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Power play in carbon trading market: How status of executives with R&D background incentives companies’ low-carbon innovation

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  • Wu, Qingyang

Abstract

Utilizing the phased initiation of carbon trading pilot projects in eight provinces and municipalities in China from 2013 to 2016 as a quasi-natural experiment, this study matches micro-level data on low-carbon patents and financial information of publicly listed companies from 2009 to 2019. Employing a staggered difference-in-differences framework, it assesses the impact and mechanisms through which the distribution of executive power, among those with R&D backgrounds, influences firms' low-carbon innovation capabilities against the backdrop of a carbon emission trading scheme. The status of executives with R&D backgrounds is characterized by their relative ranking within the team, as denoted in the companies' financial statements. Findings indicate that the carbon emission trading significantly fosters the output of low-carbon patents by firms, with executives possessing R&D backgrounds further enhancing the level of low-carbon innovation. This effect is more pronounced in privately-owned and small to medium-sized enterprises. Further analysis suggests that increasing R&D investment and personnel ratios, correcting managerial overconfidence and myopia, and enhancing corporate attention to green strategies are channels through which R&D-oriented executives lead to an improvement in firms' low-carbon innovation capabilities. The paper proposes government tax incentives and funds for R&D staff, stricter ESG standards for executive accountability, and regulations mandating green goals in company planning.

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  • Wu, Qingyang, 2024. "Power play in carbon trading market: How status of executives with R&D background incentives companies’ low-carbon innovation," Energy Policy, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:enepol:v:188:y:2024:i:c:s0301421524000697
    DOI: 10.1016/j.enpol.2024.114049
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    1. Wu, Qingyang & Li, Shanhong, 2024. "Decarbonization by digits: How data factors drive nonlinear sustainable dynamics in manufacturing," Applied Energy, Elsevier, vol. 374(C).

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