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!

Biodiversity and energy must-reads

Massey ecologist Mike Joy has written a must-read opinion piece in the Dominion Post on how short-term profit-seeking, economic analysis and the legal system, the Government, the Resource Management Act, and well, all of us, have failed to slow New Zealand's decline in biodiversity. Government failures and ecological apathy bite back.

Joy highlights the role of consultants and lawyers acting for resource developers.

Simply put, it is expensive to limit environmental damage. So predictably the economic incentive for the evasion of the laws that are there for a public good, quickly became a big money-spinner for lawyers and consulting firms.

These law firms and environmental consultancies have been successful at helping their clients evade the RMA. They have increased profits for developers/polluters while undermining the ecosystems that sustain us all; effectively allowing private profit from public loss.

And Claire Browning has a good post on Gerry Brownlee's 'fossilised' 'fuelish' energy strategy Filty rich: our developing energy strategy.

Claire Browning asks:

Can we, in clean green conscience, keep digging for coal and drilling for oil, postponing the inevitable, and increasing the global carbon burden? How do we justify that, to the rest of the world?

No, its insanity.

The seventy percent fossil fooled country

Quite often I hear politicians or journalists say that New Zealand can't really be expected to reduce carbon dioxide emissions from fossil fuels as 70% of our energy is renewable, due mainly to our hydro power schemes.

A typical recent example was business journalist Fran O'Sullivan, writing in the New Zealand Herald on December 9, 2009.

O'Sullivan wrote this about NZ's positioning on climate change prior to the UNFCCC summit at Copenhagen.
"Where the Government negotiators will focus is on getting across New Zealand's position...the fact that 70 per cent of energy is renewable leaves little room for gains on that score"

The trouble is this 'truism' is not only a bad reason for not reducing greenhouse gas emissions, it is also factually incorrect. As the barchart above shows, renewable energy including Hydro power supplies 30% of New Zealand's energy supply. Fossil fuels supply 70%.

So, I sent Fran an email.
"Fran, You state; "Where the Government negotiators will focus is [in respect of climate change negotiations] on getting across New Zealand's position...the fact that 70 per cent of energy is renewable leaves little room for gains on that score, and that agriculture makes up 50 per cent of emissions." I think you are confusing electricity generation with NZ's gross energy supply. Only 30 percent of NZ's gross energy supply is renewable. Refer to http://en.wikipedia.org/wiki/Energy_in_New_Zealand and the Ministry of Economic Development 2009 energy figures. Kind regards"

Fran replied the same morning "Yes you are right. Real oversight on my behalf will get it corrected on website".

As I write this, six weeks later, the '70%-renewable' statement is still on the NZ Herald website. We seem doomed to be seventy percent fossil fooled for a while longer.

PS.
The source of the data for the barplot is the Ministry of Economic Development 2009 Energy File. I created the barplot with the R programme.

The commands were:

engy<-c(83.1, 280.8,159.9,80.3,113.2,47.4, 1.2)

names(engy)<-c('Coal','Oil','Gas','Hydro', 'Geothermal', 'Other renewable', 'Waste')

cols <-c("brown4","green","green","brown4", "green", "brown4", "brown4")

png(file="NZ_Energy4.png",pointsize=14, width=650, height=550)

par(mar=c(6,9,6,2)+0.1,cex.main=1.5, cex.lab=1.2)

barplot(sort(engy),horiz=T,las=1,xlim=c(0,300), border=1, space=0.8, col=cols)

legend(165,4,c("Non-renewable 69%","Renewable 31%"),lty =1,lwd=4, col= c("brown4", "green"))

title(main="New Zealand Primary Energy Supply 2008", xlab= "Gross Petajoules")

mtext(side=3,line=0.25,"Source: Ministry of Economic Development")

dev.off()

I have uploaded two versions to the Wikimedia Commons website; an SVG chart and a PNG chart.