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gas_trade_figures.R
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gas_trade_figures.R
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library("reshape2")
library("stringr")
library("scales")
library("plyr")
library("readr")
library("tidyr")
library("dplyr")
library("ggplot2")
library("rgcam")
library("jgcricolors")
library("grid")
# SET WORKING DIRECTORY -----------------------------------------------------------------------------
## REQUIRED: Manually set working directory
setwd("C:/gas_trade/metarepo/")
PLOT_FOLDER <- paste(getwd(), "/figures/", sep = "")
# Create the specified output directory inside the current working directory
dir.create(PLOT_FOLDER)
# FUNCTIONS ---------------------------------------------------------------
#Function "is not an element of" (opposite of %in%)
'%!in%' <- function( x, y ) !( '%in%'( x, y ) )
aggregate_rows <- function(df, filter_var, var_name, filter_group, ...) {
group_var <- quos(...)
filter_var <- enquo(filter_var)
filter_var_name <- quo_name(filter_var)
df %>%
filter(!!filter_var %in% filter_group) %>%
group_by(!!!group_var) %>%
dplyr::summarise(value = sum(value)) %>%
dplyr::mutate(!!filter_var_name := !!var_name)
}
#returns difference from a scenario
diff_from_scen <- function(df, diff_scenarios, ref_scenario, join_var){
diff_df <- df %>%
filter(scenario %in% diff_scenarios)
ref_df <- df %>%
filter(scenario %in% ref_scenario)
output_df <- diff_df %>%
full_join(ref_df, by = join_var,
suffix = c(".diff", ".ref")) %>%
mutate(value.diff = if_else(is.na(value.diff),0,value.diff),
value.ref = if_else(is.na(value.ref),0,value.ref),
value = value.diff - value.ref)
return(output_df)
}
# DEFINE CONVERSION FACTORS ----------------------------------------------------------
EJ_Tcf <- .981
EJ_Bcfd <- .981 * 1000 / 365
MtC_GtCO2 <- 44 / (12 * 1000)
EJ_Mcf <- EJ_Tcf * 1e+6
EJ_TWh <- 277.7777777
EJ_quad <- 0.95
USD1975_2021 <- 1.18895 / 0.29829
C_CO2 <- 44/12
Tg_Gt <- 1e+12 / (1e+9 * 1e+6)
CONV_MJ_BTU <- 947.777
CONV_BTU_CF <- 1 / 1027
CONV_CF_TONLNG <- 1 / 48700
# DEFINE COLORS, SCENARIOS, GROUPS, LEVELS ------------------------------------------------------------------
pal_16 <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442","#CC79A7","#333333", "#D55E00", "#0072B2",
"#333333", "#FFCC00", "#CC6600", "#006600", "#3333CC", "#CC0033", "#0099CC", "#999966")
elec_gen_colors <- c("coal" = "gray20",
"coal CCS" = "gray40",
"refined liquids" = "#d01c2a",
"refined liquids CCS" = "#f7988f",
"gas" = "deepskyblue1",
"gas CCS" = "darkslategray1",
"biomass" = "#00931d",
"biomass CCS" = "#88c892",
"geothermal" = "#ad440c",
"hydrogen" = "peachpuff2",
"hydro" = "#3d86f9",
"nuclear" = "#ef8e27",
"solar" = "#fdfa28",
"wind" = "#fdd67b")
res_prod_colors <- c("coal" = "gray20",
"natural gas" = "deepskyblue1",
"crude oil" = "#d01c2a")
NG_trade_group <- c("traded Afr_MidE pipeline gas",
"traded EUR pipeline gas",
"traded LA pipeline gas",
"traded N.Amer pipeline gas",
"traded PAC pipeline gas",
"traded RUS pipeline gas",
"traded LNG")
pri_ene_fuels <- c("a oil", "b natural gas", "c coal", "d biomass", "e nuclear",
"f hydro", "g wind", "h solar", "i geothermal", "j traditional biomass")
pri_ene_CCS_fuels <- c("a oil", "a oil CCS", "b natural gas", "b natural gas CCS",
"c coal", "c coal CCS", "d biomass", "d biomass CCS", "e nuclear",
"f hydro", "g wind", "h solar", "i geothermal", "j traditional biomass")
#GHG emissions
res_prod_GHG_group <- c("coal", "unconventional oil", "crude oil", "natural gas")
trans_GHG_group <- c("gas pipeline", "gas processing", "refining", "H2 central production", "H2 forecourt production")
aglu_GHG_group <- c("Corn", "FiberCrop", "FodderGrass", "FodderHerb", "MiscCrop", "OilCrop", "OtherGrain", "PalmFruit", "Rice", "RootTuber","SugarCrop", "Wheat",
"biomass", "UnmanagedLand", "regional biomass", "regional biomassOil", "regional corn for ethanol", "regional sugar for ethanol",
"Beef", "Dairy", "Pork", "Poultry", "SheepGoat")
urb_GHG_group <- c("desalinated water", "urban processes")
ind_GHG_group <- c("N fertilizer", "cement",
"industrial energy use", "industrial feedstocks", "industrial processes", "process heat cement")
trn_GHG_group <- c( "trn_aviation_intl","trn_freight","trn_freight_road",
"trn_pass","trn_pass_road","trn_pass_road_LDV","trn_pass_road_LDV_4W","trn_shipping_intl")
elec_GHG_group <- c("backup_electricity", "elec_biomass (IGCC CCS)", "elec_biomass (IGCC)", "elec_biomass (conv CCS)", "elec_biomass (conv)",
"elec_coal (IGCC CCS)","elec_coal (IGCC)","elec_coal (conv pul CCS)","elec_coal (conv pul)",
"elec_gas (CC CCS)","elec_gas (CC)","elec_gas (steam/CT)",
"elec_refined liquids (CC CCS)","elec_refined liquids (CC)","elec_refined liquids (steam/CT)",
"electricity","electricity_net_ownuse", "csp_backup")
bld_GHG_group <- c("comm cooling", "comm heating", "comm others", "resid cooling", "resid heating", "resid others", "district heat")
trade_sector_levels <- c("LNG", "Afr_MidE", "EUR", "LA", "N.Amer", "PAC", "RUS")
domestic_sector_levels <- c("domestic", "LNG", "Afr_MidE", "EUR", "LA", "N.Amer", "PAC", "RUS")
grouped_region_levels <- c("USA", "Middle East", "Russia", "Europe", "SE.Asia", "Can.+Mex.", "Africa",
"C.Asia+E.Eur", "LAC", "China", "Australia+NZ", "S.Asia","India","E.Asia")
flip_grouped_region_levels <- c("E.Asia", "India", "S.Asia", "Australia+NZ", "China", "LAC", "C.Asia+E.Eur",
"Africa", "Can.+Mex.", "SE.Asia","Europe", "Russia","Middle East", "USA")
paper_scenarios <- c("Reference",
"Transition",
"Reference_LLS",
"Transition_LLS",
"Reference_LT_sshc",
"Transition_LT_sshc")
paper_scenario_labels <- c("Reference" = "Reference",
"Transition" = "Transition",
"Reference_LLS" = "Reference_LLS",
"Transition_LLS" = "Transition_LLS",
"Reference_LT_sshc" = "Reference_LT",
"Transition_LT_sshc" = "Transition_LT")
# READ IN ADDITIONAL DATA, MAPPING FILES ----------------------------------
grouped_region_mapping <- readr::read_csv("./mappings/grouped_region_mapping.csv")
grouped_region_mapping_WEO <- readr::read_csv("./mappings/grouped_region_mapping_WEO.csv")
elec_gen_mapping <- readr::read_csv("./mappings/elec_gen_mapping.csv")
GWP <- readr::read_csv("./mappings/GWP.csv")
#IPCC AR6 data
AR6_DB <- readr::read_csv("./comparison_data/AR6_Scenarios_Database_World_v1.1.csv")
AR6_DB_long <- AR6_DB %>%
pivot_longer(cols = as.character(seq(1995,2100),by=1)) %>%
dplyr::rename(year = name) %>%
mutate(year = as.numeric(year))
#IEA World Energy Outlook data
WEO_world_FFI_CO2_em <- readr::read_csv("./comparison_data/WEO_world_FFI_CO2_em.csv")
WEO_world_gas_cons <- readr::read_csv("./comparison_data/WEO_world_gas_cons.csv")
WEO_world_LNG_cons <- readr::read_csv("./comparison_data/WEO_world_LNG_cons.csv")
WEO_world_pipeline_cons <- readr::read_csv("./comparison_data/WEO_world_pipeline_cons.csv")
WEO_world_traded_gas_cons <- bind_rows(WEO_world_LNG_cons,
WEO_world_pipeline_cons) %>%
group_by(scenario, year) %>%
summarise_at(c("Bcm", "Tcf", "EJ"), sum)
WEO_reg_gas_cons <- readr::read_csv("./comparison_data/WEO_reg_gas_cons.csv")
WEO_reg_gas_prod <- readr::read_csv("./comparison_data/WEO_reg_gas_prod.csv")
# SELECT DATA ---------------------------------------------------------
prj <- rgcam::loadProject("gas_trade.dat")
prj_LUC <- rgcam::loadProject("gas_trade_LUC.dat")
# RETRIEVE QUERIES --------------------------------------------------------
reg_gas_tech <- rgcam::getQuery(prj, "regional natural gas by tech (nest)")
tra_gas_tech <- rgcam::getQuery(prj, "traded gas by tech") %>%
filter(sector %in% NG_trade_group, subsector != "statistical differences") %>%
select(-region) %>%
separate(subsector, c("region",NA), sep = " traded ")
pri_ene<- rgcam::getQuery(prj, "primary energy consumption by region (direct equivalent)")
pri_ene_CCS <- rgcam::getQuery(prj, "primary energy consumption with CCS by region (direct equivalent)")
tra_gas_tech_vintage <- rgcam::getQuery(prj, "traded gas by tech and vintage") %>%
filter(sector %in% NG_trade_group, subsector != "statistical differences") %>%
select(-region) %>%
separate(subsector, c("region",NA), sep = " traded ") %>%
separate(technology, c(NA, "vintage"), sep = ",year=")
reg_gas_tech_vintage <- rgcam::getQuery(prj, "regional natural gas by tech and vintage") %>%
separate(technology, c("technology","vintage"), sep = ",year=") %>%
select(scenario, region, technology, vintage, year, value, Units)
elec_gen <- rgcam::getQuery(prj, "elec gen by gen tech") %>%
left_join(elec_gen_mapping, by = c("technology"))
final_ene <- rgcam::getQuery(prj, "final energy consumption by sector and fuel")
res_prod <- rgcam::getQuery(prj, "resource production")
GHG_sector <- rgcam::getQuery(prj, "nonCO2 emissions by sector")
GHG_res_prod <- rgcam::getQuery(prj, "nonCO2 emissions by resource production")
LUC_em <- rgcam::getQuery(prj_LUC, "LUC emissions by region") %>%
group_by(scenario, region, year) %>%
dplyr::summarize(value = sum(value)) %>%
mutate(value = value * 44/12,
Units = "MtCO2",
sector = "LUC",
GHG = "CO2")
# DATA PROCESSING ---------------------------------------------------------
# RESOURCE PRODUCTION -----------------------------------------------------
global_res_prod <- res_prod %>%
group_by(scenario, resource, year) %>%
dplyr::summarise(value = sum(value))
# PRODUCTION, TRADE, CONSUMPTION FLOWS --------------------------------
gross_imports_domestic_ng <- reg_gas_tech %>%
filter(Units =="EJ") %>%
select(scenario, region, technology, year, value) %>%
separate(technology, c("trade", "sector"), sep = " ") %>%
mutate(sector = if_else(trade == "domestic", "domestic", sector))
gross_exports_ng <- tra_gas_tech %>%
separate(sector, c("trade", "sector"), sep = " ") %>%
mutate(trade = if_else(trade == "traded", "exported", trade)) %>%
select(-technology, -output, -Units)
gross_trade_ng <- gross_imports_domestic_ng %>%
filter(trade == "imported") %>%
mutate(value = value * -1) %>%
bind_rows(gross_exports_ng)
net_trade_ng <- gross_trade_ng %>%
group_by(scenario, region, year) %>%
dplyr::summarise(value = sum(value))
trade_ng <- gross_trade_ng %>%
left_join(net_trade_ng, by = c("scenario", "region", "year"), suffix = c("", ".net"))
gross_production_ng <- gross_imports_domestic_ng %>%
filter(trade == "domestic") %>%
bind_rows(gross_exports_ng)
gross_consumption_ng <- gross_imports_domestic_ng %>%
mutate(value = value * -1)
# AGG REGION PRODUCTION, TRADE, CONSUMPTION FLOWS -------------------------
grouped_gross_trade_ng <- gross_trade_ng %>%
left_join(grouped_region_mapping, by = "region") %>%
group_by(scenario, grouped_region, trade, sector, year) %>%
dplyr::summarise(value = sum(value))
grouped_net_trade_ng <- gross_trade_ng %>%
left_join(grouped_region_mapping, by = "region") %>%
group_by(scenario, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
grouped_trade_ng <- grouped_gross_trade_ng %>%
left_join(grouped_net_trade_ng, by = c("scenario", "grouped_region", "year"), suffix = c("", ".net")) %>%
mutate(grouped_region = factor(grouped_region, levels = flip_grouped_region_levels),
sector = factor(sector, levels = trade_sector_levels))
grouped_production_ng <- gross_production_ng %>%
left_join(grouped_region_mapping, by = "region") %>%
group_by(scenario, grouped_region, trade, sector, year) %>%
dplyr::summarise(value = sum(value)) %>%
mutate(grouped_region = factor(grouped_region, levels = flip_grouped_region_levels),
sector = factor(sector, levels = trade_sector_levels))
grouped_consumption_ng <- gross_consumption_ng %>%
left_join(grouped_region_mapping, by = "region") %>%
group_by(scenario, grouped_region, trade, sector, year) %>%
dplyr::summarise(value = sum(value)) %>%
mutate(grouped_region = factor(grouped_region, levels = flip_grouped_region_levels),
sector = factor(sector, levels = trade_sector_levels))
# DIFF AGG REGION PRODUCTION(REFERENCE_LLS AND REFERENCE_LT SCENARIOS) ----------
diff_grouped_production_ng_REF_LLS_sens <- diff_from_scen(df = grouped_production_ng,
diff_scenarios = c("Reference_LLS"),
ref_scenario = "Reference",
join_var = c("grouped_region", "trade", "sector", "year")) %>%
mutate(scenario.diff = "Reference_LLS")
diff_grouped_production_ng_REF_LT_sens <- diff_from_scen(df = grouped_production_ng,
diff_scenarios = c("Reference_LT_sshc"),
ref_scenario = "Reference",
join_var = c("grouped_region", "trade", "sector", "year")) %>%
mutate(scenario.diff = "Reference_LT_sshc")
diff_grouped_production_ng_REF_sens_net <- bind_rows(diff_grouped_production_ng_REF_LLS_sens,
diff_grouped_production_ng_REF_LT_sens)%>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_production_ng_REF_sens_plot <- bind_rows(diff_grouped_production_ng_REF_LLS_sens,
diff_grouped_production_ng_REF_LT_sens) %>%
left_join(diff_grouped_production_ng_REF_sens_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
# DIFF AGG REGION PRODUCTION (TRANSITION_LLS AND TRANSITION_LT SCENARIOS) --------
diff_grouped_production_ng_TRANS_LLS_sens <- diff_from_scen(df = grouped_production_ng,
diff_scenarios = c("Transition_LLS"),
ref_scenario = "Transition",
join_var = c("grouped_region", "trade", "sector", "year")) %>%
mutate(scenario.diff = "Transition_LLS")
diff_grouped_production_ng_TRANS_LT_sens <- diff_from_scen(df = grouped_production_ng,
diff_scenarios = c("Transition_LT_sshc"),
ref_scenario = "Transition",
join_var = c("grouped_region", "trade", "sector", "year")) %>%
mutate(scenario.diff = "Transition_LT_sshc")
diff_grouped_production_ng_TRANS_sens_net <- bind_rows(diff_grouped_production_ng_TRANS_LLS_sens,
diff_grouped_production_ng_TRANS_LT_sens) %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_production_ng_TRANS_sens_plot <- bind_rows(diff_grouped_production_ng_TRANS_LLS_sens,
diff_grouped_production_ng_TRANS_LT_sens) %>%
left_join(diff_grouped_production_ng_TRANS_sens_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
# DIFF AGG CONSUMPTION (REFERENCE_LLS AND REFERENCE_LT SCENARIOS) ---------
diff_grouped_consumption_ng_REF_LLS_sens <- diff_from_scen(df = grouped_consumption_ng,
diff_scenarios = c("Reference_LLS"),
ref_scenario = "Reference",
join_var = c("grouped_region", "trade", "sector", "year")) %>%
mutate(scenario.diff = "Reference_LLS")
diff_grouped_consumption_ng_REF_LT_sens <- diff_from_scen(df = grouped_consumption_ng,
diff_scenarios = c("Reference_LT_sshc"),
ref_scenario = "Reference",
join_var = c("grouped_region", "trade", "sector", "year"))%>%
mutate(scenario.diff = "Reference_LT_sshc")
diff_grouped_consumption_ng_REF_sens_net <- bind_rows(diff_grouped_consumption_ng_REF_LLS_sens,
diff_grouped_consumption_ng_REF_LT_sens) %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_consumption_ng_REF_sens_plot <- bind_rows(diff_grouped_consumption_ng_REF_LLS_sens,
diff_grouped_consumption_ng_REF_LT_sens) %>%
left_join(diff_grouped_consumption_ng_REF_sens_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
# DIFF AGG CONSUMPTION (TRANSITION_LLS and TRANSITION_LT SCENARIOS) -------
diff_grouped_consumption_ng_TRANS_LLS_sens <- diff_from_scen(df = grouped_consumption_ng,
diff_scenarios = c("Transition_LLS"),
ref_scenario = "Transition",
join_var = c("grouped_region", "trade", "sector", "year")) %>%
mutate(scenario.diff = "Transition_LLS")
diff_grouped_consumption_ng_TRANS_LT_sens <- diff_from_scen(df = grouped_consumption_ng,
diff_scenarios = c("Transition_LT_sshc"),
ref_scenario = "Transition",
join_var = c("grouped_region", "trade", "sector", "year")) %>%
mutate(scenario.diff = "Transition_LT_sshc")
diff_grouped_consumption_ng_TRANS_sens_net <- bind_rows(diff_grouped_consumption_ng_TRANS_LLS_sens,
diff_grouped_consumption_ng_TRANS_LT_sens) %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_consumption_ng_TRANS_sens_plot <- bind_rows(diff_grouped_consumption_ng_TRANS_LLS_sens,
diff_grouped_consumption_ng_TRANS_LT_sens) %>%
left_join(diff_grouped_consumption_ng_TRANS_sens_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
# PRIMARY ENERGY ----------------------------------------------------------
#primary energy
nat_gas_pri_ene <- pri_ene %>%
filter(fuel %in% c("traded Afr_MidE pipeline gas", "traded LNG", "traded LA pipeline gas", "traded PAC pipeline gas", "traded N.Amer pipeline gas",
"traded RUS pipeline gas", "traded EUR pipeline gas",
Units == "EJ")) %>%
group_by(scenario, region, year) %>%
dplyr::summarise(value = sum(value)) %>%
mutate(fuel = "b natural gas")
grouped_pri_ene <- pri_ene %>%
filter(fuel %!in% c("traded Afr_MidE pipeline gas", "traded LNG", "traded LA pipeline gas", "traded PAC pipeline gas", "traded N.Amer pipeline gas",
"traded RUS pipeline gas", "traded EUR pipeline gas"),
Units == "EJ") %>%
bind_rows(nat_gas_pri_ene) %>%
group_by(scenario, region, fuel, year) %>%
dplyr::summarise(value = sum(value)) %>%
filter(fuel %in% pri_ene_fuels)
global_pri_ene <- grouped_pri_ene %>%
group_by(scenario, fuel, year) %>%
dplyr::summarise(value = sum(value))
# PRIMARY ENERGY WITH CCS -------------------------------------------------
#primary energy with CCS
nat_gas_pri_ene_CCS <- pri_ene_CCS %>%
filter(fuel %in% c("traded Afr_MidE pipeline gas", "traded LNG", "traded LA pipeline gas", "traded PAC pipeline gas", "traded N.Amer pipeline gas",
"traded RUS pipeline gas", "traded EUR pipeline gas",
Units == "EJ")) %>%
group_by(scenario, region, year) %>%
dplyr::summarise(value = sum(value)) %>%
mutate(fuel = "b natural gas")
nat_gas_CCS_pri_ene_CCS <- pri_ene_CCS %>%
filter(fuel %in% c("traded Afr_MidE pipeline gas CCS", "traded LNG CCS", "traded LA pipeline gas CCS", "traded PAC pipeline gas CCS", "traded N.Amer pipeline gas CCS",
"traded RUS pipeline gas CCS", "traded EUR pipeline gas CCS",
Units == "EJ")) %>%
group_by(scenario, region, year) %>%
dplyr::summarise(value = sum(value)) %>%
mutate(fuel = "b natural gas CCS")
grouped_pri_ene_CCS <- pri_ene_CCS %>%
filter(fuel %!in% c("traded Afr_MidE pipeline gas", "traded LNG", "traded LA pipeline gas", "traded PAC pipeline gas", "traded N.Amer pipeline gas",
"traded RUS pipeline gas", "traded EUR pipeline gas",
"traded Afr_MidE pipeline gas CCS", "traded LNG CCS", "traded LA pipeline gas CCS", "traded PAC pipeline gas CCS", "traded N.Amer pipeline gas CCS",
"traded RUS pipeline gas CCS", "traded EUR pipeline gas CCS"),
Units == "EJ") %>%
bind_rows(nat_gas_pri_ene_CCS,
nat_gas_CCS_pri_ene_CCS) %>%
group_by(scenario, region, fuel, year) %>%
dplyr::summarise(value = sum(value)) %>%
filter(fuel %in% pri_ene_CCS_fuels)
global_pri_ene_CCS <- grouped_pri_ene_CCS %>%
group_by(scenario, fuel, year) %>%
dplyr::summarise(value = sum(value))
grouped_region_pri_ene_CCS <- grouped_pri_ene_CCS %>%
left_join(grouped_region_mapping, by = c("region")) %>%
group_by(scenario, grouped_region, fuel, year) %>%
dplyr::summarise(value = sum(value))
# DIFF PRIMARY ENERGY WITH CCS (LLS and LT SCENARIOS) --------------------------------------------
diff_grouped_region_pri_ene_CCS_REF <- diff_from_scen(df = grouped_region_pri_ene_CCS,
diff_scenarios = c("Reference_LLS", "Reference_LT_sshc"),
ref_scenario = "Reference",
join_var = c("grouped_region", "fuel", "year"))
diff_grouped_region_pri_ene_CCS_REF_net <- diff_grouped_region_pri_ene_CCS_REF %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_region_pri_ene_CCS_REF_plot <- diff_grouped_region_pri_ene_CCS_REF %>%
left_join(diff_grouped_region_pri_ene_CCS_REF_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
diff_grouped_region_pri_ene_CCS_TRANS <- diff_from_scen(df = grouped_region_pri_ene_CCS,
diff_scenarios = c("Transition_LLS", "Transition_LT_sshc"),
ref_scenario = "Transition",
join_var = c("grouped_region", "fuel", "year"))
diff_grouped_region_pri_ene_CCS_TRANS_net <- diff_grouped_region_pri_ene_CCS_TRANS %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_region_pri_ene_CCS_TRANS_plot <- diff_grouped_region_pri_ene_CCS_TRANS %>%
left_join(diff_grouped_region_pri_ene_CCS_TRANS_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
diff_grouped_region_pri_ene_CCS <- bind_rows(diff_grouped_region_pri_ene_CCS_REF_plot,
diff_grouped_region_pri_ene_CCS_TRANS_plot) %>%
mutate(grouped_region = factor(grouped_region, levels = flip_grouped_region_levels))
# DIFF PRIMARY ENERGY WITH CCS (TRANSITION) -------------------------------------------------
diff_grouped_region_pri_ene_CCS_TRANS_REF <- diff_from_scen(df = grouped_region_pri_ene_CCS,
diff_scenarios = c("Transition"),
ref_scenario = "Reference",
join_var = c("grouped_region", "fuel","year"))
diff_grouped_region_pri_ene_CCS_TRANS_REF_net <- diff_grouped_region_pri_ene_CCS_TRANS_REF %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_region_pri_ene_CCS_TRANS_REF_plot <- diff_grouped_region_pri_ene_CCS_TRANS_REF %>%
left_join(diff_grouped_region_pri_ene_CCS_TRANS_REF_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
# ELECTRICITY GENERATION ----------------------------------------------------------------
grouped_region_elec_gen <- elec_gen %>%
left_join(grouped_region_mapping, by = c("region")) %>%
group_by(scenario, grouped_region, sector, year) %>%
dplyr::summarise(value = sum(value))
global_elec_gen <- elec_gen %>%
group_by(scenario, sector, year) %>%
dplyr::summarise(value = sum(value))
# DIFF ELECTRICITY GENERATION (TRANSITION) --------------------------------
diff_grouped_region_elec_gen_TRANS_REF <- diff_from_scen(df = grouped_region_elec_gen,
diff_scenarios = c("Transition"),
ref_scenario = "Reference",
join_var = c("grouped_region", "sector","year"))
diff_grouped_region_elec_gen_TRANS_REF_net <- diff_grouped_region_elec_gen_TRANS_REF %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_region_elec_gen_TRANS_REF_plot <- diff_grouped_region_elec_gen_TRANS_REF %>%
left_join(diff_grouped_region_elec_gen_TRANS_REF_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
# FINAL ENERGY ------------------------------------------------------------
grouped_region_final_ene <- final_ene %>%
left_join(grouped_region_mapping, by = c("region")) %>%
group_by(scenario, grouped_region, input, year) %>%
dplyr::summarise(value = sum(value))
global_final_ene <- final_ene %>%
group_by(scenario, input, year) %>%
dplyr::summarise(value = sum(value))
# DIFF FINAL ENERGY -------------------------------------------------------
diff_grouped_region_final_ene_TRANS_REF <- diff_from_scen(df = grouped_region_final_ene,
diff_scenarios = c("Transition"),
ref_scenario = "Reference",
join_var = c("grouped_region", "input","year"))
diff_grouped_region_final_ene_TRANS_REF_net <- diff_grouped_region_final_ene_TRANS_REF %>%
group_by(scenario.diff, grouped_region, year) %>%
dplyr::summarise(value = sum(value))
diff_grouped_region_final_ene_TRANS_REF_plot <- diff_grouped_region_final_ene_TRANS_REF %>%
left_join(diff_grouped_region_final_ene_TRANS_REF_net, by = c("scenario.diff", "grouped_region", "year"), suffix = c("", ".net"))
# EXPORT INFRASTRUCTURE CAPACITY ------------------------------------------
# "Gross Additions and Retirements by Vintage" ----------------------------
conv_traded_gas_tech_vintage <- tra_gas_tech_vintage %>%
mutate(value = value * CONV_MJ_BTU * CONV_BTU_CF * CONV_CF_TONLNG * 10^6,
Units = "MTPA",
vintage = as.numeric(vintage))
# total export capacity in MTPA
conv_traded_gas_tech <- conv_traded_gas_tech_vintage %>%
group_by(Units, scenario, sector, region, output, year) %>%
dplyr::summarise(value = sum(value))
conv_global_traded_gas_tech <- conv_traded_gas_tech %>%
group_by(scenario, year) %>%
dplyr::summarise(value = sum(value))
# cumulative total export capacity in MTPA
cum_conv_traded_gas_tech <- conv_traded_gas_tech %>%
group_by(Units, scenario, sector, region, output) %>%
dplyr::mutate(cum_value = cumsum(value))
# Calculate additions by vintage
conv_traded_gas_tech_vintage %>%
filter(vintage > 2015) %>%
mutate(additions = if_else(vintage == year, value, 0)) -> gas_vintage_add
# Calculate retirements by vintage
conv_traded_gas_tech_vintage %>%
filter(vintage >= 2015) %>%
group_by(scenario, region, sector, Units, vintage) %>%
mutate(prev_year = lag(value, n = 1L)) %>%
ungroup() %>%
mutate(prev_year = if_else(is.na(prev_year), 0, prev_year),
retirements = prev_year - value,
retirements = if_else(retirements < 0, 0, retirements)) %>%
arrange(vintage, sector, region) -> gas_vintage_ret
# "Expected Natural Retirements" ------------------------------------------
# Calculate s-curve output fraction
# parameters (from A_ff_TradedTechnology_NG.csv)
half.life <- 23
steepness <- 0.2
lifetime <- 45
conv_traded_gas_tech_vintage %>%
# for base years only
filter(vintage == 2015) %>%
mutate(s_curve_frac = if_else(year > vintage,
(1 / (1 + exp( steepness * ((year - vintage) - half.life )))),
1)) %>%
# Adjust s-curve output fraction to ensure that all of the capacity is retired at the end of lifetime
mutate(s_curve_adj = if_else(year - vintage >= lifetime, 0, s_curve_frac),
s_curve_adj = if_else(is.na(s_curve_adj), 1, s_curve_adj)) %>%
select(scenario, region, sector, vintage, Units, year, value, s_curve_adj) -> s_curve_frac_adj
# Expected gas capacity assuming natural shutdowns only
# Create variable reflecting each tech/ vintage generation in year of installment (OG_gas_capacity)
s_curve_frac_adj %>%
left_join(conv_traded_gas_tech_vintage %>%
filter(vintage == year) %>%
select(-year) %>%
rename(OG_gas_capacity = value),
by = c("scenario", "region", "sector", "vintage", "Units")) %>%
mutate(gas_expect = OG_gas_capacity * s_curve_adj) %>%
# Expected natural retirements
group_by(scenario, region, sector, Units, vintage) %>%
mutate(prev_yr_expect = lag(gas_expect, n = 1L),
natural_retire = if_else(year > vintage & prev_yr_expect > gas_expect, prev_yr_expect - gas_expect, 0)) %>%
ungroup() -> gas_retire_expect
# "Gross Additions and Retirements by Sector" -------------------------
# Total additions per region/ sector/ year (in EJ)
gas_vintage_add %>%
group_by(scenario, region, sector, Units, year) %>%
summarise(additions = sum(additions)) %>%
ungroup() -> gas_total_add
# Total retirements per region/ sector/ year (in EJ)
gas_vintage_ret %>%
left_join(gas_retire_expect %>%
select(scenario, sector, region, vintage, year, output, Units, natural_retire),
by = c("Units", "scenario", "sector", "region", "vintage", "output", "year")) %>%
# vintages > 2015 have no expected natural retirements - replace NA values with zero
replace_na(list(natural_retire = 0)) %>%
# adjust retirements to account for expected natural retirements
mutate(retirements = retirements - natural_retire,
# make sure we don't have any negative values for retirement after adjustment
retirements = if_else(retirements < 0, 0, retirements)) %>%
group_by(scenario, region, sector, Units, year) %>%
summarise(retirements = sum(retirements)) %>%
ungroup() -> gas_total_ret
# "Adjusted Additions and Retirements" -------------------------
# Merge total additions and retirements data tables
gas_total_add %>%
left_join(gas_total_ret, by = c("scenario", "region", "sector", "Units", "year")) %>%
mutate(add_adj = if_else(additions > retirements, additions - retirements, 0),
ret_adj = if_else(retirements > additions, retirements - additions, 0)) -> gas_add_ret
global_total_add <- gas_add_ret %>%
group_by(scenario, sector, year) %>%
dplyr::summarise(additions = sum(add_adj))
# Cumulative Additions and Retirements ------------------------------------
cum_gas_total_add <- gas_add_ret %>%
group_by(scenario, region, sector, Units) %>%
dplyr::mutate(cum_additions = cumsum(add_adj))
cum_gas_total_ret <- gas_add_ret %>%
group_by(scenario, region, sector, Units) %>%
dplyr::mutate(cum_retirements = cumsum(ret_adj))
cum_global_total_add <- cum_gas_total_add %>%
group_by(scenario, sector, year) %>%
dplyr::summarise(cum_additions = sum(cum_additions))
cum_global_total_ret <- cum_gas_total_ret %>%
group_by(scenario, sector, year) %>%
dplyr::summarise(cum_retirements = sum(cum_retirements))
cum_agg_region_total_add <- cum_gas_total_add %>%
left_join(grouped_region_mapping, by = c("region")) %>%
group_by(scenario, grouped_region, sector, year) %>%
dplyr::summarise(cum_additions = sum(cum_additions))
cum_agg_region_total_ret <- cum_gas_total_ret %>%
left_join(grouped_region_mapping, by = c("region")) %>%
group_by(scenario, grouped_region, sector, year) %>%
dplyr::summarise(cum_retirements = sum(cum_retirements))
# ANNUAL INFRASTRUCTURE COST ----------------------------------------------
#Convert from MTPA -> $ (via EJ * $/GJ)
global_total_add_LNG_cost <- global_total_add %>%
filter(sector == "traded LNG") %>%
dplyr::mutate(cost = additions * 2.02 * 10^9 * (1/(CONV_MJ_BTU * CONV_BTU_CF * CONV_CF_TONLNG * 10^6)) * (1/0.13) * (1/5))
global_total_add_pipeline_cost <- global_total_add %>%
filter(sector != "traded LNG") %>%
dplyr::mutate(cost = additions * 0.57 * 10^9 * (1/(CONV_MJ_BTU * CONV_BTU_CF * CONV_CF_TONLNG * 10^6)) * (1/0.13) * (1/5))
global_total_add_ng_cost <- bind_rows(global_total_add_LNG_cost,
global_total_add_pipeline_cost)
# IMPORT INFRASTRUCTURE CAPACITY ------------------------------------------
# "Gross Additions and Retirements by Vintage" ----------------------------
#convert to MTPA (million tons per annum)
conv_reg_gas_tech_vintage <- reg_gas_tech_vintage %>%
mutate(value = value * CONV_MJ_BTU * CONV_BTU_CF * CONV_CF_TONLNG * 10^6,
Units = "MTPA",
vintage = as.numeric(vintage))
# Total cumulative capacity
conv_reg_gas_tech <- conv_reg_gas_tech_vintage %>%
group_by(scenario, region, technology, year) %>%
dplyr::summarise(value = sum(value))
# Calculate additions by vintage
conv_reg_gas_tech_vintage %>%
filter(vintage > 2015) %>%
mutate(additions = if_else(vintage == year, value, 0)) -> gas_vintage_add_reg
# Calculate retirements by vintage
conv_reg_gas_tech_vintage %>%
filter(vintage >= 2015) %>%
group_by(scenario, region, technology, Units, vintage) %>%
mutate(prev_year = lag(value, n = 1L)) %>%
ungroup() %>%
mutate(prev_year = if_else(is.na(prev_year), 0, prev_year),
retirements = prev_year - value,
retirements = if_else(retirements < 0, 0, retirements)) %>%
arrange(vintage, technology, region) -> gas_vintage_ret_reg
# "Expected Natural Retirements" ------------------------------------------
# Calculate s-curve output fraction
# parameters (from A_ff_TradedTechnology_NG.csv)
half.life <- 23
steepness <- 0.2
lifetime <- 45
conv_reg_gas_tech_vintage %>%
# for base years only
filter(vintage == 2015) %>%
mutate(s_curve_frac = if_else(year > vintage,
(1 / (1 + exp( steepness * ((year - vintage) - half.life )))),
1)) %>%
# Adjust s-curve output fraction to ensure that all of the capacity is retired at the end of lifetime
mutate(s_curve_adj = if_else(year - vintage >= lifetime, 0, s_curve_frac),
s_curve_adj = if_else(is.na(s_curve_adj), 1, s_curve_adj)) %>%
select(scenario, region, technology, vintage, Units, year, value, s_curve_adj) -> s_curve_frac_adj_reg
# Expected gas capacity assuming natural shutdowns only
# Create variable reflecting each tech/ vintage generation in year of installment (OG_gas_capacity)
s_curve_frac_adj_reg %>%
left_join(conv_reg_gas_tech_vintage %>%
filter(vintage == year) %>%
select(-year) %>%
rename(OG_gas_capacity = value),
by = c("scenario", "region", "technology", "vintage", "Units")) %>%
mutate(gas_expect = OG_gas_capacity * s_curve_adj) %>%
# Expected natural retirements
group_by(scenario, region, technology, Units, vintage) %>%
mutate(prev_yr_expect = lag(gas_expect, n = 1L),
natural_retire = if_else(year > vintage & prev_yr_expect > gas_expect, prev_yr_expect - gas_expect, 0)) %>%
ungroup() -> gas_retire_expect_reg
# "Gross Additions and Retirements by Sector" -------------------------
# Total additions per region/ technology/ year (in EJ)
gas_vintage_add_reg %>%
group_by(scenario, region, technology, Units, year) %>%
summarise(additions = sum(additions)) %>%
ungroup() -> gas_total_add_reg
# Total retirements per region/ technology/ year (in EJ)
gas_vintage_ret_reg %>%
left_join(gas_retire_expect_reg %>%
select(scenario, technology, region, vintage, year, Units, natural_retire),
by = c("Units", "scenario", "technology", "region", "vintage", "year")) %>%
# vintages > 2015 have no expected natural retirements - replace NA values with zero
replace_na(list(natural_retire = 0)) %>%
# adjust retirements to account for expected natural retirements
mutate(retirements = retirements - natural_retire,
# make sure we don't have any negative values for retirement after adjustment
retirements = if_else(retirements < 0, 0, retirements)) %>%
group_by(scenario, region, Units, technology, year) %>%
summarise(retirements = sum(retirements)) %>%
ungroup() -> gas_total_ret_reg
# "Adjusted Additions and Retirements" -------------------------
# Merge total additions and retirements data tables
gas_total_add_reg %>%
left_join(gas_total_ret_reg, by = c("scenario", "region", "technology", "Units", "year")) %>%
mutate(add_adj = if_else(additions > retirements, additions - retirements, 0),
ret_adj = if_else(retirements > additions, retirements - additions, 0)) -> gas_add_ret_reg
# Cumulative Additions and Retirements ------------------------------------
cum_gas_total_add_reg <- gas_add_ret_reg %>%
group_by(scenario, region, technology, Units) %>%
dplyr::mutate(cum_additions = cumsum(add_adj))
cum_gas_total_ret_reg <- gas_add_ret_reg %>%
group_by(scenario, region, technology, Units) %>%
dplyr::mutate(cum_retirements = cumsum(ret_adj))
cum_global_total_add_reg <- cum_gas_total_add_reg %>%
group_by(scenario, technology, year) %>%
dplyr::summarise(cum_additions = sum(cum_additions))
cum_global_total_ret_reg <- cum_gas_total_ret_reg %>%
group_by(scenario, technology, year) %>%
dplyr::summarise(cum_retirements = sum(cum_retirements))
cum_agg_region_total_add_reg <- cum_gas_total_add_reg %>%
left_join(grouped_region_mapping, by = c("region")) %>%
group_by(scenario, grouped_region, technology, year) %>%
dplyr::summarise(cum_additions = sum(cum_additions))
cum_agg_region_total_ret_reg <- cum_gas_total_ret_reg %>%
left_join(grouped_region_mapping, by = c("region")) %>%
group_by(scenario, grouped_region, technology, year) %>%
dplyr::summarise(cum_retirements = sum(cum_retirements))
# PROCESS IPCC AR6 DATA -------------------------------------------------------
#Temperature data
AR6_temp <- AR6_DB_long %>%
filter(Variable == "AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|50.0th Percentile", year == 2100) %>%
mutate(temp_category = case_when(value < 1.5 ~ "A_below 1.5C",
value >= 1.5 & value < 2.0 ~ "B_1.5-2C",
value >= 2.0 & value < 3.0 ~ "C_2-3C",
value >= 3.0 & value < 4.0 ~ "D_3-4C",
value >= 4.0 ~ "E_above 4C")) %>%
select(Model, Scenario, temp_category)
AR6_temp$temp_category <- factor(AR6_temp$temp_category,
levels = c("A_below 1.5C",
"B_1.5-2C",
"C_2-3C",
"D_3-4C",
"E_above 4C"))
#Global gas demand
AR6_pri_ene_gas <- AR6_DB_long %>%
filter(Variable == "Primary Energy|Gas") %>%
left_join(AR6_temp, by = c("Model", "Scenario")) %>%
na.omit() %>%
mutate(category = "AR6") %>%
rename(Model_Scenario = Scenario,
scenario = temp_category)
#Global primary energy
AR6_pri_ene <- AR6_DB_long %>%
filter(Variable == "Primary Energy") %>%
left_join(AR6_temp, by = c("Model", "Scenario")) %>%
na.omit()
#Global gas in electricity
AR6_elec_gas <- AR6_DB_long %>%
filter(Variable == "Secondary Energy|Electricity|Gas") %>%
left_join(AR6_temp, by = c("Model", "Scenario")) %>%
na.omit()
#FFI CO2 emissions
AR6_FFI_CO2_em <- AR6_DB_long %>%
filter(Variable == "Emissions|CO2|Energy and Industrial Processes") %>%
left_join(AR6_temp, by = c("Model", "Scenario")) %>%
na.omit() %>%
mutate(category = "AR6") %>%
rename(Model_Scenario = Scenario,
scenario = temp_category)
# GHG EMISSIONS -----------------------------------------------------------
GHG <- GHG_res_prod %>%
select(-resource) %>%
rename(sector = subresource) %>%
bind_rows(GHG_sector) %>%
rename(GHG = ghg) %>%
left_join(GWP, by = c("GHG" = "ghg")) %>%
mutate(Units = "MTCO2e", value = value * AR5all, SAR = NULL, AR5 = NULL,
AR4 = NULL, SARall = NULL, AR5all = NULL, AR4all = NULL) %>%
na.omit() %>%
bind_rows(LUC_em)
grouped_region_GHG <- GHG %>%
left_join(grouped_region_mapping, by = "region") %>%
group_by(scenario, grouped_region, GHG, sector, year) %>%
dplyr::summarise(value = sum(value))
global_GHG <- grouped_region_GHG %>%
group_by(scenario, year) %>%
dplyr::summarise(value = sum(value))
global_CO2 <- GHG %>%
filter(GHG == "CO2") %>%
group_by(scenario, year) %>%
dplyr::summarise(value = sum(value))
global_nonCO2 <- GHG %>%
filter(GHG != "CO2") %>%
group_by(scenario, year) %>%
dplyr::summarise(value = sum(value))
# GHG EMISSIONS BY AGGREGATED SECTOR --------------------------------------
global_CO2_res_prod <- GHG_res_prod %>%
group_by(scenario, resource, year) %>%
rename(GHG = ghg) %>%
left_join(GWP, by = c("GHG" = "ghg")) %>%
mutate(Units = "MTCO2e", value = value * AR5all, SAR = NULL, AR5 = NULL,
AR4 = NULL, SARall = NULL, AR5all = NULL, AR4all = NULL) %>%
na.omit() %>%
filter(GHG == "CO2") %>%
dplyr::summarise(value = sum(value)) %>%
rename(sector = resource)
global_CO2_sector <- GHG_sector %>%
group_by(scenario, sector, year) %>%
rename(GHG = ghg) %>%
left_join(GWP, by = c("GHG" = "ghg")) %>%
mutate(Units = "MTCO2e", value = value * AR5all, SAR = NULL, AR5 = NULL,
AR4 = NULL, SARall = NULL, AR5all = NULL, AR4all = NULL) %>%
na.omit() %>%
filter(GHG == "CO2") %>%
dplyr::summarise(value = sum(value)) %>%
bind_rows(LUC_em)
global_CO2_sector_res_prod <- bind_rows(global_CO2_res_prod,
global_CO2_sector)
global_CO2_agg_sector_res_prod <- bind_rows(aggregate_rows(global_CO2_sector_res_prod, sector, "Buildings", bld_GHG_group, scenario, year),
aggregate_rows(global_CO2_sector_res_prod, sector, "Electricity", elec_GHG_group, scenario, year),
aggregate_rows(global_CO2_sector_res_prod, sector, "Transportation", trn_GHG_group, scenario, year),
aggregate_rows(global_CO2_sector_res_prod, sector, "Industry", ind_GHG_group, scenario, year),
aggregate_rows(global_CO2_sector_res_prod, sector, "Urban", urb_GHG_group, scenario, year),
aggregate_rows(global_CO2_sector_res_prod, sector, "Agriculture and Land Use", c(aglu_GHG_group,"LUC"), scenario, year),
aggregate_rows(global_CO2_sector_res_prod, sector, "Transformation", trans_GHG_group, scenario, year),
aggregate_rows(global_CO2_sector_res_prod, sector, "Resource Production", res_prod_GHG_group, scenario, year))
global_GHG_res_prod <- GHG_res_prod %>%
group_by(scenario, resource, year) %>%
rename(GHG = ghg) %>%
left_join(GWP, by = c("GHG" = "ghg")) %>%
mutate(Units = "MTCO2e", value = value * AR5all, SAR = NULL, AR5 = NULL,
AR4 = NULL, SARall = NULL, AR5all = NULL, AR4all = NULL) %>%
na.omit() %>%
dplyr::summarise(value = sum(value)) %>%
rename(sector = resource)
global_GHG_sector <- GHG_sector %>%
group_by(scenario, sector, year) %>%
rename(GHG = ghg) %>%
left_join(GWP, by = c("GHG" = "ghg")) %>%
mutate(Units = "MTCO2e", value = value * AR5all, SAR = NULL, AR5 = NULL,
AR4 = NULL, SARall = NULL, AR5all = NULL, AR4all = NULL) %>%
na.omit() %>%
dplyr::summarise(value = sum(value)) %>%
bind_rows(LUC_em)
global_GHG_sector_res_prod <- bind_rows(global_GHG_res_prod,
global_GHG_sector)
global_GHG_agg_sector_res_prod <- bind_rows(aggregate_rows(global_GHG_sector_res_prod, sector, "Buildings", bld_GHG_group, scenario, year),
aggregate_rows(global_GHG_sector_res_prod, sector, "Electricity", elec_GHG_group, scenario, year),
aggregate_rows(global_GHG_sector_res_prod, sector, "Transportation", trn_GHG_group, scenario, year),
aggregate_rows(global_GHG_sector_res_prod, sector, "Industry", ind_GHG_group, scenario, year),
aggregate_rows(global_GHG_sector_res_prod, sector, "Urban", urb_GHG_group, scenario, year),
aggregate_rows(global_GHG_sector_res_prod, sector, "Agriculture and Land Use", c(aglu_GHG_group,"LUC"), scenario, year),
aggregate_rows(global_GHG_sector_res_prod, sector, "Transformation", trans_GHG_group, scenario, year),
aggregate_rows(global_GHG_sector_res_prod, sector, "Resource Production", res_prod_GHG_group, scenario, year))
# GAS GHG EMISSIONS BY AGGREGATED SECTOR ----------------------------------
gas_res_prod <- GHG_res_prod %>%