1.安徽理工大学空间信息与测绘工程学院,安徽 淮南 232001
2.煤炭无人化开采数智技术全国重点实验室, 安徽 淮南 232001
3.安徽理工大学地球与环境学院,安徽 淮南 232001
张兴辉(2002—),男,硕士研究生,主要从事碳收支与碳补偿研究。E-mail:2023201605@aust.edu.cn
张坤(1985—),男,博士,讲师,主要从事矿区生态修复研究。E-mail:chzk@aust.edu.cn
收稿:2025-03-04,
修回:2025-04-02,
录用:2025-04-15,
网络出版:2025-05-27,
纸质出版:2025-10-01
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张兴辉,张坤,范廷玉,等.面向SDGs的安徽省碳生态补偿空间网络与分区治理[J].水土保持学报,2025,39(5):335-348.
ZHANG Xinghui, ZHANG Kun, FAN Tingyu, et al. Carbon ecological compensation network and zone management in Anhui Province under SDGs[J]. Journal of Soil and Water Conservation,2025,39(5):335-348.
张兴辉,张坤,范廷玉,等.面向SDGs的安徽省碳生态补偿空间网络与分区治理[J].水土保持学报,2025,39(5):335-348. DOI: 10.13870/j.cnki.stbcxb.2025.05.029. CSTR: 32310.14.stbcxb.2025.05.029.
ZHANG Xinghui, ZHANG Kun, FAN Tingyu, et al. Carbon ecological compensation network and zone management in Anhui Province under SDGs[J]. Journal of Soil and Water Conservation,2025,39(5):335-348. DOI: 10.13870/j.cnki.stbcxb.2025.05.029. CSTR: 32310.14.stbcxb.2025.05.029.
目的
2
在“双碳”目标与可持续发展背景下,探究区域碳生态补偿机制对平衡土地开发利用过程中经济发展与生态保护之间矛盾具有重要意义。
方法
2
以安徽省为研究对象,构建“时空分异-网络关联-补偿分区”的系统框架,结合网络分析法、碳生态补偿模型和熵权-TOPSIS等方法,通过K-means算法建立基于可持续发展目标(SDGs)下的差异化碳补偿方案。
结果
2
1)研究期内安徽省土地利用碳排放显著增长且空间差异显著,整体表现为“北高南低、东高西低”的空间分布特征。2)区域碳关联网络逐步形成以合肥为核心,芜湖、马鞍山和淮南为主要节点的“核心-边缘”结构且整体网络特征在持续增强。3)碳补偿价值空间差异明显,总计碳支付、碳受偿金额分别为109.89×10
8
、25.23×10
8
元,确定7个支付区和10个受偿区。4)结合城市的可持续发展,最终形成7类碳综合生态补偿管理分区,并针对每一类型区提出“梯度补偿-协同治理”的差别化碳生态补偿建议。
结论
2
研究结果为协调区域碳公平、推动“双碳”目标与SDGs协同发展提供借鉴,对完善跨区域生态补偿政策具有参考价值。
Objective
2
Against the backdrop of the dual-carbon goals and sustainable development, this study investigates the critical role of regional carbon ecological compensation mechanisms in addressing the conflict between economic development and ecological preservation during land development and utilization.
Methods
2
Focusing on Anhui Province, this study constructed a systematic framework of "spatiotemporal differentiation-network connectivity-compensation zoning". Using network analysis, carbon ecological compensation modeling, and the entropy weight-TOPSIS method, a differentiated carbon compensation scheme based on Sustainable Development Goals (SDGs) was established through the K-means algorithm.
Results
2
1) During the study period, land-use carbon emissions in Anhui Province increased significantly with notable spatial differences, demonstrating an overall spatial distribution of "high-north-low-south, high-east-low-west". 2) The regional carbon association network gradually formed a "core-periphery" structure centered on Hefei, with Wuhu, Ma'anshan, and Huainan as the main nodes, and the overall network connectivity continued to strengthen. 3) The spatial variation in carbon compensation values was significant, with total carbon payments and carbon compensation amounts reaching 109.89×10
8
yuan and 25.23×10
8
yuan, respectively. Seven payment zones and ten compensation zones were identified. 4) In line with urban sustainable development, seven types of carbon-integrated ecological compensation management zones were formed. For each type, differentiated strategies of "gradient compensation-collaborative governance" were proposed.
Conclusion
2
The findings offer insights for enhancing regional carbon equity and promoting the coordinated development of the dual-carbon goals and the SDGs, serving as a reference for improving cross-regional ecological compensation policies.
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