湖泊生态系统服务功能支付意愿的影响因素--以洪泽湖为例

Influencing factors analysis of willingness to pay for ecosystem services evaluation of Hongze Lake

为更好的保护湖泊生态系统,同时为了提供湖泊生态安全管理、宣传、预防等方面的建议,应用目前评价非使用价值较成熟的条件价值法(CVM),以洪泽湖为研究对象,采用实地调研的方式,运用支付意愿问卷调查的形式,结合数据相关分析和回归分析的方法,分析了湖泊生态系统服务功能支付意愿的影响因素。共发放支付卡式CVM问卷520份,回收有效问卷498份,获得支付意愿的分布形态和规律。运用Logit回归模型分析湖泊生态服务功能价值评估过程中的主要影响因素,对比分析了不同人群对恢复该湖泊生态服务功能支付意愿的差异。通过多元统计分析得到在洪泽湖生态系统恢复计划案例中,受访者的支付意愿主要受到家庭年收入、对政府的信任程度和对环境保护的意识等因素的影响,并展开了较为详细的讨论和解释。最后针对模型分析的结果,提出具体可行的增强公众环保态度和环保意愿的建议,为政府相关政策的制定提供理论基础,为决策者选择有效目标人群提供参考依据。

In order to protect the lake ecosystem services,and provide some useful advices on the security management,advocacy and prevention recommendations,the relatively mature method—Contingent Valuation Method（CVM）was used to be associated with a questionnaire survey to evaluate the valuation of the ecosystem services of Hongze lake.Using field research methods and the questionnaire survey with the form of willingness to pay（WTP）,we evaluated the influencing factors to pay for the lake ecosystem services combined with correlation analysis and regression analysis.As a case study,520 payment card CVM questionnaires were surveyed and 498 as useful questionnaires were feed back.Distribution shape and rule of willingness to pay were probed.Logit model was taken to analysis the main impacting factors in the process of evaluation.At the same time,comparative analysis of WTP was made in the different groups of people for restoring the function of the lake ecosystem services.Through multivariate statistical analysis in the case of Hongze Lake ecosystem recovery,some key factors were found to be quite important to WTP,such as the annual income of the family,trust to the government and the attitude to the environmental protection.At last,based on the results of the logistic models,some reasonable and useful suggestions were provided to the government,and some effective references were given to the policy-makers for choosing the target groups of people about public communication and public education programs.