The contribution of demographics to global warming

n°294 – december 2023

Summary

According to the latest IPCC reports, world population growth over the 2010-2019 period was second only to per capita GDP growth as the most important driver of the increase in CO2 emissions from fossil fuel combustion. Yet the contribution of demographics to greenhouse gas emissions is not mentioned in the “summaries for decision-makers” of the latest IPCC assessment report, even though the body of the report provides interesting information on this subject.

Some work on the correlation between population trends and CO2 emissions of “fossil” origin suggests that, in many countries, the contribution of demographics to these emissions is greater than that resulting from Kaya’s identity. Population growth has a major influence on the growth of emissions associated with land-use change and deforestation in many low-income countries, where economic growth per capita has often remained very low or even non-existent.

Policies and measures likely to influence demographics with a view to reducing emissions have never been assessed by the IPCC, even though they could make a substantial contribution to mitigating global warming, particularly in many developing countries where emissions associated with deforestation are strongly correlated with population growth. The IPCC currently takes demographic change into account in its projections as an exogenous variable, which makes it difficult to test policies that address demographics.

In view of the considerable impact of population on nature and climate, and consequently on the degradation of most people’s lives, it is important and urgent for the socio-economic community and the IPCC to look into the impact of population policies and measures on climate.

Authors

Gillet Marc

Consultant in meteorology and climate, Ingénieur général des Ponts, des Eaux et de Forêts, was IPCC Focal Point for France, Director of the Observatoire national des effets du réchauffement climatique and Director of International Relations at Météo-France.

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Introduction

In the Summary for Policymakers of its contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, see IPCC, 2014), IPCC Working Group 3, whose focus is climate change mitigation, stated that “at the global level, economic and population growth continue to be the most important drivers of increases in CO2 emissions from fossil fuel combustion”. This statement is not reproduced in the Summary for Policymakers of Group 3’s contribution to the Sixth Assessment Report (IPCC, 2022). However, it does appear in the technical summary and in the body of this report, in the following form: “Globally, growth in gross domestic product (GDP) per capita and population growth have remained the most important drivers of CO2 emissions from fossil fuel combustion over the past decade”, with this result presented with a high degree of confidence. Despite this, the drivers of changes in greenhouse gas (GHG) emissions – economic and population growth – are not even mentioned in the synthesis report of the sixth assessment cycle (IPCC, 2023).

Here we recall the main conclusions reached by the IPCC in its last two reports on the subject of the contribution of demographics to global warming. First, we present the method used to estimate this contribution, based on the Kaya equation, which applies to CO2 emissions from fossil fuels. However, some work based on regression methods produces higher contributions for this same type of emissions. We then examine CO2 emissions from land use and land use change, which cannot be assessed using the Kaya equation, and which are likely to be strongly influenced by demographic trends in a number of developing countries. Overall, it appears that the influence of demographics on GHG emissions could be even greater than estimated by the IPCC.

It should be remembered that the socio-economic modelling carried out by the IPCC uses demographics as an exogenous variable, which makes it difficult to estimate the effects on climate of policies and measures affecting demographics.

In order to provide an overview of the GHG emissions to be discussed, the following box recalls certain precautions to be taken when examining emissions data. Tab. 1 summarizes the figures we have been able to find in IPCC reports and recent publications for the main sources of emissions discussed in this article.

The figures available on GHG emissions are not always exhaustive or comparable, and can lead to confusion. Very often, they refer only to the CO2 produced by the combustion of fossil fuels (oil, gas, coal, etc.), as recorded by the International Energy Agency (IEA). GHGs from industrial processes other than combustion (cement, chemicals, steel, etc.) are often considered separately, as are gases from agriculture, forestry and other land uses (AFOLU). The IPCC (2023) estimated that, on average over the period 1990-2019, net CO2 emissions from AFOLU accounted for 22% of total GHG emissions, or around 5.9 GteqCO2/year, while only less than 0.8 tonnes were reported to the United Nations Framework Convention on Climate Change (UNFCCC). Furthermore, in the case of the so-called LULUCF (Land Use, Land Use Change and Forestry) sector, which is included in AFOLU, a distinction must be made between “emissions” and “net emissions”. The latter corresponds to emissions less the amount of carbon captured by vegetation. Net emissions from LULUCF are most often negative for countries in the Northern Hemisphere, and positive for countries between the tropics (Houghton and Nassikas, 2017). The IPCC estimates (IPCC, 2019) that gross emissions from AFOLU (one-third of global GHG emissions) are more indicative of the mitigation potential of deforestation reductions than net emissions (13% of global emissions), which include compensation from afforestation (high confidence). The AFOLU “net” CO2 flux is made up of two opposing “gross” fluxes: (i) gross emissions (20 GtCO2/year) due to deforestation, soil cultivation and oxidation of wood products, and (ii) gross removals (-14 GtCO2/year), largely from forest growth after timber harvesting and agricultural abandonment (medium confidence). Friedlingstein et al., 2022, obtain lower, but nonetheless high values: over the last ten years, global gross emissions due to deforestation would have averaged 6.6 GtCO2/year, of which 3.3 Gt would have been sequestered, with a net result of 3.3 GtCO2/year due to deforestation.

It is therefore very important to be sure of the precise meaning of the figures we are dealing with each time. The methods recommended by the IPCC for drawing up inventories are established by the Task Force on National Greenhouse Gas Inventories (see in particular IPCC, 2006). It should be noted that, as uncertainties are sometimes very large, especially in the case of AFOLU, some inconsistencies between assessments may remain.

Estimates of emissions from the main sources and sinks discussed in this article are given in Table 1.

Early work on the link between demographics and climate

The link between demographics and global warming, or more generally between demographics and environmental degradation, has been the subject of a number of landmark publications since Ehrlich and Holdren (1971). These include O’Neill et al (2001), who already offer a comprehensive overview of the issue, and Pont (2023).

A milestone in our understanding of the subject was the article by O’Neill et al. (2010). Using an elaborate socio-economic model, they estimate that a slowdown in population growth corresponding to the UN’s low scenario at the time (UNFPA, 2004) could deliver, depending on the mitigation scenario followed, between 16% and 29% of the cumulative reductions in CO2 emissions considered necessary between 2000 and 2050 to avoid dangerous climate change. Between 2000 and 2100, this cumulative reduction potential would be between 37 and 41%. They also conclude that while reducing population growth may not be the most effective mitigation measure, it could make a substantial contribution, particularly in the long term.

The role of demographics as seen by the IPCC

Although it does not appear in the Summary for Policymakers of its Sixth Assessment Report (IPCC, 2022), in Chapter 2 of this report, IPCC Group 3 assesses the role of population growth in increasing GHG emissions over the period 1990-2019. The last line of Table (c) in Fig. 1, taken from this report, shows that, on a global average annual basis and over this period, CO2 emissions excluding LULUCF grew by 1.1%, with positive contributions of 1.2% from population growth and 2.3% from growth in GDP per capita. Conversely, improvements in energy intensity would have contributed to a 2% reduction in global emissions, and a 0.3% decrease in carbon intensity. In view of these results, population growth would therefore account for 34% of the increase in emissions, with the remainder attributable to growth in GDP per capita.

These estimates are deduced from the so-called Kaya identity (Kaya, 1990). This identity applies only to CO2 emissions from fossil fuel combustion, noted here as F, and is written as :

F = P x (G/P) x (E/G) x (F/E) (1)

It expresses F as the product of four functions of time:

The number of inhabitants, denoted P.

Average per capita income, (G/P), where G stands for GDP. Note that there is no obligation to use GDP in this equation. Other development indices could be used, such as the Human Development Index (HDI), which takes into account access to health and knowledge.

Energy intensity, (E/G), where E represents the quantity of energy consumed. Energy intensity depends in particular on industrial techniques and fuel prices.

Carbon intensity, (F/E), corresponding to the CO2 emitted by the production of a unit of energy. This term can be reduced, for example, by developing nuclear and renewable energies, by saving energy, and by capturing and burying the carbon dioxide released by the combustion of fossil fuels.

It is possible to demonstrate that relative variations in emissions are the sum of relative variations in the four factors of the Kaya equation.

Kaya’s identity can be applied to any population for which sufficient statistical data are available (world, region, country, etc.). Globally (see Tab. 1), CO2 emissions from fossil fuels in 2019 were 38 Gt. This amount corresponds to 64% of total net (i.e. taking into account absorption by forestry) GHG emissions, estimated at 59 Gt in CO2 equivalent (IPCC, 2022). It is not possible to include emissions from AFOLU in Kaya’s formula, since energy intensity is meaningless in this case. Similarly, GHGs other than CO2 are not included. We will see that for many developing countries, GHG emissions from AFOLU are much higher than CO2 emissions from fossil fuel combustion. AFOLU emissions are predominant in Africa, South America and Southeast Asia (IPCC, 2022).

The previous assessment report by IPCC Group 3 (IPCC, 2014) provided details of the evolution over time of the influence of the 4 Kaya identity factors over the four decades prior to 2010. The results are shown in Fig. 2, and show a steady downward trend in the annual growth rate of the world population since 1963, when the rate [1] was 2.1%. However, Fig. 2 shows that the contribution of population growth to CO2 emissions from fossil fuel combustion has been stable since the 1970s.

The main criticism of the Kaya equation, particularly in IPCC reports, is that it uses averages applied to highly inhomogeneous situations. The IPCC states that it does not use this equation to deduce causal links (IPCC, 2000).

It should be noted that chapter 5.3.2.1 of the IPCC’s Fifth Assessment Report (IPCC, 2014) states that the literature presents contradictory results as to whether it is in rich or poor countries that population growth contributes most to the increase in GHG emissions. The report cites the work of Poumanyvong and Kaneko (2010), who estimate elasticities between population and fossil CO2 emissions at 1.12 for high-income countries, 1.23 for middle-income countries and 1.75 for low-income countries. Jorgenson and Clark (2010) find values of 1.65 for developed countries and 1.27 for developing countries. According to the results of these two studies, the Kaya identity, which corresponds to an elasticity of value 1 only, strongly underestimates the correlation between population growth and CO2 emissions growth. It should also be remembered that these estimates were limited to national CO2 emissions data from fossil fuels.

If AFOLU emissions and those of other GHGs were taken into account, the elasticities obtained could be even different, particularly in the case of countries subject to significant deforestation.

Highly contrasting demographic projections by continent

Demographic trends, both observed and projected, are highly contrasted between continents, countries and even within countries, particularly between urban and rural areas. A detailed table of world demographic prospects can be found in Pison, 2018. Demographic projections are closely linked to fertility projections. From this point of view, the demographic transition has been observed for several years in developed countries, and one surprise has been the faster-than-expected decline in fertility in Asia and Latin America, but not in Africa. Several factors may explain why the decline in fertility is currently slower in Africa than it was a few decades ago in Asia and Latin America.

One of the major demographic changes to come is the tremendous growth in Africa’s population, which, including North Africa, could more than quadruple in a century, from one billion in 2010 to 4.5 billion in 2100 according to the United Nations’ medium scenario as already mentioned (Fig.3).

Meeting the basic needs and demographic growth of Africa’s populations will imply strong growth in energy requirements, and could be a major source of GHG production in the future, as will the growth in emissions associated with changes in land use and deforestation (as will many other environmental pressures (water, biodiversity, etc.).

In the Climate Convention, these problems of divergent development are cited under the heading of “common but differentiated responsibilities”, a formula periodically recalled by developing countries, but one whose consequences remain poorly defined.

Table (c) in Fig. 1 shows a breakdown by 10 regions of the world of the factors involved in the Kaya identity applied to each of these regions. The list of countries making up these ten regions can be found in Annex 2 of the IPCC, 2022 report. The IPCC has a policy of not naming any countries, and has used this grouping throughout its sixth assessment period. This approach often masks very significant inhomogeneities within regions.

Note that this Table (c), included in Fig.1, concerns only CO2 emissions excluding AFOLU, whereas the adjacent illustration (b) shows the distribution of emissions of all GHGs according to the 10 regions defined by the IPCC. This figure highlights significant differences with regard to CO2 emissions from combustion. For example, in terms of total GHG emissions, Latin America accounts for 10% of global emissions, and Africa for 9%. It would therefore appear useful to take a closer look at the drivers of AFOLU emissions at a disaggregated level, particularly if we are interested in the influence of demographics on these emissions.

The case of poor countries faced with deforestation

The contribution of some poor, populous countries to climate change may be much higher than indicated by CO2 emissions from fossil fuel combustion alone. Without undertaking an exhaustive analysis, we present here some data relating to certain LDC (Least Developed Countries) countries. These examples suffice to highlight the current gaps in our understanding of the influence of demographic change on global warming. The list of LDCs maintained by the United Nations comprises 46 countries, including most of the countries of inter-tropical Africa. The total population of the LDCs is around 1 billion. The LDCs do not constitute a “region” for the IPCC, but are scattered mainly between the African and South Asian regions.

Fig. 3 shows that LDC emissions from LULUCF would be around six or seven times higher than their emissions from fossil fuel combustion [2]. In 2019, total GHG emissions from LDCs, excluding CO2 from LULUCF, were estimated at 3.3% of global emissions, or 1.95 Gt (IPCC, 2022). If we add the LULUCF CO2 of these countries, estimated here, assuming constant proportions from one country to another, at 50% of AFOLU emissions, which are of the order of 2 Gt, the total contribution of LDCs to global emissions would be at least 3 GteqCO2, or 5% of global emissions.

The IPCC specifies that the main AFOLU-related activities in LDCs are cultivation, subsistence livestock farming and the use of wood for cooking and heating (see IPCC, 2022, Box 5.23 and Fig.4 ). According to the FAO [3] , on the whole, the use of fuelwood (Fig.4) is growing at the same rate as the population, i.e. between 3 and 4 percent per year depending on the country (Amous, 2000). In the Democratic Republic of Congo, for example, charcoal made by charcoal-makers in the forests, usually near the towns that constitute their market, produces the bulk of the energy consumed in households. This is particularly the case in Kinshasa, the world’s most populous French-speaking city, where vehicles of all kinds, overloaded with bags of charcoal, are constantly seen on the roads heading for the city [4] . In addition, several emerging countries with large areas of forest, such as Indonesia and Brazil, also have very high levels of GHG emissions in the AFOLU sector, but these are linked more to the expansion of livestock farming and agriculture. However, the poor populations of these countries also make extensive use of wood as a fuel.

According to the IPCC (IPCC,2022, chapter 7), traditional fuelwood and charcoal continue to account for the largest share of total wood consumption in low-income countries (Barger et al., 2018). Regionally, the percentage of total harvested wood used as fuelwood varies from 90% in Africa, 62% in Asia, 50% in America to less than 20% in Europe, North America and Oceania.

This highlights the importance of the fight against deforestation and for reforestation, as promoted in particular by the REDD+ program [5], which can bring substantial emissions reductions through better forest management. And in the longer term, a smaller population using forest resources can only reinforce the benefits of such a program.

For many LDCs, and for other more advanced countries whose forests are under pressure, the preponderance of AFOLU emissions over those from buildings, industry and transport (Fig. 4) suggests that population growth could be the main contributor to emissions growth in these countries (IPCC, 2022). The three countries with the largest deforested areas between 1990 and 2020 are Brazil (356,287 km²), Indonesia (101,977 km²) and the Democratic Republic of Congo (94,495 km², which is an LDC). [6].

The activities responsible for deforestation vary between continents and countries. According to Tyukavina et al. (2018), in the case of the Congo Basin, forests are cleared mainly by manual means. This small-scale deforestation for agriculture is responsible for 84% of total forest loss between 2000 and 2014. Again according to these authors, in the Democratic Republic of Congo and Cameroon, this deforestation is strongly correlated with population growth.

The IPCC (2022) has estimated that AFOLU contributes 22% of net global emissions, half of which is CO2 from LULUCF, with deforestation predominating. It states that “Projections show that the mitigation potential on AFOLU, between 2020 and 2050 and at costs below 100 USD per tonne CO2e, is 8 to 14 Gteq CO2 per year (high confidence). 30 to 50% of this potential is achievable for less than 20 USD per tonne CO2e, and could be realized in the short term in most regions (high confidence)”. These costs are much lower than the costs of emission reductions in the energy sector.

These examples show that high population growth in certain forest regions of developing countries can have a significant influence on CO2 emissions, through deforestation and forest degradation. It is also to be feared that this high birth rate will have a negative impact on the standard of living of the populations concerned. Unfortunately, it does not seem certain that the per capita income of the least developed countries will improve in the near future. While extreme poverty is declining worldwide, the number of people living in destitution is currently on the rise in sub-Saharan Africa [7].

One may well wonder whether the very strong demographic growth currently observed in these regions does not play an important role in this economic stagnation.

Demographics in the IPCC climate projections

The results to be expected from policies and measures to reduce greenhouse gas emissions are assessed in a variety of ways. The most sophisticated methods are based on socio-economic modelling, using integrated assessment models (IAMs), described for example in Annex III of IPCC, 2022. Such models are developed and used by dozens of economic and social science research centers, which have defined consensual rules to ensure that their results are comparable. The IPCC has set up a special team on socio-economic scenarios, and the Integrated Assessment Models Consortium (IAMC) has been formed. Many renowned intergovernmental and academic bodies are members of this consortium, including the International Institute for Applied Systems Analysis (IIASA), the OECD, the World Bank, the European Commission’s Joint Research Centre, Cired and universities in various countries. The approach is transparent, since most IAMC member publications are readily available from documentation centers, on the Internet or on request from the authors. [8].

Despite this considerable investment in socio-economic research, the IPCC reports provide no information on the effect that policies aimed at reducing the birth rate might have on climate change. When we consult the lists of mitigation policies and measures examined by the IPCC, we look in vain for any action on demographics. In the framework of the IPCC scenarios, population is in fact taken into account as an “exogenous” variable, i.e. given a priori and not modifiable by the IAM models.

The IPCC is not the only intergovernmental group to remain silent on the possibilities of action through demographics. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has classified demography as one of the “indirect drivers” of natural degradation, the consequences of which are difficult to quantify (IPBES, 2019).

To all appearances, there is a political and scientific consensus not to discuss the possibilities of mitigating climate change through population control measures. No doubt there are enough areas of disagreement between rich and poor countries for some to hesitate to open a discussion on the rate of population reproduction. The United Nations recently declared (UNFPA, 2023a): “Instead of focusing on the rate at which human beings reproduce, leaders must ask themselves whether individuals, especially women, are free to make their own reproductive choices – a question that is still too often answered in the negative”, and “We will not succeed in making the countries that emit the most carbon responsible by blaming climate change on fertility rates”. The organization’s latest report (UNFPA, 2023b) is along the same lines. We won’t get into an argument here about the possible contradictions between the freedom of women today and the serious difficulties that could arise for future generations in a situation of overpopulation. As intergovernmental groups, the missions of the IPCC and IPBES are to shed scientific light on the questions posed by governments. No doubt they therefore avoid insisting on issues that governments do not wish to see debated.

The IPCC’s assumptions on “exogenous variables”, or “indirect drivers”, are clearly presented. In addition to population, these exogenous variables include GDP per capita (income), the energy intensity of GDP and the carbon intensity of energy (IPCC, 2014, Chapter 2, and IPCC, 2022, Annex 3). Their temporal evolutions are estimated by different methods, supposed to give results consistent with the five visions of the future of the IPCC (called Illustrative mitigation pathways). Each of these frameworks is based on a storyline, qualitatively describing the main characteristics of the anticipated evolution, and a quantitative analysis providing consistent numerical values of the indirect drivers.

The future evolution of population and GDP are then used as exogenous variables.

Demographic trends are therefore taken into account by the IPCC, but as background trends, which are not modified by the mitigation policies and measures tested by the IEMs. These trends were selected by consensus of experts, during extensive meetings organized by IIASA and the Wittgenstein Center for Global Demography and Human Capital. These demographic scenarios, detailed by country, fall within the range of UN estimates, with a world population of between 7 and 13 billion by 2100.

Each of these demographic scenarios is associated with one of five families of general evolution of socioeconomic conditions (or SSP for Shared Socioeconomic Pathway), which differ mainly in the degree of “sustainability” of development and the level of globalization of trade (O’Neill et al., 2017). Tab. 2 presents some general characteristics of the five frameworks that have been selected.

Each of these five scenario families is therefore based on a single demographic evolution curve (Fig. 6), intended to be consistent with the general socio-economic assumptions adopted. In short, the consensus of the experts who chose these scenarios was that low population growth should correspond to an advanced degree of globalization with rapidly enriching populations, or to a sustainable mode of development.

More detailed descriptions of the design of the IPCC scenarios can be found in Gillet, 2021. Schematically, in addition to the two “business-as-usual” scenarios, SSP2 and SSP4, we have two “virtuous” scenarios: SSP1, corresponding to the European vision of a sustainable future, and SSP5, corresponding to the North American vision of a future that makes full use of fossil fuels. The SSP3 scenario corresponds to a fragmented world, with little international trade, and is the one to which the IPCC has attributed the highest population growth and the highest GHG emissions (red curve in Fig. 6).

Assumptions about future income trends, derived from macroeconomic models such as those used by the OECD or the IMF, are also accepted by expert consensus as exogenous variables. For example, the most “optimistic” scenarios, SSP1 and SSP5, show exponential growth in global income, which could be questioned in a world of limited natural resources and political and economic uncertainties. The IPCC itself points out that, while the SSP scenarios facilitate the harmonization of work, they are not unique and do not explore the entire range of possibilities (IPCC, 2022, Annex 3).

Very few studies have tested different demographic projections within the same family of socio-economic models. Two of them conclude, for example, that following the low variant of the UN population projections, rather than their medium variant, could achieve a reduction in annual global carbon emissions of 40% (O’Neill et al., 2010) and 35% (Casey and Galor, 2017) by the end of the century.

On the basis of a less elaborate model applied solely to the case of France, Pont (2023) concludes that even severe birth control policies would have a small cumulative effect on CO2 emissions. A “one-child” birth rate would contribute 3% to total emissions reductions between 2020 and 2100 if the annual rate of emissions reduction resulting from other mitigation measures were 6% (close to the carbon neutrality trajectory in 2050). In the case, closer to the current situation, where the rate of reduction due to these other measures is only 2% per year, the cumulative contribution of a one-child policy would be 11%. Still with a “one-child” policy, annual CO2 emissions would be reduced by 7% in 2050 and 52% in 2100.

This result, which reflects the well-known fact that the effect of a reduction in the number of births on emissions is small during the two or three decades following this reduction, is lower than that obtained by O’Neill et al. (2010), who range from 16% to 29% of the reductions in CO2 emissions considered necessary before 2050 to avoid dangerous climate change. The main reason for this difference is certainly that the starting point of O’Neill’s projections is 2000, whereas Pont starts to take into account the reduction in the birth rate 20 years later: it is therefore not surprising that the effect in 2050 is much less significant in his case. Furthermore, Pont’s “zero-carbon” scenario, corresponding to a 6% annual reduction in emissions for France, is much more rigorous than the B2 scenario used by O’Neill et al. Above all, Pont shows that if almost all emissions were already abolished by other measures, a reduction in the birth rate would be of less interest. Indeed, at an annual reduction rate of 6% starting in 2020, emissions in 2050 would represent only 16% of those in 2020. It would therefore be advisable to compare comparable things, bearing in mind that the figures in one case are for the world and in the other for France, and relying on a detailed description of the method used, as in O’Neill et al., 2010.

It should also be noted that these estimates by O’Neill and de Pont relate only to CO2 emissions from energy sources, and exclude emissions from AFOLU in particular. We have shown above that these are significant and strongly linked to demographic change, particularly in many developing countries.

Demographic policies and measures for mitigation

Since the scientific community first became interested in climate change mitigation, a very wide range of mitigation policies and measures have been studied, notably in the fields of energy, agriculture, forestry, land use, urban systems, transport and industry. However, the effectiveness of mitigation policies addressing the birth rate has rarely been scientifically evaluated, as indicated by Dodson et al. (2020), and it seems that no state has implemented such policies.

Various measures are possible to slow down or reverse demographic change, which would improve the living conditions of unborn generations without worsening those of currently living humans. The causes of fertility undoubtedly vary according to country, culture or religion, production methods, financial resources, social protection, access to old-age insurance, etc. Depending on the context, it is possible to implement measures that respect individual freedoms and the principles of equality before the law. In France, for example, the NGO Démographie Responsable [9] proposes that, while public support for the second child should be maintained, there should be no increase in benefits for subsequent children. In concrete terms, benefits would be capped at 2 children.

Several developing countries have successfully implemented measures to reduce births, including Tunisia, Egypt, Ethiopia and Bangladesh. It has been shown that improving family planning in countries where it is underdeveloped can make a significant contribution to reducing the birth rate. Tunisia implemented a family planning policy as early as 1964, with very positive results. Developed countries could encourage this type of action through non-coercive measures, for example by requiring that development aid be conditional on the widespread introduction of family planning.

We shall limit ourselves here to these few examples of birth reduction policies, in the hope that future research will be able to assess the possible contributions of such measures to the fight against climate change.

Conclusions

We have mentioned several reasons why the influence of population growth on global warming should be more accurately assessed and taken into account in climate change mitigation policies. While no one argues that having children can be good for the climate, some downplay this influence or gloss over it in favor of other political priorities, to the extent that the issue is not even mentioned in the summaries for decision-makers of the latest IPCC assessment report. In this area, we need to distinguish between the short term and the long term, and between developed and developing countries, among which those with high levels of deforestation are a special case.

Considering the case of France, Pont (2022) concludes that “the faster we reduce emissions (which we should do), the less useful the demographic lever is”. Even if his calculations significantly reduce the effect of demographics, his conclusion remains valid for a timeframe of less than twenty or thirty years. But in the longer term, the effect of a reduction in the number of births is increasing. Assuming that an emissions reduction train of 6% per annum by other means is feasible – which is not certain – this would require considerable efforts on the part of the population.

A reduction in the birth rate would alleviate this constraint, and could enable the generations that will live in the decades to come to temper the obligation of sobriety and enjoy greater freedom. The zero-carbon objective will require a great deal of discipline in lifestyles, which could be tempered a little in the long term if the population is smaller. On the other hand, the inhabitants of countries currently experiencing strong demographic growth could in future achieve a higher per capita income if there were fewer of them.

Pending more precise indicators and results on the contribution of population growth to GHG emissions, it appears in conclusion that the assessments made by the IPCC using the Kaya identity are underestimated, particularly in the presence of significant AFOLU activities, but even for CO2 emissions from fossil fuel combustion. This review also shows that the rate of population growth has an important relative influence on emissions growth in many low-income countries, with high emissions from forests and land use, and where economic growth per capita has remained very low or even non-existent, and is likely to remain so unless vigorous measures are applied.

Population growth, or even the fact that the number of human beings will remain above the 8 billion mark by 2022, is a subject of concern to a very wide public, and one that regularly attracts media attention. The few existing publications on the link between population and climate indicate that any relative variation in population eventually leads to at least an equal variation in GHG emissions. They are often ignored, or accused outright of Malthusianism and incitement to degrowth.

The IPCC’s inertia in this area is understandable, given that it is an intergovernmental working group operating on a consensus basis to inform governments. To date, governments have not asked the research community or the IPCC to assess the possible effects of demographic measures on global warming. The same applies to the effects of demographics on biodiversity loss, which could be examined by IPBES.

It’s easy to see how a consensus could have been reached to avoid this politically sensitive subject. On the one hand, it is understandable that countries with high population growth do not wish to be accused of increasing GHG emissions (or the destruction of biodiversity) due to their demographics, which they find difficult to control. On the other hand, the governments of developed countries have no particular short-term interest in seeing the population of developing countries stabilize, either for themselves or for their companies. In the knowledge that this subject may also appeal to certain cultural sensitivities, and come on top of other negotiating topics considered more urgent, it may have been deemed preferable to avoid it. And never mind the long-term consequences, catastrophic though they may be.

In view of the obvious and considerable impact of population on climate and nature, and consequently on the degradation of the lives of most humans, and especially of future generations, it seems important and urgent that the socio-economic community and the IPCC address the climate impacts of policies and measures relating to demography.

Acknowledgements

The author is deeply grateful to Messrs. Jean-Luc Redaud, Régis Juvanon du Vachat and Philippe Waldteufel for the comments and advice they took the time to provide during the preparation of this article.

Notes

[1] See https://data.worldbank.org/indicato…

[2] francetvinfo.fr/monde/environne…

[3] fao.org/3/y4450f/y4450f10.htm

[4] Watch for example the program francetvinfo.fr/monde/afrique/r…

[5] United Nations program on reducing emissions from deforestation and forest degradation in developing countries, including conservation, sustainable management of forests and enhancement of forest carbon stocks.

[6] https://worldpopulationreview.com/c…

[7] World Bank Group, 2022, https://pip.worldbank.org/home

[8] See for exemple :iamcdocumentation.eu/index.php/…

[9] demographie-responsable.org/

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