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Evaluating the Causal Effects of Emissions Trading Policy on Emission Reductions Based on Nonlinear Difference-In-Difference Model

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  • Lianyan Fu

    (School of Mathematics and Statistics, Liaoning University, Liaoning 110036, China
    School of Economics, Liaoning University, Liaoning 110036, China
    These authors contributed equally to this work.)

  • Lin Zhou

    (School of Economics, Liaoning University, Liaoning 110036, China
    These authors contributed equally to this work.)

  • Peili Wu

    (School of Economics, Liaoning University, Liaoning 110036, China)

  • Zhichuan Zhu

    (School of Mathematics and Statistics, Liaoning University, Liaoning 110036, China
    School of Economics, Liaoning University, Liaoning 110036, China)

  • Zhuoxi Yu

    (School of Mathematics and Statistics, Liaoning University, Liaoning 110036, China
    School of Economics, Liaoning University, Liaoning 110036, China)

  • Dehui Wang

    (School of Mathematics and Statistics, Liaoning University, Liaoning 110036, China)

Abstract

Based on panel data from 30 provinces, cities, and autonomous regions from 2001 to 2019, this paper uses the nonlinear difference-in-difference ( D I D ) method to estimate the distribution of causal effects of emissions trading policy on emission reduction in Chinese industrial enterprises, and examines the heterogeneity of the effects. The empirical results show that (1) the emissions trading policy has a significant effect on industrial S O 2 emissions reduction in China, where the reduction effect is larger in non-pilot areas than in pilot areas; (2) the policy effects are not proportional to the regional S O 2 emissions intensity, and the emissions trading policy is not more effective in regions with higher industrial S O 2 emissions intensities. One advantage of this paper is the use of nonlinear D I D to estimate the emissions reduction effect, which eliminates the bias problem caused by the strict linearity assumption of the classical D I D method. Another advantage is that the combination of the random forest method avoids the subjectivity in the selection of control variables and uses distribution effects for multilevel comparisons. This method improves the validity of estimating the effect of emissions trading policy and provides targeted policy suggestions for the effective promotion of system implementation, all of which have academic and application value.

Suggested Citation

  • Lianyan Fu & Lin Zhou & Peili Wu & Zhichuan Zhu & Zhuoxi Yu & Dehui Wang, 2022. "Evaluating the Causal Effects of Emissions Trading Policy on Emission Reductions Based on Nonlinear Difference-In-Difference Model," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15726-:d:984515
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    References listed on IDEAS

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