How does China's carbon emissions trading (CET) policy affect the investment of CET-covered enterprises?

被引:133
|
作者
Zhang, Yue-Jun [1 ,2 ]
Wang, Wei [1 ,2 ]
机构
[1] Hunan Univ, Business Sch, Changsha 410082, Hunan, Peoples R China
[2] Hunan Univ, Ctr Resource & Environm Management, Changsha 410082, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon emissions trading; CET-covered enterprises; DID model; DDD model; Investment; RESEARCH-AND-DEVELOPMENT; ENVIRONMENTAL-REGULATION; CORPORATE-INVESTMENT; PROPENSITY SCORE; EU-ETS; IMPACT; INNOVATION; GREEN; IMPLEMENTATION; PERFORMANCE;
D O I
10.1016/j.eneco.2021.105224
中图分类号
F [经济];
学科分类号
02 ;
摘要
The carbon emissions trading (CET) policy realizes the internalization of emission reduction costs of related enterprises, which may affect their investment and management decisions, but has seldom received attention. Therefore, based on the panel data of A-share listed enterprises in eight energy-and-carbon-intensive industries in China during 2009-2018, this paper employs the difference-in-differences (DID) and the difference-in -differences based propensity score matching methods (PSM-DID) to empirically evaluate the impact of China's CET policy on investment expenditure of CET-covered enterprises in seven pilot regions. The results indicate that: first of all, the investment expenditure of CET-covered enterprises has been reduced by 0.2449% due to China's CET policy during the sample period in general. Second, this impact shows significant industrial and regional heterogeneity. The CET policy reduces the investment expenditure of related enterprises in the building material and steel industries, but not in the other six industries. It also has a reducing impact on the investment expenditure of related enterprises in Beijing and Guangdong, but not in the other five pilot regions. Third, the impact began to appear in the second year of the implementation of CET policy, and it has shown an increasingly strengthening trend with continuous implementation. In addition, the central findings remain robust when we test the parallel trend assumption and apply the difference-in-differences-in-differences (DDD) method to further eliminate the interference of other factors. (c) 2021 Elsevier B.V. All rights reserved.
引用
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页数:13
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