城市垃圾分类政策路径与效能——以西北地区试点城市为例

【目的】为了完善城市垃圾分类政策从而提升政策执行效果。【方法】本文基于“路径-效能”分析框架，以西北地区垃圾分类试点城市政策为分析对象，首先运用文本挖掘方法厘清垃圾分类的政策路径，再以此为基础构建PMC指数评价模型，对政策效能进行全面评估。【结果】①推进垃圾分类的政策要素包括导向要素（政策目标）、资源要素（基础设施、技术支持、人员配套、资助体系）和保障要素（宣传教育、监督考核、处罚方式、奖励机制），最终形成以政策目标为导向，给予配套资源，并为资源运用提供保障的政策路径。②6个试点城市的政策评级分别为良（西宁）、中（兰州、西安、银川、咸阳）、差（乌鲁木齐），整体平均水平为中，除基础设施外，其余变量均有待提升，其中奖励机制的效能提升空间最大。③各城市政策均在部分变量上存在内容或实施细则的缺漏，而不同政策的内容缺失方面和程度都不尽相同，致使政策改进的参考路线也有所区别。【结论】从补充政策内容细则和制订差异化完善路线两方面提出政策优化建议。

Path and effectiveness of municipal waste classification policies: Taking pilot cities in northwest China as an example

[Objective] In order to improve municipal waste classification policies and thus enhance the policy implementation effect, [Methods] this study used the “path-effectiveness” analysis framework to analyze the policies of the pilot cities in Northwest China. The text mining method was used to clarify the policy path of waste classification, and then the policy modeling consistency (PMC) index evaluation model was built to comprehensively evaluate the policy effectiveness. [Results] The results show that: (1) The policy elements for promoting waste classification include orientation (policy goals), resources (infrastructure, technical support, personnel support, and funding system), and guarantee (publicity and education, supervision and assessment, punishment methods, and incentive mechanisms), which ultimately form a policy path guided by policy goals, supplying supporting resources, and providing guarantees for resource use. (2) The policy ratings of the six pilot cities are good (Xining), medium (Lanzhou, Xi’an, Yinchuan, Xianyang), and poor (Urumqi), with an overall rating of medium. All variables, except infrastructure, need to be improved, and the effectiveness of incentive mechanisms has the greatest room for improvement. (3) The policies of all cities have missing content or implementation details in some variables, and the aspect and extent of missing content vary from one policy to another, so the reference routes for policy improvement are also different. [Conclusion] Finally, policy optimization recommendations were made in terms of supplementing policy content details and developing differentiated improvement routes.