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Structural, Innovation and Efficiency Effects of Environmental Regulation: Evidence from China’s Carbon Emissions Trading Pilot

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Abstract

Conventional wisdom argues that environmental regulation can trigger both structural adjustments and enhanced innovation. We test this conjecture by using a difference-in-differences approach to analyze the impacts of China’s carbon emission trading (CET) pilot policy on energy consumption. We find that compliance with the CET regulation has triggered statistically significant adjustments in energy structure, industrial structure, and technological innovation. Adjustments in industrial structure also contribute to enhanced total factor energy efficiency, whereas increased technological innovation has mixed effects on energy efficiency. We show that in the short run, government-led innovation does not immediately contribute to improvement in energy efficiency, whereas enterprise-led innovation has a negative impact. It indicates that CET regulation can affect energy efficiency through industrial structure and technological innovation. Overall, our results provide new evidence for the strong version of the Porter hypothesis. Our results also provide strong scientific support for China’s recent transition towards market-based carbon mitigation strategies.

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Notes

  1. Chinese carbon prices are spot prices sourced from China Carbon Trading Network. (http://k.tanjiaoyi.com/); European carbon prices are futures prices for EU allowances (EUA) sourced from the Intercontinental Exchange (ICE) (https://www.theice.com/products/197/EUA-Futures); the implied range of US carbon prices ($5-$19 per ton) was estimated based on the failed Waxman-Markey bill (Meng 2017); the Australian carbon tax came into effect on 1 July 2012. The price of an emission permit for one metric ton of carbon was fixed at $23 for the 2012–13 financial years and rose to $24.15 for the 2013-14 financial year. The tax was revoked from 1 July 2014 onwards (http://www.cleanenergyregulator.gov.au/Infohub/CPM/About-the-mechanism).

  2. We also run the regressions of Eqs. (4) and (5) using log(X + 1) to test the robustness of our results. Overall, our main results are robust to this alternative specification of variables. All results are available upon request from the authors.

  3. It should be noted that our findings only indicate that the CET resulted in positive outcomes on the whole; however, it does not imply that other mechanisms could not work as well or even better. Price based mechanisms could provide even better innovation incentives due to less uncertainty at the firm level. We thank an anonymous referee for raising this point.

  4. A number of recent papers have cautioned that conventional pre-trends tests often have low power against meaningful violations of parallel trends (Freyaldenhoven et al. 2018; Kahn-Lang and Lang 2019; Bilinski and Hatfield 2018; Roth 2019).

  5. http://www.gov.cn/zhengce/content/2012-01/13/content_1294.htm.

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Acknowledgements

The authors are grateful for financial support from the National Natural Science Foundation of China (NSFC) (Grant No. 71673083, 71873057, 71420107027, 71771082 and 71371067), Hunan Provincial Natural Science Foundation of China (No. 2017JJ1012), Scientific Research Project of Hunan Provincial Education Department (No. 19B090) and Fellowship Support Grant at the University of Western Australia.

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Authors and Affiliations

  1. School of Economy and Trade, Hunan University, Changsha, 410079, Hunan, China

    Cenjie Liu & Rui Xie

  2. School of Engineering Management, Hunan University of Finance and Economics, Changsha, 410205, Hunan, China

    Cenjie Liu

  3. Agricultural and Resource Economics, UWA School of Agriculture and Environment, University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia

    Chunbo Ma

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Appendix

Appendix

We select ten variables, including GDP per capita (PGDP), industrial structure (IND), government-led innovation (RD_GOV), enterprise-led innovation (RD_PRIVATE), FDI, government intervention (GOV), urbanization (URBAN), the ratio of capital to labor (K/L) and energy intensity (EI), to calculate the propensity. The Logit regression results are shown in Table 9. According to the matching process, we obtain a matched sample of 3519 observations. We find that most coefficients are statistically significant at the 1% level.

Table 9 The Logit regression results for the PSM†
Full size table

Table 10 compares variable means between pilots and non-pilots before and after matching. There are statistically significant differences between the two groups for most variables before matching; however such differences become insignificant after matching.

Table 10 Comparison between variables before and after employing matching
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The comparison of logit regressions using unmatched and matched samples are shown in Table 11. Pseudo-R indicates the goodness-of-fit of the logit model based on the unmatched and matched samples. The LR statistic tests the joint insignificance of all variables in the model using unmatched and matched samples. B(%) denotes the standardized bias difference between the unmatched and matched samples. The different statistics all suggest that the systematic difference between the treatment group and control group is substantially reduced. Overall, there is insignificant difference between pilot cities and matched non-pilot cities after matching. The matched samples are now appropriate for our identification purposes.

Table 11 Results of test before and after matching
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Liu, C., Ma, C. & Xie, R. Structural, Innovation and Efficiency Effects of Environmental Regulation: Evidence from China’s Carbon Emissions Trading Pilot. Environ Resource Econ 75, 741–768 (2020). https://doi.org/10.1007/s10640-020-00406-3

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