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.
关键词
生态补偿/支付意愿/XGBoost/SHAP/可解释机器学习/太湖流域
Key words
eco-compensation/willingness to pay/XGBoost/SHAP/interpretable machine learning/Taihu Basin
维普
万方数据