标題: Assessing Assisted Natural Regeneration as a Cost-Efficient Mitigation for Climate Change and Biodiversity Loss in China

作者: Qiu, ML (Qiu, Ming-Li); Liu, DF (Liu, Dian-Feng); Zhao, YX (Zhao, Yu-Xin); Tong, ZM (Tong, Zhao-Min); He, JH (He, Jian-Hua); Fortin, MJ (Fortin, Marie-Josee); Huang, JL (Huang, Jun-Long)

來源出版物: EARTHS FUTURE 卷: 13 期: 3 文獻号: e2024EF005257 DOI: 10.1029/2024EF005257 Published Date: 2025 MAR

摘要: Global deforestation results in climate change and biodiversity loss. Assisted natural regeneration (ANR) emerges as a promising approach to achieving global forest restoration targets, yet its potential and benefits for climate and biodiversity in China remain underexplored. Here, we assessed ANR potential across China and modeled spatial prioritization strategies targeting climate mitigation, biodiversity conservation, and cost savings, individually and in combination, as well as strategies considering spatial constraints from current forest restoration projects and fragmentation mitigation. From 1995 to 2015, 3.40 million hectares of land naturally regenerated into forests, with an additional 5.11 million hectares identified as potential regeneration areas, which could contribute to 12.41% of China's restoration goal in 2035. Spatial prioritization revealed limited overlap among the three single-objective ANR strategies, while a multi-objective optimization strategy emerged as the most effective solution to achieve synergies among goals. The top 30% of prioritized areas under the multi-objective strategy could sequester 46.54 gigatons of CO2, reduce extinction risks of forest-dependent species by 40.64%, and lower implementation costs by 31.55% compared to monoculture tree plantations. Our findings highlight that strategic spatial prioritization of ANR could mitigate climate change and biodiversity loss in a cost-efficient manner and have the potential to reinforce current forest restoration projects.

作者關鍵詞: assisted natural regeneration; forest restoration; carbon sequestration; species extinction risk; multi-objective optimization; NSGA-II

KeyWords Plus: DEFORESTATION; DATASET; FORESTS; FUTURE

地址: [Qiu, Ming-Li; Liu, Dian-Feng; Zhao, Yu-Xin; Tong, Zhao-Min; He, Jian-Hua] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Liu, Dian-Feng; He, Jian-Hua] Minist Nat Resources, Key Lab Digital Cartog & Land Informat Applicat En, Wuhan, Peoples R China.

[Fortin, Marie-Josee; Huang, Jun-Long] Univ Toronto, Dept Ecol & Evolutionary Biol, Toronto, ON, Canada.

[Huang, Jun-Long] Chongqing Univ, Coll Environm & Ecol, Chongqing, Peoples R China.

通訊作者地址: Liu, DF (通訊作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

Liu, DF (通訊作者)，Minist Nat Resources, Key Lab Digital Cartog & Land Informat Applicat En, Wuhan, Peoples R China.

Huang, JL (通訊作者)，Univ Toronto, Dept Ecol & Evolutionary Biol, Toronto, ON, Canada.

Huang, JL (通訊作者)，Chongqing Univ, Coll Environm & Ecol, Chongqing, Peoples R China.

電子郵件地址: liudianfeng@whu.edu.cn; jlhuang@cqu.edu.cn

影響因子：7.3