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Commit 4905c491 authored by Ingram Jaccard's avatar Ingram Jaccard
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analysis/preprocessing/full_code.Rmd
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......@@ -55,83 +55,93 @@ Each year is available as a .zip file ('IOT_year_ixi' or 'IOT_year_pxp') from th
```{r exiobase, eval = FALSE}
# EXIOBASE_cluster_ixi_version
# data directories (on cluster)
#data_dir_exiobase = paste("/",file.path("data","metab","Exiobase", fsep=.Platform$file.sep),sep="")
# 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)
L = solve(diag(dim(A)[1])-A) # this solves the Leontief inverse initially
# solve the Leontief inverse
L = solve(diag(dim(A)[1])-A)
L[is.na(L)]=0
# final demand
# 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"))
# total output
# 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"))
# direct environmental vectors
# 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"))
# direct environmental vectors on final demand
# 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
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
......@@ -149,8 +159,7 @@ TIV_country_breakdown_co2_combustion_air_w_labels = t(TIV_breakdown_co2_combusti
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
# CO2 - non-combustion - air
## cement
CO2_noncombustion_cement_air = satellite[93,]
DIV_co2_noncombustion_cement_air = CO2_noncombustion_cement_air/total_output
......@@ -187,8 +196,7 @@ TIV_country_breakdown_co2_noncombustion_lime_air_w_labels = t(TIV_breakdown_co2_
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 - 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
......@@ -206,7 +214,7 @@ TIV_country_breakdown_co2_agriculture_peatdecay_air_w_labels = t(TIV_breakdown_c
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
# CO2 - waste - air
## biogenic
CO2_waste_biogenic_air = satellite[438,]
DIV_co2_waste_biogenic_air = CO2_waste_biogenic_air/total_output
......@@ -243,8 +251,7 @@ TIV_country_breakdown_co2_waste_fossil_air_w_labels = t(TIV_breakdown_co2_waste_
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
CH4_combustion_air = satellite[25,]
CH4_combustion_air = CH4_combustion_air*28
DIV_ch4_combustion_air = CH4_combustion_air/total_output
......@@ -263,8 +270,7 @@ TIV_country_breakdown_ch4_combustion_air_w_labels = t(TIV_breakdown_ch4_combusti
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 noncombustion air
# CH4 - non-combustion - air
## gas
CH4_noncombustion_gas_air = satellite[68,]
CH4_noncombustion_gas_air = CH4_noncombustion_gas_air*28
......@@ -417,8 +423,7 @@ TIV_country_breakdown_ch4_noncombustion_oilrefinery_air_w_labels = t(TIV_breakdo
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
CH4_agriculture_air = satellite[427,]
CH4_agriculture_air = CH4_agriculture_air*28
DIV_ch4_agriculture_air = CH4_agriculture_air/total_output
......@@ -437,8 +442,7 @@ TIV_country_breakdown_ch4_agriculture_air_w_labels = t(TIV_breakdown_ch4_agricul
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
CH4_waste_air = satellite[436,]
CH4_waste_air = CH4_waste_air*28
DIV_ch4_waste_air = CH4_waste_air/total_output
......@@ -457,8 +461,7 @@ TIV_country_breakdown_ch4_waste_air_w_labels = t(TIV_breakdown_ch4_waste_air_w_l
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
N2O_combustion_air = satellite[26,]
N2O_combustion_air = N2O_combustion_air*265
DIV_n2o_combustion_air = N2O_combustion_air/total_output
......@@ -477,8 +480,7 @@ TIV_country_breakdown_n2o_combustion_air_w_labels = t(TIV_breakdown_n2o_combusti
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
N2O_agriculture_air = satellite[430,]
N2O_agriculture_air = N2O_agriculture_air*265
DIV_n2o_agriculture_air = N2O_agriculture_air/total_output
......@@ -497,8 +499,7 @@ TIV_country_breakdown_n2o_agriculture_air_w_labels = t(TIV_breakdown_n2o_agricul
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
SF6_air = satellite[424,]
SF6_air = SF6_air*23500
DIV_sf6_air = SF6_air/total_output
......@@ -517,8 +518,7 @@ TIV_country_breakdown_sf6_air_w_labels = t(TIV_breakdown_sf6_air_w_labels %>%
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
HFC_air = satellite[425,]
DIV_hfc_air = HFC_air/total_output
DIV_hfc_air[is.na(DIV_hfc_air)]=0
......@@ -536,8 +536,7 @@ TIV_country_breakdown_hfc_air_w_labels = t(TIV_breakdown_hfc_air_w_labels %>%
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
PFC_air = satellite[426,]
DIV_pfc_air = PFC_air/total_output
DIV_pfc_air[is.na(DIV_pfc_air)]=0
......@@ -555,8 +554,7 @@ TIV_country_breakdown_pfc_air_w_labels = t(TIV_breakdown_pfc_air_w_labels %>%
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
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
......@@ -577,79 +575,90 @@ write.csv(TIV_country_breakdown_e_u_w_labels, paste0(data_dir_exiobase, "/IOT_",
}
# EXIOBASE_cluster_pxp_version
##### 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)
L = solve(diag(dim(A)[1])-A) # this solves the Leontief inverse initially
# solve the Leontief inverse
L = solve(diag(dim(A)[1])-A)
L[is.na(L)]=0
# final demand
# 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"))
# total output
# 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"))
# direct environmental vectors
# 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"))
# direct environmental vectors on final demand
# 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 save to 'data_dir_exiobase'
# CO2 combustion air
# 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
......@@ -667,8 +676,7 @@ TIV_country_breakdown_co2_combustion_air_w_labels = t(TIV_breakdown_co2_combusti
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
# CO2 - non-combustion - air
## cement
CO2_noncombustion_cement_air = satellite[93,]
DIV_co2_noncombustion_cement_air = CO2_noncombustion_cement_air/total_output
......@@ -705,8 +713,7 @@ TIV_country_breakdown_co2_noncombustion_lime_air_w_labels = t(TIV_breakdown_co2_
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 - 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
......@@ -724,8 +731,7 @@ TIV_country_breakdown_co2_agriculture_peatdecay_air_w_labels = t(TIV_breakdown_c
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
# CO2 - waste - air
## biogenic
CO2_waste_biogenic_air = satellite[438,]
DIV_co2_waste_biogenic_air = CO2_waste_biogenic_air/total_output
......@@ -762,8 +768,7 @@ TIV_country_breakdown_co2_waste_fossil_air_w_labels = t(TIV_breakdown_co2_waste_
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
CH4_combustion_air = satellite[25,]
CH4_combustion_air = CH4_combustion_air*28
DIV_ch4_combustion_air = CH4_combustion_air/total_output
......@@ -782,8 +787,7 @@ TIV_country_breakdown_ch4_combustion_air_w_labels = t(TIV_breakdown_ch4_combusti
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
# CH4 - non-combustion - air
## gas
CH4_noncombustion_gas_air = satellite[68,]
CH4_noncombustion_gas_air = CH4_noncombustion_gas_air*28
......@@ -936,8 +940,7 @@ TIV_country_breakdown_ch4_noncombustion_oilrefinery_air_w_labels = t(TIV_breakdo
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
CH4_agriculture_air = satellite[427,]
CH4_agriculture_air = CH4_agriculture_air*28
DIV_ch4_agriculture_air = CH4_agriculture_air/total_output
......@@ -956,8 +959,7 @@ TIV_country_breakdown_ch4_agriculture_air_w_labels = t(TIV_breakdown_ch4_agricul
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
CH4_waste_air = satellite[436,]
CH4_waste_air = CH4_waste_air*28
DIV_ch4_waste_air = CH4_waste_air/total_output
......@@ -976,8 +978,7 @@ TIV_country_breakdown_ch4_waste_air_w_labels = t(TIV_breakdown_ch4_waste_air_w_l
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
N2O_combustion_air = satellite[26,]
N2O_combustion_air = N2O_combustion_air*265
DIV_n2o_combustion_air = N2O_combustion_air/total_output
......@@ -996,8 +997,7 @@ TIV_country_breakdown_n2o_combustion_air_w_labels = t(TIV_breakdown_n2o_combusti
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
N2O_agriculture_air = satellite[430,]
N2O_agriculture_air = N2O_agriculture_air*265
DIV_n2o_agriculture_air = N2O_agriculture_air/total_output
......@@ -1016,8 +1016,7 @@ TIV_country_breakdown_n2o_agriculture_air_w_labels = t(TIV_breakdown_n2o_agricul
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
SF6_air = satellite[424,]
SF6_air = SF6_air*23500
DIV_sf6_air = SF6_air/total_output
......@@ -1036,8 +1035,7 @@ TIV_country_breakdown_sf6_air_w_labels = t(TIV_breakdown_sf6_air_w_labels %>%
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
HFC_air = satellite[425,]
DIV_hfc_air = HFC_air/total_output
DIV_hfc_air[is.na(DIV_hfc_air)]=0
......@@ -1055,8 +1053,7 @@ TIV_country_breakdown_hfc_air_w_labels = t(TIV_breakdown_hfc_air_w_labels %>%
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
PFC_air = satellite[426,]
DIV_pfc_air = PFC_air/total_output
DIV_pfc_air[is.na(DIV_pfc_air)]=0
......@@ -1074,8 +1071,7 @@ TIV_country_breakdown_pfc_air_w_labels = t(TIV_breakdown_pfc_air_w_labels %>%
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
energy_carrier_use = satellite[470,]
DIV_e_u = energy_carrier_use/total_output
DIV_e_u[is.na(DIV_e_u)]=0
......
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