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Enhancing carbon sinks in China using a spatially-optimized forestation strategy
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  • Published: 12 January 2026

Enhancing carbon sinks in China using a spatially-optimized forestation strategy

  • Yanli Dong1,
  • Zhen Yu  ORCID: orcid.org/0000-0002-7729-249X1,2,3,
  • Thomas Pugh  ORCID: orcid.org/0000-0002-6242-73714,
  • Evgenios Agathokleous  ORCID: orcid.org/0000-0002-0058-48571,3,5,
  • Fangmin Zhang1,3,
  • Stephen Sitch  ORCID: orcid.org/0000-0003-1821-85616,
  • Weibin You7,8,
  • Wangya Han  ORCID: orcid.org/0000-0002-1075-15401,3,
  • Stefan Olin  ORCID: orcid.org/0000-0002-8621-33004,
  • Shirong Liu  ORCID: orcid.org/0000-0002-1103-98032,9,
  • Guoyi Zhou1,3,
  • Pedro Cabral1,10 &
  • …
  • Pengsen Sun2 

Nature Communications , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Carbon cycle
  • Forestry

Abstract

China plans expanding 49.5 million hectares of new forests by 2050 to strengthen carbon sequestration. However, estimates of the carbon benefits from this expansion rarely consider the effect of ‘forest edge’, where tree mortality increases under intensified stress from wind, drought, pests, and fire. Here we show that proximity to forest edges substantially reduces biomass carbon storage, and develop a spatial optimization strategy that prioritizes planting in areas that minimize edge effects. Our projections show that forestation optimized for edge effects results in a 51% increase in carbon gain (986 ± 22 Tg by 2060), with approximately half of the total gain driven by reduced edge effects. These findings demonstrate that ignoring edge effects can significantly overestimate carbon sink potential and highlight spatially optimized forestation as a pathway to maximize climate mitigation and ecological benefits.

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Data availability

The historical climate data is available from https://data.tpdc.ac.cn/. The future climate data is provided at: https://cds.climate.copernicus.eu/. The land cover maps used are the China Land Use and Cover Change dataset, which can be obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences: http://www.resdc.cn. The soil data is available from https://soil.geodata.cn/. The administrative boundary data were obtained from https://cloudcenter.tianditu.gov.cn. The national forest inventory data are protected and are not publicly available due to data privacy laws.

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Acknowledgements

This study was supported by National Key Research and Development Program of China (No. 2021YFD2200405 (S.R.L.)), China National Science Foundation (No. 32361143869 (Z.Y.), 32371663 (Z.Y.)), and Jiangsu Provincial Fund for Distinguished Young Scholars (BK20250044) (Z.Y.).

Author information

Authors and Affiliations

  1. State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China

    Yanli Dong, Zhen Yu, Evgenios Agathokleous, Fangmin Zhang, Wangya Han, Guoyi Zhou & Pedro Cabral

  2. Key Laboratory of Forest Ecology and Environment, China’s National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, China

    Zhen Yu, Shirong Liu & Pengsen Sun

  3. Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China

    Zhen Yu, Evgenios Agathokleous, Fangmin Zhang, Wangya Han & Guoyi Zhou

  4. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

    Thomas Pugh & Stefan Olin

  5. Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus

    Evgenios Agathokleous

  6. Faculty of Environment, Science and Economics, University of Exeter, Exeter, UK

    Stephen Sitch

  7. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China

    Weibin You

  8. Fujian Southern Forest Resources and Environmental Engineering Technology Research Center, Fujian Agriculture and Forestry University, Fuzhou, China

    Weibin You

  9. Academy of Forestry and Grassland Carbon Sink, Chinese Academy of Forestry, Beijing, China

    Shirong Liu

  10. NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Lisboa, Portugal

    Pedro Cabral

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  1. Yanli Dong
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Contributions

Z.Y. conceived and designed the research. Y.L.D. performed simulations and drew figures. Z.Y. and Y.L.D. wrote the first draft together. E.A., S.R.L., T.P., S.S., and W.B.Y. extensively edited the manuscript. G.Y.Z., F.M.Z., W.Y.H., S.O., P.C., and P.S.S. provided essential suggestions to improve the manuscript.

Corresponding author

Correspondence to Zhen Yu.

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Dong, Y., Yu, Z., Pugh, T. et al. Enhancing carbon sinks in China using a spatially-optimized forestation strategy. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68288-5

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  • Received: 17 September 2024

  • Accepted: 02 January 2026

  • Published: 12 January 2026

  • DOI: https://doi.org/10.1038/s41467-026-68288-5

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