Journal
ENERGY POLICY
Volume 145, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.enpol.2020.111765
Keywords
Environmental regulations; SYS-GMM; Propensity score matching; Difference-in-Difference; CO2 emissions; Heavy industries
Funding
- Peak Discipline Construction Project of Education at East China Normal University, China
- National Natural Science Foundation of China [71603084, 71673230, 71704148]
- Fundamental Research Funds for the Central Universities [20720191006]
- National Fund of Philosophy and Social Science of China [18ZDA106]
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As the most energy intensive industries, heavy industries are decisive for the realization of energy saving and emission reduction commitments. This study investigates factors behind CO2 emissions mitigation in China's heavy industries based on the system generalized method of moments (SYS-GMM) model. Results indicate that industrial structure (IS), fixed asset investment (F) and historical emissions are drivers for sectoral CO2 emission increase, while energy efficiency (EE) is a key factor for carbon emissions reduction. In order to further explore the effect of environmental regulations, we treat 2011 mandatory emission trading scheme (ETS) in high energy-consuming industries as a quasi-natural experiment, and conduct a Propensity Score Matching and Difference-in-Difference (PSM-DID) approach to analyze the policy effect. We find that the implementation of the mandatory emission reduction policy can reduce CO2 emissions of heavy industries, and the results are robust by testing the randomness of the policies. The policy implications are put forward to optimizing industrial structure and enhancing the environmental regulations in China's heavy industries.
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