD_fp)) %>%
rename(year = year_national_fp,
geo = V1,
fd_category = V2,
co2_combustion = "rowSums(t(national_CO2_combustion_footprints))",
co2_noncombustion_cement = "rowSums(t(national_CO2_noncombustion_cement_footprints))",
co2_noncombustion_lime = "rowSums(t(national_CO2_noncombustion_lime_footprints))",
co2_agriculture_peatdecay = "rowSums(t(national_CO2_agriculture_peatdecay_footprints))",
co2_waste_biogenic = "rowSums(t(national_CO2_waste_biogenic_footprints))",
co2_waste_fossil = "rowSums(t(national_CO2_waste_fossil_footprints))",
ch4_combustion = "rowSums(t(national_CH4_combustion_footprints))",
ch4_noncombustion_gas = "rowSums(t(national_CH4_noncombustion_gas_footprints))",
ch4_noncombustion_oil = "rowSums(t(national_CH4_noncombustion_oil_footprints))",
ch4_noncombustion_anthracite = "rowSums(t(national_CH4_noncombustion_anthracite_footprints))",
ch4_noncombustion_bituminouscoal = "rowSums(t(national_CH4_noncombustion_bituminouscoal_footprints))",
ch4_noncombustion_cokingcoal = "rowSums(t(national_CH4_noncombustion_cokingcoal_footprints))",
ch4_noncombustion_lignite = "rowSums(t(national_CH4_noncombustion_lignite_footprints))",
ch4_noncombustion_subbituminouscoal = "rowSums(t(national_CH4_noncombustion_subbituminouscoal_footprints))",
ch4_noncombustion_oilrefinery = "rowSums(t(national_CH4_noncombustion_oilrefinery_footprints))",
ch4_agriculture = "rowSums(t(national_CH4_agriculture_footprints))",
ch4_waste = "rowSums(t(national_CH4_waste_footprints))",
n2o_combustion = "rowSums(t(national_N2O_combustion_footprints))",
n2o_agriculture = "rowSums(t(national_N2O_agriculture_footprints))",
sf6 = "rowSums(t(national_SF6_footprints))",
hfc = "rowSums(t(national_HFC_footprints))",
pfc = "rowSums(t(national_PFC_footprints))",
energy = "rowSums(t(national_energy_footprints))") %>%
select(year,
geo,
fd_category,
co2_combustion,
direct_FD_co2_combustion,
co2_noncombustion_cement,
direct_FD_co2_noncombustion_cement,
co2_noncombustion_lime,
direct_FD_co2_noncombustion_lime,
co2_agriculture_peatdecay,
direct_FD_co2_agriculture_peatdecay,
co2_waste_biogenic,
direct_FD_co2_waste_biogenic,
co2_waste_fossil,
direct_FD_co2_waste_fossil,
ch4_combustion,
direct_FD_ch4_combustion,
ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_gas,
ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_oil,
ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_anthracite,
ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_bituminouscoal,
ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_cokingcoal,
ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_lignite,
ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_subbituminouscoal,
ch4_noncombustion_oilrefinery,
direct_FD_ch4_noncombustion_oilrefinery,
ch4_agriculture,
direct_FD_ch4_agriculture,
ch4_waste,
direct_FD_ch4_waste,
n2o_combustion,
direct_FD_n2o_combustion,
n2o_agriculture,
direct_FD_n2o_agriculture,
sf6,
direct_FD_sf6,
hfc,
direct_FD_hfc,
pfc,
direct_FD_pfc,
energy,
direct_FD_energy)
national_fp = rbind(national_fp, look_national_fp)
}
# option holding HBS exp ratios
mean_expenditure_by_quintile_toggle = mean_expenditure_by_quintile_long_bp %>%
filter(!(quintile %in% c("TOTAL","UNK"))) %>%
group_by(geo,year) %>%
mutate(euro_pps_bp = as.numeric(euro_pps_bp),
mean_exp_shares = euro_pps_bp/sum(euro_pps_bp))
ala = total_fd %>%
left_join(mean_expenditure_by_quintile_toggle, by = c("geo","year","quintile"))
join_ala = mean_expenditure_by_coicop_sector_long_bp %>%
left_join(ala, by = c("geo","quintile","year")) %>%
mutate(year = as.numeric(year),
eurostat_countries_colsums = as.numeric(eurostat_countries_colsums),
pm_bp = as.numeric(pm_bp),
fd_me = pm_bp*((eurostat_countries_colsums*mean_exp_shares)/1000))
Eurostat_countries_hh_fd_mean_TIV = as.data.frame(Eurostat_countries_hh_fd) %>% select(-year)
weighted_mean_TIV_with_labels = cbind(TIVs,Eurostat_countries_hh_fd_mean_TIV) %>%
gather(geo,fd,-country_of_production,-year,-sector,-coicop,-five_sectors,
-TIV_CO2_combustion,-TIV_CO2_noncombustion_cement,-TIV_CO2_noncombustion_lime,
-TIV_CO2_agriculture_peatdecay,-TIV_CO2_waste_biogenic,
-TIV_CO2_waste_fossil,-TIV_CH4_combustion,
-TIV_CH4_noncombustion_gas,-TIV_CH4_noncombustion_oil,
-TIV_CH4_noncombustion_anthracite,-TIV_CH4_noncombustion_bituminouscoal,
-TIV_CH4_noncombustion_cokingcoal,-TIV_CH4_noncombustion_lignite,
-TIV_CH4_noncombustion_subbituminouscoal,-TIV_CH4_noncombustion_oilrefinery,
-TIV_CH4_agriculture,-TIV_CH4_waste,
-TIV_N2O_combustion,-TIV_N2O_agriculture,-TIV_SF6,-TIV_HFC,-TIV_PFC,
-TIV_energy) %>%
group_by(geo,year,coicop) %>%
mutate(fd = as.numeric(fd)) %>%
mutate(TIV_CO2_combustion_weighted_average = sum((fd/sum(fd))*TIV_CO2_combustion),
TIV_CO2_noncombustion_cement_weighted_average = sum((fd/sum(fd))*TIV_CO2_noncombustion_cement),
TIV_CO2_noncombustion_lime_weighted_average = sum((fd/sum(fd))*TIV_CO2_noncombustion_lime),
TIV_CO2_agriculture_peatdecay_weighted_average = sum((fd/sum(fd))*TIV_CO2_agriculture_peatdecay),
TIV_CO2_waste_biogenic_weighted_average = sum((fd/sum(fd))*TIV_CO2_waste_biogenic),
TIV_CO2_waste_fossil_weighted_average = sum((fd/sum(fd))*TIV_CO2_waste_fossil),
TIV_CH4_combustion_weighted_average = sum((fd/sum(fd))*TIV_CH4_combustion),
TIV_CH4_noncombustion_gas_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_gas),
TIV_CH4_noncombustion_oil_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_oil),
TIV_CH4_noncombustion_anthracite_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_anthracite),
TIV_CH4_noncombustion_bituminouscoal_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_bituminouscoal),
TIV_CH4_noncombustion_cokingcoal_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_cokingcoal),
TIV_CH4_noncombustion_lignite_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_lignite),
TIV_CH4_noncombustion_subbituminouscoal_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_subbituminouscoal),
TIV_CH4_noncombustion_oilrefinery_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_oilrefinery),
TIV_CH4_agriculture_weighted_average = sum((fd/sum(fd))*TIV_CH4_agriculture),
TIV_CH4_waste_weighted_average = sum((fd/sum(fd))*TIV_CH4_waste),
TIV_N2O_combustion_weighted_average = sum((fd/sum(fd))*TIV_N2O_combustion),
TIV_N2O_agriculture_weighted_average = sum((fd/sum(fd))*TIV_N2O_agriculture),
TIV_SF6_weighted_average = sum((fd/sum(fd))*TIV_SF6),
TIV_HFC_weighted_average = sum((fd/sum(fd))*TIV_HFC),
TIV_PFC_weighted_average = sum((fd/sum(fd))*TIV_PFC),
TIV_energy_weighted_average = sum((fd/sum(fd))*TIV_energy)) %>%
select(year,geo,coicop,TIV_CO2_combustion_weighted_average,
TIV_CO2_noncombustion_cement_weighted_average,
TIV_CO2_noncombustion_lime_weighted_average,
TIV_CO2_agriculture_peatdecay_weighted_average,
TIV_CO2_waste_biogenic_weighted_average,
TIV_CO2_waste_fossil_weighted_average,
TIV_CH4_combustion_weighted_average,
TIV_CH4_noncombustion_gas_weighted_average,
TIV_CH4_noncombustion_oil_weighted_average,
TIV_CH4_noncombustion_anthracite_weighted_average,
TIV_CH4_noncombustion_bituminouscoal_weighted_average,
TIV_CH4_noncombustion_cokingcoal_weighted_average,
TIV_CH4_noncombustion_lignite_weighted_average,
TIV_CH4_noncombustion_subbituminouscoal_weighted_average,
TIV_CH4_noncombustion_oilrefinery_weighted_average,
TIV_CH4_agriculture_weighted_average,
TIV_CH4_waste_weighted_average,
TIV_N2O_combustion_weighted_average,
TIV_N2O_agriculture_weighted_average,
TIV_SF6_weighted_average,
TIV_HFC_weighted_average,
TIV_PFC_weighted_average,
TIV_energy_weighted_average) %>%
unique()
weighted_mean_europe_TIV_with_labels = cbind(europe_TIVs, Eurostat_countries_hh_fd_mean_TIV) %>%
gather(geo,fd,-country_of_production,-year,-sector,-coicop,-five_sectors,
-TIV_CO2_combustion_europe,-TIV_CO2_combustion_not_europe,
-TIV_CO2_noncombustion_cement_europe,-TIV_CO2_noncombustion_cement_not_europe,
-TIV_CO2_noncombustion_lime_europe, -TIV_CO2_noncombustion_lime_not_europe,
-TIV_CO2_agriculture_peatdecay_europe,-TIV_CO2_agriculture_peatdecay_not_europe,
-TIV_CO2_waste_biogenic_europe, -TIV_CO2_waste_biogenic_not_europe,
-TIV_CO2_waste_fossil_europe, -TIV_CO2_waste_fossil_not_europe,
-TIV_CH4_combustion_europe,-TIV_CH4_combustion_not_europe,
-TIV_CH4_noncombustion_gas_europe, -TIV_CH4_noncombustion_gas_not_europe,
-TIV_CH4_noncombustion_oil_europe,-TIV_CH4_noncombustion_oil_not_europe,
-TIV_CH4_noncombustion_anthracite_europe,-TIV_CH4_noncombustion_anthracite_not_europe,
-TIV_CH4_noncombustion_bituminouscoal_europe,-TIV_CH4_noncombustion_bituminouscoal_not_europe,
-TIV_CH4_noncombustion_cokingcoal_europe,-TIV_CH4_noncombustion_cokingcoal_not_europe,
-TIV_CH4_noncombustion_lignite_europe,-TIV_CH4_noncombustion_lignite_not_europe,
-TIV_CH4_noncombustion_subbituminouscoal_europe,-TIV_CH4_noncombustion_subbituminouscoal_not_europe,
-TIV_CH4_noncombustion_oilrefinery_europe, -TIV_CH4_noncombustion_oilrefinery_not_europe,
-TIV_CH4_agriculture_europe, -TIV_CH4_agriculture_not_europe,
-TIV_CH4_waste_europe,-TIV_CH4_waste_not_europe,
-TIV_N2O_combustion_europe,-TIV_N2O_combustion_not_europe,
-TIV_N2O_agriculture_europe,-TIV_N2O_agriculture_not_europe,
-TIV_SF6_europe,-TIV_SF6_not_europe,
-TIV_HFC_europe,-TIV_HFC_not_europe,-TIV_PFC_europe,-TIV_PFC_not_europe,
-TIV_energy_europe,-TIV_energy_not_europe) %>%
group_by(geo,year,coicop) %>%
mutate(fd = as.numeric(fd)) %>%
mutate(TIV_CO2_combustion_europe_weighted_average = sum((fd/sum(fd))*TIV_CO2_combustion_europe),
TIV_CO2_noncombustion_cement_europe_weighted_average = sum((fd/sum(fd))*TIV_CO2_noncombustion_cement_europe),
TIV_CO2_noncombustion_lime_europe_weighted_average = sum((fd/sum(fd))*TIV_CO2_noncombustion_lime_europe),
TIV_CO2_agriculture_peatdecay_europe_weighted_average = sum((fd/sum(fd))*TIV_CO2_agriculture_peatdecay_europe),
TIV_CO2_waste_biogenic_europe_weighted_average = sum((fd/sum(fd))*TIV_CO2_waste_biogenic_europe),
TIV_CO2_waste_fossil_europe_weighted_average = sum((fd/sum(fd))*TIV_CO2_waste_fossil_europe),
TIV_CH4_combustion_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_combustion_europe),
TIV_CH4_noncombustion_gas_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_gas_europe),
TIV_CH4_noncombustion_oil_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_oil_europe),
TIV_CH4_noncombustion_anthracite_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_anthracite_europe),
TIV_CH4_noncombustion_bituminouscoal_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_bituminouscoal_europe),
TIV_CH4_noncombustion_cokingcoal_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_cokingcoal_europe),
TIV_CH4_noncombustion_lignite_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_lignite_europe),
TIV_CH4_noncombustion_subbituminouscoal_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_subbituminouscoal_europe),
TIV_CH4_noncombustion_oilrefinery_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_oilrefinery_europe),
TIV_CH4_agriculture_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_agriculture_europe),
TIV_CH4_waste_europe_weighted_average = sum((fd/sum(fd))*TIV_CH4_waste_europe),
TIV_N2O_combustion_europe_weighted_average = sum((fd/sum(fd))*TIV_N2O_combustion_europe),
TIV_N2O_agriculture_europe_weighted_average = sum((fd/sum(fd))*TIV_N2O_agriculture_europe),
TIV_SF6_europe_weighted_average = sum((fd/sum(fd))*TIV_SF6_europe),
TIV_HFC_europe_weighted_average = sum((fd/sum(fd))*TIV_HFC_europe),
TIV_PFC_europe_weighted_average = sum((fd/sum(fd))*TIV_PFC_europe),
TIV_energy_europe_weighted_average = sum((fd/sum(fd))*TIV_energy_europe)) %>%
select(year,geo,coicop,TIV_CO2_combustion_europe_weighted_average,
TIV_CO2_noncombustion_cement_europe_weighted_average,
TIV_CO2_noncombustion_lime_europe_weighted_average,
TIV_CO2_agriculture_peatdecay_europe_weighted_average,
TIV_CO2_waste_biogenic_europe_weighted_average,
TIV_CO2_waste_fossil_europe_weighted_average,
TIV_CH4_combustion_europe_weighted_average,
TIV_CH4_noncombustion_gas_europe_weighted_average,
TIV_CH4_noncombustion_oil_europe_weighted_average,
TIV_CH4_noncombustion_anthracite_europe_weighted_average,
TIV_CH4_noncombustion_bituminouscoal_europe_weighted_average,
TIV_CH4_noncombustion_cokingcoal_europe_weighted_average,
TIV_CH4_noncombustion_lignite_europe_weighted_average,
TIV_CH4_noncombustion_subbituminouscoal_europe_weighted_average,
TIV_CH4_noncombustion_oilrefinery_europe_weighted_average,
TIV_CH4_agriculture_europe_weighted_average,
TIV_CH4_waste_europe_weighted_average,
TIV_N2O_combustion_europe_weighted_average,
TIV_N2O_agriculture_europe_weighted_average,
TIV_SF6_europe_weighted_average,
TIV_HFC_europe_weighted_average,
TIV_PFC_europe_weighted_average,
TIV_energy_europe_weighted_average) %>%
unique()
domestic_TIVs_Eurostat = domestic_TIVs %>%
filter(geo %in% c("AT",
"BG",
"BE",
"CY",
"CZ",
"DE",
"DK",
"EE",
"EL",
"ES",
"FI",
"FR",
"HR",
"HU",
"IE",
"IT",
"LT",
"LU",
"LV",
"MT",
"NL",
"NO",
"PL",
"PT",
"RO",
"SE",
"SI",
"SK",
"TR",
"UK"))
Eurostat_countries_hh_fd_long = as.data.frame(Eurostat_countries_hh_fd) %>%
gather(geo,fd,-year) %>%
arrange(year, match(geo, c("AT",
"BE",
"BG",
"CY",
"CZ",
"DE",
"DK",
"EE",
"ES",
"FI",
"FR",
"UK",
"EL",
"HR",
"HU",
"IE",
"IT",
"LT",
"LU",
"LV",
"MT",
"NL",
"NO",
"PL",
"PT",
"RO",
"SE",
"SI",
"SK",
"TR"))) %>% select(-year,-geo)
weighted_mean_domestic_TIV_with_labels = cbind(domestic_TIVs_Eurostat,Eurostat_countries_hh_fd_long) %>%
group_by(geo,year,coicop) %>%
mutate(fd = as.numeric(fd)) %>%
mutate(TIV_CO2_combustion_domestic_weighted_average = sum((fd/sum(fd))*TIV_CO2_combustion_domestic),
TIV_CO2_noncombustion_cement_domestic_weighted_average = sum((fd/sum(fd))*TIV_CO2_noncombustion_cement_domestic),
TIV_CO2_noncombustion_lime_domestic_weighted_average = sum((fd/sum(fd))*TIV_CO2_noncombustion_lime_domestic),
TIV_CO2_agriculture_peatdecay_domestic_weighted_average = sum((fd/sum(fd))*TIV_CO2_agriculture_peatdecay_domestic),
TIV_CO2_waste_biogenic_domestic_weighted_average = sum((fd/sum(fd))*TIV_CO2_waste_biogenic_domestic),
TIV_CO2_waste_fossil_domestic_weighted_average = sum((fd/sum(fd))*TIV_CO2_waste_fossil_domestic),
TIV_CH4_combustion_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_combustion_domestic),
TIV_CH4_noncombustion_gas_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_gas_domestic),
TIV_CH4_noncombustion_oil_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_oil_domestic),
TIV_CH4_noncombustion_anthracite_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_anthracite_domestic),
TIV_CH4_noncombustion_bituminouscoal_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_bituminouscoal_domestic),
TIV_CH4_noncombustion_cokingcoal_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_cokingcoal_domestic),
TIV_CH4_noncombustion_lignite_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_lignite_domestic),
TIV_CH4_noncombustion_subbituminouscoal_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_subbituminouscoal_domestic),
TIV_CH4_noncombustion_oilrefinery_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_noncombustion_oilrefinery_domestic),
TIV_CH4_agriculture_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_agriculture_domestic),
TIV_CH4_waste_domestic_weighted_average = sum((fd/sum(fd))*TIV_CH4_waste_domestic),
TIV_N2O_combustion_domestic_weighted_average = sum((fd/sum(fd))*TIV_N2O_combustion_domestic),
TIV_N2O_agriculture_domestic_weighted_average = sum((fd/sum(fd))*TIV_N2O_agriculture_domestic),
TIV_SF6_domestic_weighted_average = sum((fd/sum(fd))*TIV_SF6_domestic),
TIV_HFC_domestic_weighted_average = sum((fd/sum(fd))*TIV_HFC_domestic),
TIV_PFC_domestic_weighted_average = sum((fd/sum(fd))*TIV_PFC_domestic),
TIV_energy_domestic_weighted_average = sum((fd/sum(fd))*TIV_energy_domestic)) %>%
select(year,geo,coicop,TIV_CO2_combustion_domestic_weighted_average,
TIV_CO2_noncombustion_cement_domestic_weighted_average,
TIV_CO2_noncombustion_lime_domestic_weighted_average,
TIV_CO2_agriculture_peatdecay_domestic_weighted_average,
TIV_CO2_waste_biogenic_domestic_weighted_average,
TIV_CO2_waste_fossil_domestic_weighted_average,
TIV_CH4_combustion_domestic_weighted_average,
TIV_CH4_noncombustion_gas_domestic_weighted_average,
TIV_CH4_noncombustion_oil_domestic_weighted_average,
TIV_CH4_noncombustion_anthracite_domestic_weighted_average,
TIV_CH4_noncombustion_bituminouscoal_domestic_weighted_average,
TIV_CH4_noncombustion_cokingcoal_domestic_weighted_average,
TIV_CH4_noncombustion_lignite_domestic_weighted_average,
TIV_CH4_noncombustion_subbituminouscoal_domestic_weighted_average,
TIV_CH4_noncombustion_oilrefinery_domestic_weighted_average,
TIV_CH4_agriculture_domestic_weighted_average,
TIV_CH4_waste_domestic_weighted_average,
TIV_N2O_combustion_domestic_weighted_average,
TIV_N2O_agriculture_domestic_weighted_average,
TIV_SF6_domestic_weighted_average,
TIV_HFC_domestic_weighted_average,
TIV_PFC_domestic_weighted_average,
TIV_energy_domestic_weighted_average) %>%
unique()
ok = join_ala %>%
mutate(year = as.character(year)) %>%
left_join(weighted_mean_TIV_with_labels, by = c("geo","year","coicop")) %>%
left_join(weighted_mean_europe_TIV_with_labels, by = c("geo","year","coicop")) %>%
left_join(weighted_mean_domestic_TIV_with_labels, by = c("geo", "year", "coicop")) %>%
mutate(co2_kg = fd_me*(TIV_CO2_combustion_weighted_average + TIV_CO2_noncombustion_cement_weighted_average +
TIV_CO2_noncombustion_lime_weighted_average + TIV_CO2_agriculture_peatdecay_weighted_average +
TIV_CO2_waste_biogenic_weighted_average + TIV_CO2_waste_fossil_weighted_average),
co2_domestic_kg = fd_me*(TIV_CO2_combustion_domestic_weighted_average + TIV_CO2_noncombustion_cement_domestic_weighted_average +
TIV_CO2_noncombustion_lime_domestic_weighted_average + TIV_CO2_agriculture_peatdecay_domestic_weighted_average +
TIV_CO2_waste_biogenic_domestic_weighted_average + TIV_CO2_waste_fossil_domestic_weighted_average),
co2_europe_kg = fd_me*((TIV_CO2_combustion_europe_weighted_average + TIV_CO2_noncombustion_cement_europe_weighted_average +
TIV_CO2_noncombustion_lime_europe_weighted_average + TIV_CO2_agriculture_peatdecay_europe_weighted_average +
TIV_CO2_waste_biogenic_europe_weighted_average + TIV_CO2_waste_fossil_europe_weighted_average) - (TIV_CO2_combustion_domestic_weighted_average + TIV_CO2_noncombustion_cement_domestic_weighted_average +
TIV_CO2_noncombustion_lime_domestic_weighted_average + TIV_CO2_agriculture_peatdecay_domestic_weighted_average +
TIV_CO2_waste_biogenic_domestic_weighted_average + TIV_CO2_waste_fossil_domestic_weighted_average)),
co2eq_kg = fd_me*(TIV_CO2_combustion_weighted_average +
TIV_CO2_noncombustion_cement_weighted_average +
TIV_CO2_noncombustion_lime_weighted_average + TIV_CO2_agriculture_peatdecay_weighted_average +
TIV_CO2_waste_biogenic_weighted_average + TIV_CO2_waste_fossil_weighted_average +
TIV_CH4_combustion_weighted_average +
TIV_CH4_noncombustion_gas_weighted_average +
TIV_CH4_noncombustion_oil_weighted_average +
TIV_CH4_noncombustion_anthracite_weighted_average +
TIV_CH4_noncombustion_bituminouscoal_weighted_average +
TIV_CH4_noncombustion_cokingcoal_weighted_average +
TIV_CH4_noncombustion_lignite_weighted_average +
TIV_CH4_noncombustion_subbituminouscoal_weighted_average +
TIV_CH4_noncombustion_oilrefinery_weighted_average +
TIV_CH4_agriculture_weighted_average +
TIV_CH4_waste_weighted_average +
TIV_N2O_combustion_weighted_average +
TIV_N2O_agriculture_weighted_average +
TIV_SF6_weighted_average +
TIV_HFC_weighted_average +
TIV_PFC_weighted_average),
co2eq_domestic_kg = fd_me*(TIV_CO2_combustion_domestic_weighted_average +
TIV_CO2_noncombustion_cement_domestic_weighted_average +
TIV_CO2_noncombustion_lime_domestic_weighted_average + TIV_CO2_agriculture_peatdecay_domestic_weighted_average +
TIV_CO2_waste_biogenic_domestic_weighted_average + TIV_CO2_waste_fossil_domestic_weighted_average +
TIV_CH4_combustion_domestic_weighted_average +
TIV_CH4_noncombustion_gas_domestic_weighted_average +
TIV_CH4_noncombustion_oil_domestic_weighted_average +
TIV_CH4_noncombustion_anthracite_domestic_weighted_average +
TIV_CH4_noncombustion_bituminouscoal_domestic_weighted_average +
TIV_CH4_noncombustion_cokingcoal_domestic_weighted_average +
TIV_CH4_noncombustion_lignite_domestic_weighted_average +
TIV_CH4_noncombustion_subbituminouscoal_domestic_weighted_average +
TIV_CH4_noncombustion_oilrefinery_domestic_weighted_average +
TIV_CH4_agriculture_domestic_weighted_average +
TIV_CH4_waste_domestic_weighted_average +
TIV_N2O_combustion_domestic_weighted_average +
TIV_N2O_agriculture_domestic_weighted_average +
TIV_SF6_domestic_weighted_average +
TIV_HFC_domestic_weighted_average +
TIV_PFC_domestic_weighted_average),
co2eq_europe_kg = fd_me*((TIV_CO2_combustion_europe_weighted_average +
TIV_CO2_noncombustion_cement_europe_weighted_average +
TIV_CO2_noncombustion_lime_europe_weighted_average + TIV_CO2_agriculture_peatdecay_europe_weighted_average +
TIV_CO2_waste_biogenic_europe_weighted_average + TIV_CO2_waste_fossil_europe_weighted_average +
TIV_CH4_combustion_europe_weighted_average +
TIV_CH4_noncombustion_gas_europe_weighted_average +
TIV_CH4_noncombustion_oil_europe_weighted_average +
TIV_CH4_noncombustion_anthracite_europe_weighted_average +
TIV_CH4_noncombustion_bituminouscoal_europe_weighted_average +
TIV_CH4_noncombustion_cokingcoal_europe_weighted_average +
TIV_CH4_noncombustion_lignite_europe_weighted_average +
TIV_CH4_noncombustion_subbituminouscoal_europe_weighted_average +
TIV_CH4_noncombustion_oilrefinery_europe_weighted_average +
TIV_CH4_agriculture_europe_weighted_average +
TIV_CH4_waste_europe_weighted_average +
TIV_N2O_combustion_europe_weighted_average +
TIV_N2O_agriculture_europe_weighted_average +
TIV_SF6_europe_weighted_average +
TIV_HFC_europe_weighted_average +
TIV_PFC_europe_weighted_average) -
(TIV_CO2_combustion_domestic_weighted_average +
TIV_CO2_noncombustion_cement_domestic_weighted_average +
TIV_CO2_noncombustion_lime_domestic_weighted_average + TIV_CO2_agriculture_peatdecay_domestic_weighted_average +
TIV_CO2_waste_biogenic_domestic_weighted_average + TIV_CO2_waste_fossil_domestic_weighted_average +
TIV_CH4_combustion_domestic_weighted_average +
TIV_CH4_noncombustion_gas_domestic_weighted_average +
TIV_CH4_noncombustion_oil_domestic_weighted_average +
TIV_CH4_noncombustion_anthracite_domestic_weighted_average +
TIV_CH4_noncombustion_bituminouscoal_domestic_weighted_average +
TIV_CH4_noncombustion_cokingcoal_domestic_weighted_average +
TIV_CH4_noncombustion_lignite_domestic_weighted_average +
TIV_CH4_noncombustion_subbituminouscoal_domestic_weighted_average +
TIV_CH4_noncombustion_oilrefinery_domestic_weighted_average +
TIV_CH4_agriculture_domestic_weighted_average +
TIV_CH4_waste_domestic_weighted_average +
TIV_N2O_combustion_domestic_weighted_average +
TIV_N2O_agriculture_domestic_weighted_average +
TIV_SF6_domestic_weighted_average +
TIV_HFC_domestic_weighted_average +
TIV_PFC_domestic_weighted_average)),
energy_use_TJ = fd_me*(TIV_energy_weighted_average),
energy_use_domestic_TJ = fd_me*(TIV_energy_domestic_weighted_average),
energy_use_europe_TJ = fd_me*(TIV_energy_europe_weighted_average -
TIV_energy_domestic_weighted_average))
# direct from FD - to go back to results without direct FD fp, do not run this next chunk and do not bind_rows with 'results'
env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_pefasu_1_Data.csv")) %>%
filter(TIME == 2015) %>%
mutate(geo = dplyr::recode(GEO,"Austria" = "AT",
"Belgium" = "BE",
"Cyprus" = "CY",
"Czechia" = "CZ",
"Denmark" = "DK",
"Estonia" = "EE",
"Finland" = "FI",
"France" = "FR",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Greece" = "EL",
"Hungary" = "HU",
"Ireland" = "IE",
"Italy" = "IT",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Malta" = "MT",
"Netherlands" = "NL",
"Norway" = "NO",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovakia" = "SK",
"Slovenia" = "SI",
"Spain" = "ES",
"Sweden" = "SE",
"United Kingdom" = "UK",
"Bulgaria" = "BG",
"Croatia" = "HR")) %>%
select(NACE_R2,geo,Value) %>%
mutate(Value = parse_number(Value),
Value = as.numeric(Value)) %>%
spread(NACE_R2,Value) %>%
clean_names() %>%
mutate(HH_HEAT = heating_cooling_activities_by_households/total_activities_by_households,
HH_TRA = transport_activities_by_households/total_activities_by_households,
HH_OTH = other_activities_by_households/total_activities_by_households) %>%
select(geo,HH_HEAT,HH_TRA,HH_OTH)
env_ac_pefasu_TR = env_ac_pefasu_no_TR %>%
filter(geo == "BG") %>%
mutate(geo = dplyr::recode(geo,
"BG" = "TR"))
env_ac_pefasu = rbind(env_ac_pefasu_no_TR,env_ac_pefasu_TR) %>%
gather(sector,share_of_total_energy,-geo)
env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_ainah_r2_1_Data.csv")) %>%
filter(TIME == 2015) %>%
mutate(geo = dplyr::recode(GEO,"Austria" = "AT",
"Belgium" = "BE",
"Cyprus" = "CY",
"Czechia" = "CZ",
"Denmark" = "DK",
"Estonia" = "EE",
"Finland" = "FI",
"France" = "FR",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Greece" = "EL",
"Hungary" = "HU",
"Ireland" = "IE",
"Italy" = "IT",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Malta" = "MT",
"Netherlands" = "NL",
"Norway" = "NO",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovakia" = "SK",
"Slovenia" = "SI",
"Spain" = "ES",
"Sweden" = "SE",
"Turkey" = "TR",
"United Kingdom" = "UK",
"Bulgaria" = "BG",
"Croatia" = "HR")) %>%
select(NACE_R2,AIRPOL,geo,Value) %>%
mutate(Value = parse_number(Value),
Value = as.numeric(Value)) %>%
spread(NACE_R2,Value) %>%
clean_names() %>%
mutate(HH_HEAT = heating_cooling_activities_by_households/total_activities_by_households,
HH_TRA = transport_activities_by_households/total_activities_by_households,
HH_OTH = other_activities_by_households/total_activities_by_households) %>%
select(geo,airpol,HH_HEAT,HH_TRA,HH_OTH)
env_ac_ainah_r2_co2 = env_ac_ainah_r2 %>%
filter(airpol == "Carbon dioxide") %>%
select(-airpol) %>%
gather(sector,share_of_total_co2,-geo)
env_ac_ainah_r2_ch4 = env_ac_ainah_r2 %>%
filter(airpol == "Methane") %>%
select(-airpol) %>%
gather(sector,share_of_total_ch4,-geo)
env_ac_ainah_r2_n2o = env_ac_ainah_r2 %>%
filter(airpol == "Nitrous oxide") %>%
select(-airpol) %>%
gather(sector,share_of_total_n2o,-geo)
direct_FD_fp_long = national_fp %>%
filter(fd_category == "Final consumption expenditure by households",
geo %in% c("AT",
"BE", "BG", "CY", "CZ",
"DE" , "DK" , "EE" ,
"ES" , "FI" , "FR" ,
"UK", "EL", "HR" ,
"HU" , "IE" , "IT" ,
"LT" , "LU" , "LV" ,
"MT" , "NL" , "PL" ,
"PT" , "TR" , "SK" ,
"SI" , "SE" , "RO" ,
"NO")) %>%
select(year,geo,fd_category,direct_FD_co2_combustion,
direct_FD_co2_noncombustion_cement,
direct_FD_co2_noncombustion_lime,
direct_FD_co2_agriculture_peatdecay,
direct_FD_co2_waste_biogenic,
direct_FD_co2_waste_fossil,
direct_FD_ch4_combustion,
direct_FD_ch4_noncombustion_gas,
direct_FD_ch4_noncombustion_oil,
direct_FD_ch4_noncombustion_anthracite,
direct_FD_ch4_noncombustion_bituminouscoal,
direct_FD_ch4_noncombustion_cokingcoal,
direct_FD_ch4_noncombustion_lignite,
direct_FD_ch4_noncombustion_subbituminouscoal,
direct_FD_ch4_noncombustion_oilrefinery,
direct_FD_ch4_agriculture,
direct_FD_ch4_waste,
direct_FD_n2o_combustion,
direct_FD_n2o_agriculture,
direct_FD_sf6,
direct_FD_hfc,
direct_FD_pfc,
direct_FD_energy) %>%
slice(rep(1:n(), each = 3))
sector = rep(c("HH_HEAT","HH_TRA","HH_OTH"), nrow(direct_FD_fp_long)/3)
direct_FD_fp_long_disagg = cbind(sector,direct_FD_fp_long) %>%
mutate(coicop = ifelse(sector == "HH_TRA","CP072",
ifelse(sector == "HH_HEAT","CP045","CP05")),
five_sectors = ifelse(sector == "HH_TRA", "transport",
ifelse(sector == "HH_HEAT", "shelter", "manufactured goods"))) %>%
left_join(env_ac_ainah_r2_co2, by = c("geo","sector")) %>%
left_join(env_ac_ainah_r2_ch4, by = c("geo","sector")) %>%
left_join(env_ac_ainah_r2_n2o, by = c("geo","sector")) %>%
left_join(env_ac_pefasu, by = c("geo","sector")) %>%
mutate(direct_FD_co2 = (direct_FD_co2_combustion +
direct_FD_co2_noncombustion_cement +
direct_FD_co2_noncombustion_lime +
direct_FD_co2_agriculture_peatdecay +
direct_FD_co2_waste_biogenic +
direct_FD_co2_waste_fossil)*share_of_total_co2,
direct_FD_ch4 = (direct_FD_ch4_combustion +
direct_FD_ch4_noncombustion_gas +
direct_FD_ch4_noncombustion_oil +
direct_FD_ch4_noncombustion_anthracite +
direct_FD_ch4_noncombustion_bituminouscoal +
direct_FD_ch4_noncombustion_cokingcoal +
direct_FD_ch4_noncombustion_lignite +
direct_FD_ch4_noncombustion_subbituminouscoal +
direct_FD_ch4_noncombustion_oilrefinery +
direct_FD_ch4_agriculture +
direct_FD_ch4_waste)*share_of_total_ch4,
direct_FD_n2o = (direct_FD_n2o_combustion +
direct_FD_n2o_agriculture)*share_of_total_n2o,
direct_FD_energy = direct_FD_energy*share_of_total_energy) %>%
left_join(shares, by = c("year","geo","coicop")) %>%
mutate(disaggregated_direct_FD_co2 = direct_FD_co2*share,
disaggregated_direct_FD_ch4 = direct_FD_ch4*share,
disaggregated_direct_FD_n2o = direct_FD_n2o*share,
disaggregated_direct_FD_energy = direct_FD_energy*share) %>%
select(year,geo,sector, quintile,
coicop, five_sectors,
disaggregated_direct_FD_co2,
disaggregated_direct_FD_ch4,
disaggregated_direct_FD_n2o,
disaggregated_direct_FD_energy)
direct_FD_co2 = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_co2) %>%
spread(quintile,disaggregated_direct_FD_co2) %>%
rename(q1_co2 = QUINTILE1,
q2_co2 = QUINTILE2,
q3_co2 = QUINTILE3,
q4_co2 = QUINTILE4,
q5_co2 = QUINTILE5) %>%
mutate(q1_co2_domestic = q1_co2,
q2_co2_domestic = q2_co2,
q3_co2_domestic = q3_co2,
q4_co2_domestic = q4_co2,
q5_co2_domestic = q5_co2,
co2_total = q1_co2+q2_co2+q3_co2+q4_co2+q5_co2,
co2_total_domestic = q1_co2_domestic+
q2_co2_domestic+q3_co2_domestic+
q4_co2_domestic+q5_co2_domestic)
direct_FD_ch4 = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_ch4) %>%
spread(quintile,disaggregated_direct_FD_ch4) %>%
rename(q1_ch4 = QUINTILE1,
q2_ch4 = QUINTILE2,
q3_ch4 = QUINTILE3,
q4_ch4 = QUINTILE4,
q5_ch4 = QUINTILE5) %>%
mutate(q1_ch4_domestic = q1_ch4,
q2_ch4_domestic = q2_ch4,
q3_ch4_domestic = q3_ch4,
q4_ch4_domestic = q4_ch4,
q5_ch4_domestic = q5_ch4,
ch4_total = q1_ch4+q2_ch4+q3_ch4+q4_ch4+q5_ch4,
ch4_total_domestic = q1_ch4_domestic+
q2_ch4_domestic+q3_ch4_domestic+
q4_ch4_domestic+q5_ch4_domestic)
direct_FD_n2o = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_n2o) %>%
spread(quintile,disaggregated_direct_FD_n2o) %>%
rename(q1_n2o = QUINTILE1,
q2_n2o = QUINTILE2,
q3_n2o = QUINTILE3,
q4_n2o = QUINTILE4,
q5_n2o = QUINTILE5) %>%
mutate(q1_n2o_domestic = q1_n2o,
q2_n2o_domestic = q2_n2o,
q3_n2o_domestic = q3_n2o,
q4_n2o_domestic = q4_n2o,
q5_n2o_domestic = q5_n2o,
n2o_total = q1_n2o+q2_n2o+q3_n2o+q4_n2o+q5_n2o,
n2o_total_domestic = q1_n2o_domestic+
q2_n2o_domestic+q3_n2o_domestic+
q4_n2o_domestic+q5_n2o_domestic)
direct_FD_energy = direct_FD_fp_long_disagg %>%
select(year,geo,sector,quintile,coicop,five_sectors,disaggregated_direct_FD_energy) %>%
spread(quintile,disaggregated_direct_FD_energy) %>%
rename(q1_energy = QUINTILE1,
q2_energy = QUINTILE2,
q3_energy = QUINTILE3,
q4_energy = QUINTILE4,
q5_energy = QUINTILE5) %>%
mutate(q1_energy_domestic = q1_energy,
q2_energy_domestic = q2_energy,
q3_energy_domestic = q3_energy,
q4_energy_domestic = q4_energy,
q5_energy_domestic = q5_energy,
energy_total = q1_energy+q2_energy+q3_energy+q4_energy+q5_energy,
energy_total_domestic = q1_energy_domestic+
q2_energy_domestic+q3_energy_domestic+
q4_energy_domestic+q5_energy_domestic)
direct_FD_fp_wide = direct_FD_co2 %>%
left_join(direct_FD_ch4, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
left_join(direct_FD_n2o, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
left_join(direct_FD_energy, by = c("year","geo",
"sector","coicop",
"five_sectors")) %>%
mutate(country_of_production = geo) %>%
mutate(q1_co2eq = q1_co2 + q1_ch4 + q1_n2o,
q2_co2eq = q2_co2 + q2_ch4 + q2_n2o,
q3_co2eq = q3_co2 + q3_ch4 + q3_n2o,
q4_co2eq = q4_co2 + q4_ch4 + q4_n2o,
q5_co2eq = q5_co2 + q5_ch4 + q5_n2o,
co2eq_total = q1_co2eq +
q2_co2eq + q3_co2eq +
q4_co2eq + q5_co2eq,
q1_co2eq_domestic = q1_co2_domestic + q1_ch4_domestic + q1_n2o_domestic,
q2_co2eq_domestic = q2_co2_domestic + q2_ch4_domestic + q2_n2o_domestic,
q3_co2eq_domestic = q3_co2_domestic + q3_ch4_domestic + q3_n2o_domestic,
q4_co2eq_domestic = q4_co2_domestic + q4_ch4_domestic + q4_n2o_domestic,
q5_co2eq_domestic = q5_co2_domestic + q5_ch4_domestic + q5_n2o_domestic,
co2eq_total_domestic = q1_co2eq_domestic +
q2_co2eq_domestic + q3_co2eq_domestic +
q4_co2eq_domestic + q5_co2eq_domestic) %>%
select(-q1_ch4,
-q2_ch4,
-q3_ch4,
-q4_ch4,
-q5_ch4,
-ch4_total,
-q1_ch4_domestic,
-q2_ch4_domestic,
-q3_ch4_domestic,
-q4_ch4_domestic,
-q5_ch4_domestic,
-ch4_total_domestic,
-q1_n2o,
-q2_n2o,
-q3_n2o,
-q4_n2o,
-q5_n2o,
-n2o_total,
-q1_n2o_domestic,
-q2_n2o_domestic,
-q3_n2o_domestic,
-q4_n2o_domestic,
-q5_n2o_domestic,
-n2o_total_domestic)
direct_FD_fp_wide_all = direct_FD_fp_wide %>%
clean_names() %>%
select(year,geo,coicop,q1_co2:q5_co2,
q1_co2_domestic:q5_co2_domestic,
q1_co2eq:q5_co2eq,
q1_co2eq_domestic:q5_co2eq_domestic,
q1_energy:q5_energy,
q1_energy_domestic:q5_energy_domestic)
## extract co2 and pivot long
cols_co2 = c("q1_co2", "q2_co2", "q3_co2", "q4_co2", "q5_co2")
tmp_co2 = direct_FD_fp_wide_all %>%
select(year, geo, coicop, cols_co2) %>%
pivot_longer(cols = cols_co2,
names_to = "quintile",
values_to = "co2_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2 domestic and pivot long
cols_co2_domestic = c("q1_co2_domestic", "q2_co2_domestic", "q3_co2_domestic", "q4_co2_domestic", "q5_co2_domestic")
tmp_co2_domestic = direct_FD_fp_wide_all %>%
select(year, geo, coicop, cols_co2_domestic) %>%
pivot_longer(cols = cols_co2_domestic,
names_to = "quintile",
values_to = "co2_domestic_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq and pivot long
cols_co2eq = c("q1_co2eq", "q2_co2eq", "q3_co2eq", "q4_co2eq", "q5_co2eq")
tmp_co2eq = direct_FD_fp_wide_all %>%
select(year, geo, coicop, cols_co2eq) %>%
pivot_longer(cols = cols_co2eq,
names_to = "quintile",
values_to = "co2eq_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract co2eq domestic and pivot long
cols_co2eq_domestic = c("q1_co2eq_domestic", "q2_co2eq_domestic", "q3_co2eq_domestic", "q4_co2eq_domestic", "q5_co2eq_domestic")
tmp_co2eq_domestic = direct_FD_fp_wide_all %>%
select(year, geo, coicop, cols_co2eq_domestic) %>%
pivot_longer(cols = cols_co2eq_domestic,
names_to = "quintile",
values_to = "co2eq_domestic_kg") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy use and pivot long
cols_energy = c("q1_energy","q2_energy","q3_energy","q4_energy","q5_energy")
tmp_energy = direct_FD_fp_wide_all %>%
select(year, geo, coicop, cols_energy) %>%
pivot_longer(cols = cols_energy,
names_to = "quintile",
values_to = "energy_use_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
## extract energy domestic and pivot long
cols_energy_domestic = c("q1_energy_domestic","q2_energy_domestic","q3_energy_domestic","q4_energy_domestic","q5_energy_domestic")
tmp_energy_domestic = direct_FD_fp_wide_all %>%
select(year, geo, coicop, cols_energy_domestic) %>%
pivot_longer(cols = cols_energy_domestic,
names_to = "quintile",
values_to = "energy_use_domestic_TJ") %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile)
direct_FD_fp_wide_recombined = tmp_co2 %>%
left_join(tmp_co2_domestic, by=c("year", "geo", "coicop", "quint")) %>%
left_join(tmp_co2eq, by=c("year", "geo", "coicop", "quint")) %>%
left_join(tmp_co2eq_domestic, by=c("year", "geo", "coicop", "quint")) %>%
left_join(tmp_energy, by=c("year", "geo", "coicop", "quint")) %>%
left_join(tmp_energy_domestic, by=c("year", "geo", "coicop", "quint")) %>%
clean_names() %>%
mutate(year = as.numeric(year))
results = ok %>%
filter(!(geo %in% c("EA","EA12","EA13","EA17",
"EA18","EA19","EEA28","EEA30_2007",
"EFTA","EU15","EU25",
"EU27_2007", "EU27_2020",
"EU28","XK", "RS",
"MK", "ME")),
!(quintile %in% c("TOTAL","UNK")),
!(year %in% c(1988,1994,1999))) %>%
group_by(geo,quintile,year,coicop) %>%
summarise(fd_me = sum(fd_me, na.rm = TRUE),
co2_kg = sum(co2_kg, na.rm = TRUE),
co2_domestic_kg = sum(co2_domestic_kg, na.rm = TRUE),
co2_europe_kg = sum(co2_europe_kg, na.rm = TRUE),
co2eq_kg = sum(co2eq_kg, na.rm = TRUE),
co2eq_domestic_kg = sum(co2eq_domestic_kg, na.rm = TRUE),
co2eq_europe_kg = sum(co2eq_europe_kg, na.rm = TRUE),
energy_use_TJ = sum(energy_use_TJ, na.rm = TRUE),
energy_use_domestic_TJ = sum(energy_use_domestic_TJ, na.rm = TRUE),
energy_use_europe_TJ = sum(energy_use_europe_TJ, na.rm = TRUE)) %>%
ungroup() %>%
mutate(year = as.numeric(year)) %>%
na.omit()
results_formatted = results %>%
clean_names() %>%
mutate(quint = parse_number(quintile)) %>%
select(-quintile) %>%
filter(coicop %in% c("CP011",
"CP012",
"CP02",
"CP03",
"rent",
"CP043",
"CP044",
"CP045",
"CP05",
"CP06",
"CP071",
"CP072",
"CP073",
"CP08",
"CP09",
"CP10",
"CP11",
"CP12"))
results_formatted_with_direct_FD_fp = bind_rows(results_formatted,direct_FD_fp_wide_recombined)
write.csv(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified_footprints, "/results_formatted_method2_ixi.csv"))
write_rds(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified_footprints, "/results_formatted_method2_ixi.rds"))
European expenditure deciles
- need to download EUROSTAT households and Norway households
# set target number of quantiles
target_eu_ntiles = 10
##### main paper results (main paper method, EXIOBASE industry-by-industry version)
# 1) load income-stratified-footprints formatted results file from previous code chunk
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
"results_formatted_method1_ixi.rds")) %>%
ungroup() %>%
mutate(year= strtoi(year)) %>%
rename(iso2 = geo)
# get iso3 country codes to join with household data
country_codes = ISOcodes::ISO_3166_1 %>%
select(iso2 = Alpha_2, iso3 = Alpha_3) %>%
# resolve inconsistency between EUROSTAT and ISO for Greece and UK/Great Britain
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
# read in total private households data from EUROSTAT and Norway, merge, and write .csv
## set 4 digits per value
options(digits=4)
## EUROSTAT total private households
total_private_households_Eurostat = read.csv(here("/analysis/preprocessing/income-stratified-footprints/lfst_hhnhtych_1_Data.csv")) %>%
filter(!(GEO %in% c("European Union - 27 countries (from 2020)",
"Euro area - 19 countries (from 2015)",
"European Union - 28 countries (2013-2020)",
"European Union - 15 countries (1995-2004)"))) %>%
mutate(geo = dplyr::recode(GEO,
"Belgium" = "BE",
"Bulgaria" = "BG",
"Czechia" = "CZ",
"Denmark" = "DK",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Estonia" = "EE",
"Ireland" = "IE",
"Greece" = "EL",
"Spain" = "ES",
"France" = "FR",
"Croatia" = "HR",
"Italy" = "IT",
"Cyprus" = "CY",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Hungary" = "HU",
"Malta" = "MT",
"Netherlands" = "NL",
"Austria" = "AT",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovenia" = "SI",
"Slovakia" = "SK",
"Finland" = "FI",
"Sweden" = "SE",
"United Kingdom" = "UK",
"Montenegro" = "ME",
"North Macedonia" = "MK",
"Serbia" = "RS",
"Turkey" = "TR")) %>%
select(TIME,geo,Value) %>%
rename(year = TIME, total_private_households = Value) %>%
mutate(total_private_households = as.character(total_private_households),
total_private_households = parse_number(total_private_households),
total_private_households = as.numeric(total_private_households),
total_private_households = total_private_households*1000)
## Norway total private households
total_private_households_Norway = read.csv(here("/analysis/preprocessing/income-stratified-footprints/Privathusholdninger.csv")) %>%
gather(year, total_private_households, Private.households.2005:Private.households.2019) %>%
mutate(geo = dplyr::recode(region,
"0 The whole country" = "NO"),
year = dplyr::recode(year,
"Private.households.2005" = 2005,
"Private.households.2006" = 2006,
"Private.households.2007" = 2007,
"Private.households.2008" = 2008,
"Private.households.2009" = 2009,
"Private.households.2010" = 2010,
"Private.households.2011" = 2011,
"Private.households.2012" = 2012,
"Private.households.2013" = 2013,
"Private.households.2014" = 2014,
"Private.households.2015" = 2015,
"Private.households.2016" = 2016,
"Private.households.2017" = 2017,
"Private.households.2018" = 2018,
"Private.households.2019" = 2019)) %>%
select(year,geo,total_private_households)
## merge EUROSTAT and Norway total private households data
total_private_households = rbind(total_private_households_Eurostat,
total_private_households_Norway) %>%
mutate(geo = as.character(geo),
year = as.numeric(year),
total_private_households = as.numeric(total_private_households))
## write .csv file with all total private households data
write_csv(total_private_households, here("/analysis/preprocessing/income-stratified-footprints/total_private_households.csv"))
# the original 'total_private_households_original.csv' is in 'income-stratified-footprints-in-europe/data' - if something goes wrong
# with the main paper results (pulling from the mrio results file written later) then the problem is here, when I added
# the as.character mutation in the total_private_households_Eurostat
## return to 2 digits per value
options(digits=2)
# 2) load merged total private households data
hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"total_private_households.csv")) %>%
mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>%
rename(iso2 = geo) %>%
group_by(iso2) %>%
# impute households with next available neighbour
mutate(hh = na_locf(total_private_households)) %>%
left_join(country_codes, by="iso2") %>%
select(-total_private_households)
#3) load EUROSTAT mean expenditures per household income quintile per household and per adult equivalent (written and saved in the previous, income-stratified-footprints code chunk)
df_expenditure_long = read_csv(here("analysis",
"preprocessing",
"income-stratified-footprints",
"mean_expenditure_by_quintile_long.csv"),
na = ":") %>%
#filter(year >=2010, geo != "IT") %>%
#filter(year >=2005, geo != "IT") %>%
#decide here
filter(year >=2005) %>%
mutate(imputed = if_else(is.na(mean_expenditure), TRUE, FALSE)) %>%
group_by(geo,unit,quintile) %>%
mutate(value = na_locf(mean_expenditure)) %>%
select(-mean_expenditure) %>%
ungroup()
## calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>%
pivot_wider(id_cols = c(iso2, year, quintile, imputed),
names_from = unit,
values_from = value) %>%
clean_names() %>%
mutate(adult_e_p_hh = pps_hh/pps_ae) %>%
left_join(country_codes, by="iso2") %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile))
## add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>%
left_join(hh_data, by=c("iso2", "year")) %>%
mutate(hh_quintile = hh/5) %>% # population per country quinitle
select(-hh) %>%
rename(hh_imputed = imputed) %>%
left_join(df_adult_e_p_hh %>%
select(iso2, year, quint, imputed_ae = imputed, adult_e_p_hh),
by=c("iso2", "year", "quint")) %>%
mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>%
select(-c(hh_quintile, adult_e_p_hh))
## for the European expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>%
ungroup() %>%
filter(co2_kg>0) %>%
select(iso2, year, co2_kg) %>%
pivot_wider(id_cols = c(iso2), names_from = year, values_from = co2_kg) %>%
drop_na() %>%
select(iso2) %>%
pull()
df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
mutate(quint = parse_number(quintile))
# calculate European expenditure deciles based on loaded income-stratified-footprints result file and adult equivalents.
# returns country quintiles mapped to European ntile rank and European ntile boundaries
# helper function called by function below
calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
country_data_annual_sorted = summary_country_fd %>%
ungroup() %>%
filter(year==pyear) %>%
arrange(fd_pae_e) %>%
mutate(idx = 1:n(),
eu_q_rank = 0) # later to be filled with euro quintile rank
# total European adult equivalents (of included countries) in year
total_ae_in_year = sum(country_data_annual_sorted$ae_quintile)
# quantile target ae population
eu_decile_adult_eq = total_ae_in_year/pquantile_count
# country quintles must be split to allocate ae population according to eu quantile target ae population
# filtering by condition that can't be fulfilled is a lazy way to create an empty dataframe
# of the same structure as country_data_annual_sorted
additional_rows = country_data_annual_sorted %>%
filter(year==1)
# store quantile split values
eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0)
# loops through the ordered dataset, assigns European quantile rank
# and splits quintiles where necessary
eu_ae_current = 0
euro_q_rank_current = 1
for (row_idx in 1:nrow(country_data_annual_sorted)) {
row = country_data_annual_sorted[row_idx,]
if (row["ae_quintile"] + eu_ae_current <= eu_decile_adult_eq) {
eu_ae_current = eu_ae_current + row["ae_quintile"]
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
} else {
ae_diff = eu_decile_adult_eq - eu_ae_current
## write rest of this European decile (split country quintile)
new_row = country_data_annual_sorted[row_idx, ]
new_row[1, "eu_q_rank"] = euro_q_rank_current
new_row[1, "ae_quintile"] = ae_diff
## record European quantile boundary
eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] =
country_data_annual_sorted[row_idx, "fd_pae_e"]
## put first part of population in overflow dataframe
additional_rows = additional_rows %>%
bind_rows(new_row)
## classify rest of country quintile population to next European quantile
country_data_annual_sorted[row_idx, "ae_quintile"] =
country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001)
euro_q_rank_current = euro_q_rank_current + 1
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
eu_ae_current = country_data_annual_sorted[row_idx, "ae_quintile"]
}
}
country_data_eu_quantiles = country_data_annual_sorted %>%
bind_rows(additional_rows) %>%
arrange(fd_pae_e, eu_q_rank) %>%
mutate(idx = 1:n())
# add zeroth and nth quantile (min and max)
eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e)
list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries)
}
# maps income-stratified-footprint results to European ntile ranks, returns mapping and ntile European boundaries
map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles)
df_eu_ntiles_data = df_eu_ntiles$df_q_data
sector_mapping = mrio_results_with_adult_eq %>%
group_by(sector_id) %>%#
summarise(sector_agg_id = first(sector_agg_id)) %>%
ungroup()
df_mapped_data = mrio_results_with_adult_eq %>%
select(year,
iso2,
quint,
sector_id,
fd_me,
co2_kg,
co2_domestic_kg,
co2_europe_kg,
co2eq_kg,
co2eq_domestic_kg,
co2eq_europe_kg,
energy_use_TJ,
energy_use_domestic_TJ,
energy_use_europe_TJ,
ae_quintile) %>%
filter(year==pyear) %>%
# calculate per adult equivalent values in quintiles
mutate(fd_pae_e = fd_me*1000000/ae_quintile,
co2_pae_kg = co2_kg/ae_quintile,
co2_pae_dom_kg = co2_domestic_kg/ae_quintile,
co2_pae_eu_kg = co2_europe_kg/ae_quintile,
co2eq_pae_kg = co2eq_kg/ae_quintile,
co2eq_pae_dom_kg = co2eq_domestic_kg/ae_quintile,
co2eq_pae_eu_kg = co2eq_europe_kg/ae_quintile,
energy_use_pae_tj = energy_use_TJ/ae_quintile,
energy_use_dom_pae_tj = energy_use_domestic_TJ/ae_quintile,
energy_use_eu_pae_tj = energy_use_europe_TJ/ae_quintile) %>%
# remove totals
select(-c(fd_me,
co2_kg,
co2_domestic_kg,
co2_europe_kg,
co2eq_kg,
co2eq_domestic_kg,
co2eq_europe_kg,
energy_use_TJ,
energy_use_domestic_TJ,
energy_use_europe_TJ,
year, ae_quintile)) %>%
full_join(df_eu_ntiles_data %>%
rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>%
rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population
# recalculate totals
mutate(fd_me = fd_pae_e*adult_eq/1000000,
co2_kg = co2_pae_kg*adult_eq,
co2_dom_kg = co2_pae_dom_kg*adult_eq,
co2_eu_kg = co2_pae_eu_kg*adult_eq,
co2eq_kg = co2eq_pae_kg*adult_eq,
co2eq_dom_kg = co2eq_pae_dom_kg*adult_eq,
co2eq_eu_kg = co2eq_pae_eu_kg*adult_eq,
energy_use_tj = energy_use_pae_tj*adult_eq,
energy_use_dom_tj = energy_use_dom_pae_tj*adult_eq,
energy_use_eu_tj = energy_use_eu_pae_tj*adult_eq
) %>%
left_join(sector_mapping, by="sector_id")
list("df_mapped_data" = df_mapped_data, "df_ntile_boundaries" = df_eu_ntiles$df_q_boundaries)
}
# filter only countries with complete info for years 2005, 2010, 2015
mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>%
filter(iso2 %in% complete_countries)
# summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for complete countries
summary_country_fd = mrio_results_with_adult_eq %>%
group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000)/(ae_quintile))
# summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for all countries
summary_country_fd_all = mrio_results_with_adult_eq_all %>%
group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000)/(ae_quintile))
df_mapped_result_2005 = map_mrio_results_to_eu_ntiles(2005, target_eu_ntiles)
df_mapped_result_2005_data = df_mapped_result_2005$df_mapped_data
df_mapped_result_2005_ntiles = df_mapped_result_2005$df_ntile_boundaries
df_mapped_result_2010 = map_mrio_results_to_eu_ntiles(2010, target_eu_ntiles)
df_mapped_result_2010_data = df_mapped_result_2010$df_mapped_data
df_mapped_result_2010_ntiles = df_mapped_result_2010$df_ntile_boundaries
df_mapped_result_2015 = map_mrio_results_to_eu_ntiles(2015, target_eu_ntiles)
df_mapped_result_2015_data = df_mapped_result_2015$df_mapped_data
df_mapped_result_2015_ntiles = df_mapped_result_2015$df_ntile_boundaries
df_mapped_result_data = df_mapped_result_2005_data %>%
bind_rows(df_mapped_result_2010_data) %>%
bind_rows(df_mapped_result_2015_data)
write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, ".csv")))
###### SI results, main paper method, EXIOBASE product-by-product version
# 1) load income-stratified-footprints formatted results file
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
"results_formatted_method1_pxp.rds")) %>%
ungroup() %>%
mutate(year= strtoi(year)) %>%
rename(iso2 = geo)
# get iso3 country codes to join with household data
country_codes = ISOcodes::ISO_3166_1 %>%
select(iso2 = Alpha_2, iso3 = Alpha_3) %>%
# resolve inconsistency between EUROSTAT and ISO for Greece and UK/Great Britain
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
# read in total private households data from EUROSTAT and Norway, merge, and write .csv
## set 4 digits per value
options(digits=4)
## EUROSTAT total private households
total_private_households_Eurostat = read.csv(here("/analysis/preprocessing/income-stratified-footprints/lfst_hhnhtych_1_Data.csv")) %>%
filter(!(GEO %in% c("European Union - 27 countries (from 2020)",
"Euro area - 19 countries (from 2015)",
"European Union - 28 countries (2013-2020)",
"European Union - 15 countries (1995-2004)"))) %>%
mutate(geo = dplyr::recode(GEO,
"Belgium" = "BE",
"Bulgaria" = "BG",
"Czechia" = "CZ",
"Denmark" = "DK",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Estonia" = "EE",
"Ireland" = "IE",
"Greece" = "EL",
"Spain" = "ES",
"France" = "FR",
"Croatia" = "HR",
"Italy" = "IT",
"Cyprus" = "CY",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Hungary" = "HU",
"Malta" = "MT",
"Netherlands" = "NL",
"Austria" = "AT",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovenia" = "SI",
"Slovakia" = "SK",
"Finland" = "FI",
"Sweden" = "SE",
"United Kingdom" = "UK",
"Montenegro" = "ME",
"North Macedonia" = "MK",
"Serbia" = "RS",
"Turkey" = "TR")) %>%
select(TIME,geo,Value) %>%
rename(year = TIME, total_private_households = Value) %>%
mutate(total_private_households = as.character(total_private_households),
total_private_households = parse_number(total_private_households),
total_private_households = as.numeric(total_private_households),
total_private_households = total_private_households*1000)
## Norway total private households
total_private_households_Norway = read.csv(here("/analysis/preprocessing/income-stratified-footprints/Privathusholdninger.csv")) %>%
gather(year, total_private_households, Private.households.2005:Private.households.2019) %>%
mutate(geo = dplyr::recode(region,
"0 The whole country" = "NO"),
year = dplyr::recode(year,
"Private.households.2005" = 2005,
"Private.households.2006" = 2006,
"Private.households.2007" = 2007,
"Private.households.2008" = 2008,
"Private.households.2009" = 2009,
"Private.households.2010" = 2010,
"Private.households.2011" = 2011,
"Private.households.2012" = 2012,
"Private.households.2013" = 2013,
"Private.households.2014" = 2014,
"Private.households.2015" = 2015,
"Private.households.2016" = 2016,
"Private.households.2017" = 2017,
"Private.households.2018" = 2018,
"Private.households.2019" = 2019)) %>%
select(year,geo,total_private_households)
## merge EUROSTAT and Norway total private households data
total_private_households = rbind(total_private_households_Eurostat,
total_private_households_Norway) %>%
mutate(geo = as.character(geo),
year = as.numeric(year),
total_private_households = as.numeric(total_private_households))
## write .csv file with all total private households data
write_csv(total_private_households, here("/analysis/preprocessing/income-stratified-footprints/total_private_households.csv"))
# the original 'total_private_households_original.csv' is in 'income-stratified-footprints-in-europe/data' - if something goes wrong
# with the main paper results (pulling from the mrio results file written later) then the problem is here, when I added
# the as.character mutation in the total_private_households_Eurostat
## return to 2 digits per value
options(digits=2)
# 2) load merged total private households data
hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"total_private_households.csv")) %>%
mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>%
rename(iso2 = geo) %>%
group_by(iso2) %>%
# impute households with next available neighbour
mutate(hh = na_locf(total_private_households)) %>%
left_join(country_codes, by="iso2") %>%
select(-total_private_households)
#3) load EUROSTAT mean expenditures per household income quintile per household and per adult equivalent (written and saved in the previous, income-stratified-footprints code chunk)
df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"mean_expenditure_by_quintile_long.csv"),
na = ":") %>%
#filter(year >=2010, geo != "IT") %>%
#filter(year >=2005, geo != "IT") %>%
#decide here
filter(year >=2005) %>%
mutate(imputed = if_else(is.na(mean_expenditure), TRUE, FALSE)) %>%
group_by(geo,unit,quintile) %>%
mutate(value = na_locf(mean_expenditure)) %>%
select(-mean_expenditure) %>%
ungroup()
## calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>%
pivot_wider(id_cols = c(iso2, year, quintile, imputed),
names_from = unit,
values_from = value) %>%
clean_names() %>%
mutate(adult_e_p_hh = pps_hh/pps_ae) %>%
left_join(country_codes, by="iso2") %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile))
## add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>%
left_join(hh_data, by=c("iso2", "year")) %>%
mutate(hh_quintile = hh/5) %>% # population per country quinitle
select(-hh) %>%
rename(hh_imputed = imputed) %>%
left_join(df_adult_e_p_hh %>%
select(iso2, year, quint, imputed_ae = imputed, adult_e_p_hh),
by=c("iso2", "year", "quint")) %>%
mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>%
select(-c(hh_quintile, adult_e_p_hh))
## for the European expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>%
ungroup() %>%
filter(co2_kg>0) %>%
select(iso2, year, co2_kg) %>%
pivot_wider(id_cols = c(iso2), names_from = year, values_from = co2_kg) %>%
drop_na() %>%
select(iso2) %>%
pull()
df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
mutate(quint = parse_number(quintile))
# calculate European expenditure deciles based on loaded income-stratified-footprints result file and adult equivalents.
# returns country quintiles mapped to European ntile rank and European ntile boundaries
# helper function called by function below
calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
country_data_annual_sorted = summary_country_fd %>%
ungroup() %>%
filter(year==pyear) %>%
arrange(fd_pae_e) %>%
mutate(idx = 1:n(),
eu_q_rank = 0) # later to be filled with euro quintile rank
# total European adult equivalents (of included countries) in year
total_ae_in_year = sum(country_data_annual_sorted$ae_quintile)
# quantile target ae population
eu_decile_adult_eq = total_ae_in_year/pquantile_count
# country quintiles must be split to allocate ae population according to European quantile target ae population
# filtering by condition that cant be fulfilled is a lazy way to create an empty dataframe
# of the same structure as country_data_annual_sorted
additional_rows = country_data_annual_sorted %>%
filter(year==1)
# store quantile split values
eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0)
# loops through the ordered dataset, assignes euro quantile rank
# and splits quintiles where necessary
eu_ae_current = 0
euro_q_rank_current = 1
for (row_idx in 1:nrow(country_data_annual_sorted)) {
row = country_data_annual_sorted[row_idx,]
if (row["ae_quintile"] + eu_ae_current <= eu_decile_adult_eq) {
eu_ae_current = eu_ae_current + row["ae_quintile"]
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
} else {
ae_diff = eu_decile_adult_eq - eu_ae_current
## write rest of this European decile (split country quintile)
new_row = country_data_annual_sorted[row_idx, ]
new_row[1, "eu_q_rank"] = euro_q_rank_current
new_row[1, "ae_quintile"] = ae_diff
## record European quantile boundary
eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] =
country_data_annual_sorted[row_idx, "fd_pae_e"]
## put first part of population in overflow dataframe
additional_rows = additional_rows %>%
bind_rows(new_row)
## classify rest of country quintile population to next European quantile
country_data_annual_sorted[row_idx, "ae_quintile"] =
country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001)
euro_q_rank_current = euro_q_rank_current + 1
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
eu_ae_current = country_data_annual_sorted[row_idx, "ae_quintile"]
}
}
country_data_eu_quantiles = country_data_annual_sorted %>%
bind_rows(additional_rows) %>%
arrange(fd_pae_e, eu_q_rank) %>%
mutate(idx = 1:n())
# add zeroth and nth quantile (min and max)
eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e)
list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries)
}
# maps income-stratified-footprint results to European ntile ranks, returns mapping and ntile European boundaries
map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles)
df_eu_ntiles_data = df_eu_ntiles$df_q_data
sector_mapping = mrio_results_with_adult_eq %>%
group_by(sector_id) %>%#
summarise(sector_agg_id = first(sector_agg_id)) %>%
ungroup()
df_mapped_data = mrio_results_with_adult_eq %>%
select(year,
iso2,
quint,
sector_id,
fd_me,
co2_kg,
co2_domestic_kg,
co2_europe_kg,
co2eq_kg,
co2eq_domestic_kg,
co2eq_europe_kg,
energy_use_TJ,
energy_use_domestic_TJ,
energy_use_europe_TJ,
ae_quintile) %>%
filter(year==pyear) %>%
# calculate per adult equivalent values in quintiles
mutate(fd_pae_e = fd_me*1000000/ae_quintile,
co2_pae_kg = co2_kg/ae_quintile,
co2_pae_dom_kg = co2_domestic_kg/ae_quintile,
co2_pae_eu_kg = co2_europe_kg/ae_quintile,
co2eq_pae_kg = co2eq_kg/ae_quintile,
co2eq_pae_dom_kg = co2eq_domestic_kg/ae_quintile,
co2eq_pae_eu_kg = co2eq_europe_kg/ae_quintile,
energy_use_pae_tj = energy_use_TJ/ae_quintile,
energy_use_dom_pae_tj = energy_use_domestic_TJ/ae_quintile,
energy_use_eu_pae_tj = energy_use_europe_TJ/ae_quintile) %>%
# remove totals
select(-c(fd_me,
co2_kg,
co2_domestic_kg,
co2_europe_kg,
co2eq_kg,
co2eq_domestic_kg,
co2eq_europe_kg,
energy_use_TJ,
energy_use_domestic_TJ,
energy_use_europe_TJ,
year, ae_quintile)) %>%
full_join(df_eu_ntiles_data %>%
rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>%
rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population
# recalculate totals
mutate(fd_me = fd_pae_e*adult_eq/1000000,
co2_kg = co2_pae_kg*adult_eq,
co2_dom_kg = co2_pae_dom_kg*adult_eq,
co2_eu_kg = co2_pae_eu_kg*adult_eq,
co2eq_kg = co2eq_pae_kg*adult_eq,
co2eq_dom_kg = co2eq_pae_dom_kg*adult_eq,
co2eq_eu_kg = co2eq_pae_eu_kg*adult_eq,
energy_use_tj = energy_use_pae_tj*adult_eq,
energy_use_dom_tj = energy_use_dom_pae_tj*adult_eq,
energy_use_eu_tj = energy_use_eu_pae_tj*adult_eq
) %>%
left_join(sector_mapping, by="sector_id")
list("df_mapped_data" = df_mapped_data, "df_ntile_boundaries" = df_eu_ntiles$df_q_boundaries)
}
# filter only countries with complete info for years 2005, 2010, 2015
mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>%
filter(iso2 %in% complete_countries)
# summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for complete countries
summary_country_fd = mrio_results_with_adult_eq %>%
group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000)/(ae_quintile))
# summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for all countries
summary_country_fd_all = mrio_results_with_adult_eq_all %>%
group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000)/(ae_quintile))
df_mapped_result_2005 = map_mrio_results_to_eu_ntiles(2005, target_eu_ntiles)
df_mapped_result_2005_data = df_mapped_result_2005$df_mapped_data
df_mapped_result_2005_ntiles = df_mapped_result_2005$df_ntile_boundaries
df_mapped_result_2010 = map_mrio_results_to_eu_ntiles(2010, target_eu_ntiles)
df_mapped_result_2010_data = df_mapped_result_2010$df_mapped_data
df_mapped_result_2010_ntiles = df_mapped_result_2010$df_ntile_boundaries
df_mapped_result_data = df_mapped_result_2005_data %>%
bind_rows(df_mapped_result_2010_data)
write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_pxp.csv")))
##### SI results, alternative method, EXOIBASE industry-by-industry version
# 1) load income-stratified-footprints formatted results file
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
"results_formatted_method2_ixi.rds")) %>%
ungroup() %>%
mutate(year= strtoi(year)) %>%
rename(iso2 = geo)
# get iso3 country codes to join with hh data
country_codes = ISOcodes::ISO_3166_1 %>%
select(iso2 = Alpha_2, iso3 = Alpha_3) %>%
# resolve inconsistency between EUROSTAT and ISO for Greece and UK/Great Britain
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
# read in total private households data from EUROSTAT and Norway, merge, and write .csv
## set 4 digits per value
options(digits=4)
## EUROSTAT total private households
total_private_households_Eurostat = read.csv(here("/analysis/preprocessing/income-stratified-footprints/lfst_hhnhtych_1_Data.csv")) %>%
filter(!(GEO %in% c("European Union - 27 countries (from 2020)",
"Euro area - 19 countries (from 2015)",
"European Union - 28 countries (2013-2020)",
"European Union - 15 countries (1995-2004)"))) %>%
mutate(geo = dplyr::recode(GEO,
"Belgium" = "BE",
"Bulgaria" = "BG",
"Czechia" = "CZ",
"Denmark" = "DK",
"Germany (until 1990 former territory of the FRG)" = "DE",
"Estonia" = "EE",
"Ireland" = "IE",
"Greece" = "EL",
"Spain" = "ES",
"France" = "FR",
"Croatia" = "HR",
"Italy" = "IT",
"Cyprus" = "CY",
"Latvia" = "LV",
"Lithuania" = "LT",
"Luxembourg" = "LU",
"Hungary" = "HU",
"Malta" = "MT",
"Netherlands" = "NL",
"Austria" = "AT",
"Poland" = "PL",
"Portugal" = "PT",
"Romania" = "RO",
"Slovenia" = "SI",
"Slovakia" = "SK",
"Finland" = "FI",
"Sweden" = "SE",
"United Kingdom" = "UK",
"Montenegro" = "ME",
"North Macedonia" = "MK",
"Serbia" = "RS",
"Turkey" = "TR")) %>%
select(TIME,geo,Value) %>%
rename(year = TIME, total_private_households = Value) %>%
mutate(total_private_households = as.character(total_private_households),
total_private_households = parse_number(total_private_households),
total_private_households = as.numeric(total_private_households),
total_private_households = total_private_households*1000)
## Norway total private households
total_private_households_Norway = read.csv(here("/analysis/preprocessing/income-stratified-footprints/Privathusholdninger.csv")) %>%
gather(year, total_private_households, Private.households.2005:Private.households.2019) %>%
mutate(geo = dplyr::recode(region,
"0 The whole country" = "NO"),
year = dplyr::recode(year,
"Private.households.2005" = 2005,
"Private.households.2006" = 2006,
"Private.households.2007" = 2007,
"Private.households.2008" = 2008,
"Private.households.2009" = 2009,
"Private.households.2010" = 2010,
"Private.households.2011" = 2011,
"Private.households.2012" = 2012,
"Private.households.2013" = 2013,
"Private.households.2014" = 2014,
"Private.households.2015" = 2015,
"Private.households.2016" = 2016,
"Private.households.2017" = 2017,
"Private.households.2018" = 2018,
"Private.households.2019" = 2019)) %>%
select(year,geo,total_private_households)
## merge EUROSTAT and Norway total private households data
total_private_households = rbind(total_private_households_Eurostat,
total_private_households_Norway) %>%
mutate(geo = as.character(geo),
year = as.numeric(year),
total_private_households = as.numeric(total_private_households))
## write .csv file with all total private households data
write_csv(total_private_households, here("/analysis/preprocessing/income-stratified-footprints/total_private_households.csv"))
# the original 'total_private_households_original.csv' is in 'income-stratified-footprints-in-europe/data' - if something goes wrong
# with the main paper results (pulling from the mrio results file written later) then the problem is here, when I added
# the as.character mutation in the total_private_households_Eurostat
## return to 2 digits per value
options(digits=2)
# 2) load EUROSTAT household data
hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"total_private_households.csv")) %>%
mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>%
rename(iso2 = geo) %>%
group_by(iso2) %>%
# impute households with next available neighbour
mutate(hh = na_locf(total_private_households)) %>%
left_join(country_codes, by="iso2") %>%
select(-total_private_households)
#3) EUROSTAT mean expenditures per household income quintile per household and per adult equivalent
df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"mean_expenditure_by_quintile_long.csv"),
na = ":") %>%
#filter(year >=2010, geo != "IT") %>%
#filter(year >=2005, geo != "IT") %>%
#decide here
filter(year >=2005) %>%
mutate(imputed = if_else(is.na(mean_expenditure), TRUE, FALSE)) %>%
group_by(geo,unit,quintile) %>%
mutate(value = na_locf(mean_expenditure)) %>%
select(-mean_expenditure) %>%
ungroup()
## calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>%
pivot_wider(id_cols = c(iso2, year, quintile, imputed),
names_from = unit,
values_from = value) %>%
clean_names() %>%
mutate(adult_e_p_hh = pps_hh/pps_ae) %>%
left_join(country_codes, by="iso2") %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile))
## add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>%
left_join(hh_data, by=c("iso2", "year")) %>%
mutate(hh_quintile = hh/5) %>% # population per country quinitle
select(-hh) %>%
rename(hh_imputed = imputed) %>%
left_join(df_adult_e_p_hh %>%
select(iso2, year, quint, imputed_ae = imputed, adult_e_p_hh),
by=c("iso2", "year", "quint")) %>%
mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>%
select(-c(hh_quintile, adult_e_p_hh))
## for the European expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>%
ungroup() %>%
filter(co2_kg>0) %>%
select(iso2, year, co2_kg) %>%
pivot_wider(id_cols = c(iso2), names_from = year, values_from = co2_kg) %>%
drop_na() %>%
select(iso2) %>%
pull()
df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
mutate(quint = parse_number(quintile))
# calculate European expenditure deciles based on loaded income-stratified-footprints result file and adult equivalents.
# returns country quintiles mapped to European ntile rank and European ntile boundaries
# helper function called by function below
calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
country_data_annual_sorted = summary_country_fd %>%
ungroup() %>%
filter(year==pyear) %>%
arrange(fd_pae_e) %>%
mutate(idx = 1:n(),
eu_q_rank = 0) # later to be filled with euro quintile rank
# total European adult equivalents (of included countries) in year
total_ae_in_year = sum(country_data_annual_sorted$ae_quintile)
# quantile target ae population
eu_decile_adult_eq = total_ae_in_year/pquantile_count
# country quintiles must be split to allocate ae population according to European quantile target ae population
# filtering by condition that can't be fulfilled is a lazy way to create an empty dataframe
# of the same structure as country_data_annual_sorted
additional_rows = country_data_annual_sorted %>%
filter(year==1)
# store quantile split values
eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0)
## loops through the ordered dataset, assigns European quantile rank
## and splits quintiles where necessary
eu_ae_current = 0
euro_q_rank_current = 1
for (row_idx in 1:nrow(country_data_annual_sorted)) {
row = country_data_annual_sorted[row_idx,]
if (row["ae_quintile"] + eu_ae_current <= eu_decile_adult_eq) {
eu_ae_current = eu_ae_current + row["ae_quintile"]
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
} else {
ae_diff = eu_decile_adult_eq - eu_ae_current
## write rest of this European decile (split country quintile)
new_row = country_data_annual_sorted[row_idx, ]
new_row[1, "eu_q_rank"] = euro_q_rank_current
new_row[1, "ae_quintile"] = ae_diff
## record European quantile boundary
eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] =
country_data_annual_sorted[row_idx, "fd_pae_e"]
## put first part of population in overflow dataframe
additional_rows = additional_rows %>%
bind_rows(new_row)
## classify rest of country quintile population to next European quantile
country_data_annual_sorted[row_idx, "ae_quintile"] =
country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001)
euro_q_rank_current = euro_q_rank_current + 1
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
eu_ae_current = country_data_annual_sorted[row_idx, "ae_quintile"]
}
}
country_data_eu_quantiles = country_data_annual_sorted %>%
bind_rows(additional_rows) %>%
arrange(fd_pae_e, eu_q_rank) %>%
mutate(idx = 1:n())
# add zeroth and nth quantile (min and max)
eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e)
list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries)
}
# maps income-stratified-footprint results to European ntile ranks, returns mapping and ntile European boundaries
map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles)
df_eu_ntiles_data = df_eu_ntiles$df_q_data
sector_mapping = mrio_results_with_adult_eq %>%
group_by(coicop) %>%#
summarise(coicop = first(coicop)) %>%
ungroup()
df_mapped_data = mrio_results_with_adult_eq %>%
select(year,
iso2,
quint,
coicop,
fd_me,
co2_kg,
co2_domestic_kg,
co2_europe_kg,
co2eq_kg,
co2eq_domestic_kg,
co2eq_europe_kg,
energy_use_tj,
energy_use_domestic_tj,
energy_use_europe_tj,
ae_quintile) %>%
filter(year==pyear) %>%
# calculate per adult equivalent values in quintiles
mutate(fd_pae_e = fd_me*1000000/ae_quintile,
co2_pae_kg = co2_kg/ae_quintile,
co2_pae_dom_kg = co2_domestic_kg/ae_quintile,
co2_pae_eu_kg = co2_europe_kg/ae_quintile,
co2eq_pae_kg = co2eq_kg/ae_quintile,
co2eq_pae_dom_kg = co2eq_domestic_kg/ae_quintile,
co2eq_pae_eu_kg = co2eq_europe_kg/ae_quintile,
energy_use_pae_tj = energy_use_tj/ae_quintile,
energy_use_dom_pae_tj = energy_use_domestic_tj/ae_quintile,
energy_use_eu_pae_tj = energy_use_europe_tj/ae_quintile) %>%
# remove totals
select(-c(fd_me,
co2_kg,
co2_domestic_kg,
co2_europe_kg,
co2eq_kg,
co2eq_domestic_kg,
co2eq_europe_kg,
energy_use_tj,
energy_use_domestic_tj,
energy_use_europe_tj,
year, ae_quintile)) %>%
full_join(df_eu_ntiles_data %>%
rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>%
rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population
# recalculate totals
mutate(fd_me = fd_pae_e*adult_eq/1000000,
co2_kg = co2_pae_kg*adult_eq,
co2_dom_kg = co2_pae_dom_kg*adult_eq,
co2_eu_kg = co2_pae_eu_kg*adult_eq,
co2eq_kg = co2eq_pae_kg*adult_eq,
co2eq_dom_kg = co2eq_pae_dom_kg*adult_eq,
co2eq_eu_kg = co2eq_pae_eu_kg*adult_eq,
energy_use_tj = energy_use_pae_tj*adult_eq,
energy_use_dom_tj = energy_use_dom_pae_tj*adult_eq,
energy_use_eu_tj = energy_use_eu_pae_tj*adult_eq
) #%>%
#left_join(sector_mapping, by="sector_id") # comment out joining sectors (only working with coicop categories in method2)
list("df_mapped_data" = df_mapped_data, "df_ntile_boundaries" = df_eu_ntiles$df_q_boundaries)
}
# filter only countries with complete info for years 2005, 2010, 2015
mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>%
filter(iso2 %in% complete_countries)
# summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for complete countries
summary_country_fd = mrio_results_with_adult_eq %>%
group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000, na.rm = T)/(ae_quintile))
# summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for all countries
summary_country_fd_all = mrio_results_with_adult_eq_all %>%
group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000, na.rm = T)/(ae_quintile))
df_mapped_result_2005 = map_mrio_results_to_eu_ntiles(2005, target_eu_ntiles)
df_mapped_result_2005_data = df_mapped_result_2005$df_mapped_data
df_mapped_result_2005_ntiles = df_mapped_result_2005$df_ntile_boundaries
df_mapped_result_2010 = map_mrio_results_to_eu_ntiles(2010, target_eu_ntiles)
df_mapped_result_2010_data = df_mapped_result_2010$df_mapped_data
df_mapped_result_2010_ntiles = df_mapped_result_2010$df_ntile_boundaries
df_mapped_result_2015 = map_mrio_results_to_eu_ntiles(2015, target_eu_ntiles)
df_mapped_result_2015_data = df_mapped_result_2015$df_mapped_data
df_mapped_result_2015_ntiles = df_mapped_result_2015$df_ntile_boundaries
df_mapped_result_data = df_mapped_result_2005_data %>%
bind_rows(df_mapped_result_2010_data) %>%
bind_rows(df_mapped_result_2015_data)
write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_method2_ixi.csv")))