geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_point(aes(x = SL.SRV.EMPL.ZS, y = EN.ATM.CO2E.PP.GD.KD
), size = 0.1, col = "dodgerblue4") +
scale_x_log10("Service Sector (% of Total Employment)", breaks = c(2, 5, 10, 20, 50, 100)) +
scale_y_log10("kg CO2 per $2011 PPP",
breaks = c(0.01, 0.1, 1, 10), labels = c(0.01, 0.1, 1, 10)  ) +
coord_cartesian(xlim = c(4, 100)) +
ggtitle("B.  CO2 Intensity of GDP vs. Service Employment") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(0.9), hjust = 0.5, vjust = -0.1),
axis.line = element_line(color = "black"),
axis.title.x=element_text(vjust=0, size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", x = 5, y = 2, label = intensity.r2, parse = T,family="Times", size=3)
# VALUE ADDED
# energy intensity vs. service gdp
###################################################
# regression
x = log(d$NV.SRV.TETC.ZS)
y = log(d$EG.EGY.PRIM.PP.KD*d$EG.USE.COMM.FO.ZS/100)
x = x[y != -Inf]
y = y[y != -Inf]
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(110), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.99))
regression = data.frame(x = exp(x), regression)
intensity.r2 =  paste("R^2 == ", round(r.square,4))
options(scipen=10000)
energy_gdp = ggplot(data = d) +
geom_ribbon(data = regression, aes( x = x, ymin = lwr, ymax = upr), fill = "black", alpha = 0.05) +
geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_point(aes(x = NV.SRV.TETC.ZS, y = EG.EGY.PRIM.PP.KD*EG.USE.COMM.FO.ZS/100), size = 0.1, col = "red") +
scale_x_log10("Service Sector (% of Total Value Added)", breaks = c(2, 5, 10, 20, 50, 100)) +
scale_y_log10("MJ Fossil Fuel per $2011 PPP",
breaks = c(0.1, 1, 10, 100), labels = c(0.1, 1, 10, 100)   ) +
coord_cartesian(xlim = c(4, 90), ylim = c(0.1, 100)) +
ggtitle("C.  Fossil Fuel Intensity of GDP vs. Service Value Added") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(0.9), hjust = 0.5, vjust = -0.1),
axis.line = element_line(color = "black"),
axis.title.x = element_text(vjust=0, size=rel(0.9)),
axis.title.y = element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", x = 5.5, y = 100, label = intensity.r2, parse = T,family="Times", size=3)
# carbon intensity vs. service value added
###################################################
# regression
x = log(d$NV.SRV.TETC.ZS)
y = log(d$EN.ATM.CO2E.PP.GD.KD)
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(100), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.99))
regression = data.frame(x = exp(x), regression)
intensity.r2 =  paste("R^2 == ", round(r.square,2))
summary(r)
summary(r)$p
summary(fit)$coefficients[,4]
summary(r)$coefficients[,4]
summary(r)$coefficients[,4] /10^6
summary(r)$coefficients[,4] *10^6
summary(r)$coefficients[,4]
summary(r)$coefficients[,4]*10^6
summary(r)$coefficients[,4]*10^9
summary(r)$coefficients[,4]*10^12
g = summary(r)$coefficients[,4]
g
g*10^9
g*10^16
g
formatC(g, format = "e", digits = 2)
summary(r)
g = summary(r)$coefficients[,4]
g
source('~/Desktop/service/figures/absolute_dematerial.R')
dir()
dir
source('~/Desktop/service/figures/sector_share_plot.R')
dir
wd = gsub("figures", "data/world bank")
wd = gsub("figures", "data/world bank", dir)
setwd(wd)
g = fread("sector_energy.csv")
global_sector_time = ggplot() +
geom_path(data = g, aes(x = year, y = sector.frac*100, col = sector)) +
scale_x_continuous("Year") +
scale_y_continuous("% of Total Employment", breaks = seq(0, 100, 10)) +
ggtitle("A.  Sector Composition Changes Over Time") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(1), hjust = 0.5, vjust = -0.5),
axis.line = element_line(color = "black"),
axis.title.x = element_text(size = rel(0.9)),
axis.title.y=element_text(size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,2,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", label = "Agriculture", x = 1850, y = 80, family = "Times", size = 3) +
annotate("text", label = "Industry", x = 2000, y = 35, family = "Times", size = 3) +
annotate("text", label = "Services", x = 1940, y = 70, family = "Times", size = 3)
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
source('~/Desktop/service/figures/sector_share_plot.R')
library(data.table)
library(ggplot2)
library(here)
library(gridExtra)
library(grid)
library(gtable)
dir = here()
wd = gsub("figures", "data/world bank", dir)
setwd(wd)
d = fread("data_format.csv")
d$EG.USE.PCAP.KG.OE = d$EG.USE.PCAP.KG.OE*4.187/100
text.size = 10
# intensity
#############################################################
# regression
x = log(d$SL.SRV.EMPL.ZS)
y = log(d$EG.EGY.PRIM.PP.KD)
x = x[d$EG.USE.COMM.FO.ZS > 0]
y = y[d$EG.USE.COMM.FO.ZS > 0]
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(100), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.99))
regression = data.frame(x = exp(x), regression)
intensity.r2 =  paste("R^2 == ", round(r.square,2))
intensity = ggplot() +
geom_ribbon(data = regression, aes( x = x, ymin = lwr, ymax = upr), fill = "black", alpha = 0.05) +
geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_point(data = d, aes(x = SL.SRV.EMPL.ZS, y = EG.EGY.PRIM.PP.KD), size = 0.1, col = "dodgerblue4") +
scale_x_log10("Service Sector (% of Employment)", breaks = c(0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100)) +
scale_y_log10("MJ per $2011 PPP GDP", breaks = c(0.1, 1, 10, 100), labels = c(0.1, 1, 10, 100)) +
coord_cartesian(xlim = c(4, 100), ylim = c(0.1, 100)) +
ggtitle("A. Energy Intensity of GDP") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(1), hjust = 0.5, vjust = -0.1),
axis.line = element_line(color = "black"),
axis.title.x=element_text(size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", x = 5, y = 100, label = intensity.r2, parse = T,family="Times", size=3)
# fossil fuel fraction
#############################################################
# regression
x = log(d$SL.SRV.EMPL.ZS)
y = log(d$EG.USE.COMM.FO.ZS)
x = x[d$EG.USE.COMM.FO.ZS > 0]
y = y[d$EG.USE.COMM.FO.ZS > 0]
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(100), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.99))
regression = data.frame(x = exp(x), regression)
fossil.r2 =  paste("R^2 == ", round(r.square,2))
fossil = ggplot() +
geom_ribbon(data = regression, aes( x = x, ymin = lwr, ymax = upr), fill = "black", alpha = 0.05) +
geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_point(data = d, aes(x = SL.SRV.EMPL.ZS, y = EG.USE.COMM.FO.ZS), size = 0.1, col = "dodgerblue4") +
scale_x_log10("Service Sector (% of Employment)", breaks = c(2, 5, 10, 20, 50, 100)) +
scale_y_log10("Fossil Fuels (% Total Energy)",
breaks = c(2, 5, 10, 20, 50, 100) ) +
coord_cartesian(xlim = c(4, 100), ylim = c(1.5, 100)) +
ggtitle("B.  Percentage of Energy from Fossil Fuels") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(1), hjust = 0.5, vjust = -0.1),
axis.line = element_line(color = "black"),
axis.title.x=element_text(size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", x = 5, y = 100, label = fossil.r2, parse = T,family="Times", size=3)
# total energy
####################################################################
# regression
x = log(d$SL.SRV.EMPL.ZS)
y = log(d$EG.USE.PCAP.KG.OE)
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(100), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.99))
regression = data.frame(x = exp(x), regression)
energy.r2 =  paste("R^2 == ", round(r.square,2))
energy = ggplot() +
geom_ribbon(data = regression, aes( x = x, ymin = lwr, ymax = upr), fill = "black", alpha = 0.05) +
geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_point(data = d, aes(x = SL.SRV.EMPL.ZS, y = EG.USE.PCAP.KG.OE), size = 0.1, col = "dodgerblue4") +
scale_x_log10("Service Sector (% of Employment)", breaks = c(2, 5, 10, 20, 50, 100)) +
scale_y_log10("Energy Use per Capita (GJ)", breaks =  c(1,10, 100, 1000)  ) +
coord_cartesian(xlim = c(4, 100), ylim = c(0.4, 1000)) +
ggtitle("C.  Energy Use per Capita") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(1), hjust = 0.5, vjust = -0.1),
axis.line = element_line(color = "black"),
axis.title.x=element_text(size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", x = 5, y = 1000, label = energy.r2, parse = T,family="Times", size=3)
# global energy
######################################################################
g =  fread("sector_energy.csv")
g = g[sector == "Service"]
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
x = g$sector.frac
y = g$energy_pc
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(100), length.out = 50)
x = data.frame(x)
x = seq(20, 60, length.out = 20)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.99))
regression = data.frame(x = exp(x), regression)
energy.r2 =  paste("R^2 == ", round(r.square,2))
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
source('~/Desktop/service/figures/disag.R')
7.5/2
library(data.table)
library(ggplot2)
library(here)
library(gridExtra)
library(grid)
library(gtable)
library(reshape2)
text.size = 10
# EMPLOYMENT
# fossil fuel intensity vs. service employment
###################################################
dir = here()
wd = gsub("figures", "data/world bank", dir)
setwd(wd)
d = fread("data_format.csv")
library(data.table)
library(ggplot2)
library(here)
library(gridExtra)
library(grid)
library(gtable)
library(reshape2)
text.size = 10
# EMPLOYMENT
# fossil fuel intensity vs. service employment
###################################################
dir = here()
wd = gsub("figures", "data/world bank", dir)
setwd(wd)
d = fread("data_format.csv")
# regression
x = log(d$SL.SRV.EMPL.ZS)
y = log(d$NV.SRV.TETC.ZS)
x = x[y != -Inf]
y = y[y != -Inf]
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(110), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.99))
regression = data.frame(x = exp(x), regression)
intensity.r2 =  paste("R^2 == ", round(r.square,2))
library(data.table)
library(ggplot2)
library(here)
library(gridExtra)
library(grid)
library(gtable)
library(reshape2)
text.size = 10
# EMPLOYMENT
# fossil fuel intensity vs. service employment
###################################################
dir = here()
wd = gsub("figures", "data/world bank", dir)
setwd(wd)
d = fread("data_format.csv")
# regression
x = d$SL.SRV.EMPL.ZS
y = d$NV.SRV.TETC.ZS
x = x[y != -Inf]
y = y[y != -Inf]
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(30, 60, length.out = 50)
x = data.frame(x)
regression = predict(r, x,  interval = "prediction", level = 0.95)
regression = data.frame(x = x, regression)
intensity.r2 =  paste("R^2 == ", round(r.square,2))
r2 =  paste("R^2 == ", round(r.square,2))
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/srv_emp_vs_value.R')
source('~/Desktop/service/figures/absolute_dematerial.R')
source('~/Desktop/service/figures/relative_dematerial.R')
library(data.table)
library(ggplot2)
library(here)
library(gridExtra)
library(grid)
library(gtable)
library(reshape2)
text.size = 10
# EMPLOYMENT
# fossil fuel intensity vs. service employment
###################################################
dir = here()
wd = gsub("figures", "data/world bank", dir)
setwd(wd)
d = fread("data_format.csv")
# regression
x = log(d$SL.SRV.EMPL.ZS)
y = log(d$EG.EGY.PRIM.PP.KD*d$EG.USE.COMM.FO.ZS/100)
x = x[y != -Inf]
y = y[y != -Inf]
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(110), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.95))
regression = data.frame(x = exp(x), regression)
intensity.r2 =  paste("R^2 == ", round(r.square,2))
energy_emp = ggplot(data = d) +
geom_ribbon(data = regression, aes( x = x, ymin = lwr, ymax = upr), fill = "black", alpha = 0.05) +
geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_point(aes(x = SL.SRV.EMPL.ZS, y = EG.EGY.PRIM.PP.KD*EG.USE.COMM.FO.ZS/100), size = 0.1, col = "dodgerblue4") +
scale_x_log10("Service Sector (% of Total Employment)", breaks = c(2, 5, 10, 20, 50, 100)) +
scale_y_log10("MJ Fossil Fuel per $2011 PPP",
breaks = c(0.1, 1, 10, 100), labels = c(0.1, 1, 10, 100)   ) +
coord_cartesian(xlim = c(4, 90), ylim = c(0.1, 100)) +
ggtitle("A.  Fossil Fuel Intensity of GDP vs. Service Employment") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(0.9), hjust = 0.5, vjust = -0.1),
axis.line = element_line(color = "black"),
axis.title.x = element_text(vjust=0, size=rel(0.9)),
axis.title.y = element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", x = 5, y = 95, label = intensity.r2, parse = T,family="Times", size=3)
# carbon intensity vs. service employment
###################################################
# regression
x = log(d$SL.SRV.EMPL.ZS)
y = log(d$EN.ATM.CO2E.PP.GD.KD)
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(100), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.95))
regression = data.frame(x = exp(x), regression)
intensity.r2 =  paste("R^2 == ", round(r.square,2))
carbon_intensity_emp = ggplot(data = d) +
geom_ribbon(data = regression, aes( x = x, ymin = lwr, ymax = upr), fill = "black", alpha = 0.05) +
geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_point(aes(x = SL.SRV.EMPL.ZS, y = EN.ATM.CO2E.PP.GD.KD
), size = 0.1, col = "dodgerblue4") +
scale_x_log10("Service Sector (% of Total Employment)", breaks = c(2, 5, 10, 20, 50, 100)) +
scale_y_log10("kg CO2 per $2011 PPP",
breaks = c(0.01, 0.1, 1, 10), labels = c(0.01, 0.1, 1, 10)  ) +
coord_cartesian(xlim = c(4, 100)) +
ggtitle("B.  CO2 Intensity of GDP vs. Service Employment") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(0.9), hjust = 0.5, vjust = -0.1),
axis.line = element_line(color = "black"),
axis.title.x=element_text(vjust=0, size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times")) +
annotate("text", x = 5, y = 2, label = intensity.r2, parse = T,family="Times", size=3)
# VALUE ADDED
# energy intensity vs. service gdp
###################################################
# regression
x = log(d$NV.SRV.TETC.ZS)
y = log(d$EG.EGY.PRIM.PP.KD*d$EG.USE.COMM.FO.ZS/100)
x = x[y != -Inf]
y = y[y != -Inf]
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(log(1), log(110), length.out = 50)
x = data.frame(x)
regression = exp(predict(r, x,  interval = "prediction", level = 0.95))
regression = data.frame(x = exp(x), regression)
intensity.r2 =  paste("R^2 == ", round(r.square,4))
options(scipen=10000)
summary(r)
source('~/Desktop/aggregation/Supplementary Material/Data/World Bank/format.R')
