in Ecology

Comparing potential biodiversity conflicts from renewable energy expansion in China at different centralization levels

85 Views


Abstract

The rapid deployment of renewable energy creates urgent trade-offs with biodiversity conservation. The uneven distribution of renewable energy potential and biodiversity creates a critical governance challenge: at which administrative level should goal setting and spatial planning decisions be set to best mitigate these conflicts? Here we model the expansion of solar and wind energy in China through 2060 to reach its carbon neutrality goal, to compare a centralized national planning scenario against the current provincial (more decentralized) planning approach. We find that national sitings’ spatial conflict with richness-based conservation priorities is 4–7% less and overlaps 11–33% fewer species on average than the provincial planning. However, these gains are offset by greater conflicts with functional diversity and with open or dry biomes, including associated species such as the endangered marbled polecat (Vormela peregusna). By contrast, decentralized planning disproportionately overlapped more with the habitats of small-ranged forest species. Such divergence reveals the limitations of any single governance level in addressing these trade-offs, showing the imperative for integrated, multilevel siting frameworks. Effectively meeting dual climate and biodiversity targets requires strategies that synthesize strengths across administrative scales.

Access through your institution

Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Distribution of terrestrial solar and wind energy sites and their proportional overlap with major biomes (doughnut charts).
The alternative text for this image may have been generated using AI.
Fig. 2: Bivariate overlap between energy potentials and biodiversity metrics.
The alternative text for this image may have been generated using AI.
Fig. 3: Overlaps of existing and projected energy sites with overall biodiversity.
The alternative text for this image may have been generated using AI.
Fig. 4: Overlaps of existing and projected energy sites with four vertebrate taxa.
The alternative text for this image may have been generated using AI.
Fig. 5: AOH of individual species overlapped by projected energy sites.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Biodiversity benefits of China’s 20-year efforts in forest restoration

Article
Open access
28 November 2025

The impact of wind energy on plant biomass production in China

Article
Open access
15 December 2023

Impacts of climate change on the geographical distribution of rare and endangered Cyatheaceae in China: a MaxEnt model-based prediction

Article
Open access
29 April 2026

Data availability

The data used in this study are available from sources cited in Methods and listed below. Species distribution range polygons were obtained from the IUCN (https://www.iucnredlist.org/) for amphibians, reptiles and mammals, and from BirdLife International (http://datazone.birdlife.org/species/requestdis) for birds. Species conservation status and habitat preferences are available from the IUCN (https://www.iucnredlist.org/). The 2015 IUCN habitat classification map developed by ref. 88 is available for use via Google Earth Engine ((https://uploads.users.earthengine.app/view/habitat-types-map). Species traits and digitized solar panels were available from existing studies cited in Methods. Point data of operating solar farms between 2021 and 2023 were obtained from Global Energy Monitor (https://globalenergymonitor.org/). Wind turbine localities in 2020 and 2023 and powerlines used in the random forest model were obtained from existing studies and OpenStreetMap (https://www.openstreetmap.org/). Solar and wind power potentials were obtained from Global Solar Atlas (https://globalsolaratlas.info/) and Global Wind Atlas (https://globalwindatlas.info/). The ESA-CCI landcover map is available at www.esa-landcover-cci.org. The population density map is available at https://landscan.ornl.gov/. Terrestrial biomes are available at https://ecoregions.appspot.com/. Data necessary for analysis are available via Figshare at https://doi.org/10.6084/m9.figshare.29929544 (ref. 119).

Code availability

The code necessary for the analysis is available via Figshare at https://doi.org/10.6084/m9.figshare.29929544 (ref. 119).

References

  1. United Nations Framework Convention on Climate Change (UNFCCC) Summary of Global Climate Action at COP 28 (2023); https://unfccc.int/sites/default/files/resource/Summary_GCA_COP28.pdf

  2. Rehbein, J. A. et al. Renewable energy development threatens many globally important biodiversity areas. Glob. Change Biol. 26, 3040–3051 (2020).

    Article 

    Google Scholar 

  3. Bennun, L. et al. Mitigating Biodiversity Impacts Associated with Solar and Wind Energy Development. Guidelines for Project Developers (IUCN and The Biodiversity Consultancy, 2021).

  4. Sonter, L. J., Dade, M. C., Watson, J. E. & Valenta, R. K. Renewable energy production will exacerbate mining threats to biodiversity. Nat. Commun. 11, 4174 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  5. Peng, Y., Luo, Y., Xu, Z. & Jin, T. Ecological impacts of centralized large-scale photovoltaics and wind farms: progress and prospects. Biodivers. Sci. 32, 23212 (2024).

    Article 

    Google Scholar 

  6. Tinsley, E., Froidevaux, J. S., Zsebők, S., Szabadi, K. L. & Jones, G. Renewable energies and biodiversity: impact of ground-mounted solar photovoltaic sites on bat activity. J. Appl. Ecol. 60, 1752–1762 (2023).

    Article 

    Google Scholar 

  7. Lai, Y. C. et al. Endangered black-faced spoonbills alter migration across the Yellow Sea due to offshore wind farms. Ecology 106, e4485 (2025).

  8. Weber, J., Steinkamp, T. & Reichenbach, M. Competing for space? A multi-criteria scenario framework intended to model the energy–biodiversity–land nexus for regional renewable energy planning based on a German case study. Energy Sustain. Soc. 13, 1–33 (2023).

    Google Scholar 

  9. Dhunny, A. Z., Allam, Z., Lobine, D. & Lollchund, M. R. Sustainable renewable energy planning and wind farming optimization from a biodiversity perspective. Energy 185, 1282–1297 (2019).

    Article 

    Google Scholar 

  10. Stokes, D. Renewable energy federalism. Minn. Law Rev. 106, 1757 (2021).

    Article 

    Google Scholar 

  11. Saurer, J. & Monast, J. Renewable energy federalism in Germany and the United States. Transnatl. Environ. Law 10, 293–320 (2021).

    Article 

    Google Scholar 

  12. Reutter, F. & Lehmann, P. Environmental trade-offs of (de)centralized renewable electricity systems. Energy Sustain. Soc. 14, 37 (2024).

    Article 

    Google Scholar 

  13. Wiehe, J., Haaren, C. & Walter, A. How to achieve the climate targets? Spatial planning in the context of the German energy transition. Energy Sustain. Soc. 10, 1–12 (2020).

    Google Scholar 

  14. Harlan, T. Low-carbon frontier: renewable energy and the new resource boom in western China. China Q. 255, 591–610 (2023).

    Article 

    Google Scholar 

  15. National Development and Reform Commission of China (NDRC) et al. 14th Five-Year Plan for Renewable Energy Development (Public Version) [in Chinese]. (National Development and Reform Commission of China, 2021); https://www.ndrc.gov.cn/xwdt/tzgg/202206/P020220602315650388122.pdf

  16. Cai, B. et al. Assessment of China’s Wind and Solar Power Generation Potential [in Chinese]. (Chinese Academy of Environmental Planning, Ministry of Ecology and Environment, 2024); http://www.caep.org.cn/sy/tdftzhyjzx/zxdt/202406/t20240614_1075803.shtml

  17. The Biodiversity Committee of Chinese Academy of Sciences. Catalogue of Life China: 2022 Annual Checklist. Version 1.1. (Chinese Academy of Sciences, 2023); https://doi.org/10.15468/nwt6bh

  18. Mi, X. et al. The global significance of biodiversity science in China: an overview. Natl Sci. Rev. 8, nwab032 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  19. Liu, Y. & Zhou, Y. Territory spatial planning and national governance system in China. Land Use Policy 102, 105288 (2021).

    Article 

    Google Scholar 

  20. Heilmann, S. From local experiments to national policy: the origins of China’s distinctive policy process. China J. 59, 1–30 (2008).

    Article 

    Google Scholar 

  21. Zhu, M., Qi, Y., Belis, D., Lu, J. & Kerremans, B. The China wind paradox: the role of state-owned enterprises in wind power investment versus wind curtailment. Energy Policy 127, 200–212 (2019).

    Article 

    Google Scholar 

  22. Popescu, V. D. et al. Quantifying biodiversity trade-offs in the face of widespread renewable and unconventional energy development. Sci. Rep. 10, 7603 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  23. Bøe, V., Holden, E. & Linnerud, K. Measuring renewables’ impact on biosphere integrity: a review. Ecol. Indic. 156, 111135 (2023).

    Article 

    Google Scholar 

  24. Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).

    Article 

    Google Scholar 

  25. Orme, C. D. L. et al. Global hotspots of species richness are not congruent with endemism or threat. Nature 436, 1016–1019 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  26. Li, B. V. & Pimm, S. L. China’s endemic vertebrates sheltering under the protective umbrella of the giant panda. Conserv. Biol. 30, 329–339 (2016).

    Article 
    PubMed 

    Google Scholar 

  27. National Development and Reform Commission of China (NDRC) 13th Five-Year Plan for Renewable Energy Development (Public Release Version) [in Chinese]. (National Development and Reform Commission of China, 2016); https://www.ndrc.gov.cn/fggz/fzzlgh/gjjzxgh/201706/W020191104624285140648.pdf

  28. Wang, Y., Zhang, D., Ji, Q. & Shi, X. Regional renewable energy development in China: a multidimensional assessment. Renew. Sustain. Energy Rev. 124, 109797 (2020).

    Article 

    Google Scholar 

  29. Tian, J., Zhou, S. & Wang, Y. Assessing the technical and economic potential of wind and solar energy in China—a provincial-scale analysis. Environ. Impact Assess. Rev. 102, 107161 (2023).

    Article 

    Google Scholar 

  30. Liu, J. China’s renewable energy law and policy: a critical review. Renew. Sustain. Energy Rev. 99, 212–219 (2019).

    Article 

    Google Scholar 

  31. Li, S. et al. Analysis of status of photovoltaic and wind power abandoned in China. J. Power Energy Eng. 5, 91–100 (2017).

    Article 

    Google Scholar 

  32. Liu, J., Wang, Q., Song, Z. & Fang, F. Bottlenecks and countermeasures of high-penetration renewable energy development in China. Engineering 7, 1611–1622 (2021).

    Article 

    Google Scholar 

  33. Wu, J., Zuidema, C., Gugerell, K. & Roo, G. Mind the gap! Barriers and implementation deficiencies of energy policies at the local scale in urban China. Energy Policy 106, 201–211 (2017).

    Article 

    Google Scholar 

  34. National Development and Reform Commission of China (NDRC) and National Energy Administration (NEA). 14th Five-Year Plan: Modern Energy System Planning [in Chinese]. (National Development and Reform Commission of China, 2022); https://www.ndrc.gov.cn/xxgk/zcfb/ghwb/202203/P020220322582066837126.pdf

  35. National Forestry and Grassland Administration (NFSA) and National Energy Administration (NEA) Notice on Supporting the Standardized Use of Forest and Grassland Land for Wind Power Development and Construction [in Chinese]. (National Forestry and Grassland Administration, 2026); https://www.forestry.gov.cn/lyj/1/lczc/20260129/658544.html

  36. Hu, Y. et al. Spatial patterns and conservation of genetic and phylogenetic diversity of wildlife in China. Sci. Adv. 7, 5725 (2021).

    Article 

    Google Scholar 

  37. Jiang, D. et al. Effects of climate change and anthropogenic activity on ranges of vertebrate species endemic to the Qinghai–Tibet Plateau over 40 years. Conserv. Biol. 37, e14069 (2023).

    Article 
    PubMed 

    Google Scholar 

  38. Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).

    Article 

    Google Scholar 

  39. Lewin, A., Murali, G., Rachmilevitch, S. & Roll, U. Global evaluation of current and future threats to drylands and their vertebrate biodiversity. Nat. Ecol. Evol. 8, 1448–1458 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  40. Wang, X. et al. Desert ecosystems in China: past, present, and future. Earth Sci. Rev. 234, 104206 (2022).

    Article 
    CAS 

    Google Scholar 

  41. Gong, C. & Shao, L. Study on anti-birds strategy of overhead transmission line based on migration and breeding habits of Ciconia boyciana in Heilongjiang province. Int. J. New Dev. Eng. Soc. 7, 6–13 (2023).

    Google Scholar 

  42. Shobrak, M. et al. Electric infrastructure poses a significant threat at congregation sites of the globally threatened steppe eagle Aquila nipalensis in Saudi Arabia. Bird Conserv. Int. 32, 313–321 (2022).

    Article 

    Google Scholar 

  43. Chichorro, F., Juslén, A. & Cardoso, P. A review of the relation between species traits and extinction risk. Biol. Conserv. 237, 220–229 (2019).

    Article 

    Google Scholar 

  44. Wang, Z. N., Yang, L., Fan, P. F. & Zhang, L. Species bias and spillover effects in scientific research on Carnivora in China. Zool. Res. 42, 354 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  45. Wong, M. H. G., Mo, Y. & Chan, B. P. L. Past, present and future of the globally endangered Eld’s deer (Rucervus eldii) on Hainan Island, China. Glob. Ecol. Conserv. 26, e01505 (2021).

    Google Scholar 

  46. Shao, M. et al. A review of multi-criteria decision making applications for renewable energy site selection. Renew. Energy 157, 377–403 (2020).

    Article 

    Google Scholar 

  47. Wang, Y., Liu, B., Peng, H. & Jiang, Y. Locating the suitable large-scale solar farms in China’s deserts with environmental considerations. Sci. Total Environ. 955, 176911 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  48. Chen, W., Li, H. & Wu, Z. Western China energy development and west to east energy transfer: application of the western China sustainable energy development model. Energy Policy 38, 7106–7120 (2010).

    Article 

    Google Scholar 

  49. An Energy Sector Roadmap to Carbon Neutrality in China. (International Energy Agency, 2021); https://www.iea.org/reports/an-energy-sector-roadmap-to-carbon-neutrality-in-china

  50. Sun, Y., Li, Y., Wang, R. & Ma, R. Modelling potential land suitability of large-scale wind energy development using explainable machine learning techniques: applications for China, USA and EU. Energy Convers. Manag. 302, 118131 (2024).

    Article 

    Google Scholar 

  51. Sun, Y., Zhu, D., Li, Y., Wang, R. & Ma, R. Spatial modelling the location choice of large-scale solar photovoltaic power plants: application of interpretable machine learning techniques and the national inventory. Energy Convers. Manag. 289, 117198 (2023).

    Article 

    Google Scholar 

  52. ESMAP Global Solar Atlas 2.0 Technical Report (World Bank, 2019).

  53. Global horizontal irradiation. Global Solar Atlas 2.0 (World Bank Group) https://globalsolaratlas.info

  54. Lorenz, E. et al. Benchmarking of different approaches to forecast solar irradiance. In 24th European Photovoltaic Solar Energy Conference 21–25 (2009).

  55. Inman, R. H., Pedro, H. T. & Coimbra, C. F. Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 39, 535–576 (2013).

    Article 

    Google Scholar 

  56. Davis, N. N. et al. The Global Wind Atlas: a high-resolution dataset of climatologies and associated web-based application. Bull. Am. Meteorol. Soc. 104, E1507–E1525 (2023).

    Article 

    Google Scholar 

  57. Wind power density. Global Wind Atlas version 4.0 (World Bank Group); https://globalwindatlas.info

  58. Lebakula, V. et al. LandScan Silver Edition. (Oak Ridge National Laboratory, 2024); https://doi.org/10.48690/1531770

  59. Rose, A., McKee, J., Urban, M., Bright, E. & Sims, K. LandScan Global 2018. (Oak Ridge National Laboratory, 2019) https://doi.org/10.48690/1524213

  60. OpenStreetMap (OpenStreetMap, 2023); https://www.openstreetmap.org/

  61. Land Cover CCI Product User Guide Version 2 Tech. Rep. (European Space Agency, 2017); http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

  62. Defourny, P. et al. Observed Annual Global Land-Use Change from 1992 to 2020 Three Times More Dynamic Than Reported by Inventory-Based Statistics. in preparation, (2023); https://maps.elie.ucl.ac.be/CCI/viewer/download.php

  63. Liu, J., Wang, J. & Li, L. Vectorized solar photovoltaic installation dataset across China in 2015 and 2020. Sci. Data 11, 1446 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  64. Feng, Q. et al. A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020. Sci. Data 11, 198 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  65. Dunnett, S., Sorichetta, A., Taylor, G. & Eigenbrod, F. Harmonised global datasets of wind and solar farm locations and power. Sci. Data 7, 130 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  66. Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

    Google Scholar 

  67. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2024).

  68. Wickham, H., François, R., Henry, L., Müller, K. & Vaughan, D. dplyr: A Grammar of Data Manipulation. R version 1.1.4 (2023).

  69. Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).

    Article 

    Google Scholar 

  70. Li, M. et al. High-resolution data shows China’s wind and solar energy resources are enough to support a 2050 decarbonized electricity system. Appl. Energy 306, 117996 (2022).

    Article 

    Google Scholar 

  71. Hijmans, R. terra: Spatial Data Analysis. R version 1.8-29 https://CRAN.R-project.org/package=terra (2025).

  72. China Interim Administrative Measures for Examination and Approval of Constructing Facilities in National Nature Reserves. (National Forestry and Grassland Administration, 2018).

  73. Guan, J. Westerly breezes and easterly gales: a comparison of legal, policy and planning regimes governing onshore wind in Germany and China. Energy Res. Soc. Sci. 67, 101506 (2020).

    Article 

    Google Scholar 

  74. Wang, Z. et al. Research paradigm for high-precision, large-scale wind energy potential: an example of a geographically-constrained multi-criteria decision analysis model at the km-level in China. J. Clean. Prod. 429, 139614 (2023).

    Article 

    Google Scholar 

  75. Global Solar Power Tracker. (Global Energy Monitor, 2025); https://globalenergymonitor.org/projects/global-solar-power-tracker/

  76. Gaultier, S. P., Lilley, T. M., Vesterinen, E. J. & Brommer, J. E. The presence of wind turbines repels bats in boreal forests. Landsc. Urban Plan. 231, 104636 (2023).

    Article 

    Google Scholar 

  77. Marques, A. T. et al. Wind turbines cause functional habitat loss for migratory soaring birds. J. Anim. Ecol. 89, 93–103 (2020).

    Article 
    PubMed 

    Google Scholar 

  78. Tolvanen, A., Routavaara, H., Jokikokko, M. & Rana, P. How far are birds, bats, and terrestrial mammals displaced from onshore wind power development?—A systematic review. Biol. Conserv. 288, 110382 (2023).

    Article 

    Google Scholar 

  79. Olson, D. M. & Dinerstein, E. The Global 200: priority ecoregions for global conservation. Ann. Mo. Bot. Gard. 89, 199–224 (2002).

    Article 

    Google Scholar 

  80. Li, B. V. & Pimm, S. L. How China expanded its protected areas to conserve biodiversity. Curr. Biol. 30, 1334–1340 (2020).

    Article 

    Google Scholar 

  81. The IUCN Red List of Threatened Species Version 2021-3 (International Union for Conservation of Nature, 2022); https://www.iucnredlist.org

  82. BirdLife International & Handbook of the Birds of the World. Bird Species Distribution Maps of the World (Version 2021.1) (BirdLife Internatonal, 2021); http://datazone.birdlife.org/species/requestdis

  83. Mair, L. et al. A metric for spatially explicit contributions to science-based species targets. Nat. Ecol. Evol. 5, 836–844 (2021).

    Article 
    PubMed 

    Google Scholar 

  84. Brooks, T. M. et al. Measuring terrestrial area of habitat (AOH) and its utility for the IUCN Red List. Trends Ecol. Evol. 34, 977–986 (2019).

    Article 
    PubMed 

    Google Scholar 

  85. A Global Standard for the Identification of Key Biodiversity Areas. Version 1.0. (IUCN, 2016).

  86. Lumbierres, M. et al. Area of habitat maps for the world’s terrestrial birds and mammals. Sci. Data 9, 749 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  87. Farr, T. G. et al. The Shuttle Radar Topography Mission. Rev. Geophys. 45, 2005RG000183 (2007).

    Article 

    Google Scholar 

  88. Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  89. Chamberlain, S. rredlist: ‘IUCN’ Red List Client. R version 00 (2022).

  90. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article 

    Google Scholar 

  91. Scott, J. M., Csuti, B., Jacobi, J. D. & Estes, J. E. Species richness. Bioscience 37, 782–788 (1987).

    Article 

    Google Scholar 

  92. Whittaker, R. J., Willis, K. J. & Field, R. Scale and species richness: towards a general, hierarchical theory of species diversity. J. Biogeogr. 28, 453–470 (2001).

    Article 

    Google Scholar 

  93. Schleuter, D., Daufresne, M., Massol, F. & Argillier, C. A user’s guide to functional diversity indices. Ecol. Monogr. 80, 469–484 (2010).

    Article 

    Google Scholar 

  94. Institute of Zoology, Chinese Academy of Sciences. China Animal Scientific Database (V1). (National Science and Technology Infrastructure, National Basic Science Data Center, 2021); https://cstr.cn/16666.11.nbsdc.rkdf5jqo

  95. Li, B. V., Jenkins, C. N. & Xu, W. Strategic protection of landslide vulnerable mountains for biodiversity conservation under land-cover and climate change impacts. Proc. Natl Acad. Sci. USA 119, e2113416118 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  96. Botta-Dukát, Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 16, 533–540 (2005).

    Article 

    Google Scholar 

  97. Song, Y.-F., Chen, C.-W. & Wang, Y.-P. A dataset on the life-history and ecological traits of Chinese amphibians [in Chinese]. Biodivers. Sci. 30, 1005–0094 (2022).

    Article 

    Google Scholar 

  98. Wang, Y. et al. A dataset on the life-history and ecological traits of Chinese birds [in Chinese]. Biodivers. Sci. 29, 1149–1153 (2021).

    Article 

    Google Scholar 

  99. Ding, C. et al. A dataset on the morphological, life-history, and ecological traits of mammals in China [in Chinese]. Biodivers. Sci. 30, 21520 (2022).

    Article 

    Google Scholar 

  100. Wang, J. et al. A dataset of the morphological, life-history, and ecological traits of snakes in China [in Chinese]. Biodivers. Sci. 31, 23126 (2023).

    Article 

    Google Scholar 

  101. Zhong, Y., Chen, C. & Wang, Y. A dataset on the life-history and ecological traits of Chinese lizards [in Chinese]. Biodivers. Sci. 30, 22071 (2022).

    Article 

    Google Scholar 

  102. Etard, A., Morrill, S. & Newbold, T. Global gaps in trait data for terrestrial vertebrates. Glob. Ecol. Biogeogr. 29, 2143–2158 (2020).

    Article 

    Google Scholar 

  103. Feldman, A., Sabath, N., Pyron, R. A., Mayrose, I. & Meiri, S. Body sizes and diversification rates of lizards, snakes, amphisbaenians and the tuatara. Glob. Ecol. Biogeogr. 25, 187–197 (2016).

    Article 

    Google Scholar 

  104. The IUCN Red List of Threatened Species (Version 2024-2). (International Union for Conservation of Nature, 2025); https://www.iucnredlist.org

  105. Uetz, P. et al. (eds.) The Reptile Database (accessed 5 March 2025); https://reptile-database.reptarium.cz/

  106. Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).

    Article 
    PubMed 

    Google Scholar 

  107. Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring Functional Diversity from Multiple Traits, and Other Tools for Functional Ecology. R version 1.0–12.3 https://doi.org/10.32614/CRAN.package.FD (2014).

  108. Wickham, H., Hester, J. & Bryan, J. readr: Read Rectangular Text Data. R version 2.1.5 https://CRAN.R-project.org/package=readr (2024).

  109. Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).

    Article 

    Google Scholar 

  110. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  111. Staude, I. R., Navarro, L. M. & Pereira, H. M. Range size predicts the risk of local extinction from habitat loss. Glob. Ecol. Biogeogr. 29, 16–25 (2020).

    Article 

    Google Scholar 

  112. Harris et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  113. The Pandas Development Team pandas-dev/pandas: Pandas. Zenodo https://doi.org/10.5281/zenodo.3509134 (2026).

  114. Bossche, J. V. et al. geopandas/geopandas: v1.0.1. Zenodo https://doi.org/10.5281/zenodo.12625316 (2024).

  115. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  116. Caswell, T. A. et al. matplotlib/matplotlib: REL: v3.6.3. Zenodo https://doi.org/10.5281/zenodo.7527665 (2023).

  117. Gillies, S. Rasterio: geospatial raster I/O for Python programmers. Github https://github.com/rasterio/rasterio (2013).

  118. Da Costa-Luis, C. et al. tqdm: a fast, extensible progress bar for Python and CLI. Zenodo https://doi.org/10.5281/zenodo.7697295 (2023).

  119. Zhou, Z., Sun, S., Zhang, J., Fang, Y. & Li, B. V. Code and data for Zhou et al. “Comparing potential biodiversity conflicts from renewable energy expansion in China at different centralization levels”. figshare https://doi.org/10.6084/m9.figshare.29929544.v1 (2026).

Download references

Acknowledgements

We thank F. Xie and H. Guo for their insights on China’s renewable energy policy. We are grateful for the support of the Chancellors’ Scholarship and the Guo Tingting Scholarship iMEP Master Project Award from Duke Kunshan University (to Z.Z.) and funding support from the National Natural Science Foundation of China (32422056 to B.V.L. and 72373058 to J.Z.).

Author information

Author notes

  1. These authors contributed equally: Zhijie Zhou, Siyu Sun.

Authors and Affiliations

  1. Environmental Research Center, Duke Kunshan University, Kunshan, China

    Zhijie Zhou 
    (周智杰), Siyu Sun 
    (孙思雨), Junjie Zhang 
    (张俊杰), Yixin Fang 
    (方意欣) & Binbin V. Li 
    (李彬彬)

  2. Department of Geography and Geographic Information Science, The University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Zhijie Zhou 
    (周智杰)

  3. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Junjie Zhang 
    (张俊杰) & Binbin V. Li 
    (李彬彬)

  4. Department of Wildland Resources, Utah State University, Logan, UT, USA

    Yixin Fang 
    (方意欣)

Authors

  1. Zhijie Zhou 
    (周智杰)

    View author publications

    Search author on:PubMed Google Scholar

  2. Siyu Sun 
    (孙思雨)

    View author publications

    Search author on:PubMed Google Scholar

  3. Junjie Zhang 
    (张俊杰)

    View author publications

    Search author on:PubMed Google Scholar

  4. Yixin Fang 
    (方意欣)

    View author publications

    Search author on:PubMed Google Scholar

  5. Binbin V. Li 
    (李彬彬)

    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization: Z.Z. and B.V.L. Methodology: Z.Z., B.V.L., J.Z. and S.S. Data curation: Z.Z., Y.F. and S.S. Formal analysis: S.S. and Z.Z. Visualization: S.S., Z.Z. and B.V.L. Writing—original draft: Z.Z. and S.S. Writing—review and editing: S.S., B.V.L., Z.Z., J.Z. and Y.F. Project administration: B.V.L. Supervision: B.V.L. Funding acquisition: B.V.L.

Corresponding author

Correspondence to
Binbin V. Li 
(李彬彬).

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Jose Rehbein and Andrea Santangeli for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Major topographic features and biomes in China.

a, Map of major topographic features of China derived from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM)87. b, Map of major biomes in China based on refs. 109,110 and obtained from https://ecoregions.appspot.com/. All maps were created using ArcGIS Pro (Esri). Province map reproduced from Tianditu (map approval number GS(2024)0605; www.tianditu.gov.cn).

Extended Data Fig. 2 Spatial overlaps between renewable energy potential and integrated vertebrate biodiversity.

Maps show bivariate overlap between solar and wind energy potentials and integrated biodiversity scores of amphibians (a-b), reptiles (c-d), birds (e-f), and mammals (g-h). Province map reproduced from Tianditu (map approval number GS(2024)0605; www.tianditu.gov.cn). Silhouettes from PhyloPic under a Creative Commons license: frog by Steven Traver and leopard by Margot Michaud (CC0 1.0); lizard by Vijay Karthick and bird by Xgirouxb (PDM 1.0). Solar panel and wind turbine icons from Freepik (www.freepik.com).

Extended Data Fig. 3 Area of habitat (AOH) of individual species overlapped by existing energy sites.

Bar graphs showed the top 5 species of concern (IUCN-listed as threatened or endemic to China) most subject to existing solar and wind energy, ranked by the percentage (%) of AOH in China overlapped (orange) and the absolute (km2) AOH overlapped (blue). Each species is denoted by its scientific name and the corresponding taxon icon. Bars with shade indicate small-ranged species (AOH < 10,000 km2). See Supplementary Table 9 and 10 for additional information. Silhouettes from PhyloPic under a Creative Commons license: frog by Steven Traver and leopard by Margot Michaud (CC0 1.0); lizard by Vijay Karthick and bird by Xgirouxb (PDM 1.0). Solar panel and wind turbine icons from Freepik (www.freepik.com).

Supplementary information

Supplementary Information (download PDF )

Supplementary Tables 1–12 and Supplementary Analysis.

Reporting Summary (download PDF )

Peer Review File (download PDF )

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Zhou, Z., Sun, S., Zhang, J. et al. Comparing potential biodiversity conflicts from renewable energy expansion in China at different centralization levels.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-03098-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41559-026-03098-y

Source: Ecology - nature.com

Mapping an ocean metabolite’s path

How the proposal for a new regulation for new genomic techniques affects the European Union’s food system sustainability objectives