通过使用多种监督机器学习模型,探索中国2800多个县的农村定居模式对碳排放的时空异质性
Xinxin Huang1, Yansui Liu2, Rudi Stouffs3
1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Journal of environmental management
|January 5, 2025
概括
中国中国中国中国.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 机器学习应用 机器学习应用
背景情况:
- 中国是世界上最大的碳排放国,朝着碳中和的进步至关重要.
- 农村定居点对国家碳排放做出了重大贡献.
- 了解景观影响是制定有效的气候减缓战略的关键.
研究的目的:
- 系统地分析景观指数对中国农村定居点碳排放的影响.
- 确定哪些景观指标对农村碳排放影响最大.
- 评估这些影响的区域差异.
主要方法:
- 利用八种监督机器学习模型分析来自2800多个中国县 (2000-2020) 的数据.
- 用于变量影响评估的Shapley添加式扩展 (SHAP) 和累积局部效应 (ALE).
- 确定了渐变增强回归树 (GBRT) 作为最有效的模型.
主要成果:
- 从2000年到2020年,中国农村定居点的碳排放量显著增加,东北地区的增长率最高 (259.52%).
- 平均补丁面积 (MPA) 指数显示对碳排放的影响比补丁密度 (PD),边缘密度 (ED) 和聚合指数 (AI) 更大.
- 每个景观指数都表现出独特的影响特征和区域趋势.
结论:
- 景观结构,特别是中层区域,对中国农村的碳排放有很大影响.
- 调查结果为有针对性的环境政策提供了关键的见解,以实现碳中和和可持续发展.
- 区域差异需要量身定制的减排策略方法.
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