New Zealands gross greenhouse gas emissions per capita compared

Update: 7 February 2016. There was a mistake in the chart. The labels for India and Africa were interposed. So I fixed that and uploaded a new version to Wikimedia Commons. I also changed the type of point used to mark the India data into an upside-down triangle.

I have made another chart and uploaded it to Wikimedia Commons. Today's chart is a comparison of gross per capita greenhouse gas emissions 1990 to 2012 for seven entities: New Zealand, United Kingdom, the European Union, China, India, Africa (the whole continent) and also the world average.

Its my own work of course. So it's by Mrfebruary and following Wikimedia's practice, it's released under a Creative Commons Attribution-ShareAlike 4.0 International licence, via Wikimedia Commons.

I have used some html mark-up from Wikimedia Commons here and not the Blogger tools.

The original source of the data is the CAIT Climate Data Explorer which is provided by the Washington-based World Resources Institute. I selected the countries/entities and the type of data and downloaded it. I have stashed the data I used on Google Sheets.

Look at China's trend in per capita emissions. In 1990 China had more-or-less the same per capita emissions as India or the average for Africa as a continent. Then from the late 1990s onwards, globalisation of trade and China's rapid economic growth, particularly of its export manufacturing, caused per capita emissions to grow until they are now similar to the emissions in the European Union.

The two trends are of course linked; as China becomes the industrial manufacturer for the rest of the world, the developed OECD countries, including New Zealand, become importers of manufactured goods and exporters of the greenhouse gas emissions. The manufacturing industries decline and the developed economies become more reliant on low-emissions service industries.

Here is the R code for the chart.

As an experiment, I am going to insert a large .png of the graph, 1280 pixels wide. One feature of Wikimedia Commons is that once you have uploaded an image in scalable vector graphic format, you can then get portable network graphic (png) format images in different sizes. Blogger calls this 'medium' size. It doesn't reproduce crisply. The Wikimedia Commons html mark-up looks better. Still, double click on this and it looks crisper in it's glorious 1280 pixel width.

China's primary energy

I have made a chart showing the trend in China's primary energy production and consumption.

I got the raw data from International Energy Statistics part of the US Energy Information Administration webpage. I downloaded two sets of time series data, production and cosumption, from 1980 to 2011 as Excel spreadsheets. I opened each sheet with a spreadsheet program (Gnumeric) and saved each file as a comma separated values file. I then opened each CSV file with a text editor and copied the numbers separated by commas into the script sheet of Rkward, the script editor I use with R.

That made it easy to read the data into R

China Total Primary Energy Consumption (Quadrillion Btu)
c<-c(17.28743,17.19205,17.93384,19.00967,20.45343,22.00581,23.23741,24.75911,26.4463,26.95795,26.9986,28.15789,29.26607,30.03293,34.11018,34.75689,35.5535,37.74754,37.03911,36.51697,36.35199,38.41189,43.90983,51.15546,62.9191,68.2469,72.89202,77.29039,84.67288,90.25787)

Being American, the data is expressed in the bizarre unit Quadrillion British thermal units (BTU) WTF? It's the amount of energy needed to heat one pound of water by one degree Fahrenheit. Not even the British use the BTU. The conversion factor to joules is 1 Quadrillion BTU = 1055 × 10^15 joules.

jc<-c(c*1055*10^15)
str(jc)
#num [1:30] 1.82e+19 1.81e+19 1.89e+19 2.01e+19 2.16e+19 ...(telling us the data is a numeric R object, consisting of 30 numbers )
max(jc)
#[1] 9.522205e+19 (the maximum number in the data series is 9.522205 x 10^19)
ejc<-c(jc/10^18) #convert data from joules to exajoules
max(ejc)
[1] 95.22205
p<-c(18.12198,17.9451,18.92106,20.2361,22.12683,24.30279,25.04091,25.93877,27.14594,28.77554,29.38537,29.62193,30.32492,31.97204,34.13204,35.04591,35.668,37.81456,36.38594,34.99918,34.19953,37.5048,42.8456,49.44335,59.38067,64.44807,66.78499,70.841,78.34819,81.88698) #China Total Primary Energy Production
pj<-c(p*1055*10^15)
pej<-c(j/10^18)
Year<-c(1980:2009) # need a time integer
png("China-primary-energy-ej-2009v1.png", width=650, height=500, pointsize = 16)
plot(Year,ejc,las=1,ylim=c(0,95),type="n",cex.main=1.6, cex.lab=1.2, main="China Total Primary Energy 1980 to 2009",xlab="Year", ylab="exajoules (10^18 joules)")
lines(Year,ejc,lwd=3,type="l",col=2)
lines(Year,pej,lwd=3,type="l",col=4)
mtext(side=1,line=-2,"Data: US EIA")
legend("top",bty='n',bg="white", c("Energy consumption","Energy production"),lty = 1,lwd=3,col = c(2,4))
box(lwd=2)
grid()
dev.off()

Give that a go!