排污权交易对污染物排放的影响——基于双重差分法的准自然实验分析

摘要/Abstract

摘要： 2007年我国在十一个省份开展排污权交易试点，为研究排污权交易制度对污染物的政策影响提供了一次良好的准自然实验。本文基于中国2004-2017年的省际面板数据，使用双重差分等方法评估了排污权交易试点政策对约束性总量控制污染物的影响效果。研究发现：排污权交易对二氧化硫、氨氮和化学需氧量三类约束性总量控制污染物具有显著的减排效应，并且排污权交易的环境影响并未受到排污费和"两控区"政策的干扰；东、中、西部地区具有异质性，东部地区依托其市场和技术优势，能够实现三类污染物的有效减排，而中部地区对二氧化硫和氨氮的减排规模占实验组排放的比例更高；环境规制强度深刻影响着排污权交易的实施效果，规制强度更高的地区，排污权交易对三类污染物的减排效果更为明显。当前我国的排污权交易试点政策已经取得了良好的成效，应进一步扩大交易范围和主体，缩小地区间技术与市场发育水平的差异，加快构建全国统一的排污权交易市场。

关键词: 排污权交易, 总量控制污染物, 政策效应, 异质性环境效应, 双重差分

Abstract: In 2007, China launched a pilot policy on emissions trading in eleven provinces of the country, which provided a good quasi-natural experiment for studying the policy impact of emissions trading system on pollutants. Based on China's inter-provincial panel data from 2004 to 2017, this paper uses double differential and other methods to evaluate the effect of the pilot emissions trading policy on binding total pollutant control. The study finds that emissions trading has significant emission reduction effects on three types of binding total pollutants:sulfur dioxide, nitrogen oxygen and chemical oxygen demand, and the environmental impact of emissions trading is not disturbed by sewage charges and "two control zones"; In areas with higher regulatory intensity, emissions trading has a more obvious effect on the emission reduction of three types of pollutants. Hitherto, China's emissions trading pilot policy has achieved good results. It should further expand the scope and subject of transactions, narrow the differences between the level of technological and market development between regions, and speed up the construction of a unified national emissions trading market.

Key words: emissions trading, total control of pollutants, policy effects, heterogeneity environmental effects, Difference-in-Differences

斯丽娟, 曹昊煜. 排污权交易对污染物排放的影响——基于双重差分法的准自然实验分析[J]. 管理评论, 2020, 32(12): 15-26.

Si Lijuan, Cao Haoyu. The Impact of Emissions Trading on Pollution Reduction: Quasi-natural Experimental Analysis Based on Difference-in-differences Model[J]. Management Review, 2020, 32(12): 15-26.

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