The Worst Carbon Dioxide Emitters

I won’t be going into detail about climate change, global warming or the greenhouse effect here, I’ll simply be looking at carbon dioxide emissions. I’ll assume people know the link between carbon dioxide, the greenhouse effect and global warming, but I’m planning on doing another post on that later.

I wanted to find out how much carbon dioxide was emitted by different countries over the course of a year. Carbon dioxide pollution is generally accepted as one of the principle cause of global warming, and with attempts to reduce pollution in recent years, I thought the pollution levels would decrease.


The Drax Coal-Fired Power Station in the UK, one of many sources of carbon dioxide emissions, one of the contributors to the greenhouse effect and climate change. [Source: Wikimedia | Paul Glazzard | CC-BY-SA-2.0 ]

Total Emissions

So I began my investigation into emissions by heading over to the CIA World Factbook website, where I knew data for carbon emissions was collected. Sure enough, there was a page of figures for every country on the planet, in millions of metric tons. I took the top five emitters and a random selection of other countries, and decided to put the data into a bar graph. I also decided to mark the BRICS (Brazil, Russia, India, China and South Africa) nations as red columns, because of their development status. The results for this are below.

Carbon Dioxide emissions for 14 selected countries, using the most recent data from the CIA World Factbook. Horizontal line indicates the 1 billion tons of CO2 mark.

This graph clearly shows the big difference between the two worst emitters; with China emitting almost double that of the US. The five worst emitters – Japan, India, Russia, the US and China – each put out more than a billion tons of carbon dioxide a year, with China putting out 10 billion tons.

Emissions Per Capita

I did, however, find a problem with total emisions. Logically, if a country wants to sustain a large population, the country as a whole should use a lot more resources compared to a less populous country. Resources like water, food and energy. All countries have a high reliance on fossil-fuels for electricity generation and powering vehicles. So, a larger population requires more energy which means more fossil fuels will be burnt.

Which means, to cancel out the effect of a large population on the emission values, I found the populations for each of these countries, and divided the emissions by it. This gave me a value for the CO2 emissions per capita, (per person). I graphed the new data and the changes were clear.

The graph shows how much CO2 is emitted, on average, by each person in a country annually.

The US comes out worst, with 17 tons of CO2 per person per year, whereas Indian’s produce around a ton each. Australia, Canada and Russia, have relatively small populations. The fact that Russia and Canada are generally very cold countries means that the people there require heating for most of the year, whereas Australia is a hot place and therefore the population use air conditioning systems.

GDP For Emissions

But thinking about it – fossil fuels are used to generate electricity, which in turn allows development and the generation of money (or GDP – gross domestic product). What about a graph which shows how environmentally efficient the country is at generating energy for GDP? In other words, how many US dollars does a ton of CO2 make in each country? The higher the number, the better – it means the country makes money with as little environmental impact.

This graph merely shows how efficient a country is, based on the assumption that all the energy generated by burning fossil fuels goes towards making money for the country. I divided the GDP by the emissions from the first graph, which gave me a dollar-per-ton measure of “environmental efficiency”. (Alternatively, I could divide the per capita GDP by the per capita emissions)

The “monetary value of CO2” in each country, in US dollars

The UK comes a clear top, with the worst offenders being South Africa, China and Russia. It is likely that the reason for this is that industrial countries put out vast amounts of CO2 in relation to their GDP. The UK has a large banking sector and a small industrial sector, and a general acceptance of nuclear and wind power, which is used fairly extensively – as does France, which has the second-largest nuclear power capacity in the world and a small reliance on fossil fuels.

The aim of the countries on the left is to head towards the right – thereby increasing GDP and reducing emissions. It is no coincidence then, that the more developed nations are on the right-hand side and the less developed nations on the left. Normally, the more developed nations have a better chance to reduce emissions due to technologies they can develop to harness energy sources which aren’t fossil fuels.


I think of the three methods – total emissions, emission per capita and GDP-per-emission – the per-capita method made the most sense, since it accounts for large populations such as China. It shows how the energy policies of the countries near the high end of the chart rely too much on fossil fuels, despite the US’ large nuclear capacity. There is a also a consumption issue; the top three are highly developed nations and therefore the lack of non-CO2-emitting power generating technologies is not due to poor development, and rather due to choice. So the final conclusion to draw from the graph is that high consumption leads to high CO2 emissions, if the energy sector is limited to fossil fuels.

The GDP-per-emission method shows how economics and environmentalism are closely linked – and it proves, in part, that a move towards a more environmentally friendly society won’t always have a negative impact on the economy. This is a claim made by some people who oppose the enforcement of environmental laws. The data is based on assumptions but the idea is that a balance between an environmentally-friendly energy sector and a high GDP is possible.

But it is the total emissions which matter the most – it’s this figure which determines a country’s carbon footprint and therefore a government’s environmental policy. It’s the figures which everyone knows or has an idea of. It’s actually the fairest statistic towards people in a country who aren’t responsible for large CO2 emissions, unlike the per-capita data. It doesn’t make assumptions about the source of a country’s GDP, like the GDP-per-emission method does. One way to “cheat” your way to reducing the emissions is to scrap your manufacturing sector and instead import everything, which means the emissions made during the manufacturing process are another country’s burden. Taking this into account is difficult to say the least.

It’s also to see that the attempts to reduce emissions have largely failed for the biggest emitters. The success of the UK’s, France’s and other environmental policies are offset by the failures on the part of the US’ and Japan’s, both of which are capable of reducing emissions using high-end technology. There is, of course, benefits to using fossil fuels. They’re relatively cheap, in supply (at the moment) and the infrastructure to utilise the fuels is already present (in the form of oil, gas and coal-fired powerstations). In essence, China and the US is the biggest contributor to the greenhouse effect and in turn global warming.

My final statement is this: the shift from polluting fossil fuels to nuclear power and renewables will be slow, but the it will have to happen. Even if the environment is not taken into account, the supply of fossil fuels is finite. What is clear is that when the oil, gas and coal run out, countries with the necessary renewable energy sectors will likely be the most successful economically. Countries forced to make the change at the last minute will see a sizeable drop in GDP, something which might be hard to recover from. But what will be hard to recover from are the expected consequences of climate change, something most scientists have agreed is caused, in some part, by human emissions of CO2.

If you would like to use one of these graphs, please ask using the Contact page. As with other images I have created myself, they are under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.


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