【摘要】 在对太湖流域居民生态补偿支付意愿调查的基础上，基于可解释机器学习模型XGBoost-SHAP分析了居民生态补偿支付意愿的影响因素，并比较了有支付意愿和没有支付意愿居民之间影响因素的差异。结果表明：影响太湖流域居民生态补偿支付意愿最重要的3个因素为学历、收入和生态环境保护意愿；单个居民之间的支付意愿影响因素呈现一定的差异，尤其是有支付意愿和没有支付意愿居民之间的影响因素差异显著；总体而言，增强居民生态环境保护意识和加大生态补偿政策的宣传可以提升流域居民参与生态补偿的意愿。更多还原

【Abstract】 Based on the survey of residents’ willingness to pay for eco-compensation in the Taihu Basin, this paper analyzes the important factors that influence the residents’ willingness to pay for eco-compensation using the interpretable machine learning model XGBoot-SHAP and then compares the difference between those who are willing and those who are unwilling to pay for eco-compensation. The results show that the three most important influencing factors are education, annual income, and the willingness to protect the ecological environment. The important influencing factors are different among individuals, especially those who are willing and those who are unwilling to pay for eco-compensation are obviously different. Enhancing the awareness of ecological environment protection of public and increasing the publicity of eco-compensation policies can improve the willingness to pay for eco-compensation.更多还原