Last week I talked about Dieter Helm’s book, where he portrayed a future in which oil companies are going broke and fossil fuel prices are collapsing due to their practically infinite supply (via fracking and horizontal drilling). Growing awareness of climate change has led to strong public pressure to reduce our carbon footprints and we are experiencing major shifts in how we source and use energy. This includes the growing conversion to electric power. The energy industry was (and is still) structured to accommodate the now-outdated perception that energy supply is finite and shrinking and electrical distribution continues to rely on archaic structures. Helm presents a rosy future for the US because we can adapt to this post-peak-oil reality. But his prediction does not factor in our election of President Trump, who is bent on returning us to the yester-world in terms of our treatment of the physical environment.
As I mentioned in last week’s blog, I agree with some of Helm’s observations and disagree with others. I do feel, however, that it is easy to make predictions about a far future that many of us will not live to see (nor will we be held accountable for our predictions). Helm’s book suffers from that sort of glib certainty and I am aware that I have to some extent shared in the same practice throughout the five years I have been writing this blog.
Helm’s book triggered in me a strong desire to change my ways so I will be starting a series about the recent past. My jumping-off point is the IPAT identity that I have repeatedly referenced here (the first reference was from November 26, 2012). Below is a much more recent mention of this important identity:
There is a useful identity that correlates the environmental impacts (greenhouse gases, in Governor’s Romney statement) with the other indicators. The equation is known as the IPAT equation (or I=PAT), which stands for Impact Population Affluence Technology. The equation was proposed independently by two research teams; one consists of Paul R. Ehrlich and John Holdren (now President Obama’s Science Adviser), while the other is led by Barry Commoner (P.R. Ehrlich and J.P. Holdren; Bulletin of Atmospheric Science 28:16 (1972). B. Commoner; Bulletin of Atmospheric Science 28:42 (1972).)
The identity takes the following form:
Impact = Population x Affluence x Technology
Almost all of the future scenarios for climate change make separate estimates of the indicators in this equation. The difference factor of 15 in GDP/Person (measure of affluence), between the average Chinese and average American makes it clear that the Chinese and the rest of the developing world will do everything they can to try to “even the score” with the developed world. The global challenge is how to do this while at the same time minimizing the environmental impact.
The Population and Affluence terms are self-explanatory. The Impact term in this case refers to emissions of carbon dioxide. The Technology term consists of the three terms combined below:
Technology = (Energy/GDP)x(Fossil/Energy)x(CO2/Fossil)
The first term in this equation refers to Energy Intensity – how much energy we need to generate a unit of GDP (Gross Domestic Product, used here as an Affluence metric). The second term represents the fraction of the total energy that is being generated from fossil fuels. The last term specifies the kind of fossil fuel that is being used (coal, natural gas or oil).
The actual decomposition of global CO2 emissions is shown in Figure 2, which clearly demonstrates that for at least the last decade, Affluence has been the dominant contributor to emissions.
What I referred to in that blog as Figure 2 I am reposting here as Figure 1.
Figure 1 – Decomposition of the change in total global CO2 emissions from fossil fuel combustion by decade (January 24, 2017 blog) (IPCC report analyzing the IPAT identity)
In past blogs I referred to the various indicators in the identity in global terms. Here, and in the next few blogs I will instead cite individual countries. There are two main reasons for doing so. First, our global system is made up of sovereign countries, each of which can (for the most part) enforce actions only within its own borders. The second reason is that once I focus on individual countries I can emphasize those that are more/less successful at transitioning their energy away from carbon-based sources.
Table 1 includes population and GDP/Capita in current US$ for 12 countries. As Figure 1 shows, these are the two main indicators that drive carbon dioxide emissions. The population information came from the United Nations population review and the GDP/Capita came from the World Bank data case.
The sum total of the current population of the 10 most populous countries amounts to more than half of the global total so it is a fair indicator of the whole number.
Table 1 – Population and GDP/Capita of the twelve countries that I will use to analyze the global energy transition (in order of current population)
I have added Ethiopia and the Democratic Republic of Congo because the UN is projecting that given their current growth rates they will be among the 10 most populous countries by 2050. According to World Bank classification, this table includes one high-income (the US), three low-income (Bangladesh, Ethiopia, and DR Congo), four lower-middle income (India, Indonesia, Pakistan, and Nigeria) and four upper-middle income economies (China, Brazil, Russia, and Mexico). In other words, it represents the full range of global income distribution.
The table includes each of the twelve countries’ current GDP/Capita, growth rate, and population as well as their projected populations for 2030 and 2050.
Next week I will focus on trends in primary energy use, emphasizing alternative energy and the resulting change in carbon footprints that these newly diversified economies generate. I will follow it with a compilation of electricity use and the drivers being used to collect the electricity.