CO2 Emissions and Corporate Performance: Japan's Evidence with Double Machine Learning

Ryo Aruga, Keiichi Goshima and Takashi Chiba
Additional contact information
Ryo Aruga: Associate Director and Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: ryou.aruga@boj.or.jp)
Keiichi Goshima: Assistant Professor, School of Commerce, Waseda University, and Economist, Institute for Monetary and Economic Studies, Bank of Japan (currently, UTokyo Economic Consulting and Adjunct Researcher, Research Institute of Business Administration, Waseda University, E-mail: keiichi@utecon.net)
Takashi Chiba: Economist, Institute for Monetary and Economic Studies, Bank of Japan (currently, Sumitomo Mitsui Banking Corporation, E-mail: Chiba_Takashi@dn.smbc.co.jp)

No 22-E-01, IMES Discussion Paper Series from Institute for Monetary and Economic Studies, Bank of Japan

Abstract: This paper empirically examines the relationship between CO2 emissions and corporate performance in terms of long-term performance, short-term performance, and cost of capital, using available firm-level data in the First Section of the Tokyo Stock Exchange from FY2011 to FY2019. To address potential biases in previous empirical studies, we employ double machine learning, which is one of the semiparametric models introduced by Chernozhukov et al. [2018], for our empirical analysis. We find that corporations with lower CO2 emissions have (i) better long-term corporate performance and (ii) lower cost of equity. These results suggest that investors estimate that corporations with lower CO2 emissions have lower business risks, setting their risk premium to be low, which results in higher market value of such corporations. In addition, our analysis indicates that corporations with lower CO2 emissions have higher short-term performance and lower cost of debt, but also shows that the results of previous studies of these relationships may contain biases and should be evaluated with caution.

Keywords: CO2 Emissions; Corporate Performance; Double Machine Learning (search for similar items in EconPapers)
JEL-codes: G30 M14 Q54 (search for similar items in EconPapers)
Date: 2022-02
New Economics Papers: this item is included in nep-big, nep-cfn, nep-ene and nep-env
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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