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Nat Commun
Nat Commun
Nature Communications
2041-1723
Nature Publishing Group UK London

39223143
51355
10.1038/s41467-024-51355-0
Article
Green spaces provide substantial but unequal urban cooling globally
Li Yuxiang 1
http://orcid.org/0000-0002-3415-0862
Svenning Jens-Christian 2
http://orcid.org/0000-0001-7323-4906
Zhou Weiqi 345
http://orcid.org/0000-0003-1587-3317
Zhu Kai 6
http://orcid.org/0000-0003-0411-8519
Abrams Jesse F. 7
http://orcid.org/0000-0002-6725-7498
Lenton Timothy M. 7
Ripple William J. 8
http://orcid.org/0000-0003-4576-4541
Yu Zhaowu 9
Teng Shuqing N. shuqingteng@nju.edu.cn

1
Dunn Robert R. rrdunn@ncsu.edu

10
http://orcid.org/0000-0002-1841-9032
Xu Chi xuchi@nju.edu.cn

1
1 https://ror.org/01rxvg760 grid.41156.37 0000 0001 2314 964X School of Life Sciences, Nanjing University, Nanjing, China
2 https://ror.org/01aj84f44 grid.7048.b 0000 0001 1956 2722 Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), Department of Biology, Aarhus University, Aarhus, Denmark
3 grid.9227.e 0000000119573309 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
4 https://ror.org/05qbk4x57 grid.410726.6 0000 0004 1797 8419 University of Chinese Academy of Sciences, Beijing, China
5 grid.9227.e 0000000119573309 Beijing Urban Ecosystem Research Station, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
6 https://ror.org/00jmfr291 grid.214458.e 0000 0004 1936 7347 School for Environment and Sustainability, University of Michigan, Ann Arbor, MI USA
7 https://ror.org/03yghzc09 grid.8391.3 0000 0004 1936 8024 Global Systems Institute, University of Exeter, Exeter, UK
8 https://ror.org/00ysfqy60 grid.4391.f 0000 0001 2112 1969 Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR USA
9 https://ror.org/013q1eq08 grid.8547.e 0000 0001 0125 2443 Department of Environmental Science and Engineering, Fudan University, Shanghai, China
10 https://ror.org/04tj63d06 grid.40803.3f 0000 0001 2173 6074 Department of Applied Ecology, North Carolina State University, Raleigh, NC USA
2 9 2024
2 9 2024
2024
15 71086 12 2023
5 8 2024
© The Author(s) 2024
2024
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Climate warming disproportionately impacts countries in the Global South by increasing extreme heat exposure. However, geographic disparities in adaptation capacity are unclear. Here, we assess global inequality in green spaces, which urban residents critically rely on to mitigate outdoor heat stress. We use remote sensing data to quantify daytime cooling by urban greenery in the warm seasons across the ~500 largest cities globally. We show a striking contrast, with Global South cities having ~70% of the cooling capacity of cities in the Global North (2.5 ± 1.0 °C vs. 3.6 ± 1.7 °C). A similar gap occurs for the cooling adaptation benefits received by an average resident in these cities (2.2 ± 0.9 °C vs. 3.4 ± 1.7 °C). This cooling adaptation inequality is due to discrepancies in green space quantity and quality between cities in the Global North and South, shaped by socioeconomic and natural factors. Our analyses further suggest a vast potential for enhancing cooling adaptation while reducing global inequality.

A 1.5-fold gap exists in green space cooling adaptation between cities in the Global South and North. Enhancing urban green space quality and quantity offers vast potential for improving outdoor cooling adaptation and reducing its global inequality.

Subject terms

Urban ecology
Climate-change mitigation
Geography
https://doi.org/10.13039/501100001809 National Natural Science Foundation of China (National Science Foundation of China) 32061143014 Xu Chi https://doi.org/10.13039/501100001732 Danmarks Grundforskningsfond (Danish National Research Foundation) DNRF173 Svenning Jens-Christian https://doi.org/10.13039/100008398 Villum Fonden (Villum Foundation) 16549 Svenning Jens-Christian https://doi.org/10.13039/100000919 Open Society Foundations OR2021-82956 OR2021-82956 Abrams Jesse F. Lenton Timothy M. Partial funding was received from the Roger Worthington.issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Heat extremes are projected to be substantially intensified by global warming1,2, imposing a major threat to human mortality and morbidity in the coming decades3–6. This threat is particularly concerning as a majority of people now live in cities7, including those cities suffering some of the hottest climate extremes. Cities face two forms of warming: warming due to climate change and warming due to the urban heat island effect8–10. These two forms of warming have the potential to be additive, or even multiplicative. Climate change in itself is projected to result in rising maximum temperatures above 50 °C for a considerable fraction of the world if 2 °C global warming is exceeded2; the urban heat island effect will cause up to >10 °C additional (surface) warming11. Exposures to temperatures above 35 °C with high humidity or above 40 °C with low humidity can lead to lethal heat stress for humans12. Even before such lethal temperatures are reached, worker productivity13 and general health and well-being14 can suffer. Heat extremes are especially risky for people living in the Global South15,16 due to warmer climates at low latitudes. Climate models project that the lethal temperature thresholds will be exceeded with increasing frequencies and durations, and such extreme conditions will be concentrated in low-latitude regions17–19. These low-latitude regions overlap with the major parts of the Global South where population densities are already high and where population growth rates are also high. Consequently, the number of people exposed to extreme heat will likely increase even further, all things being equal16,20. That population growth will be accompanied by expanded urbanization and intensified urban heat island effects21,22, potentially exacerbating future Global North-Global South heat stress exposure inequalities.

Fortunately, we know that heat stress can be buffered, in part, by urban vegetation23. Urban green spaces, and especially urban forests, have proven an effective means through which to ameliorate heat stress through shading24,25 and transpirational cooling26,27. The buffering effect of urban green spaces is influenced by their area (relative to the area of the city) and their spatial configuration28. In this context, green spaces become a kind of infrastructure that can and should be actively managed. At broad spatial scales, the effect of this urban green infrastructure is also mediated by differences among regions, whether in their background climate29, composition of green spaces30, or other factors31–34. The geographic patterns of the buffering effects of green spaces, whether due to geographic patterns in their areal extent or region-specific effects, have so far been poorly characterized.

On their own, the effects of climate change and urban heat islands on human health are likely to become severe. However, these effects will become even worse if they fall disproportionately in cities or countries with less economic ability to invest in green space35 or in other forms of cooling36,37. A number of studies have now documented the so-called ‘luxury effect,’ wherein lower-income parts of cities tend to have less green space and, as a result, reduced biodiversity38,39. Where the luxury effect exists, green space and its benefits become, in essence, a luxury good40. If the luxury effect holds among cities, and lower-income cities also have smaller green spaces, the Global South may have the least potential to mitigate the combined effects of climate warming and urban heat islands, leading to exacerbated and rising inequalities in heat exposure41.

Here, we assess the global inequalities in the cooling capability of existing urban green infrastructure across urban areas worldwide. To this end, we use remotely sensed data to quantify three key variables, i.e., (1) cooling efficiency, (2) cooling capacity, and (3) cooling benefit of existing urban green infrastructure for ~500 major cities across the world. Urban green infrastructure and temperature are generally negatively and relatively linearly correlated at landscape scales, i.e., higher quantities of urban green infrastructure yield lower temperatures42,43. Cooling efficiency is widely used as a measure of the extent to which a given proportional increase in the area of urban green infrastructure leads to a decrease in temperature, i.e., the slope of the urban green infrastructure-temperature relationship42,44,45 (see Methods for details). This simple metric allows quantifying the quality of urban green infrastructure in terms of ameliorating the urban heat island effect. Meanwhile, the extent to which existing urban green infrastructure cools down an entire city’s surface temperatures (compared to the non-vegetated built-up areas) is referred to as cooling capacity. Hence, cooling capacity is a function of the total quantity of urban green infrastructure and its cooling efficiency (see Methods).

As a third step, we account for the spatial distributions of urban green infrastructure and populations to quantify the benefit of cooling mitigation received by an average urban inhabitant in each city given their location. This cooling benefit is a more direct measure of the cooling realized by people, after accounting for the within-city geography of urban green infrastructure and population density. We focus on cooling capacity and cooling benefit as the measures of the cooling capability of individual cities for assessing their global inequalities. We are particularly interested in linking cooling adaptation inequality with income inequality40,46. While this can be achieved using existing income metrics for country classifications47, here we use the traditional Global North/South classification due to its historical ties to geography which is influential in climate research.

Results and discussion

Our analyses indicate that existing green infrastructure of an average city has a capability of cooling down surface temperatures by ~3 °C during warm seasons. However, a concerning disparity is evident; on average Global South cities have only two-thirds the cooling capacity and cooling benefit compared to Global North cities. This inequality is attributable to the differences in both quantity and quality of existing urban green infrastructure among cities. Importantly, we find that there exists considerable potential for many cities to enhance the cooling capability of their green infrastructure; achieving this potential could dramatically reduce global inequalities in adaptation to outdoor heat stress.

Quantifying cooling inequality

Our analyses showed that both the quantity and quality of the existing urban green infrastructure vary greatly among the world’s ~500 most populated cities (see Methods for details, and Fig. 1 for examples). The quantity of urban green infrastructure measured based on remotely sensed indicators of spectral greenness (Normalized Difference Vegetation Index, NDVI, see Methods) had a coefficient of variation (CV) of 35%. Similarly, the quality of urban green infrastructure in terms of cooling efficiency (daytime land surface temperatures during peak summer) had a CV of 37% (Supplementary Figs. 1, 2). The global mean value of cooling capacity is 2.9 °C; existing urban green infrastructure ameliorates warm-season heat stress by 2.9 °C of surface temperature in an average city. In truth, however, the variation in cooling capacity was great (global CV in cooling capacity as large as ~50%), such that few cities were average. This variation is strongly geographically structured. Cities closer to the equator - tropical and subtropical cities - tend to have relatively weak cooling capacities (Fig. 2a, b). As Global South countries are predominantly located at low latitudes, this pattern leads to a situation in which Global South cities, which tend to be hotter and relatively lower-income, have, on average, approximately two-thirds the cooling capacity of the Global North cities (2.5 ± 1.0 vs. 3.6 ± 1.7°C, Wilcoxon test, p = 2.7e-12; Fig. 2c). The cities that most need to rely on green infrastructure are, at present, those that are least able to do so.Fig. 1 Distributions of city-scale land surface temperature, green space (Landsat NDVI), and derived local cooling efficiency, local cooling capacity, and local cooling benefit exemplified by four mega cities across different continents.

a, e, i, m, q Los Angeles, US. b, f, j, n, r Paris, France. c, g, k, o, s Shanghai, China. d, h, l, p, t Cairo, Egypt. Local cooling efficiency is calculated for different local climate zone types to account for within-city heterogeneity. In densely populated parts of cities, local cooling capacity tends to be lower due to reduced green space area, whereas local cooling benefit (local cooling capacity multiplied by a weight term of local population density relative to city mean) tends to be higher as more urban residents can receive cooling amelioration.

Fig. 2 Global pattern of cooling capacity.

a Global distribution of cooling capacity for the 468 major urbanized areas. b Latitudinal pattern of cooling capacity. c Cooling capacity difference between the Global North and South cities. The cooling capacity offered by urban green infrastructure evinces a latitudinal pattern wherein lower-latitude cities have weaker cooling capacity (b, cubic-spline fitting of cooling capacity with 95% confidence interval is shown), representing a significant inequality between Global North and South countries: city-level cooling capacity for Global North cities are about 1.5-fold higher than in Global South cities (c). Data are presented as box plots, where median values (center black lines), 25th percentiles (box lower bounds), 75th percentiles (box upper bounds), whiskers extending to 1.5-fold of the interquartile range (IQR), and outliers are shown. The tails of the cooling capacity distributions are truncated at zero as all cities have positive values of cooling capacity. Notice that no cities in the Global South have a cooling capacity greater than 5.5 °C (c). This is because no cities in the Global South have proportional green space areas as great as those seen in the Global North (see also Fig. 4b). A similar pattern is found for cooling benefit (Supplementary Fig. 3). The two-sided non-parametric Wilcoxon test was used for statistical comparisons.

When we account for the locations of urban green infrastructure relative to humans within cities, the cooling benefit of urban green infrastructure realized by an average urban resident generally becomes slightly lower than suggested by cooling capacity (see Methods; Supplementary Fig. 3). Urban residents tend to be densest in the parts of cities with less green infrastructure. As a result, the average urban resident experiences less cooling amelioration than expected. However, this heterogeneity has only a minor effect on global-scale inequality. As a result, the geographic trends in cooling capacity and cooling benefit are similar: mean cooling benefit for an average urban resident also presents a 1.5-fold gap between Global South and North cities (2.2 ± 0.9 vs. 3.4 ± 1.7 °C, Wilcoxon test, p = 3.2e-13; Supplementary Fig. 3c). Urban green infrastructure is a public good that has the potential to help even the most marginalized populations stay cool; unfortunately, this public benefit is least available in the Global South. When walking outdoors, the average person in an average Global South city receives only two-thirds the cooling amelioration from urban green infrastructure experienced by a person in an average Global North city. The high cooling amelioration capacity and benefit of the Global North cities is heavily influenced by North America (specifically, Canada and the US), which have both the highest cooling efficiency and the largest area of green infrastructure, followed by Europe (Supplementary Fig. 4).

One way to illustrate the global inequality of cooling capacity or benefit is to separately look at the cities that are most and least effective in ameliorating outdoor heat stress. Our results showed that ~85% of the 50 most effective cities (with highest cooling capacity or cooling benefit) are located in the Global North, while ~80% of the 50 least effective are Global South cities (Fig. 3, Supplementary Fig. 5). This is true without taking into account the differences in the background temperatures and climate warming of these cities, which will exacerbate the effects on human health; cities in the Global South are likely to be closer to the limits of human thermal comfort and even, increasingly, the limits of the temperatures and humidities (wet-bulb temperatures) at which humans can safely work or even walk, such that the ineffectiveness of green spaces in those cities in cooling will lead to greater negative effects on human health48, work14, and gross domestic product (GDP)49. In addition, Global South cities commonly have higher population densities (Fig. 3, Supplementary Fig. 5) and are projected to have faster population growth50. This situation will plausibly intensify the urban heat island effect because of the need of those populations for housing (and hence tensions between the need for buildings and the need for green spaces). It will also increase the number of people exposed to extreme urban heat island effects. Therefore, it is critical to increase cooling benefit via expanding urban green spaces, so that more people can receive the cooling mitigation from a given new neighboring green space if they live closer to each other. Doing so will require policies that incentivize urban green spaces as well as architectural innovations that make innovations such as plant-covered buildings easier and cheaper to implement.Fig. 3 Contrast between the 50 cities with highest (right hand bars) and those with lowest (left hand bars) cooling capacities.

The axes on the right are an order of magnitude greater than those on the left, such that the cooling capacity of Charlotte in the United States is about 37-fold greater than that of Mogadishu (Somalia) and 29-fold greater than that of Sana’a (Yemen). The cities presenting lowest cooling capacities are most associated with Global South cities at higher population densities.

Of course, cities differ even within the Global North or within the Global South. For example, some Global South cities have high green space areas (or relatively high cooling efficiency in combination with moderate green space areas) and hence high cooling capacity. These cities, such as Pune (India), will be important to study in more detail, to shed light on the mechanistic details of their cooling abilities as well as the sociopolitical and other factors that facilitated their high green area coverage and cooling capabilities (Supplementary Figs. 6, 7).

We conducted our primary analyses using a spatial grain of 100-m grid cells and Landsat NDVI data for quantifying spectral greenness. Our results, however, were robust at the coarser spatial grain of 1 km. We find a slightly larger global cooling inequality (~2-fold gap between Global South and North cities) at the 1-km grain using MODIS data (see Methods and Supplementary Fig. 17). MODIS data have been frequently used for quantifying urban heat island effects and cooling mitigation44,45,51. Our results reinforce its robustness for comparing urban thermal environments between cities across broad scales.

Influencing factors

The global inequality of cooling amelioration could have a number of proximate causes. To understand their relative influence, we first separately examined the effects of quality (cooling efficiency) and quantity (NDVI as a proxy indicator of urban green space area) of urban green infrastructure. The simplest null model is one in which cooling capacity (at the city scale) and cooling benefit (at the human scale) are driven primarily by the proportional area in a city dedicated to green spaces. Indeed, we found that both cooling capacity and cooling benefit were strongly correlated with urban green space area (Fig. 4, Supplementary Fig. 8). This finding is useful with regards to practical interventions. In general, cities that invest in saving or restoring more green spaces will receive more cooling benefits from those green spaces. By contrast, differences among cities in cooling efficiency played a more minor role in determining the cooling capacity and benefit of cities (Fig. 4, Supplementary Fig. 8).Fig. 4 Cooling capacity is significantly correlated with quality (cooling efficiency) and quantity (green space area) of urban green infrastructure, which are jointly shaped by natural and social economic factors.

a Relationship between cooling efficiency and cooling capacity. b Relationship between green space area (measured by mean Landsat NDVI in the hottest month of 2018) and cooling capacity. Note that the highest level of urban green space area in the Global South cities is much lower than that in the Global North (dashed line in b). Gray bands indicate 95% confidence intervals. Two-sided t-tests were conducted. c A piecewise structural equation model based on assumed direct and indirect (through influencing cooling efficiency and urban green space area) effects of essential natural and socioeconomic factors on cooling capacity. Mean annual temperature and precipitation, and topographic variation (elevation range) are selected to represent basic background natural conditions; GDP per capita is selected to represent basic socioeconomic conditions. The spatial extent of built-up areas is included to correct for city size. A bi-directional relationship (correlation) is fitted between mean annual temperature and precipitation. Red and blue solid arrows indicate significantly negative and positive coefficients with p ≤ 0.05, respectively. Gray dashed arrows indicate p > 0.05. The arrow width illustrates the effect size. Similar relationships are found for cooling benefits realized by an average urban resident (see Supplementary Fig. 8).

A further question is what shapes the quality and quantity of urban green infrastructure (which in turn are driving cooling capacity)? Many inter-correlated factors are possibly operating at multiple scales, making it difficult to disentangle their effects, especially since experiment-based causal inference is usually not feasible for large-scale urban systems. From a macroscopic perspective, we test the simple hypothesis that the background natural and socioeconomic conditions of cities jointly affect their cooling capacity and benefit in both direct and indirect ways. To this end, we constructed a minimal structural equation model including only the most essential variables reflecting background climate (mean annual temperature and precipitation), topographic variation (elevation range), as well as gross domestic product (GDP) per capita and city area (see Methods; Fig. 4c).

We found that the quantity of green spaces in a city (again, in proportion to its size) was positively correlated with GDP per capita and city area; wealthier cities have more green spaces. It is well known that wealth and green spaces are positively correlated within cities (the luxury effect)40,46; our analysis shows that a similar luxury effect occurs among them at a global scale. In addition, larger cities often have proportionally more green spaces, an effect that may be due to the tendency for large cities (particularly in the US and Canada) to have lower population densities. Cities that were hotter and had more topographic variation tended to have fewer green spaces and those that were more humid tended to have more green spaces. Given that temperature and humidity are highly correlated with the geography of the Global South and Global North, it is difficult to know whether these effects are due to the direct effects of temperature and precipitation, for example, on the growth rate of vegetation and hence the transition of abandoned lots into green spaces, or are associated with historical, cultural and political differences that via various mechanisms correlate to climate. Our structural equation model explained only a small fraction of variation among cities in their cooling efficiency, which is to say the quality of their green space. Cooling efficiency was modestly influenced by background temperature and precipitation—the warmer a city, the greater the cooling efficiency in that city; conversely, the more humid a city the less the cooling efficiency of that city.

Our analyses suggested that the lower cooling adaptation capabilities of Global South cities can be explained by their lower quantity of green infrastructure and, to a much lesser extent, their weaker cooling efficiency (quality; Supplementary Fig. 2). These patterns appear to be in part structured by GDP, but are also associated with climatic conditions39, and other factors. A key question, unresolved by our work, is whether the climatic correlates of the size of green spaces in cities are due to the effects of climate per se or if they, instead, reflect correlates between contemporary climate and the social, cultural, and political histories of cities in the Global South52. Since urban planning has much inertia, especially in big cities, those choices might be correlated with climate because of the climatic correlates of political histories. It is also possible that these dynamics relate, in part, to the ways in which climate influences vegetation structure. However, this seems less likely given that under non-urban conditions vegetation cover (and hence cooling capacity) is normally positively correlated with mean annual temperature across the globe, opposite to our observed negative relationships for urban systems (Supplementary Fig. 9g). Still, it is possible that increased temperatures in cities due to the urban heat island effects may lead to temperature-vegetation cover-cooling capacity relationships that differ from those in natural environments53,54. Indeed, a recent study found that climate warming will put urban forests at risk, and the risk is disproportionately higher in the Global South55.

Our model serves as a starting point for unraveling the mechanisms underlying global cooling inequality. We cannot rule out the possibility that other unconsidered factors correlated with the studied variables play important roles. We invite systematic studies incorporating detailed sociocultural and ecological variables to address this question across scales.

Potential of enhancing cooling and reducing inequality

Can we reduce the inequality in cooling capacity and benefits that we have discovered among the world’s largest cities? Nuanced assessments of the potential to improve cooling mitigation require comprehensive considerations of socioeconomic, cultural, and technological aspects of urban management and policy. It is likely that cities differ greatly in their capacity to implement cooling through green infrastructure, whether as a function of culture, governance, policy or some mix thereof. However, any practical attempts to achieve greater cooling will occur in the context of the realities of climate and existing land use. To understand these realities, we modeled the maximum additional cooling capacity that is possible in cities, given existing constraints. We assume that this capacity depends on the quality (cooling efficiency) and quantity of urban green infrastructure. Our approach provides a straightforward metric of the cooling that could be achieved if all parts of a city’s green infrastructure were to be enhanced systematically.

The positive outlook is that our analyses suggest a considerable potential of improving cooling capacity by optimizing urban green infrastructure. An obvious way is through increases in urban green infrastructure quantity. We employ an approach in which we consider each local climate zone56 to have a maximum NDVI and cooling efficiency (see Methods). For a given local climate zone, the city with the largest NDVI values or cooling efficiency sets the regional upper bounds for urban green infrastructure quantities or quality that can be achieved. Notably, these maxima are below the maxima for forests or other non-urban spaces for the simple reason that, as currently imagined, cities must contain gray (non-green) spaces in the form of roads and buildings. In this context, we conduct a thought experiment. What if we could systematically increase NDVI of all grid cells in each city, per local climate zone type, to a level corresponding to the median NDVI of grid cells in that upper bound city while keeping cooling efficiency unchanged (see Methods). If we were able to achieve this goal, the cooling capacity of cities would increase by ~2.4 °C worldwide. The increase would be even greater, ~3.8°C, if the 90th percentile (within the reference maximum city) was reached (Fig. 5a). The potential for cooling benefit to the average urban resident is similar to that of cooling capacity (Supplementary Fig. 10a). There is also potential to reduce urban temperatures if we can enhance cooling efficiency. However, the benefits of increases in cooling efficiency are modest (~1.5 °C increases at the 90th percentile of regional upper bounds) when holding urban green infrastructure quantity constant. In theory, if we could maximize both quantity and cooling efficiency of urban green infrastructure (to 90th percentiles of their regional upper bounds respectively), we would yield increases in cooling capacity and benefit up to ~10 °C, much higher than enhancing green space area or cooling efficiency alone (Fig. 5a, Supplementary Fig. 10a). Notably, such co-maximization of green space area and cooling efficiency would substantially reduce global inequality to Gini <0.1 (Fig. 5b, Supplementary Fig. 10b). Our analyses thus provide an important suggestion that enhancing both green space quantity and quality can yield a synergistic effect leading to much larger gains than any single aspect alone.Fig. 5 Estimated potential of enhancing cooling capacity and reducing its inequality.

a The potential of enhancing cooling capacity via either enhancing urban green infrastructure quality (i.e., cooling efficiency) while holding quantity (i.e., green space area) fixed (yellow), or enhancing quantity while holding quality fixed (blue) is much lower than that of enhancing both quantity and quality (green). The x-axis indicates the targets of enhancing urban green infrastructure quantity and/or quality relative to the 50–90th percentiles of NDVI or cooling efficiency, see Methods). The dashed horizontal lines indicate the median cooling capacity of current cities. Data are presented as median values with the colored bands corresponding to 25–75th percentiles. b The potential of reducing cooling capacity inequality is also higher when enhancing both urban green infrastructure quantity and quality. The Gini index weighted by population density is used to measure inequality. Similar results were found for cooling benefit (Supplementary Fig. 10).

Different estimates of cooling capacity potential may be reached based on varying estimates and assumptions regarding the maximum possible quantity and quality of urban green infrastructure. There is no single, simple way to make these estimates, especially considering the huge between-city differences in society, culture, and structure across the globe. Our example case (above) begins from the upper bound city’s median NDVI, taking into account different local climate zone types and background climate regions (regional upper bounds). This is based on the assumption that for cities within the same climate regions, their average green space quantity may serve as an attainable target. Still, urban planning is often made at the level of individual cities, often only implemented to a limited extent and made with limited consideration of cities in other regions and countries. A potentially more realistic reference may be taken from the existing green infrastructure (again, per local climate zone type) within each particular city itself (see Methods): if a city’s sparsely vegetated areas was systematically elevated to the levels of 50–90th percentiles of NDVI within their corresponding local climate zones within the city, cooling capacity would still increase, but only by 0.5–1.5 °C and with only slightly reduced inequalities among cities (Supplementary Fig. 11). This highlights that ambitious policies, inspired by the greener cities worldwide, are necessary to realize the large cooling potential in urban green infrastructure.

Outlook

In summary, our results demonstrate clear inequality in the extent to which urban green infrastructure cools cities and their denizens between the Global North and South. Much attention has been paid to the global inequality of indoor heat adaptation arising from the inequality of resources (e.g., less affordable air conditioning and more frequent power shortages in the Global South)36,57–59. Our results suggest that the inequality in outdoor adaptation is particularly concerning, especially as urban populations in the Global South are growing rapidly and are likely to face the most severe future temperature extremes60.

Previous studies have been focusing on characterizing urban heat island effects, urban vegetation patterns, resident exposure, and cooling effects in particular cities26,28,34,61, regions22,25,62, or continents32,44,63. Recent studies start looking at global patterns with respect to cooling efficiency or green space exposure35,45,64,65. Our approach is one drawn from the fields of large-scale ecology and macroecology. This approach is complementary to and, indeed, can, in the future, be combined with (1) mechanism driven biophysical models66,67 to predict the influence of the composition and climate of green spaces on their cooling efficiency, (2) social theory aimed at understanding the factors that govern the amount of green space in cities as well as the disparity among cities68, (3) economic models of the effects of policy changes on the amount of greenspace and even (4) artist-driven projects that seek to understand the ways in which we might reimagine future cities69. Our simple explanatory model is, ultimately, one lens on a complex, global phenomenon.

Our results convey some positive outlook in that there is considerable potential to strengthen the cooling capability of cities and to reduce inequalities in cooling capacities at the same time. Realizing this nature-based solution, however, will be challenging. First, enhancing urban green infrastructure requires massive investments, which are more difficult to achieve in Global South cities. Second, it also requires smart planning strategies and advanced urban design and greening technologies37,70–72. Spatial planning of urban green spaces needs to consider not only the cooling amelioration effect, but also their multifunctional aspects that involve multiple ecosystem services, mental health benefits, accessibility, and security73. In theory, a city can maximize its cooling while also maximizing density through the combination of high-density living, ground-level green spaces, and vertical and rooftop gardens (or even forests). In practice, the current cities with the most green spaces tend to be lower-density cities74 (Supplementary Fig. 12). Still, innovation and implementation of new technologies that allow green spaces and high-density living to be combined have the potential to reduce or disconnect the negative relationship between green space area and population density71,75. However, this development has yet to be realized. Another dimension of green spaces that deserves more attention is the geography of green spaces relative to where people are concentrated within cities. A critical question is how best should we distribute green spaces within cities to maximize cooling efficiency76 and minimize within-city cooling inequality towards social equity77? Last but not least, it is crucial to design and manage urban green spaces to be as resilient as possible to future climate stress78. For many cities, green infrastructure is likely to remain the primary means people will have to rely on to mitigate the escalating urban outdoor heat stress in the coming decades79.

Methods

Urban data

We used the world population data from the World’s Cities in 2018 Data Booklet80 to select 502 major cities with population over 1 million people (see Supplementary Data 1 for the complete list of the studied cities). Cities are divided into the Global North and Global South based on the Human Development Index (HDI) from the Human Development Report 201981. For each selected city, we used the 2018 Global Artificial Impervious Area (GAIA) data at 30 m resolution82 to determine its geographic extent. The derived urban boundary polygons thus encompass a majority of the built-up areas and urban residents. In using this approach, rather than urban administrative boundaries, we can focus on the relatively densely populated areas where cooling mitigation is most needed, and exclude areas dominated by (semi) natural landscapes that may bias the subsequent quantifications of the cooling effect. Our analyses on the cooling effect were conducted at the 100 m spatial resolution using Landsat data and WorldPop Global Project Population Data of 201883. In order to test for the robustness of the results to coarser spatial scales, we also repeated the analyses at 1 km resolution using MODIS data, which have been extensively used for quantifying urban heat island effects and cooling mitigation44,45,51. We discarded the five cities with sizes <30 km2 as they were too small for us to estimate their cooling efficiency based on linear regression (see section below for details). We combined closely located cities that form contiguous urban areas or urban agglomerations, if their urban boundary polygons from GAIA merged (e.g., Phoenix and Mesa in the United States were combined). Our approach yielded 468 polygons, each representing a major urbanized area that were the basis for all subsequent analyses. Because large water bodies can exert substantial and confounding cooling effects, we excluded permanent water bodies including lakes, reservoirs, rivers, and oceans using the Copernicus Global Land Service (CGLS) Land Cover data for 2018 at 10 m resolution84.

Quantifying the cooling effect

As a first step, we calculated cooling efficiency for each studied city within the GAIA-derived urban boundary. Cooling efficiency quantifies the extent to which a given area of green spaces in a city can reduce temperatures. It is a measure of the effectiveness (quality) of urban green spaces in terms of heat amelioration. Cooling efficiency is typically measured by calculating the slope of the relationship between remotely-sensed land surface temperature (LST) and vegetation cover through ordinary least square regression42,44,45. It is known that cooling efficiency varies between cities. Influencing factors might include background climate29, species composition30,85, landscape configuration28, topography86, proximity to large water bodies33,87, urban morphology88, and city management practices31. However, the mechanism underlying the global pattern of cooling efficiency remains unclear.

We used Landsat satellite data provided by the United States Geological Survey (USGS) to calculate the cooling efficiency of each studied city. We used the cloud-free Landsat 8 Level 2 LST and NDVI data. For each city we calculated the mean LST in each month of 2018 to identify the hottest month, and then derived the hottest month LST; we used the cloud-free Landsat 8 data to calculate the mean NDVI for the hottest month correspondingly.

We quantified cooling efficiency for different local climate zones56 separately for each city, to account for within-city variability of thermal environments. To this end, we used the Copernicus Global Land Service data (CGLS)84 and Global Human Settlement Layers (GHSL) Built-up height data89 of 2018 at the 100 m resolution to identify five types of local climate zones: non-tree vegetation (shrubs, herbaceous vegetation, and cultivated vegetation according to the CGLS classification system), low-rise buildings (built up and bare according to the CGLS classification system, with building heights ≤10 m according to the GHSL data), medium-high-rise buildings (built up and bare areas with building heights >10 m), open tree cover (open forest with tree cover 15–70% according to the CGLS system), and closed tree cover (closed forest with tree cover >70%).

For each local climate zone type in each city, we constructed a regression model with NDVI as the predictor variable and LST as the response variable (using the ordinary least square method). We took into account the potential confounding factors including topographic elevation (derived from MERIT DEM dataset90), building height (derived from the GHSL dataset89), and distance to water bodies (derived from the GSHHG dataset91), the model thus became: LST ~ NDVI + topography + building height + distance to water. Cooling efficiency was calculated as the absolute value of the regression coefficient of NDVI, after correcting for those confounding factors. To account for the multi-collinearity issue, we conducted variable selection based on the variance inflation factor (VIF) to achieve VIF < 5. Before the analysis, we discarded low-quality Landsat pixels, and filtered out the pixels with NDVI < 0 (normally less than 1% in a single city). Cooling efficiency is known to be influenced by within-city heterogeneity92,93, and, as a result, might sometimes better fit non-linear relationships at local scales65,76. However, our central aim is to assess global cooling inequality based on generalized relationships that fit the majority of global cities. Previous studies have shown that linear relationships can do this job42,44,45, therefore, here we used linear models to assess cooling efficiency.

As a second step, we calculated the cooling capacity of each city. Cooling capacity is a positive function of the magnitude of cooling efficiency and the proportional area of green spaces in a city and is calculated based on NDVI and the derived cooling efficiency (Eq. 1, Supplementary Fig. 13):1 CClcz=∑i=1n(NDVIi−NDVImin)×CElczn

where CClcz and CElcz are the cooling capacity and cooling efficiency for a given local climate zone type in a city, respectively; NDVIi is the mean NDVI for 100-m grid cell i; NDVImin is the minimum NDVI across the city; and n is the total number of grid cells within the local climate zone. Local cooling capacity for each grid cell i (Fig. 1, Supplementary Fig. 7) can be derived in this way as well (Supplementary Fig. 13). For a particular city, cooling capacity may be dependent on the spatial configuration of its land use/cover28,94, but here we condensed cooling capacity to city average (Eq. 2), thus did not take into account these local-scale factors.2 CC=∑CClcz×nlczm

where CC is the average cooling capacity of a city; nlcz is the number of grid cells of the local climate zone; m is the total number of grid cells within the whole city.

As a third step, we calculated the cooling benefit realized by an average urban resident (cooling benefit in short) in each city. Cooling benefit depends not only on the cooling capacity of a city, but also on where people live within a city relative to greener or grayer areas of the city. For example, cooling benefits in a city might be low even if the cooling capacity is high if the green parts and the dense-population parts of a city are inversely correlated. Here, we are calculating these averages while aware that in any particular city the exposure of a particular person will depend on the distribution of green spaces in a city, and the occupation, movement trajectories of a person, etc. On the scale of a city, we calculated cooling benefit following a previous study35, that is, simply adding a weight term of population size per 100-m grid cell into cooling capacity in Eq. (1):3 CBlcz=∑i=1nNDVIi−NDVImin×CElcz×popipop¯×n

Where CBlcz is the cooling benefit of a given local climate zone type in a specific city, popi is the number of people within grid cell i, pop¯ is the mean population of the city.4 CB=∑CBlcz×nlczm

Where CB is the average cooling benefit of a city. The population data were obtained from the 100-m resolution WorldPop Global Project Population Data of 201883. Local cooling benefit for a given grid cell i can be calculated in a similar way, i.e., local cooling capacity multiplied by a weight term of local population density relative to mean population density. Local cooling benefits were mapped for example cities for the purpose of illustrating the effect of population spatial distribution (Fig. 1, Supplementary Fig. 7), but their patterns were not examined here.

Based on the aforementioned three key variables quantified at 100 m grid cells, we conducted multivariate analyses to examine if and to what extent cooling efficiency and cooling benefit are shaped by essential natural and socioeconomic factors, including background climate (mean annual temperature from ECMWF ERA5 dataset95 and precipitation from TerraClimate dataset96), topography (elevation range90), and GDP per capita97, with city size (geographic extent) corrected for. We did not include humidity because it is strongly correlated with temperature and precipitation, causing serious multi-collinearity problems. We used piecewise structural equation modeling to test the direct effects of these factors and indirect effects via influencing cooling efficiency and vegetation cover (Fig. 4c, Supplementary Fig. 8c). To account for the potential influence of spatial autocorrelation, we used spatially autoregressive models (SAR) to test for the robustness of the observed effects of natural and socioeconomic factors on cooling capacity and benefit (Supplementary Fig. 14).

Testing for robustness

We conducted the following additional analyses to test for robustness. We obtained consistent results from these robustness analyses.

(1) We looked at the mean hottest-month LST and NDVI within 3 years (2017-2019) to check the consistency between the results based on relatively short (1 year) vs. long (3-year average) time periods (Supplementary Fig. 15).

(2) We carried out the approach at a coarser spatial scale of 1 km, using MODIS-derived NDVI and LST, as well as the population data83 in the hottest month of 2018. In line with our finer-scale analysis of Landsat data, we selected the hottest month and excluded low-quality grids affected by cloud cover and water bodies98 (water cover > 20% in 1 × 1 km2 grid cells) of MODIS LST, and calculated the mean NDVI for the hottest month. We ultimately obtained 441 cities (or urban agglomerations) for analysis. At the 1 km resolution, some local climate zone types would yield insufficient samples for constructing cooling efficiency models. Therefore, instead of identifying local climate zone explicitly, we took an indirect approach to account for local climate confounding factors, that is, we constructed a multiple regression model for a whole city incorporating the hottest-month local temperature95, precipitation96, and humidity (based on NASA FLDAS dataset99), albedo (derived from the MODIS MCD43A3 product100), aerosol loading (derived from the MODIS MCD19A2 product101), wind speed (based on TerraClimate dataset96), topography elevation90, distance to water91, urban morphology (building height102), and human activity intensity (VIIRS nighttime light data as a proxy indicator103). We used the absolute value of the linear regression coefficient of NDVI as the cooling efficiency of the whole city (model: LST ~ NDVI + temperature + precipitation + humidity + distance to water + topography + building height + albedo + aerosol + wind speed + nighttime light), and calculated cooling capacity and cooling benefit based on the same method. Variable selection was conducted using the criterion of VIF < 5.

Our results indicated that MODIS-based cooling capacity and cooling benefit are significantly correlated with the Landsat-based counterparts (Supplementary Fig. 16); importantly, the gap between the Global South and North cities is around two-fold, close to the result from the Landsat-based result (Supplementary Fig. 17).

(3) For the calculation of cooling benefit, we considered different spatial scales of human accessibility to green spaces: assuming the population in each 100 × 100 m2 grid cell could access to green spaces within neighborhoods of certain extents, we calculated cooling benefit by replacing NDVIi in Eq. (3) with mean NDVI within the 300 × 300 m2 and 500 × 500 m2 extents centered at the focal grid cell (Supplementary Fig. 18).

(4) Considering cities may vary in minimum NDVI, we assessed if this variation could affect resulting cooling capacity patterns. To this end, we calculated the cooling capacity for each studied city using NDVI = 0 as the reference (i.e., using NDVI = 0 instead of minimum NDVI in Supplementary Fig. 13b), and correlated it with that using minimum NDVI as the reference (Supplementary Fig. 19).

Quantifying between-city inequality

Inequalities in access to the benefits of green spaces in cities exist within cities, as is increasingly well-documented104. Here, we focus instead on the inequalities among cities. We used the Gini coefficient to measure the inequality in cooling capacity and cooling benefit between all studied cities across the globe as well as between Global North or South cities. We calculated Gini using the population-density weighted method (Fig. 5b), as well as the unweighted and population-size weighted methods (Supplementary Fig. 20).

Estimating the potential for more effective and equal cooling amelioration

We estimated the potential of enhancing cooling amelioration based on the assumptions that urban green space quality (cooling efficiency) and quantity (NDVI) can be increased to different levels, and that relative spatial distributions of green spaces and population can be idealized (so that their spatial matches can maximize cooling benefit). We assumed that macro-climate conditions act as the constraints of vegetation cover and cooling efficiency. We calculated the 50th, 60th, 70th, 80th, and 90th percentiles of NDVI within each type of local climate zone of each city. For a given local climate zone type, we obtained the city with the highest NDVI per percentile value as the regional upper bounds of urban green infrastructure quantity. The regional upper bounds of cooling efficiency are derived in a similar way. For each local climate zone in a city, we generated a potential NDVI distribution where all grid cells reach the regional upper bound values for the 50th, 60th, 70th, 80th, or 90th percentile of urban green space quantity or quality, respectively. NDVI values below these percentiles were increased, whereas those above these percentiles remained unchanged. The potential estimates are essentially dependent on the references, i.e., the optimal cooling efficiency and NDVI that a given city can reach. However, such references are obviously difficult to determine, because complex natural and socioeconomic conditions could play important roles in determining those cooling optima, and the dominant factors are unknown at a global scale. We employed the simplifying assumption that background climate could act as an essential constraint according to our results. We therefore used the Köppen climate classification system105 to determine the reference separately in each climate region (tropical, arid, temperate, and continental climate regions were involved for all studied cities).

We calculated potential cooling capacity and cooling benefit based on these potential NDVI maps (Fixed cooling efficiency in Fig. 5). We then calculated the potentials if cooling efficiency of each city can be enhanced to 50–90th percentile across all urban local climate zones within the corresponding biogeographic region (Fixed green space area in Fig. 5). We also calculated the potentials if both NDVI and cooling efficiency were enhanced (Enhancing both in Fig. 5) to a certain corresponding level (i.e., ith percentile NDVI + ith percentile cooling efficiency). We examined if there are additional effects of idealizing relative spatial distributions of urban green spaces and humans on cooling benefits. To this end, the pixel values of NDVI or population amount remained unchanged, but their one-to-one correspondences were based on their ranking: the largest population corresponds to the highest NDVI, and so forth. Under each scenario, we calculated cooling capacity and cooling benefit for each city, and the between-city inequality was measured by the Gini coefficient.

We used the Google Earth Engine to process the spatial data. The statistical analyses were conducted using R v4.3.3106, with car v3.1-2107, piecewiseSEM v2.1.2108, and ineq v0.2-13109 packages. The global maps of cooling were created using the ArcGIS v10.3 software.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information

Peer Review File

Description of Additional Supplementary Files

Supplementary Data 1

Reporting Summary

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-024-51355-0.

Acknowledgements

We thank all the data providers. We thank Marten Scheffer for valuable discussion. C.X. is supported by the National Natural Science Foundation of China (Grant No. 32061143014). J.-C.S. was supported by Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by Danish National Research Foundation (grant DNRF173), and his VILLUM Investigator project “Biodiversity Dynamics in a Changing World”, funded by VILLUM FONDEN (grant 16549). W.Z. was supported by the National Science Foundation of China through Grant No. 42225104. T.M.L. and J.F.A. are supported by the Open Society Foundations (OR2021-82956). W.J.R. is supported by the funding received from Roger Worthington.

Author contributions

Y.L., S.N.T., R.R.D., and C.X. designed the study. Y.L. collected the data, generated the code, performed the analyses, and produced the figures with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., Z.Y., S.N.T., R.R.D. and C.X. Y.L., S.N.T., R.R.D. and C.X. wrote the first draft with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., and Z.Y. All coauthors interpreted the results and revised the manuscript.

Peer review

Peer review information

Nature Communications thanks Chris Webster and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

City population statistics data is collected from the Population Division of the Department of Economic and Social Affairs of the United Nations (https://www.un.org/development/desa/pd/content/worlds-cities-2018-data-booklet). Global North-South division is based on Human Development Report 2019 which from United Nations Development Programme (https://hdr.undp.org/content/human-development-report-2019). Global urban boundaries from GAIA data are available from Star Cloud Data Service Platform (https://data-starcloud.pcl.ac.cn/resource/14). Global water data is derived from 2018 Copernicus Global Land Service (CGLS 100-m) data (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global), European Space Agency (ESA) WorldCover 10 m 2020 product (https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100), and GSHHG (A Global Self-consistent, Hierarchical, High-resolution Geography Database) at https://www.soest.hawaii.edu/pwessel/gshhg/. Landsat 8 LST and NDVI data with 30 m resolution are available at https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2. Land surface temperature (LST) data with 1 km from MODIS Aqua product (MYD11A1) is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1. NDVI (1 km) dataset from MYD13A2 is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A2. Population data (100 m) is derived from WorldPop (https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop). Local climate zones are also based on 2018 CGLS data (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global), and built-up height data is available from Global Human Settlement Layers (GHSL, 100 m) (https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_H). Temperature data is calculated from ERA5-Land Monthly Aggregated dataset (https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR). Precipitation and wind data are calculated from TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho) (https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE). Humidity data is calculated from Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001). Topography data from MERIT DEM (Multi-Error-Removed Improved-Terrain DEM) product is available at https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3. GDP from Gross Domestic Product and Human Development Index dataset is available at 10.5061/dryad.dk1j0. VIIRS nighttime light data is available at https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG. City building volume data from Global 3D Building Structure (1 km) is available at 10.34894/4QAGYL. Albedo data is derived from the MODIS MCD43A3 product (https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3), and aerosol data is derived from the MODIS MCD19A2 product (https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES). All data used for generating the results are publicly available at 10.6084/m9.figshare.26340592.v1.

Code availability

The codes used for data collection and analyses are publicly available at 10.6084/m9.figshare.26340592.v1.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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