In my previous role as a quantitative allocation researcher a very well-respected colleague taught me two appealing lessons, which can be applied to a wide range of problems and challenges. These lessons were:
Both rules are very helpful and important but combining them makes them even more powerful. Today is about the onion and I will peel the layers of country carbon emissions.
The carbon emissions of a country can be represented as follows: CO2=POP*GDP/POP*(CO2)/GDP
If you look at that formula you will see that POP (population) and GDP cancel out and an CO2 identity remains. Therefore, when you first look at it, you might question the usefulness of this formula. However, the three terms on the right side are a powerful tool in explaining and predicting country emissions.
The first term is the population and evidently is positively related to carbon emissions. The second term is called affluence and proxies the country’s wealth as GDP per capita. In general, higher economic activity results in higher emissions. The last term is the state of green technologies in a country, which is measured by the emissions per unit GDP. Obviously, the better the technological state the lower the emissions.
In 2019, the aggregated emissions in the US were around 5100 megatons of CO2, while China emitted around 11,500 megatons. Together they account for almost half of all global emissions. In the figure below I have decomposed the cumulative percentual change since 1990 in CO2 emissions for these two counties.1
The total emissions of the US in 2019 are only 1% higher than in 1990. The population growth (+29%) and affluence (+49%) are offset by advances in greener technologies (-77%). If we look at China a different picture emerges. There, the carbon emissions have increased by 380%, mainly driven by the country becoming richer (+624%).
Emissions particularly increased between 2000 and 2010, as the rise due to wealth growth was not yet compensated by technological advances. Only since 2010 have the greener technological advances become more effective, slowing down the growth rate of emissions.
Predicting future emissions can be a difficult task. Breaking down emissions in these components might be helpful. The population for instance can be predicted reasonably accurately, the possible availability of certain green technologies can be taken into account, and the wealth growth of some developing countries cannot last forever.
So, by predicting the different components an indication of the future growth of the country emissions can be obtained.
1I used a logarithmic mean divisia index approach to attribute the total change in emissions to the components