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Contrasting effects of urbanization on vegetation between the Global South and Global North

Nature Sustainability volume 8, pages 373–384 (2025)Cite this article

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Abstract

Urban vegetation, the core component of green infrastructure and critical for sustainable cities, is profoundly affected by the process of urbanization. Urbanization not only leads to substantial vegetation loss (direct impact) but also fosters urban vegetation growth (indirect impact). However, the extent to which these direct and indirect impacts affect vegetation dynamics across cities worldwide and how urban greening will change in the future remain unclear. Using satellite-based greenness and impervious surface datasets, we show that positive indirect impacts mitigated 56.85% of the negative direct impacts across 4,718 cities worldwide from 2000 to 2019. Notably, the offsetting coefficient is much greater in Global North cities (79.13%) than in Global South cities (38.01%) partly due to their socioeconomic differences. This disparity in urban greening dynamics will continue in the future. Approximately 60% of Global North cities and 30% of Global South cities will become greener by 2040. Our results reveal the divergent trade-offs between vegetation loss and enhanced vegetation growth in cities of different socioeconomic levels and stages of urbanization. Such insights are crucial for a comprehensive understanding of urban greening dynamics and for devising strategies to attain sustainable development goals.

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Fig. 1: Increased urbanization and the changes in vegetation greenness in different parts of the world during 2000 to 2019.
Fig. 2: Schematic diagram of the effects of urbanization and macroclimate change on vegetation growth.
Fig. 3: Direct and indirect effects of urbanization vegetation greenness from 2000 to 2019 across 4,718 cities worldwide.
Fig. 4: Vegetation growth enhancement driven by indirect effects of urbanization.
Fig. 5: Differences in the offsetting coefficients of the indirect impacts to direct impacts across 4,718 cities worldwide.
Fig. 6: The projected vegetation changes of global cities under different Shared Socioeconomic Pathways from 2020 to 2040.

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Data availability

The datasets in this study are publicly available as follows or can be obtained from Google Earth Engine. MODIS vegetation greenness data (MOD13A1) and landcover data (MCD13Q1) are available at https://ladsweb.modaps.eosdis.nasa.gov. GTOPO30 digital elevation model data are available at https://earthexplorer.usgs.gov. The GUB dataset is available at https://data-starcloud.pcl.ac.cn/resource/14. The GISA v.2.0 dataset can be obtained from http://irsip.whu.edu.cn/resources/resources_en_v2.php. The dataset of global future urban expansion can be obtained from the National Tibetan Plateau Data Center (https://doi.org/10.11888/HumanNat.tpdc.272853). The TerraClimate dataset is available from https://www.climatologylab.org/terraclimate.html. The GDP and HDI data were obtained from https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0. The WorldPop gridded population density dataset is available at https://hub.worldpop.org/project/categories?id=18. The gridded datasets for population and economy under Shared Socioeconomic Pathways are available from the Science Data Bank (https://doi.org/10.57760/sciencedb.01683). The nine CMIP6 model outputs can be obtained from the Institute Pierre-Simon Laplace server (https://esgf-node.ipsl.upmc.fr/search/cmip6-ipsl/). The administration area data used for mapping were obtained from https://www.naturalearthdata.com/downloads/50m-cultural-vectors/.

Code availability

The scripts for performing the analysis in Google Earth Engine (https://earthengine.google.com/), drafting the figures in MATLAB R2021b and estimating the projected urban vegetation changes in R v.4.2.2 are available from https://doi.org/10.5281/zenodo.14630847 (ref. 61). The scripts for performing the main analysis in Google Earth Engine can also be obtained from https://code.earthengine.google.com/?accept_repo=users/171830520nju/Urbanization_global.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (42175136, 42130602), and the Jiangsu Collaborative Innovation Center for Climate Change. T.C.’s contribution was supported by the US Department of Energy (DOE), Office of Science, Biological and Environmental Research programme through an Early Career award. Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. We also thank the dataset providers for sharing the data.

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Authors and Affiliations

  1. School of Atmospheric Sciences, Nanjing University, Nanjing, China

    Jiuyi Chen, Bo Qiu, Xin Miao, Yipeng Cao, Lingfeng Li, Siwen Zhao, Yueyang Ni, Xiaohui Tian & Weidong Guo

  2. Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, China

    Bo Qiu

  3. Pacific Northwest National Laboratory, Richland, WA, USA

    TC Chakraborty & Yun Qian

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Contributions

J.C. developed the conceptual framework of this research, performed the analysis and drafted the figures. J.C. and B.Q. drafted the initial version of the manuscript. B.Q. and W.G. provided conceptualization and supervision, and carried out funding acquisition. T.C., Y.Q., X.M., Y.C., L.L., S.Z., Y.N., X.T. and W.G. contributed to writing the final paper and the interpretation of the results.

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Correspondence to Bo Qiu or Weidong Guo.

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Extended data

Extended Data Fig. 1 Changes in the fractions of impervious surfaces area (Δβ) and EVI in urban areas during 2000–2019 for global cities.

a, c, The spatial pattern of Δβ (a) and ΔEVI (c) for global 4718 cities. b, d, Histogram of the probability density of cities with different Δβ (b) and ΔEVI (d). The red line represents the medium Δβ and ΔEVI of the cities.

Extended Data Fig. 2 Temporal trends of the fraction of impervious surfaces (β) and EVI in urban areas for global cities.

a,b, The average fraction of impervious surfaces (β) for (a) all 4718 cities and (b) seven parts of the world during 2000–2020. c, d, Same as a,b but for EVI. EAS: East Asia and the Pacific, 963 cities; ECS, Europe and Central Asia, 1512 cities; LCN: Latin America and Caribbean, 553 cities; MEA: Middle East and North Africa, 142 cities; NAC: North America, 1202 cities; SAS: South Asia, 80 cities; SSF: Sub-Saharan Africa, 266 cities. The shaded ranges in a and c denote the range of 5–95% β or EVI for global 4718 cities.

Extended Data Fig. 3 Effects of macroclimate changes on vegetation growth across cities worldwide.

a, EVI in 2000 for cities worldwide. b, Probability density of cities with different EVIs in 2000. c, The extent of vegetation growth enhancement driven by macroclimate change (γ) for global cities during 2000–2019. d, Probability density of cities with different γ. e, Hypothetical EVI changes (VI') only driven by macroclimate changes (without considering the increased urbanization) for global cities during 2000–2019. f, Probability density of cities with different VI'. The red lines are the average values of each variable of the cities weighted by the urban area.

Extended Data Fig. 4 Direct and indirect impacts of urbanization on vegetation greenness from 2000 to 2019.

a, Changes of average EVI in urban areas (ΔEVI) from 2000 to 2019 for global cities. b, Probability density of cities with different ΔEVIs. c,e, Same as a but for the EVI changes induced by the (c) direct and (e) indirect impacts of urbanization. d, f, Same as b but for the direct impact (d) and indirect (f) impacts on the EVI from 2000 to 2019. The red lines are the average values of EVI and the direct and indirect impacts of cities weighted by urban areas.

Extended Data Fig. 5 The effect of socioeconomic development levels on the direct and indirect impacts of urbanization on vegetation greenness.

a, The relationship between the rate of urban expansion (Δβ) and Human Development Index (HDI). The solid lines indicate significant trends, and the shaded areas represent 95% confidence intervals. The small dots denote global 4718 cities and the big dots denote the average Δβ for cities in each 0.025 HDI bin. Significance was determined by two-side Student’s t-test (P = 3.1 × 10−223, n = 4718). b, Same as a but for the direct effects on vegetation (ωd) (P = 1.4 × 10−225, n = 4718). c, Same as a but for the indirect effects (ωi) (P = 6.9 × 10−14, n = 4718).

Extended Data Fig. 6 The projected direct, indirect and climatic impacts on vegetation greenness for global cities under the SSP1-RCP2.6 scenario from 2020 to 2040.

a, Estimated impacts of macroclimate changes on the EVI by 2040 across global cities under SSP1-RCP2.6 scenario. b, Climatic impact on the EVI for cities in different regions across the world. EAS: East Asia and the Pacific, 963 cities; ECS, Europe and Central Asia, 1512 cities; LCN: Latin America and Caribbean, 553 cities; MEA: Middle East and North Africa, 142 cities; NAC: North America, 1202 cities; SAS: South Asia, 80 cities; SSF: Sub-Saharan Africa, 266 cities. The white dots represent the average climatic impact on the EVI weighted by urban areas for each region. The shaded boxes and vertical lines represent the ranges of 25–75% and 10–90%, respectively. c, The probability distributions of climatic impacts for the cities in the Global North and Global South. The dotted lines are the average climatic impacts weighted by urban areas for GN cities and GS cities. The numbers are the proportions of cities with positive or negative climatic impacts. d–f, Same as a–c but for projected direct impacts of urbanization. g–i, Same as a–c but for projected indirect impacts of urbanization.

Extended Data Fig. 7 The projected direct, indirect and climatic impacts on vegetation greenness for global cities under the SSP2-RCP4.5 scenario from 2020 to 2040.

a, Estimated impacts of macroclimate changes on the EVI by 2040 across global cities under SSP2-RCP4.5 scenario. b, Climatic impact on the EVI for cities in different regions across the world. EAS: East Asia and the Pacific, 963 cities; ECS, Europe and Central Asia, 1512 cities; LCN: Latin America and Caribbean, 553 cities; MEA: Middle East and North Africa, 142 cities; NAC: North America, 1202 cities; SAS: South Asia, 80 cities; SSF: Sub-Saharan Africa, 266 cities. The white dots represent the average climatic impact on the EVI weighted by urban areas for each region. The shaded boxes and vertical lines represent the ranges of 25–75% and 10–90%, respectively. c, The probability distributions of climatic impacts for the cities in the Global North and Global South. The dotted lines are the average climatic impacts weighted by urban areas for GN cities and GS cities. The numbers are the proportions of cities with positive or negative climatic impacts. d–f, Same as a–c but for projected direct impacts of urbanization. g–i, Same as a–c but for projected indirect impacts of urbanization.

Extended Data Fig. 8 The projected direct, indirect and climatic impacts on vegetation greenness for global cities under the SSP5-RCP8.5 scenario from 2020 to 2040.

a, Estimated impacts of macroclimate changes on the EVI by 2040 across global cities under SSP5-RCP8.5 scenario. b, Climatic impact on the EVI for cities in different regions across the world. EAS: East Asia and the Pacific, 963 cities; ECS, Europe and Central Asia, 1512 cities; LCN: Latin America and Caribbean, 553 cities; MEA: Middle East and North Africa, 142 cities; NAC: North America, 1202 cities; SAS: South Asia, 80 cities; SSF: Sub-Saharan Africa, 266 cities. The white dots represent the average climatic impact on the EVI weighted by urban areas for each region. The shaded boxes and vertical lines represent the ranges of 25–75% and 10–90%, respectively. c, The probability distributions of climatic impacts for the cities in the Global North and Global South. The dotted lines are the average climatic impacts weighted by urban areas for GN cities and GS cities. The numbers are the proportions of cities with positive or negative climatic impacts. d–f, Same as a–c but for projected direct impacts of urbanization. g–i, Same as a–c but for projected indirect impacts of urbanization.

Extended Data Fig. 9 The projected offsetting coefficients (η) of indirect impacts to direct impacts for global cities under different Shared Socioeconomic Pathways from 2020 to 2040.

a, Estimated η across global cities under SSP1-RCP2.6 scenario. b, The η for cities in different regions across the world. EAS: East Asia and the Pacific, 963 cities; ECS, Europe and Central Asia, 1512 cities; LCN: Latin America and Caribbean, 553 cities; MEA: Middle East and North Africa, 142 cities; NAC: North America, 1202 cities; SAS: South Asia, 80 cities; SSF: Sub-Saharan Africa, 266 cities. The white dots represent the average climatic impact on the EVI weighted by urban areas for each region. The shaded boxes and vertical lines represent the ranges of 25–75% and 10–90%, respectively. c, The probability distribution of η for cities in Global North and Global South. The dotted lines are the average η weighted by urban areas for the GN cities and GS cities. d–f, Same as a–c but for η under the SSP2-RCP4.5 scenario. g–i, Same as a–c but for η under the SSP5-RCP8.5 scenario.

Extended Data Fig. 10 Differences in climatic and socioeconomic characteristic between Global North cities and Global South cities.

The distributions of (a) mean annual temperature, (b) mean annual precipitation, (c) GDP per capita, (d) compound annual growth rate of GDP per capita, (e) population density, and (f) compound annual growth rate of population density the cities in the GS (n = 2888) and GN (n = 1830). The black lines represent the average value of each variable for the GS cities and GN cities. The shaded boxes and the vertical lines represent the ranges of 25–75% and 5–95%, respectively.

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Chen, J., Qiu, B., Chakraborty, T. et al. Contrasting effects of urbanization on vegetation between the Global South and Global North. Nat Sustain 8, 373–384 (2025). https://doi.org/10.1038/s41893-025-01520-0

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