Exploring the structure of national consumption
This entry is a direct continuation of my first and second entry on the structure of national resource consumption
library(ggplot2);library(Hmisc)
# READING IN DATA
## SETTING DIRECTORY FOR EORA DATA ON LOCAL HARD DRIVE
wd<-"G:/Documents/PostDocKVA/Data/Eora" ### data directory
setwd(wd)
## READING IN DATA
### MATERIAL USE DATA - ENERGY DATASET
energy.df<-read.csv("TradeBalance_I-ENERGY.csv",header=TRUE)
### Reading in .csv file with annual gdp and population sizes
gdppop.df<-read.csv("gdppop.csv",header=TRUE,skip=1) #skipping the first line which includes a description of the file
## REMOVING NEGATIVE AND ZERO CONSUMPTION ENTRIES
energy.df<-energy.df[which(energy.df[,"Consumption"]>0),]
## REMOVING NEGATIVE AND ZERO CONSUMPTION ENTRIES
energy.df<-energy.df[-which(as.character(energy.df$Country)=="Former USSR"),]
## merging the gdp and population size data onto the energy consumption data frame
energy.df<-merge(energy.df,gdppop.df,by=c("CountryA3","y","Country"),all.x=TRUE)
## To make consumption more comparable let's calculate per capita consumption by associating population data
### calculate per capita consumption and gdp consumption intensity by associating population data
energy.df[,"Consum.pop.int"]<-energy.df[,"Consumption"]/energy.df[,"val"]
energy.df[,"Consum.gdp.int"]<-energy.df[,"Consumption"]/energy.df[,"GDP"]
### calculating scaled consumption intensitities with respect to population size and gdp
energy.df<-energy.df[order(energy.df[,"Country"],energy.df[,"y"]),]
energy.df[,"Consum.pop.int.scale"]<-unlist(by(energy.df,energy.df[,"Country"], function(x) scale(x[,"Consum.pop.int"],center=TRUE,scale=TRUE)))
energy.df[,"Consum.gdp.int.scale"]<-unlist(by(energy.df,energy.df[,"Country"], function(x) scale(x[,"Consum.gdp.int"],center=TRUE,scale=TRUE)))
energy.df<-energy.df[-which( energy.df[,"y"] %in% c(1991,2000,2011)),]Now let’s look at the contribution of imports and domestic extration to national consumption. We will do this qucikly in the beginning since I’m running out of time today.
head(energy.df)## CountryA3 y Country TerritorialEmissions Imports Exports
## 43 AFG 1970 Afghanistan 115043 15343 1343
## 44 AFG 1971 Afghanistan 115043 13345 1262
## 45 AFG 1972 Afghanistan 115043 11725 1465
## 46 AFG 1973 Afghanistan 115043 9633 1392
## 47 AFG 1974 Afghanistan 115043 8265 1213
## 48 AFG 1975 Afghanistan 115043 7828 1150
## DirectEmissions Consumption GDP val Consum.pop.int
## 43 43301 129043 1277935 11839729 0.010899151
## 44 43301 127126 1362663 12138578 0.010472891
## 45 43301 125303 1168728 12449180 0.010065161
## 46 43301 123284 1266842 12760486 0.009661388
## 47 43301 122094 1567124 13058067 0.009350082
## 48 43301 121720 1722525 13328589 0.009132249
## Consum.gdp.int Consum.pop.int.scale Consum.gdp.int.scale
## 43 0.10097775 1.651512 2.065308
## 44 0.09329233 1.524365 1.807681
## 45 0.10721314 1.402746 2.274329
## 46 0.09731600 1.282307 1.942561
## 47 0.07790960 1.189450 1.292028
## 48 0.07066371 1.124474 1.049134### Subtracting exports from imports and domestic extraction in proportion to their size.
### This is the conceptual code
#### consum.extract <- extraction - (export * extraction/(import+extraction))
#### consum.import <- import - (export * import/(import+extraction))
energy.df[,"Extraction"]<- (energy.df[,"TerritorialEmissions"] + energy.df[,"DirectEmissions"]) ## These two categories both refer to domestically extracted resources, i thik. Check up with Wiedmann dataset.
energy.df[,"Extraction"]<- energy.df[,"TerritorialEmissions"]
energy.df[,"Consum.extract"]<- (energy.df[,"Extraction"]) -
(energy.df[,"Exports"] * ((energy.df[,"Extraction"]) /
(energy.df[,"Extraction"] + energy.df[,"Imports"])))
energy.df[,"Consum.import"]<- (energy.df[,"Imports"]) -
(energy.df[,"Exports"] * ((energy.df[,"Imports"]) /
(energy.df[,"Extraction"] + energy.df[,"Imports"])))
energy.df[,"Consum.balance"]<-(energy.df[,"Consum.extract"]-energy.df[,"Consum.import"])/energy.df[,"Consumption"]
energy.df[order(energy.df[,"Consum.balance"],decreasing=TRUE)[1:20],c("Country","y","Consum.balance")]## Country y Consum.balance
## 2987 Hong Kong 1975 1.8921038
## 3992 Liberia 1972 1.7620715
## 3993 Liberia 1973 1.3574014
## 5360 Norway 1997 1.2404283
## 3197 Indonesia 1975 1.2249416
## 7594 Viet Nam 1975 1.1396350
## 3996 Liberia 1976 1.1056177
## 4059 Libya 1997 1.0502739
## 826 Bahrain 1997 1.0426134
## 5631 Philippines 1975 1.0392322
## 5044 Malaysia 1975 1.0354441
## 7054 Turkmenistan 1981 1.0192899
## 1476 China 1975 1.0022135
## 947 Belarus 1992 0.9992896
## 945 Belarus 1990 0.9991773
## 944 Belarus 1989 0.9990176
## 943 Belarus 1988 0.9989872
## 935 Belarus 1980 0.9989454
## 942 Belarus 1987 0.9989381
## 936 Belarus 1981 0.9989218energy.df<-energy.df[-which(energy.df[,"Consum.balance"]>1),] ## removing entries that are above 1 (i.e. all resources being domestically extracted)
energy.df<-energy.df[order(energy.df[,"Country"],energy.df[,"y"]),]
energy.df[,"mean.gdp"]<-unlist(tapply(energy.df[,"GDP"],energy.df[,"Country"],function(x) rep(mean(x,na.rm=TRUE),length(x))))
energy.df[,"mean.val"]<-unlist(tapply(energy.df[,"val"],energy.df[,"Country"],function(x) rep(mean(x,na.rm=TRUE),length(x))))
ggplot(energy.df[which(energy.df[,"y"]<2008),],aes(x=y,y=Consum.balance)) + geom_line(aes(Group=Country,alpha=mean.val,size=mean.gdp/mean.val))
val.membership.df<-data.frame("mean.val"=unique(energy.df[,"mean.val"]),"val.membership"=NA)
val.membership.df[,"val.membership"]<-as.numeric(cut2(val.membership.df$mean.val,m=20))
energy.df<-merge(energy.df,val.membership.df,by="mean.val",all.x=TRUE)
ggplot(energy.df[which(energy.df[,"y"]<2008 & is.na(energy.df[,"val.membership"])==FALSE),],aes(x=y,y=Consum.balance)) + geom_line(aes(Group=Country,alpha=mean.gdp/mean.val)) + facet_wrap(~val.membership)
I will comment further on these plots later. However, it is interesting to see that the darkest lines (highest gdp per capita) in each facet plot (1 (lowest) - 9 (highest) national population size) tends to have the strongest trends toward lower extraction vs import balance. No surprise, but good to be able to see this trend in the data.