Quantifying the human cost of global warming

The costs of climate change are often estimated in monetary terms, but this raises ethical issues. Here we express them in terms of numbers of people left outside the ‘human climate niche’—defined as the historically highly conserved distribution of relative human population density with respect to mean annual temperature. We show that climate change has already put ~9% of people (>600 million) outside this niche. By end-of-century (2080–2100), current policies leading to around 2.7 °C global warming could leave one-third (22–39%) of people outside the niche. Reducing global warming from 2.7 to 1.5 °C results in a ~5-fold decrease in the population exposed to unprecedented heat (mean annual temperature ≥29 °C). The lifetime emissions of ~3.5 global average citizens today (or ~1.2 average US citizens) expose one future person to unprecedented heat by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change. As an alternative to monetary estimates, this study expresses the costs of climate change in terms of numbers of people left outside the ‘human climate niche’, which reflects the historically highly conserved distribution of human population density relative to mean annual temperature.

Calculating exposure. We provide three calculations of exposure outside of the temperature niche, due to: (i) unprecedented hot MAT, (ii) climate change only, or (iii) climate and demographic change (see Methods, Extended Data Figure 2). (i) The simplest approach 4 just considers 'hot exposure'i.e., how many people fall outside the hot edge of the temperature niche. This is calculated 4 as the percentage of population exposed to MAT ≥29 °C. Only 0.3% of the 1980 population (12 million) experienced such conditions in the 1960-1990 climate. (ii) Exposure due to climate change alone 4 , considers all places where MAT changes to a value supporting lower relative population density according to the temperature niche: To calculate this 4 , we create a spatial 'ideal distribution' that maintains the historical distribution of relative population density with respect to MAT under a changed climate, and contrast this with the 'reference distribution' of population density with respect to the 1960-1990 climate. The difference between the ideal and reference distributions integrated across space gives the % of population exposed outside of the niche due to climate-only. (iii) Demographic change can also expose an increased density of population to a less favourable climate. To provide an upper estimate of population exposure (in %) due to both climate and demographic change we calculate the geographical distribution of projected population with respect to projected climate and contrast this 'projected distribution' with the 'ideal distribution'.
Changes up to present. We find that noticeable changes in the distribution of population density with respect to temperature have occurred due to climate and demographic changes from 1980 to 2010 ( Figure 1a). Considering the 2010 population distribution (total 6.9 billion) under the observed 2000-2020 climate, global warming of 1.0 °C (0.7 °C above 1960-90) has shifted the primary peak of population density to slightly higher MAT (~13 °C) compared to 1980, and the bias of population growth towards hot places has increased population density at the secondary (~27 °C) peak. Greater observed global warming in the cooler higher northern latitudes than the tropics is visible in the changes to the distribution. Hot exposure (MAT ≥29 °C) doubled in percentage terms to 0.6% (41 million people), ~8% of the global population have been left outside the historical niche due to climate change alone, and ~12% from climate change plus demographic change. Thus, global warming of 0.7 °C since 1960-1990 has put ~550 million people in less favourable climate conditions, with demographic change adding another ~275 million.
Future exposure. To estimate future exposure, we use an ensemble of eight climate model outputs (Extended Data Table 1) and corresponding population projections from four Shared Socio-economic Pathways 35 (SSPs, Extended Data Table 2). SSP2-4.5 ('middle of the road') provides a useful reference scenario because it produces end-of-century (2081-2100) global warming 2.7 (2.1-3.5) °C corresponding to the 2.7 (2.0-3.6) °C expected under current policies 5 , and it captures largely unavoidable 36 population growth towards a peak of ~9.5 billion in 2070 (then declining to ~9.0 billion in 2100). Climate change and population growth combine to shift relative population density to higher MAT (Figure 1b). Hot exposure (Figure 2a,d) becomes significant by 2030 at ~4% or ~0.3 billion as global warming reaches 1.5 °C, and it increases near linearly to ~23% or ~2.1 billion in 2090 under 2.7 °C global warming. People left outside the niche due to climate change alone (Figure 2b,e) reaches ~14% or ~1.2 billion by 2030, more than doubling to ~29% or ~2.7 billion in 2090. People left outside the niche from climate plus demographic change (Figure 2c,f) reaches ~25% or ~2.0 billion by 2030, and ~40% or ~3.7 billion by 2090.
Variation across the SSPs. The other three Shared Socio-economic Pathways (SSPs) produce a wide range of global warming (2081-2100) from ~1.8 (1.3-2.4) °C (SSP1-2.6) to ~4.4 (3.3-5.7) °C (SSP5-8.5) and span a wide range of human development trajectories from population peaking at ~8.5 billion then declining to ~6.9 billion in 2100 (SSP1) to ongoing growth to ~12.6 billion in 2100 (SSP3) (Extended Data Table 2). Both climate and demographic change alter the distribution of relative population density with respect to MAT (Extended Data Figure 3). By 2090, hot exposure reaches 8-40% or 0.6-4.7 billion across SSPs (Figure 2a,d). People left outside of the niche due to climate-only reaches 18-47% or 1.3-4.7 billion (Figure 2b,e). Adding in demographic change increases this to 29-53% or 2.2-6.5 billion (Figure 2c,f). Estimates of exposure outside the combined temperatureprecipitation niche are roughly 20% greater than for the temperature niche alone (Extended Data Figure 4). SSP5-8.5 exposes the greatest proportion of population to unprecedented heat or being pushed out of the niche due to climate change alone, but SSP3-7.0 exposes the greatest proportion of population due to climate and demographic change combined, and the greatest absolute numbers across all three measures of exposure ( Figure 2).
Controlling for demography. Larger global populations following the SSPs place a greater proportion of people in hotter places, tending to leave more outside the historical niche (irrespective of climate change). To isolate the effects of climate policy and associated climate change on exposure, we fix the population and its distribution, exploring three different options: (i) 6.9 billion as in 2010, (i) 9.5 billion as in SSP2 in 2070, and (iii) 11.1 billion as in SSP3 in 2070. Having controlled for demography, global warming shifts the whole distribution of population density to higher temperatures (Figure 1c, Extended Data Figure 5). This results in linear relationships ( Figure 3) between global warming and % of population exposed to unprecedented heat or left outside the niche from climate-only or climate plus demographic change. Hot exposure (Figure 3a) starts to become significant above the present level of ~1.2 °C global warming and increases steeply at ~12 % °C -1 (6.9 billion) to ~17.5 % °C -1 (11.1 billion). People left outside the niche from climate change alone increases ~12 % °C -1 , above the baseline defined at 0.3 °C global warming   (Figure 3b). Factoring in demography, for a greater fixed population, the percent exposed is always greater, but the dependence on climate weakens somewhat towards ~9 % °C -1 (for 11.1 billion). The relationships between global warming and exposure are all steeper for the temperature-precipitation niche (Extended Data Figure 6a). The mean temperature experienced by an average person increases with global warming in a manner invariant to demography at +1.5 °C °C -1 (Extended Data Figure 6b), consistent with observations and models that the land warms ~1.5 times faster than the global average 37 .
Worst case scenarios. We now focus on a future world of 9.5 billion. When assessing risk it is important to consider worst case scenarios 38 . If the transient climate response to cumulative emissions is high, current policies could, in the worst case, lead to ~3.6 °C end-of-century global warming 5 (as projected under SSP3-7.0; Extended Data Table 2). This results in 33% (3.1 billion) hot exposed, 39% (3.7 billion) left outside the niche from climate-only, and 50% (4.8 billion) from climate plus demographic change ( Figure 3). There also remains the possibility that climate policies are not enacted, and the world reverts to fossil-fuelled development (SSP5-8.5), leading to ~4.4 °C end-ofcentury global warming. This gives 44% (4.2 billion) hot exposed, 49% (4.7 billion) left outside the niche from climate-only, and 59% (5.3 billion) from climate plus demography ( Figure 3).
Gains from strengthening climate policy. Having controlled for demography, strengthening climate policy reduces exposure (Figure 1c, 3), including to MAT ≥29 °C (Figure 4a,c), through reducing geographical movement of the temperature (Figure 4b,d) and temperature-precipitation (Extended Data Figure 7) niches. Following Climate Action Tracker's projections 5 , different levels of policy ambition result in ~0.3 °C changes in end-of-century global warming as follows: Current policies lead to ~2.7 (2.0-3.6) °C; meeting current 2030 Nationally Determined Contributions (NDCs) (without long-term pledges) leads to ~2.4 (1.9-3.0) °C; additional full implementation of submitted and binding long-term targets leads to ~2.1 (1.7-2.6) °C; fully implementing all targets announced at COP26 leads to ~1.8 (1.5-2.4) °C. Overall, going from ~2.7 °C global warming under current policies to meeting the Paris Agreement 1.5 °C target reduces hot exposure from 21% to 5% (2.0 to 0.5 billion) (Figure 3a). It reduces population left outside the niche due to climate-only from 28% to 14% (2.7 to 1.3 billion), and it reduces population left outside the niche by climate plus demographic change from 42% to 31% (4.0 to 2.9 billion) ( Figure 3b). Thus, each 0.3 °C decline in end-of-century warming reduces hot exposure by ~4% or ~380 million people, it reduces population left outside the niche due to climate-only by ~3.5% or ~330 million people, and population left outside the niche due to climate and demographic change by ~3% or ~260 million people.
Country-level exposure. We focus on hot exposure as the simplest and most conservative metric. The population exposed to unprecedented heat (MAT ≥29 °C) declines significantly for the most affected countries if global warming is reduced from ~2.7 °C under current policies to meeting the 1.5 °C target ( Figure 5). Assuming a future world of 9.5 billion, India has the greatest population exposed under 2.7 °C global warming, >600 million, but this reduces >6-fold to ~90 million at 1.5 °C global warming. Nigeria has the second largest population exposed, >300 million under 2.7 °C global warming, but this reduces >7-fold to <40 million at 1.5 °C global warming. For third-ranked Indonesia, hot exposure reduces >20-fold from ~100 million under 2.7 °C global warming, to <5 million at 1.5 °C global warming. For fourth and fifth-ranked Philippines and Pakistan with >80 million exposed under 2.7 °C global warming, there are even larger proportional reductions at 1.5 °C global warming. Sahelian-Saharan countries including Sudan (6th ranked) and Niger (7th) have a circa 2-fold reduction in exposure, because they still have a large fraction of land area exposed at 1.5 °C global warming (Extended Data Figure 8a). The fraction of land area exposed approaches 100% for several countries under 2.7 °C global warming (Extended Data Figure 8a). Brazil has the greatest absolute land area exposed under 2.7 °C global warming, despite almost no area being exposed at 1.5 °C, and Australia and India also experience massive increases in absolute area exposed ( Figure  4a,b; Extended Data Figure 8b). (If the future population reaches 11.1 billion, the ranking of countries by population exposed remains similar, although the numbers exposed increases.) Those most exposed under 2.7 °C global warming come from nations that today are above the median poverty rate and below the median per capita emissions ( Figure 6).
Relating present emissions to future exposure. Above the present level of 1.2 °C global warming, the increase in hot exposure of 13.8 % °C -1 for a future world of ~9.5 billion people, corresponds to 1.31×10 9 cap °C -1 . The established relationship 39 of cumulative emissions to transient global warming is ~1.65 (1.0-2.3) °C EgC -1 . Therefore 1 person will be exposed to unprecedented heat (MAT ≥29 °C) for every ~460 (330-760) tC emitted. Present (2018 data) global mean per capita CO2-equivalent emissions 40 (production-based) are 1.8 tCeq cap -1 yr -1 . Thus, during their lifetimes (72.6 years) ~3.5 global average citizens today (less than the average household of 4.9 people) emit enough carbon to expose one future person to unprecedented heat. Citizens in richer countries generally have higher emissions 40 , e.g. EU 2.4 tCeq cap -1 yr -1 , US 5.3 tCeq cap -1 yr -1 , Qatar 18 tCeq cap -1 yr -1 ( Figure 6) (and consumption-based emissions are even higher). Thus, ~2.7 average EU citizens or ~1.2 average US citizens emit enough carbon in their lifetimes to expose 1 future person to unprecedented heat, and the average citizen of Qatar emits enough carbon in their lifetime to expose ~2.8 future people to unprecedented heat. Those future people tend to be in nations that today have per capita emissions around the 25% quantile (Figure 6), including the two countries with the greatest population exposed: India 0.73 tCeq cap -1 yr -1 and Nigeria 0.55 tCeq cap -1 yr -1 . We estimate that the average future person exposed to unprecedented heat comes from a place where today per capita emissions are approximately half (56%) of the global average (or 52% in a world of 11.1 billion people).

Discussion
Our estimate that global warming since 1960-1990 has put more than half a billion people outside the historical climate niche is consistent with attributable impacts of climate change affecting 85% of the world's population 41 . Above the present level of ~1.2 °C global warming, predicted exposure to unprecedented MAT ≥29 °C increases markedly. This is consistent with extreme humid heat having more than doubled in frequency 42 since 1979, with exposure in urban areas increasing for 23% of the world's population 43 from 1983 to 2016 (due also to growing urban heat islands), and the total urban population exposed tripling 43 (due also to demographic change). Extreme humid heat can be fatal 44 for more vulnerable individuals 45 , and is associated with labour loss of 148 million full time equivalent jobs 19 . Further work should examine the relationship between unprecedented MAT ≥29 °C and exposure to extreme heat. Both India and Nigeria already show 'hotspots' of increased exposure to extreme heat due predominantly to warming 43 , consistent with our prediction that they are at greatest future risk ( Figure 5). These and other emerging economies (e.g., Indonesia, Pakistan, Thailand) dominate the total population exposed to unprecedented heat in a 2.7 °C warmer world ( Figure 5). Their climate policy commitments also play a significant role in determining end-ofcentury global warming 9 .
The huge numbers of humans left out of the climate niche in our future projections warrant critical evaluation. Combined effects of climate and demographic change are upper estimates. This is because at any given time the method limits absolute population density of the (currently secondary) higher-temperature peak based on absolute population density of the (currently primary) lower-temperature peak. Yet absolute population density is allowed to vary (everywhere) over time. (This is not an issue for the climate-only or hot-exposure estimates.) Nevertheless, a bias of population growth to hot places clearly increases the proportion (as well as the absolute number) of people exposed to harm from high temperatures 46 .
Overall, our results illustrate the huge potential human cost and the great inequity of climate change -without having considered exposure to e.g., sea-level rise 29,30 . They also inform discussions of loss and damage 47,48 . The worst-case scenarios of ~3.6 °C or even ~4.4 °C global warming could put half of the world population outside the historical climate niche, posing an existential risk. The ~2.7 °C global warming expected under current policies puts around a third of world population outside the niche. It exposes almost the entire area of some countries (e.g., Burkina Faso, Mali) to unprecedented heat, including some Small Island Developing States (SIDS) (e.g., Aruba, Netherlands Antilles) (Extended Data Figure 8b) -a group with members already facing an existential risk from sea-level rise. The gains from fully implementing all policy targets announced at COP26 and limiting global warming to ~1.8 °C are considerable but would still leave nearly 10% of people exposed to MAT ≥29 °C. Meeting the goal of the Paris Agreement to limit global warming to 1.5 °C, halves exposure outside of the historical niche relative to current policies, and limits those exposed to unprecedented hot temperatures to 5% of people. This still leaves several least-developed countries (e.g., Sudan, Niger, Burkina Faso, Mali) with large populations exposed ( Figure 5), adding adaptation challenges to an existing climate investment trap 49 . Nevertheless, our results show the huge potential for more decisive climate policy to limit the human costs and inequities of climate change.    Relationships between global warming and population exposed outside of the temperature niche for different fixed population distributions: a. Population (%) exposed to mean annual temperature (MAT) ≥29 °C for the different population distributions: 6.9 billion (n=65, coefficient=11.9 % °C -1 , r 2 =0.83); 9.5 billion (n=65, coefficient=13.8 % °C -1 , r 2 =0.83); 11.1 billion (n=65, coefficient=17.5 % °C -1 , r 2 =0.83). b. Population (%) put outside of the temperature niche due to climate change only (purple; n=65, coefficient=11.8 % °C -1 ; forcing intercept at 1960-1990 global warming of 0.3 °C), and due to the combined effects of climate and demographic change, for different fixed population distributions: 6.9 billion in 2010 (blue; n=65, coefficient=11.0 % °C -1 , r 2 =0.83); 9.5 billion following SSP2 in 2070 (green; n=65, coefficient=9.5 % °C -1 , r 2 =0.84), 11.1 billion following SSP3 in 2070 (red; n=65, coefficient=9.1 % °C -1 , r 2 =0.84).   . Country-level per capita greenhouse gas emissions 40 related to population exposed to unprecedented heat (MAT ≥29 °C) at 2.7 °C global warming ( Figure 5) and poverty rate 50 . Solid lines show the median (50% quantile) and dashed lines the 25% and 75% quantiles for emissions and heat exposure. Points are coloured by quartile of the poverty rate distribution, where poverty rate is defined as % of national population below the $1.90 poverty line. The density plots at the bottom show the distribution of emissions per capita for each poverty rate quartile.

Methods
Reassessing the climate niche. We plot the running mean of population density against mean annual temperature (MAT), with a step of 1 °C and a bin size of 2 °C, and then apply double-Gaussian fitting to the resulting curve 4 . Our previous work 4 assessed the human temperature niche by quantifying the 2015 population distribution in relation to the 1960-1990 MAT (Extended Data Figure 1; 'Old reference'). Here we re-assessed the niche changing the data to the 1980 population distribution (total 4.4 billion) under the 1960-1990 MAT, for greater internal consistency (Figure 1a, Extended Data Figure 1; '1980'). This is important because there has been significant population growth between 1980 and 2015 with a distinct bias to hotter places. The 1980 population distribution data was obtained from the HYDE (History database of the Global Environment) 3.2 database 51 . The ensemble mean 1960-1990 climate and associated uncertainty (5th/95th percentiles) were calculated from three sources: (1) WorldClim v1.4 data 52 , (2) CRU TS v.4.05 monthly data 53,54 (available at https://crudata.uea.ac.uk/cru/data/hrg/), (3) NASA GLDAS-2.1 (Global Land Data Assimilation System) 3-hourly data 55 (available at https://developers.google.com/earthengine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H). A revised temperatureprecipitation niche was also calculated from both MAT and mean annual precipitation (MAP), following the methods in ref. 4 , but using the 1980 population distribution with the 1960-1990 mean climate. This is considered in sensitivity analyses.
Projecting the niche. Hot exposure is calculated (as previously 4 ) for a given climate and population distribution as the percentage of people exposed to mean annual temperature ( Changes up to present. To calculate changes up to (near) present we construct an ensemble mean 2000-2020 climate and associated uncertainty (5th/95th percentiles) from five sources: (1) CRU TS v.4.05 monthly data 53,54 ; (2) NASA GLDAS-2.1 (Global Land Data Assimilation System) 3-hourly data 55 ; (3) ECMWF ERA5-Land monthly averaged Climate Reanalysis data 56 Table 1). We obtained SSP population data at 1-km resolution from the spatial population scenarios dataset 60,61 (available at https://www.cgd.ucar.edu/iam/modeling/spatial-populationscenarios.html). We aggregated the population data to a consistent resolution of 0.0833 degree (~10 km) to match the climate data and our previous analyses. We combine results across climate models to create a multi-model ensemble mean, and a 5-95% confidence interval, recognising that the number of models available varies somewhat between SSPs and time-slices (Extended Data Table 1).
To this end, we apply the MAT data of each climate model to plot population density against MAT and then combine the resulting curves to calculate the mean, and 5th and 95th percentiles.
To control for demography and thus isolate the effects of climate policy and associated climate change on exposure, we consider three different fixed populations and their spatial distributions: (i) 6.9 billion as in 2010, (i) 9.5 billion following SSP2 in 2070, and (iii) 11.1 billion following SSP3 in  Figure 6). However, we checked that global warming in the multi-model ensemble mean of the CMIP6 models we consider (Extended Data Table 1) matches that of the larger CMIP6 ensemble (Table SPM.1 of IPCC AR6).
Country-level estimates. Results for hot exposure were disaggregated to country scale for 2.7 °C and 1.5 °C global warming and populations of 9.5 or 11.1 billion, using GIS data for country boundaries from the World Borders Dataset (https://thematicmapping.org/downloads/world_borders.php).
Emissions and poverty rate of those exposed. Using the country-level breakdown of exposure to unprecedented heat in a 2.7 °C warmer world with 9.5 billion people ( Figure 5) we calculated a weighted average for number of people exposed multiplied by percentage of global average emissions per capita today. This uses production-based, country-level CO2-equivalent greenhouse gas emissions from the emissions database for global atmospheric research 40 , for which 2018 is the latest year. The calculation was also done for country-level exposure in a 2.7 °C warmer world of 11.1 billion. Consumption-based emissions (accounting for trade) tend to be lower than productionbased emissions in poorer countries and higher in richer countries. This would increase the inequity already apparent in the results. We also examined poverty rate defined as the percentage of population per country below the $1.90 poverty line, using the interpolated data for 2019 from the World Bank's Poverty and Inequality Platform 50 (https://pip.worldbank.org/home). The resulting distribution is heavily skewed with 25% quantile = 0.26%, 50% quantile = 1.79%, 75% quantile = 20%.

Data availability
All data analysed during this study are available at the URLs given (above), with the WorldClim v1.4 data available within https://doi.org/10.5061/dryad.fj6q573q7. All data generated during this study are available from corresponding author C.X. and will be deposited in dryad before final publication.

Code availability
Code used for the analysis is available from corresponding author C.X. and will be deposited in dryad before final publication.