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Does carbon emissions trading affect the financial performance of high energy-consuming firms in China?

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Abstract

Whether China’s carbon emissions trading (CET) has an impact on the financial performance of the market players of carbon trading, i.e., firms, is crucial to the sustainable development of national economy and carbon trading market. Based on the panel data of the listed firms in the seven high energy-consuming industries in China during 2010–2017, this paper uses the DID model to study the impact of CET on the firms’ financial performance. The empirical results show that the impact of CET on firms’ financial performance presents obvious industrial heterogeneity; CET policy reduces the financial performance of firms in the nonferrous metal industry but improves that in the power industry. In addition, with the implementation of CET policy, its impact on the financial performance of firms in the nonferrous metal and power industries is increasingly intensifying. Finally, there is a lag of 2–4 years on the impact of CET on the firms’ financial performance in the chemical, paper and aviation industries, and the effects change from negative to positive over time. That is, CET policy can hardly ensure that all firms are profitable in the short term, but there is still the possibility of profitability in the long term.

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Notes

  1. http://www.china.com.cn/19da/2017-10/18/content_41751715.htm.

  2. http://energy.people.com.cn/n1/2018/0325/c71661-29887170.html.

  3. http://news.cntv.cn/special/18jszqh/gongbao/index.shtml.

  4. Carbon emissions trading means that the emissions quotas are used for trading, and if firms with low abatement costs have overfulfilled their emissions reduction tasks, then they can sell the remaining quotas to firms with high abatement costs and insufficient quotas to obtain revenues, thus achieving the total social minimum abatement costs (Ma et al. 2018).

  5. http://news.cri.cn/20171219/65ea119b-d108-ddce-4643-473fd0ff80fd.html.

  6. In 2016, the National Development and Reform Commission promulgated the Notice on Effectively Launching the Key Work of the National Carbon Emissions Trading Market, which proposed that the main players involved in the carbon emissions trading market are the firms in eight major emission industries, including petrochemical, chemical, building material, steel, nonferrous metal, paper, power and aviation. Because the samples for chemical industry in this paper contains petrochemical, the samples in this paper are divided into seven industries.

  7. In the quasi-experiment, the selection and grouping of experimental samples is performed artificially, while it is a completely random “natural event” in the natural experiment (Dinardo 2010). The carbon trading pilots in China are not randomly selected, but are artificially selected according to the observable factors.

  8. Many scholars have combined PSM and DID, namely PSM-DID, which can make up for the shortcomings of the DID and has been widely used, such as Li et al. (2018), Michalek et al. (2018), and Liu and Zhao (2015).

  9. The k-nearest matching method matches one CET firm to k non-CET firms having the closest propensity score (Jauregui et al. 2017).

  10. Fixed effects \( \eta_{i} \) and \( \mu_{t} \) include estimates for \( \alpha_{1} \) and \( \alpha_{2} \) from the standard DID equation, i.e., Eq. (2), so the estimates for \( \alpha_{1} \) and \( \alpha_{2} \) are omitted in Eq. (3) (Moser and Voena 2012; Fan and Li 2014; Li et al. 2018).

  11. http://econ.cssn.cn/jjx/jjx_bg/201709/t20170917_3642571.shtml.

  12. Such as Several Opinions of the CPC Central Committee and the State Council on Further Deepening the Reform of the Electric Power System and the 13th Five-Year Plan for Electric Power Development (2016–2020).

  13. The reviewer proposes whether it is possible to distinct firms engaging in export or not. Thus, we collect data on whether the firm has overseas income and use it as a dummy variable (Export) for the firm’s exports, and then recalculate the results. We find that Export variable has no significant negative impact on firms’ financial performance, and the direction and significance of the variables of the main results are the same with those in Table 4, although the exact numbers have some small changes. The detailed results can be obtained upon request from the authors.

  14. For example, the entry range of the carbon trading market in Guangdong Province is the province’s firms that emit more than 20,000 tons of CO2 emissions (or their annual comprehensive energy consumption is greater than 10,000 tons of coal equivalent) in the power, steel, petrochemical and cement industries per year.

  15. http://www.miit.gov.cn/n1146295/n1146592/n3917132/n4061512/c5288595/content.html.

  16. The scale of wind power increases rapidly, and its share has increased from 3.1% in 2010 to 8.6% in 2015. Nuclear power ranks fourth in the world in terms of transport capacity, and the proportion of non-fossil energy in primary energy consumption has increased from 9.4% in 2010 to 12% in 2015.

  17. The placebo test is a counterfactual test and is based on a converse thought, referring to the contrast effect when there is no policy implementation. Its specific idea is to create a fictitious new treatment group, and then use this new treatment group to replace the original treatment group for regression. The years before the implementation of the policy can be selected as the new treatment group. If the new regression results of the DID estimator are the same as the original results, it indicates that the original results are likely to be biased, and if the new regression results of the DID estimator are in sharp contrast to the original results, it indicates that the original results are more reliable (Luong et al. 2017).

  18. The radius matching method sets a score radius R in advance, and if the difference between the scores of CET and non-CET firms is within R, then we match CET firms with non-CET firms. Based on the study of Gran et al. (2008) and the size of samples in this paper, we set the PSM matching radius R to 0.05. Kernel matching uses a weighted average of all non-CET firms to construct the counterfactual match for each CET firm, and the weight based on the distance between propensity scores for a CET firm and all non-CET firms are calculated by the kernel function (Jauregui et al. 2017).

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Acknowledgements

We are grateful to the financial support from the National Natural Science Foundation of China (Nos. 71273028, 71322103, 71774051), National Program for Support of Top-notch Young Professionals (No. W02070325), Changjiang Scholars Program of the Ministry of Education of China (No. Q2016154) and Hunan Youth Talent Program.

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  1. Business School, Hunan University, Changsha, 410082, China

    Yue-Jun Zhang & Jing-Yue Liu

  2. Center for Resource and Environmental Management, Hunan University, Changsha, 410082, China

    Yue-Jun Zhang & Jing-Yue Liu

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  1. Yue-Jun Zhang
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Correspondence to Yue-Jun Zhang.

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Zhang, YJ., Liu, JY. Does carbon emissions trading affect the financial performance of high energy-consuming firms in China?. Nat Hazards 95, 91–111 (2019). https://doi.org/10.1007/s11069-018-3434-5

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