Population growth and global warming: overpopulation or overconsumption?

Population growth and global warming:

overpopulation or overconsumption?

Student: ms. L. (Laura) van Oosten (12178543) Supervisor: Dhr. Dr. J. H. (John) van Boxel Mentor: ms. D.C. (Donya) Danesh

May 2021, Leiden

2 Abstract

The rising global temperature, caused by ongoing greenhouse gas (GHG) emissions, is changing the planet's climate. To prevent disastrous consequences and to avoid crossing so-called tipping points, it is necessary to limit global warming to 1.5°C above pre-industrial levels. Numerous measures are suggested to combat climate change, among which population reduction, since both human population as well as CO2 emissions have increased rapidly over the past decades. This research aims to investigate whether there is a causal relationship between population growth and CO2 emissions, and to what extend population growth contributes to global warming. This is done by analysing global data on population size and CO2 emissions from 2019. The main findings of this research are that the share in population and the share in CO2 emissions differs largely per region or country. Economically wealthier countries are overshooting, and emitting more CO2 than would be expected based on their population size, whereas economically poorer countries are undershooting, and emitting less CO2 than would be expected based on their population size. Economic prosperity is found to be stronger linked to high CO2 emissions as opposed to population size. A new economic approach is likely to be necessary to combat climate change.

3 Contents

1. Introduction……… 4

2. Methods and Data……….. 6

3. Results……… 8 4. Discussion……….. 13 5. Conclusion………. 17 6. Acknowledgements……… 18 7. References……….. 19 8. Appendices………. 22

4 1. Introduction

The rising global temperature, caused by ongoing greenhouse gas (GHG) emissions, is changing the planet's climate. The Intergovernmental Panel on Climate Change (IPCC) has stated that to prevent disastrous consequences and to avoid crossing so-called tipping points, it is necessary to limit global warming to 1.5°C (IPCC, 2018). The Royal Netherlands Meteorological Institute (KNMI) warned that there is a possibility that the increase of 1.5°C will be met in nine years (De Wijs, 2021). Therefore, it is necessary to take actions to combat climate change better sooner than later. However, the climate crisis is very complex. Even though global warming has been researched for decades, there is no consensus on what are exactly the right measures that need to be taken. One of the measures for fighting global warming, that has been suggested by scientists, is reducing population growth. A 2019 paper, co-signed by more than 11,000 scientists, stated that we are in a climate emergency (Ripple et al., 2020). The paper presents several human activities that are, at least partly, linked to global warming. One of these activities is population growth. Based upon a 2018 paper (Bongaarts and O’Neill, 2018), Ripple et al. (2020) conclude that reducing population growth will be beneficial in lowering the risks of climate change. The IPCC has also concluded that the increase of GHG emissions is mostly driven by both the growth regarding economics as well as population (IPCC, 2014). The main discourse surrounding population is thus that population growth is, among other factors, causing global warming. Therefore, it is interesting to investigate the relationship between population growth and GHG emissions, and explore other discourses.

Reducing population growth as a measure to combat global warming faces some controversy. The resistance comes from, for example, the Catholic Church, which is repelling to birth control (Bongaarts and O’Neill, 2018). Other concerns are brought up with the eyesight on human rights (Bongaarts and O’Neill, 2018). Population policy entails the introduction of laws upon women, which threatens bodily autonomy, and reduces or eliminates free choice (Dyett & Thomas, 2019). Besides, critiques of population policy are claiming that the main discourse of population growth as a driver of global warming is a ‘myth’ based on e.g. sexist and racist ideas, as a consequence of colonialism with influences from ecofascism (Dyett & Thomas, 2019). According to this alternative discourse, it is not overpopulation, but rather overconsumption, capitalism and the overall way of living by most people in the Global North that is causing global warming (Dyett & Thomas, 2019). In most research surrounding population policy, the aim is in particular on the world regions

with currently the highest population growth rates – mostly in the Global South, entailing Asia, Latin America, the Middle East, and Africa, and not on the Global North (Bongaarts and O’Neill, 2018; Dyett & Thomas, 2019). Attributing overpopulation as a driver to climate change is considered a way to shift the blame of global warming on marginalized people and women in the Global South who are procreating more than their counterparts in the Global North (Dyett & Thomas, 2019). This is considered

unfair, because when taking into account cumulative emissions Figure 1: Cumulative CO2

5 – historical emissions – people in the Global South have contributed far less to the climate crisis than people in the Global North, see figure 1.

Researching the contribution of population growth to global warming is important because global warming is already causing major problems for the environment. Changes in the environment have led to a climate and biodiversity crisis, harming and affecting people. Therefore, it is crucial to determine what contributes to this crisis, to be able to act on it. To research the contribution of population growth specifically is important since critiques say that population policy can have concerning consequences regarding human rights, especially for marginalized people. Moreover, this analysis is on a macro-level, meaning that it focuses on countries and regions. This focus is helpful to understand the contribution of countries and regions regarding emissions. Understanding the contribution in emissions reflects the responsibility a country or region has in the current climate crisis, which is necessary to .obtain climate justice.

The overarching aim of this research is to determine whether population growth causes increasing CO2 emissions. Therefore, the main research question is What is the role of

population growth regarding global warming? In support of this main question, several other

questions are also asked, namely Is there a causal relationship between population growth and

increasing CO2 emissions?, Is population growth contributing to global warming?, Is it necessary to reduce and/or stop population growth to combat global warming?, and lastly, What is the role of economic growth regarding global warming?

This research hypothesises that population growth does contribute to global warming since more people demand more resources to sustain life. However, nuance is needed and the contribution of other factors that cause global warming needs to be taken into consideration. More importantly, it is expected that overconsumption, economic growth and contemporary capitalism are more determining factors for increasing CO2 emissions. After all, per capita emissions are differing greatly among countries, which implies that individuals globally are not equally responsible for and equally contributing to the climate crisis.

This research focuses on the share in population size and CO2 emissions of countries and regions in 2019. Regarding the structure of this thesis: first, the methods will be introduced. After, the results will be presented. Thereafter, the results will be interpreted in the discussion, and finally, a conclusion will be given.

6 2. Methods and Data

In order to investigate the role of human population on emissions, existing data has been used for this analysis. Firstly, population and CO2 emission data were extracted from Our World in Data (Ritchie and Roser, 2020; Roser, Richtie and Ortiz-Ospina, 2019). Our World in Data publishes scientific data, targeting global issues. Our World in Data uses data from for example the World Bank and The Global Carbon Project. The most recent population and territorial CO2 emission data available at Our World in Data is from the year 2019 (Roser, Richtie and Ortiz-Ospina, 2019). To be able to compare population and territorial CO2 emissions by country or region, the share was calculated in comparison to the world population and the total CO2 emission of 2019. The share is calculated with the straightforward equation of 𝑝𝑎𝑟𝑡

𝑤ℎ𝑜𝑙𝑒 × 100%. The choice to only research the greenhouse gas CO2, and no other greenhouse gasses was made because data on this greenhouse gas is broadly available for the majority of countries around the world, including historical emissions.

Secondly, to gain insight into the role of economic growth on emissions, data on Gross Domestic Product (GDP) was extracted, from The World Bank (World Bank, 2021). GDP is widely used to indicate the level of welfare and development of a country. GDP was also calculated in percentages according to each countries’ GDP as a share of the World Domestic Product (WDP) of 2019. Moreover, the categorization developed by the World Bank (Serajuddin and Hamadeh, 2020), assigning countries’ economies to either low, low-middle, upper-middle, and high income economies based on Gross National Income (GNI) per capita, was used. Alongside countries’ categorization, the respectively territorial CO2 data and population data were assigned as well. The World Bank updates the categorization every year, and the most recent categorization, from July 1st 2020 is used in this analysis, see table 2.

Table 2: Categorization of economies by the World Bank (2020)

Category GNI per capita (USD)

Low income ≤ $1036

Lower-middle income $1036 - $4045 Upper-middle income $4046 - $12535

High income ≥ $12536

This research is a worldwide analysis. Next to the more traditional categorizations in countries, regions and continents, this analysis takes into account the North-South divide of the world, see figure 2. This divide is not based on geography but based on societal and political forces. The Global North entails the United States of America (USA), Canada, Europe, Russia, Japan, South Korea, Australia and New Zealand. The Global South entails the rest of the world, thus Africa, most of Asia, and Latin America. Most countries in the Global South share the experience of having been colonized by countries from the Global North and are considered underdeveloped. The North-South divide can also be used while analysing economic prosperity globally. The Global North is generally economically wealthy, whereas the Global South is generally economically poor. Note that there are differences among countries within this division, after all, the World Bank categorizes 4 levels of economies. The North-South divide

7 has not yet been used extensively in the field of environmental science, and can therefore bring new perspectives to the global issue of climate change.

8 3. Results

Table 1: Natural population growth, only accounting births and deaths and excluding migration flows (Roser, Ritchie, and Ortiz-Ospina, 2019)

Region Population (%)

Africa

Oceania (excl. Australia & New Zealand)

2,47% 1,85%

Latin America 0,94%

Asia 0,88%

Australia & New Zealand 0,53%

Canada & USA 0,24%

Europe -0,10%

Global North 0,22%

Global South 1,54%

Table 1 visualizes the natural population growth for the regions Africa, Oceania (excluding Australia and New Zealand), Latin America, Asia, Australia and New Zealand, Canada and USA, Europe, the Global North, and the Global South. The natural population growth excludes migration flows, thus only accounts for births and deaths. The regions in table 1 are ranked from highest natural population growth to lowest. Table 1 shows that the economically poorer regions (Africa, Oceania without Australia and New Zealand, Latin America, and Asia) have higher natural population growth rates than the economically wealthier regions (Australia & New Zealand, Canada & USA, and Europe). The natural population growth rate of Europe is even negative, meaning that based on births and deaths, population is declining. Lastly, Table 1 shows that the natural population growth in the Global South is higher than in the Global South, thus that the population is increasing.

9 Figure 3: Share in population, territorial CO2 emissions and GDP in 2019 by region (Ritchie and Roser, 2020; Roser, Ritchie and Ortiz-Ospina, 2019; World Bank, 2021)

Figure 3 visualizes three variables: population, territorial CO2 emissions, and GDP. These variables are expressed in percentages of the world total and are reflecting the share in comparison to the world’s total of 2019. Figure 3 visualizes the six biggest emitters in the world – China, the United States of America (USA), the European Union (EU-27), India, Russia and Japan – and the remaining part of the world (RoW), split into two items: the remaining countries of the Global North (GN) – Canada, New Zealand, Australia, South Korea and Europe excluding EU-27 and Russia – and the remaining countries of the Global South (GS). Figure 3 shows that the relative contribution to CO2 emissions of a country or region is not equal to the relative contribution to the human population. For countries in the Global North, the relative contribution to CO2 emissions is higher than their relative contributions to human population. For countries in the Global South, it is the other way around: the contribution to CO2 emissions is lower than their relative contributions to human population. China is an exception – the country is based in the Global South, but has a higher share in emissions than its share in population.

10 Table 2: Ratio of population and CO2 emissions by region in 2019 (Ritchie and Roser,

2020; Roser, Ritchie and Ortiz-Ospina, 2019; World Bank, 2021) Region Population (%) Territorial CO2

emissions (%) Ratio population and CO2 emissions China USA 19,3% 4,4% 30,0% 15,6% 1,55 3,55 EU-27 6,0% 8,6% 1,43 India 18,4% 7,7% 0,42 Russia 2,0% 4,9% 2,45 Japan 1,7% 3,3% 1,94 RoW (GN) 5,7% 12,3% 2,16 RoW (GS) 42,4% 17,6% 0,42

The third column of table 2 visualizes the ratio between CO2 emissions and population for the same regions. This number reflects how much more or less that particular region emits in consideration of its population size. If the share in population is equal to the share in CO2 emissions, the ratio is 1. If the share in population is higher than the share in CO2 emissions, the ratio is lower than 1. If the share in population is lower than the share in CO2 emissions, the ratio is higher than 1. This table shows that countries in the Global North, and China, have ratio’s higher than 1, whereas the countries in the Global South have ratio’s lower than 1.

11 Figure 4: Share in population and territorial CO2 emissions by economy in 2019 (Ritchie and Roser, 2020; Serajuddin and Hamadeh (2020)

Figure 4 visualizes two variables, population and territorial CO2 emissions, by the four world economies, categorized by the World Bank (2021). These variables are also expressed in percentages relative to the worlds’ total of 2019. Figure 5 shows that the relative contribution in CO2 emissions is higher than the contribution in population in the countries that are categorized as upper-middle or high income economies. Vice versa applies for the countries which are categorized as low or low-middle income: these countries’ contributions in CO2 emissions are lower than their contribution in population size.

12 Figure 5: per capita territorial CO2 emissions by region 1900-2019 (Ritchie and Roser, 2020)

Figure 5 visualizes the per capita CO2 emissions by region over the time period 1900-2019. Figure 5 shows that the regions North America, Oceania and Europe have had and still have the highest per capita CO2 emissions. The highest values in 2019 (about 11 tCO2 per capita) are for North America and Oceania. The per capita emission of Oceania is this high mainly due to Australia and New Zealand, these countries have respectively 16,31 tCO2 per capita and 7,64 tCO2 per capita. Europe is intermediate with 7.28 tCO2 per capita in 2019. The mainly Global South regions Asia, Latin America and Africa show lower emission of respectively 4.40, 2.54 and 1.12 tCO2 per capita in 2019. The ‘World’ line shows what the average person on the planet emits, in 2019 that is 4,72 tCO2 per capita. Figure 5 shows that the average person in Asia, Latin America and Africa has always been and still is below this average, whereas the average person in North America, Oceania and Europe always exceed this world average.

Figure 5 shows that the per capita emissions in the Global South are increasing. Most of the recent emissions in Asia come from China, which explains the strong increase of per capita emission in Asia after 2000. Overall, the per capita

emissions in the Global South are increasing, while on the contrary, in the Global North the per capita CO2 emissions are decreasing.

Figure 6 visualizes the relative cumulative CO2 emissions from 1900-2019. Figure 6 shows that the Global North is responsible for 68% of the cumulative territorial CO2 emissions, and the Global South for 32%. The biggest historical emitter is the USA, accounting for more than a quarter of the cumulative emissions. China, currently responsible for almost a third of the territorial CO2 emissions (see figure 3), is only responsible for 13% of the cumulative emissions.

Figure 6: Cumulative territorial CO2 emissions 1900-2019 by region (Ritchie and Roser, 2020)

13 4. Discussion

Both human population and CO2 emissions have increased over the past decades. This research wanted to investigate whether there is a causal relationship between population growth and the increasing CO2 emissions. This causal relationship is implied because the reduction of human population has been proposed to combat climate change broadly (Ripple, 2020; Bongaarts and O’Neill, 2018). If population growth indeed increases CO2 emissions, one could expect that all over the world the share in human population of a country or region is equal to its share in emissions. This would also be the case if everyone on this planet would emit the same. However, figure 3 shows that the share in population and CO2 emissions are in fact not equal. It shows that some regions have a bigger share in CO2 emissions than their share in human population, and others have a lower share in CO2 emissions compared to their share in population.

Figure 3 shows that even though only 4.4% of the world population in 2019 lived in the USA, the USA accounts for 15.6% of the total CO2 emissions of 2019. Table 2 shows that the USA has the highest ratio between contribution to total CO2 emissions and contribution to world population, namely 3.55. China, EU-27, Russia, Japan, and the remaining part of the Global North also have a ratio higher than 1. India is the only country of the six biggest emitters that has a ratio lower than 1. This means that India is emitting less than it would be accounted for based on its share in human population. The share in human population and the share in CO2 emissions are thus not equal, and there are countries or regions with relatively small population sizes, but high emissions rates. The bar ‘Rest of the World Global South’ in figure 3 shows that the majority of the countries based in the Global South are emitting relatively less compared to its share in population. If there was a strong causal relationship between population and CO2, it would be expected that the share in population and CO2 emissions would be equal globally, and the ratios between emissions and population would be 1. Since this appears not to be the case, this could mean that population size is not a determining factor for the level of CO2 emissions. This also arouses the assumption that economic prosperity is a more determining factor. After all, the Global North, which has historically and currently the highest emissions, has the largest share in the world GDP, despite having lower population numbers. This assumption is supported by the results visualized in figure 4.

Figure 4 shows that economically wealthier countries (assigned to the categories high income and upper-middle income economies) emit more CO2 than economically poorer countries (assigned to the categories low-middle income and low income economies) in comparison to their relative population sizes. This data suggests that economic wealth is a determining factor regarding high CO2 emissions; the economically wealthier the region, the more CO2 emissions are emitted. This suggestion is supported by a 2020 report that found that 10% of the richest people in the world, circa 630 million people, are responsible for 52% of the cumulative emissions from 1990-2015 (Gore, 2020). The poorest half of the world, accounting for 3.1 billion people, are responsible for only 7% of the cumulative emissions over that same time period (Gore, 2020). A relatively small and extremely wealthy fraction of the world population is thus responsible for more than half of the cumulative CO2 emissions from 1990-2015, which makes it questionable whether it is effective to reduce population in the Global South – where most of the 3.1 billion poorest people live – for cutting down emissions.

14 Moreover, the Global North is responsible for 68% of the cumulative CO2 emissions (Hickel, 2020) even though only roughly 15% of the world population is living in the Global North. The Global North is economically wealthier than the Global South, indicating that economic wealth is strongly linked to high CO2 emissions.

China’s percentage of CO2 emissions is also higher than its percentage of population. However, China functions as the ‘factory of the world’, meaning that they produce for other countries, predominately in the Global North (Tukker et al., 2014). In 2007, China exported 2,0 of its 9,3 Gt CO2-equivalence domestic emissions (Tukker et al., 2014). 0,67 Gt CO2-eq was imported by Europe, and 0,47 Gt CO2-eq by the United States (Tukker et al., 2014.). China is thus partly producing for consumption in the Global North, while environmental impacts are experienced in the producing country. Besides, when assigning responsibility for climate action, it is common to assign the responsibility to the consumers (e.g. USA and EU-27), rather than the producers (China). Moreover, looking at cumulative CO2 data, China holds even less responsibility for the climate breakdown. China is responsible for 11% of the cumulative CO2 emissions over the time period from 1850-2015, whereas the USA and EU-28 are responsible for respectively 28% and 25% (Hickel, 2020). Note that in the research from Hickel (2020), the European Union with 28 Member States was used – including Croatia – while in this research, the EU with 27 Member States is used. However, the addition of Croatia alone will not affect the outcome largely.

There is so-called ‘carbon inequality’ in different ways. Firstly, as seen in figure 3, the six biggest emitters China, USA, EU-27, India, Russia and Japan, are responsible for 70,1% of the total CO2 emissions of 2019. Secondly, figure 5 shows that the continents Europe, North America and Oceania, have always had higher per capita emissions than Asia, Africa and Latin America. These continents roughly represent the Global North and Global South, so there is also carbon inequality between those two world regions. This specific perspective of carbon inequality between the Global North and the Global South is referred to by Hickel (2020) as

atmospheric colonisation. Atmospheric colonisation is the process of economically wealthy

countries colonising the atmosphere, a ‘common good’, by emitting massive amounts of greenhouse gasses – see figure 1 and 6 that show that the Global North is responsible for 68% of the cumulative CO2 emissions. These same countries have been thriving on the exploitation of the Global South, for resources and cheap labour (Smith, 2016). In the same manner the Global North is exploiting the Global South for their own benefits while causing lowering living standards in the Global South, the Global North is also misusing the atmosphere while causing the biggest climate risks in the Global South (Hickel, 2020; IPCC, 2015).

As covered before, figure 4 shows that economically wealthier countries emit more than economically poorer countries. This suggests that economic growth is leading to higher CO2 emissions. Figure 4 is only visualizing data from 2019, leaving the question of whether economic growth has always led to higher emissions, or whether it is a recent factor. Figure 5 shows that the per capita emissions in especially in North America decrease greatly around the 1930s, from 12,4 tCO2 per capita in 1929 to 8,2 tCO2 per capita in 1933. During this time, the Great Depression occurred: an economic depression that began in the USA at the beginning of the 1930s (Bianchi, 2020; Wheelock, 2020). Economic growth was extremely low at that time, and the emissions dropped. Therefore, this finding contributes to the hypothesis that economic

15 growth is a determinative factor for high emissions, especially because the population of the USA was still growing (Roser, Ritchie and Ortiz-Ospina, 2019).

Scientists are concerned that if the Global South continues to grow in population size and will ‘develop’ to Western standards, their emissions will increase rapidly (Stephenson, Newman and Mayhew, 2010). It is however questionable whether the Global South will be able to develop to the same living and consumption standards as the main reason why the Global North has been able to ‘develop’, is through exploiting the Global South (Smith, 2016). The Global South does not have another Global South to exploit, and will therefore not be likely able to ‘develop’ as long as these (neo)colonial and imperial power structures are in place. But by far the biggest concern raised about population growth is the fear that there will not be enough resources to sustain life on earth, and thus that the world is or at least will be ‘overpopulated’ (Mann, 2015). This belief of overpopulation can be questioned in itself because this assumes that humans are inherently bad for the environment when there are ‘too many’. However, not the mere existence of humans is harming the environment, but humans’ demand for natural resources is. As stated before, only a small fraction of the world population, namely 630 million (the 10% richest people of the world) are responsible for more than half of the cumulative emissions from 1990-2015 (Gore, 2020). That leaves a majority of the world population emitting way less, and therefore being less harmful to the environment. Besides, the existence of humans can also be beneficial for the environment. For example, indigenous people account for just 5% of the world population, but they safeguard 80% of the world biodiversity (Ogar, Pecl and Mustonen, 2020). Besides, there is potential in changing the usage of natural resources and lowering the environmental impacts of human activities. Food production can for instance be more effectively and with lower environmental impact by shifting to predominately plant-based diets (Clark & Tillman, 2017). This shift to more plant-based could also make it possible to feed the expected population of 10 billion people in 2050 within the planetary boundaries (Röckstrom et al. 2009; Springmann et al., 2018). Implementing both climate adaptation and mitigation could ensure meeting human demands, also without drastic population reductions (Peterson, 2017).

Furthermore, one can also question whether population policy is effective. As Table 1 and figure 4 show, the regions with the highest population growth are emitting the most CO2. If there is a desire for population policy, it could be argued that it is more effective to aim it at the countries where people per capita emit the most, even though the population is not increasing. Besides, implementing population policy will take time and the results will not be seen in the short term. It is therefore debatable if population policy will even have the chance to contribute to combatting climate change since there is a possibility that the 1.5°C increase in global temperature will be met in nine years (De Wijs, 2020). Moreover, the United Nations (UN) estimates the human population to stabilize around 11 billion people in 2100 (United Nations, 2019), thus human population will not be exponentially growing. The IPCC (2014) found that between 2000 and 2010 the contribution of population growth to GHG emissions remained the same compared to the 30 years before (around 3 GtCO2 per year), whereas the contribution of economic growth has increased rapidly (around 3 GtCO2 per year between 1970-2000, to 6 GtCO2 per year in the period 2000-2010) (IPCC, 2014).

16 As for the limitations of this research: only territorial CO2 emissions have been used, while consumption-based emissions, used in the research of Hickel (2020) may be more accurate when accounting responsibility to regions and countries. Also, data on other greenhouse gasses or CO2 equivalents would add more depth to an analysis like this. For further research, it would be valuable to investigate what alternatives exist for the economic growth that is now causing environmental damage – such as the ‘Doughnut Economics’ proposed by economist Kate Raworth (Raworth, 2017) or degrowth, a theory that criticizes contemporary capitalism and advocates for a post-growth economy that can exist within the planetary boundaries (Van den Bergh and Kallis, 2012).

17 5. Conclusion

The main findings are that the share in population and CO2 emissions are not equal around the world. Even though a bigger population size will demand more natural resources, per capita emissions differ massively around the world. This means that not every person is equally responsible for the emissions of greenhouse gasses and eventually global warming. Economically wealthier regions emit more CO2 respectively to their population size, and economically poorer regions emit less. The six biggest emitters, China, USA, EU-27, Russia, Japan and India, are responsible for 70,1% of the territorial CO2 emissions of 2019. Thus, rather than population growth, it is predominately economic growth that causes higher CO2 emissions.

The Global North is mainly responsible for the high CO2 emissions, both currently in comparison to their population sizes as well as historically. This industrialised region has caused most of the climate crisis, and must therefore take on the burden to prevent this crisis from further damaging the environment. Since the results of this research indicate that economic growth is causing high emissions, it is not the world population that must be “stabilized – and, ideally, gradually reduced” like Ripple (2019) stated, but economic growth. Instead of exponential economic growth, an economy based on the ‘Doughnut Economics’ proposed by economist Kate Raworth or degrowth could be the new standard.

In conclusion, the relationship between population and CO2 emissions is rather weak. Economic growth is a more determining factor to higher emissions, and therefore, population reduction, with all the controversies and implications, is not a preferable measurement to combat climate change. Rather, measurements aimed at overconsumption – driven by contemporary capitalism are probably more effective and ethical.

18 6. Acknowledgements

I would like to thank my supervisor John van Boxel for his ongoing support and guidance during this project. I am grateful that I got the opportunity and the freedom to explore this topic. I would also like to thank my mentor Donya Danesh for her guidance and kind and encouraging words throughout this process.

I would also like to thank my roommates Nathalie, Nienke and Vera. I moved in with you when I started with the BSc thesis, in the beginning of March, and you have helped me throughout this bachelor project. You listened to all my ideas, gave me feedback, cooked for me when I had deadlines and gave me confidence when I needed it.

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22

8. Appendices

Appendix 1

Table 1: Natural population growth, only accounting births and deaths and excluding migration flows

Source: Data compiled by Our World in Data (https://ourworldindata.org/world-population-growth)

Based upon data by: United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects: The 2019 Revision, DVD Edition.

(https://population.un.org/wpp2019/Download/Standard/Interpolated/)

Region GN/GS Natural population growth

Africa GS 2,47% Latin America GS 0,94% Asia GS 0,88% Australia GN 0,59% New Zealand GN 0,53% USA GN 0,30% Canada GN 0,24% Europe GN -0,10% Global North 0,22% Global South 1,54%

23 Appendix 2

Figure 4: Share in population, territorial CO2 emissions and GDP in 2019 by region.

Source:

CO2 emissions data is sourced from the Global Carbon Project:

Global Carbon Project. (2020). Supplemental data of Global Carbon Budget 2020 (Version 1.0) [Data set]. Global Carbon Project.

https://doi.org/10.18160/gcp-2020

Population data is sourced from CIA World Factbook:

https://www.cia.gov/the-world-factbook/field/population/country-comparison

GDP data is sourced from CIA World Factbook:

https://www.cia.gov/the-world-factbook/field/real-gdp-per-capita/country-comparison

Region Population CO2 emissions GDP

China 1433784064 10174681100 14,34 USA 329060000 5284696657 21,433 EU-27 446824564 2916906140 15,592 India 1366418048 2616448820 2,87 Russia 145872000 1678366791 1,689 Japan 126860000 1106664426 5,148

Rest of the world (GN)

419355444 4174074625 12,77

Rest of the world (GS)

3144935372 5957746468 14,034

Region Population CO2 emissions GDP

China 19,3% 30,0% 16,3% USA 4,4% 15,6% 24,4% EU-27 6,0% 8,6% 17,7% India 18,4% 7,7% 3,3% Russia 2,0% 4,9% 1,9% Japan 1,7% 3,3% 5,9%

Rest of the world (GN)

5,7% 12,3% 14,5%

Rest of the world (GS)

24 Appendix 3

Figure 5: Share in population and territorial CO2 emissions by economy in

Source:

Population data is sourced from CIA World Factbook:

https://www.cia.gov/the-world-factbook/field/population/country-comparison

Economies data is sourced from the World Bank:

https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

Country Economy CO2 emissions 2019

Afghanistan Low income (≤ $1,035) 10720332

Albania Upper-middle income ($4,046 - $12,535)

5579015 Algeria Low-middle income ($1,036 - $4,045) 171707041

Andorra High income (≥$12,536) 470495

Angola Low-middle income ($1,036 - $4,045) 38020297 Antigua and Barbuda High income (≥$12,536) 492341 Argentina Upper-middle income ($4,046 -

$12,535)

178939546 Armenia Upper-middle income ($4,046 -

$12,535)

6005927

Aruba High income (≥$12,536) 918546

Australia High income (≥$12,536) 411015667

Austria High income (≥$12,536) 68495143

Azerbaijan Upper-middle income ($4,046 - $12,535)

39820063

Bahamas, The High income (≥$12,536) 1979149

Bahrain High income (≥$12,536) 34354328

Bangladesh Low-middle income ($1,036 - $4,045) 102161676

Barbados High income (≥$12,536) 1186602

Belarus Upper-middle income ($4,046 - $12,535)

62483877

Belgium High income (≥$12,536) 99708878

Belize Upper-middle income ($4,046 - $12,535)

632797 Benin Low-middle income ($1,036 - $4,045) 7998145

Bermuda High income (≥$12,536) 631960

Bhutan Low-middle income ($1,036 - $4,045) 1706825 Bolivia Low-middle income ($1,036 - $4,045) 22574851 Bosnia and Herzegovina Upper-middle income ($4,046 -

$12,535)

26621092 Botswana Upper-middle income ($4,046 -

$12,535)

25 Brazil Upper-middle income ($4,046 -

$12,535)

465715770

Brunei High income (≥$12,536) 9088688

Bulgaria Upper-middle income ($4,046 - $12,535)

42006473

Burkina Faso Low income (≤ $1,035) 4301291

Burundi Low income (≤ $1,035) 579786

Cambodia Low-middle income ($1,036 - $4,045) 16026972 Cameroon Low-middle income ($1,036 - $4,045) 7592750

Canada High income (≥$12,536) 576650511

Cayman Islands High income (≥$12,536) 633890

Central African Republic Low income (≤ $1,035) 307653

Chad Low income (≤ $1,035) 1030268

Chile High income (≥$12,536) 84266622

China Upper-middle income ($4,046 -

$12,535)

1,0175E+10 Colombia Upper-middle income ($4,046 -

$12,535)

102202444 Comoros Low-middle income ($1,036 - $4,045) 253114 Congo Low-middle income ($1,036 - $4,045) 3456980 Costa Rica Upper-middle income ($4,046 -

$12,535)

8507521

Croatia High income (≥$12,536) 17882170

Cuba Upper-middle income ($4,046 -

$12,535)

25988450

Cyprus High income (≥$12,536) 7315731

Czech Republic High income (≥$12,536) 101009765 Democratic Republic of

Congo

Low income (≤ $1,035) 2282718

Denmark High income (≥$12,536) 32075494

Djibouti Low-middle income ($1,036 - $4,045) 399373 Dominica Upper-middle income ($4,046 -

$12,535)

161664 Dominican Republic Upper-middle income ($4,046 -

$12,535)

27378802 Ecuador Upper-middle income ($4,046 -

$12,535)

40540282 Egypt Low-middle income ($1,036 - $4,045) 246642918 El Salvador Low-middle income ($1,036 - $4,045) 6207296 Equatorial Guinea Upper-middle income ($4,046 -

$12,535)

5633778

Eritrea Low income (≤ $1,035) 727233

Estonia High income (≥$12,536) 13888373

Eswatini Low-middle income ($1,036 - $4,045) 973848

Ethiopia Low income (≤ $1,035) 16.255.140

Faroe Islands High income (≥$12,536) 715004

Fiji Upper-middle income ($4,046 -

$12,535)

26

Finland High income (≥$12,536) 41652603

France High income (≥$12,536) 323747139

French Polynesia High income (≥$12,536) 831677

Gabon Upper-middle income ($4,046 -

$12,535)

4704935

Gambia, The Low income (≤ $1,035) 585713

Georgia Upper-middle income ($4,046 - $12,535)

10286677

Germany High income (≥$12,536) 701955108

Ghana Low-middle income ($1,036 - $4,045) 14959912

Greece High income (≥$12,536) 67183971

Greenland High income (≥$12,536) 518656

Grenada Upper-middle income ($4,046 - $12,535)

287951 Guatemala Upper-middle income ($4,046 -

$12,535)

20513439

Guinea Low income (≤ $1,035) 3153404

Guinea-Bissau Low income (≤ $1,035) 320957

Guyana Upper-middle income ($4,046 - $12,535)

2390398

Haiti Low income (≤ $1,035) 3280850

Honduras Low-middle income ($1,036 - $4,045) 10928412

Hong Kong High income (≥$12,536) 41536352

Hungary High income (≥$12,536) 49101007

Iceland High income (≥$12,536) 3321657

India Low-middle income ($1,036 - $4,045) 2616448820 Indonesia Upper-middle income ($4,046 -

$12,535)

617512648

Iran Upper-middle income ($4,046 -

$12,535)

779526533

Iraq Upper-middle income ($4,046 -

$12,535)

221383949

Ireland High income (≥$12,536) 37117688

Israel High income (≥$12,536) 64.172.118

Italy High income (≥$12,536) 337086213

Jamaica Upper-middle income ($4,046 - $12,535)

8014192

Japan High income (≥$12,536) 1106664426

Jordan Upper-middle income ($4,046 - $12,535)

26071583 Kazakhstan Upper-middle income ($4,046 -

$12,535)

313797848 Kenya Low-middle income ($1,036 - $4,045) 17315266 Kiribati Low-middle income ($1,036 - $4,045) 73383

Kuwait High income (≥$12,536) 107532047

Kyrgyzstan Low-middle income ($1,036 - $4,045) 11482589 Laos Low-middle income ($1,036 - $4,045) 32.814.784

27 Lebanon Upper-middle income ($4,046 -

$12,535)

28202071 Lesotho Low-middle income ($1,036 - $4,045) 2223231

Liberia Low income (≤ $1,035) 1321680

Libya Upper-middle income ($4,046 -

$12,535)

46427779

Liechtenstein High income (≥$12,536) 145802

Lithuania High income (≥$12,536) 13483413

Luxembourg High income (≥$12,536) 9784853

Macao High income (≥$12,536) 2.064.662

Madagascar Low income (≤ $1,035) 4014831

Malawi Low income (≤ $1,035) 1466344

Malaysia Upper-middle income ($4,046 - $12,535)

250094688 Maldives Upper-middle income ($4,046 -

$12,535)

1667430

Mali Low income (≤ $1,035) 3394412

Malta High income (≥$12,536) 1553776

Marshall Islands Upper-middle income ($4,046 - $12,535)

163074 Mauritania Low-middle income ($1,036 - $4,045) 4.092.203

Mauritius High income (≥$12,536) 4686810

Mexico Upper-middle income ($4,046 - $12,535)

438497623 Moldova Low-middle income ($1,036 - $4,045) 5957810 Mongolia Low-middle income ($1,036 - $4,045) 65.513.698 Montenegro Upper-middle income ($4,046 -

$12,535)

2461441 Morocco Low-middle income ($1,036 - $4,045) 71928924

Mozambique Low income (≤ $1,035) 8705807

Myanmar Low-middle income ($1,036 - $4,045) 26231575 Namibia Upper-middle income ($4,046 -

$12,535)

4.168.008

Nauru High income (≥$12,536) 52999

Nepal Low-middle income ($1,036 - $4,045) 13913522

Netherlands High income (≥$12,536) 154826594

New Caledonia High income (≥$12,536) 8451602

New Zealand High income (≥$12,536) 36540963

Nicaragua Low-middle income ($1,036 - $4,045) 5548625

Niger Low income (≤ $1,035) 2135276

Nigeria Low-middle income ($1,036 - $4,045) 140026463

North Korea High income (≥$12,536) 38762163

North Macedonia Upper-middle income ($4,046 - $12,535)

8.041.363

Norway High income (≥$12,536) 42440799

Oman High income (≥$12,536) 71684606

28

Panama High income (≥$12,536) 12503103

Papua New Guinea Low-middle income ($1,036 - $4,045) 7086853 Paraguay Upper-middle income ($4,046 -

$12,535)

8272283 Peru Upper-middle income ($4,046 -

$12,535)

54533175 Philippines Low-middle income ($1,036 - $4,045) 144262785

Poland High income (≥$12,536) 322626493

Portugal High income (≥$12,536) 48597835

Qatar High income (≥$12,536) 109344710

Romania High income (≥$12,536) 75084050

Russian Federation Upper-middle income ($4,046 - $12,535)

1678366791

Rwanda Low income (≤ $1,035) 1110913

Samoa Upper-middle income ($4,046 -

$12,535)

285379 São Tomé and Principe Low-middle income ($1,036 - $4,045) 129138

Saudi Arabia High income (≥$12,536) 582149599

Senegal Low-middle income ($1,036 - $4,045) 9822725 Serbia Upper-middle income ($4,046 -

$12,535)

54666675

Seychelles High income (≥$12,536) 623050

Sierra Leone Low income (≤ $1,035) 1.027.063

Singapore High income (≥$12,536) 38944802

Sint Maarten (Dutch part) High income (≥$12,536) 753208 Slovak Republic High income (≥$12,536) 33314769

Slovenia High income (≥$12,536) 13696428

Solomon Islands Low-middle income ($1,036 - $4,045) 317994

Somalia Low income (≤ $1,035) 677567

South Africa Upper-middle income ($4,046 - $12,535)

478608101

South Korea Low income (≤ $1,035) 611263215

South Sudan Low income (≤ $1,035) 1584884

Spain High income (≥$12,536) 252683217

Sri Lanka Low-middle income ($1,036 - $4,045) 24841152

Sudan Low income (≤ $1,035) 22980726

Suriname Upper-middle income ($4,046 - $12,535)

2606035

Sweden High income (≥$12,536) 42766618

Switzerland High income (≥$12,536) 37681506

Syria Low income (≤ $1,035) 26960685

Taiwan High income (≥$12,536) 262639349

Tajikistan Low income (≤ $1,035) 8979885

Tanzania Low-middle income ($1,036 - $4,045) 11626306 Thailand Upper-middle income ($4,046 -

$12,535)

29 Timor-Leste Low-middle income ($1,036 - $4,045) 554451

Togo Low income (≤ $1,035) 3261434

Tonga Upper-middle income ($4,046 -

$12,535)

175304 Trinidad and Tobago High income (≥$12,536) 37863888 Tunisia Low-middle income ($1,036 - $4,045) 31012921 Turkey Upper-middle income ($4,046 -

$12,535)

405126365 Turkmenistan Upper-middle income ($4,046 -

$12,535)

85646506 Turks and Caicos Islands High income (≥$12,536) 231474 Tuvalu Upper-middle income ($4,046 -

$12,535)

12231

Uganda Low income (≤ $1,035) 5531265

Ukraine Low-middle income ($1,036 - $4,045) 223229393 United Arab Emirates High income (≥$12,536) 190683060 United Kingdom High income (≥$12,536) 369878396

United States High income (≥$12,536) 5284696657

Uruguay High income (≥$12,536) 6378115

Uzbekistan Low-middle income ($1,036 - $4,045) 110245989 Vanuatu Low-middle income ($1,036 - $4,045) 154920 Venezuela Upper-middle income ($4,046 -

$12,535)

116687785 Vietnam Low-middle income ($1,036 - $4,045) 247708911

Yemen Low income (≤ $1,035) 10255048

Zambia Low-middle income ($1,036 - $4,045) 6720490 Zimbabwe Low-middle income ($1,036 - $4,045) 10374287

Economy Sum of emissions by

economy High income (≥$12,536) 12461081726 Low income (≤ $1,035) 758216380 Low-middle income ($1,036 - $4,045) 4742113556 Upper-middle income ($4,046 - $12,535) 17178952130

Economy Sum of population by

economy High income (≥$12,536) 1204903392 Low income (≤ $1,035) 694014000 Low-middle income ($1,036 - $4,045) 2882174040 Upper-middle income ($4,046 - $12,535) 2894317048

30

Economy Population CO2

emissions Low income (≤ $1,035) 9% 2% Low-middle income ($1,036 - $4,045) 38% 13% Upper-middle income ($4,046 - $12,535) 38% 49% High income (≥$12,536) 16% 35%

31 Appendix 3

Figure 6: Per capita CO2 emissions by region 1900-2019 in tonnes.

Source: Data compiled by Our World in Data

CO2 emissions data is sourced from the Global Carbon Project:

Global Carbon Project. (2020). Supplemental data of Global Carbon Budget 2020 (Version 1.0) [Data set]. Global Carbon Project.

https://doi.org/10.18160/gcp-2020

Africa Asia Europe South America North America Oceania World 1900 0,02 0,05 3,00 0,08 6,46 2,06 1,18 1901 0,03 0,06 2,95 0,15 6,92 2,28 1,22 1902 0,05 0,06 2,93 0,17 7,22 2,29 1,24 1903 0,06 0,06 3,02 0,17 8,27 2,26 1,34 1904 0,07 0,07 3,05 0,19 8,05 2,32 1,35 1905 0,08 0,07 3,11 0,20 8,82 2,38 1,43 1906 0,09 0,09 3,19 0,26 9,09 2,74 1,49 1907 0,10 0,10 3,50 0,27 10,41 2,90 1,68 1908 0,10 0,11 3,52 0,31 9,01 3,05 1,61 1909 0,12 0,11 3,50 0,27 9,74 2,70 1,66 1910 0,13 0,11 3,51 0,34 10,46 3,14 1,73 1911 0,13 0,13 3,56 0,37 10,28 3,28 1,75 1912 0,13 0,12 3,67 0,38 10,82 3,44 1,83 1913 0,14 0,13 3,94 0,39 11,50 3,49 1,97 1914 0,14 0,13 3,49 0,31 10,30 3,81 1,78 1915 0,14 0,13 3,30 0,27 10,49 3,41 1,74 1916 0,17 0,14 3,44 0,24 11,57 3,10 1,87 1917 0,18 0,15 3,38 0,19 12,64 3,22 1,94 1918 0,17 0,15 3,09 0,19 13,11 3,44 1,90 1919 0,18 0,16 2,63 0,21 11,10 3,17 1,64 1920 0,19 0,15 3,04 0,24 13,05 3,50 1,89 1921 0,19 0,15 2,75 0,23 10,80 3,12 1,64 1922 0,16 0,16 3,04 0,27 10,61 3,21 1,71 1923 0,19 0,17 2,88 0,30 13,52 3,24 1,92 1924 0,20 0,18 3,29 0,36 11,90 3,45 1,91 1925 0,21 0,19 3,24 0,39 11,92 3,52 1,90 1926 0,22 0,19 2,75 0,39 12,65 3,44 1,84 1927 0,22 0,20 3,48 0,42 12,17 3,52 1,99 1928 0,22 0,21 3,47 0,43 11,79 3,21 1,96 1929 0,22 0,22 3,74 0,47 12,45 2,92 2,08 1930 0,21 0,22 3,50 0,37 10,98 2,72 1,89 1931 0,18 0,21 3,24 0,33 9,21 2,38 1,67 1932 0,16 0,21 2,99 0,37 7,78 2,37 1,49 1933 0,17 0,23 3,06 0,41 8,23 2,48 1,55 1934 0,19 0,25 3,32 0,46 8,73 2,61 1,66

32 1935 0,21 0,27 3,46 0,50 8,98 2,85 1,73 1936 0,22 0,30 3,65 0,47 10,17 3,01 1,88 1937 0,23 0,31 3,93 0,59 10,58 3,18 1,99 1938 0,24 0,32 3,92 0,53 8,93 3,06 1,85 1939 0,25 0,34 3,99 0,53 9,72 3,43 1,94 1940 0,26 0,38 4,22 0,56 10,79 3,14 2,11 1941 0,28 0,38 4,05 0,55 11,59 3,64 2,14 1942 0,30 0,34 3,77 0,51 12,28 3,80 2,12 1943 0,30 0,33 3,78 0,51 12,52 3,66 2,15 1944 0,33 0,34 3,56 0,55 13,22 3,56 2,17 1945 0,33 0,25 2,32 0,54 12,57 3,40 1,79 1946 0,33 0,24 3,11 0,51 11,92 3,58 1,94 1947 0,32 0,28 3,55 0,59 12,85 3,74 2,13 1948 0,33 0,28 3,79 0,65 13,13 3,71 2,21 1949 0,35 0,34 4,10 0,64 10,76 3,58 2,12 1950 0,42 0,34 4,32 0,99 12,04 4,96 2,36 1951 0,44 0,32 4,73 1,12 12,24 5,15 2,47 1952 0,47 0,35 4,83 1,18 11,73 5,24 2,46 1953 0,46 0,37 4,92 1,14 11,76 5,01 2,48 1954 0,48 0,40 5,18 1,19 11,05 5,56 2,49 1955 0,51 0,44 5,58 1,31 11,82 5,69 2,68 1956 0,52 0,48 5,89 1,38 12,19 5,78 2,81 1957 0,54 0,53 6,08 1,41 11,88 5,80 2,85 1958 0,55 0,70 6,09 1,27 11,35 5,85 2,88 1959 0,54 0,83 6,16 1,36 11,45 6,11 2,97 1960 0,56 0,90 6,48 1,34 11,50 6,36 3,08 1961 0,56 0,80 6,63 1,32 11,27 6,40 3,03 1962 0,57 0,76 6,93 1,37 11,49 6,48 3,07 1963 0,59 0,79 7,35 1,36 11,74 6,78 3,19 1964 0,63 0,81 7,61 1,39 12,15 7,20 3,29 1965 0,67 0,87 7,79 1,41 12,45 7,77 3,37 1966 0,68 0,92 7,96 1,45 12,86 7,59 3,46 1967 0,71 0,92 8,08 1,50 13,21 7,89 3,50 1968 0,75 0,99 8,40 1,57 13,53 8,00 3,61 1969 0,79 1,09 8,81 1,66 13,98 8,25 3,78 1970 0,84 1,25 9,20 1,76 14,89 8,40 4,01 1971 0,90 1,34 9,47 1,76 14,84 8,55 4,09 1972 0,93 1,39 9,75 1,80 15,36 8,67 4,19 1973 0,97 1,45 10,14 1,91 15,81 9,32 4,33 1974 0,97 1,45 10,11 1,98 15,14 9,28 4,23 1975 0,94 1,49 10,09 1,91 14,45 9,27 4,14 1976 0,99 1,55 10,61 1,91 14,91 9,10 4,28 1977 1,00 1,62 10,62 1,93 15,12 9,65 4,32 1978 1,04 1,66 10,91 2,00 15,42 10,04 4,40 1979 1,09 1,70 11,15 2,10 15,37 9,99 4,44 1980 1,13 1,66 11,10 2,11 14,77 10,60 4,34 1981 1,13 1,62 10,65 1,99 14,07 10,81 4,15

33 1982 1,15 1,64 10,52 1,95 13,30 10,86 4,05 1983 1,16 1,68 10,50 1,89 13,12 10,29 4,02 1984 1,23 1,74 10,52 1,87 13,33 10,65 4,06 1985 1,23 1,81 10,93 1,87 13,22 10,79 4,13 1986 1,24 1,84 10,91 1,94 13,02 10,60 4,11 1987 1,20 1,88 10,89 2,00 13,43 11,11 4,17 1988 1,23 1,98 10,92 2,04 13,81 11,17 4,25 1989 1,15 2,01 10,84 2,00 13,94 11,62 4,24 1990 1,06 2,04 11,11 1,97 14,08 11,48 4,26 1991 1,07 2,10 10,67 1,96 13,79 11,38 4,28 1992 1,02 2,14 9,78 1,96 13,89 11,46 4,08 1993 1,05 2,21 9,36 2,04 13,95 11,44 4,06 1994 1,04 2,27 8,87 2,06 14,06 11,46 4,03 1995 1,09 2,33 8,84 2,13 14,02 11,70 4,06 1996 1,07 2,39 8,90 2,19 14,33 11,83 4,13 1997 1,09 2,39 8,62 2,28 14,38 12,02 4,10 1998 1,10 2,28 8,60 2,40 14,36 12,28 4,03 1999 1,06 2,32 8,48 2,41 14,39 12,48 4,03 2000 1,11 2,40 8,48 2,32 14,63 12,55 4,09 2001 1,08 2,42 8,66 2,32 14,29 12,71 4,07 2002 1,06 2,52 8,60 2,31 14,24 12,72 4,11 2003 1,12 2,72 8,80 2,29 14,29 12,86 4,26 2004 1,17 2,94 8,83 2,29 14,36 13,09 4,41 2005 1,16 3,11 8,80 2,37 14,30 13,04 4,50 2006 1,18 3,28 8,92 2,36 14,02 12,99 4,59 2007 1,19 3,42 8,83 2,38 14,07 12,99 4,67 2008 1,22 3,57 8,72 2,54 13,53 12,86 4,71 2009 1,20 3,67 8,00 2,47 12,46 12,64 4,58 2010 1,19 3,88 8,30 2,68 12,73 12,38 4,76 2011 1,18 4,12 8,17 2,69 12,41 12,13 4,86 2012 1,16 4,24 8,08 2,82 11,95 12,04 4,88 2013 1,14 4,24 7,86 2,86 12,11 11,63 4,85 2014 1,20 4,27 7,54 2,92 12,07 11,42 4,83 2015 1,13 4,26 7,53 2,80 11,71 11,47 4,77 2016 1,12 4,24 7,51 2,76 11,38 11,52 4,72 2017 1,13 4,29 7,54 2,69 11,19 11,50 4,73 2018 1,13 4,37 7,50 2,59 11,40 11,42 4,77 2019 1,12 4,40 7,28 2,54 11,04 11,21 4,72

34 Appendix 4

Figure 1: Cumulative territorial CO2 emissions 1900-2019 by region

Source: Data compiled by Our World in Data

CO2 emissions data is sourced from the Global Carbon Project:

Global Carbon Project. (2020). Supplemental data of Global Carbon Budget 2020 (Version 1.0) [Data set]. Global Carbon Project.

https://doi.org/10.18160/gcp-2020

The full CO2 emissions dataset from the Global Carbon Project has been provided by Dr Robbie Andrew:

https://folk.universitetetioslo.no/roberan/GCB2020.shtml

Country Cumulative CO2 emissions 1900-2019

(tCO2) Afghanistan 3564266175 Albania 8967302516 Algeria 91353034630 Andorra 220909154 Angola 9512023171 Anguilla 42459998

Antigua and Barbuda 616110177

Argentina 2,55E+11 Armenia 28807791327 Aruba 2281933008 Australia 5,48E+11 Austria 2,91E+11 Azerbaijan 92239998369 Bahamas 5569827765 Bahrain 19451500105 Bangladesh 22345809249 Barbados 1282833662 Belarus 1,95E+11 Belgium 8,44E+16 Belize 401863413 Benin 1443463737 Bermuda 768871001 Bhutan 187681564 Bolivia 9736655292

Bonaire Sint Eustatius and Saba 1322081528

Bosnia and Herzegovina 26984643847

Botswana 2210348110

Brazil 3,47E+11

British Virgin Islands 92497178

Brunei 10340325578

35 Burkina Faso 865581096 Burundi 258687099 Cambodia 1726362351 Cameroon 4219699485 Canada 1,19E+12 Cape Verde 219603278

Central African Republic 301576449

Chad 460312635 Chile 76174995316 China 3,83E+12 Colombia 87541525962 Comoros 86412952 Congo 1547112891 Cook Islands 40438935 Costa Rica 5020933548 Croatia 31469086843 Cuba 49037949088 Cyprus 6378834828 Czechia 5,76E+11

Democratic Republic of Congo 7269296442

Denmark 1,73E+11 Djibouti 458060347 Dominica 87198577 Dominican Republic 14368540572 Ecuador 24436564570 Egypt 1,20E+11 El Salvador 4875368145 Equatorial Guinea 1403353330 Eritrea 233634304 Estonia 54869629938 Eswatini 852843861 Ethiopia 4020217187 Faeroe Islands 749651526 Fiji 1241447584 Finland 93763563380 France 2,06E+12 French Polynesia 606443646 Gabon 6735678680 Gambia 257752512 Georgia 42720341391 Germany 5,01E+12 Ghana 7021877245 Greece 99544740677 Greenland 879656351 Grenada 152344217 Guatemala 8450928263 Guinea 1835344239

36 Guinea-Bissau 227127892 Guyana 2829676495 Haiti 1517401137 Honduras 4632048148 Hong Kong 34309490237 Hungary 2,15E+11 Iceland 4028013971 India 9,82E+11 Indonesia 2,64E+11 Iran 3,99E+11 Iraq 92519312091 Ireland 72102486533 Israel 51232876308 Italy 7,81E+11 Jamaica 12034827766 Japan 1,92E+12 Jordan 12110650537 Kazakhstan 4,10E+11 Kenya 10341912074 Kiribati 46575396 Kosovo 625918471 Kuwait 60157496165 Kyrgyzstan 33272933750 Laos 1134132104 Latvia 28126442831 Lebanon 14871586964 Lesotho 860771689 Liberia 1641792921 Libya 46057030054 Liechtenstein 97367956 Lithuania 52077740858 Luxembourg 26843008426 Macao 1117394716 Madagascar 2162749970 Malawi 1334191989 Malaysia 91132135988 Maldives 252656221 Mali 811919546 Malta 2491572882 Marshall Islands 42128884 Mauritania 1384233375 Mauritius 2021050319 Mexico 5,25E+11 Micronesia 49690643 Moldova 42897298104 Mongolia 11635913377 Montenegro 2827149681

37 Montserrat 34688911 Morocco 33725609577 Mozambique 5009673840 Myanmar 14815323657 Namibia 860027587 Nauru 146140906 Nepal 1800175226 Netherlands 4,64E+11 New Caledonia 3663747964 New Zealand 65139778674 Nicaragua 3911386583 Niger 879287992 Nigeria 84154239104 Niue 6047076

North Korea 1,74E+11

North Macedonia 20182203255 Norway 98054880502 Oman 16582759791 Pakistan 90356690728 Palestine 683486552 Panama 6371361779

Papua New Guinea 3117680891

Paraguay 3134202813 Peru 56292786351 Philippines 68846918265 Poland 1,23E+12 Portugal 73882129785 Qatar 31606328627 Romania 3,14E+11 Russia 3,85E+12 Rwanda 588489856 Saint Helena 6467645

Saint Kitts and Nevis 93395756

Saint Lucia 237965717

Saint Pierre and Miquelon 112229446

Saint Vincent and the Grenadines 123811532

Samoa 149491570

Sao Tome and Principe 65355100

Saudi Arabia 2,72E+11

Senegal 4524827688

Serbia 81935179843

Seychelles 264696458

Sierra Leone 1080588655

Singapore 49823589303

Sint Maarten (Dutch part) 2955534799

Slovakia 1,85E+11

38

Solomon Islands 202189459

Somalia 859651523

South Africa 5,93E+11

South Korea 3,09E+11

South Sudan 750941668 Spain 4,43E+11 Sri Lanka 9795263680 Sudan 8288371619 Suriname 3128529992 Sweden 2,28E+11 Switzerland 1,21E+11 Syria 40673724445 Taiwan 1,66E+11 Tajikistan 15997817310 Tanzania 4655875184 Thailand 1,17E+11 Timor 41565871 Togo 1267120816 Tonga 88059338

Trinidad and Tobago 43378725426

Tunisia 17945762223

Turkey 1,99E+11

Turkmenistan 75865843600

Turks and Caicos Islands 39838384

Tuvalu 3915366

Uganda 1994770842

Ukraine 1,11E+12

United Arab Emirates 76564574455

United Kingdom 5,46E+12

United States 1,80E+13

Uruguay 11837458387

Uzbekistan 2,05E+11

Vanuatu 109394784

Venezuela 2,25E+11

Vietnam 64590333661

Wallis and Futuna Islands 4790350

Yemen 13594213610

Zambia 8513396677