Influences of Environmental Regulations on Industrial Green Technology Innovation Efficiency in China

Wanfang Shen, Jianing Shi, Qinggang Meng, Xiaolan Chen, Yufei Liu, Ken Cheng and Wenbin Liu
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Wanfang Shen: Shandong Key Laboratory of Blockchain Finance, Shandong University of Finance and Economics, Jinan 250014, China
Jianing Shi: School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250002, China
Qinggang Meng: School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250002, China
Xiaolan Chen: Shandong Technology Innovation Center of Social Governance Intelligence, Shandong University of Finance and Economics, Jinan 250014, China
Yufei Liu: School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250002, China
Ken Cheng: Kent Business School, University of Kent, Canterbury CT1 7NZ, UK
Wenbin Liu: Centre for Evaluation Studies, Beijing Normal University, Zhuhai 519088, China

Sustainability, 2022, vol. 14, issue 8, 1-25

Abstract: The Paris Agreement marks global response to climate change after 2020 and China has proposed the dual carbon goals, carbon peaking and carbon neutrality, in response. This paper analyses the contribution to dual carbon goals by analyzing the impact of environmental regulations (ERs) on green technology innovation (GTI) in China. First, considering variances in energy consumption structure across provinces and industries, industrial CO 2 emission is calculated and set as an undesirable output of industrial GTI. Then, industrial green technology innovation efficiencies (GTIE) of 29 provinces in China between 2005–2017 are calculated using a non-oriented two-stage network SBM-DEA model assuming variable returns to scale. Last, dynamic evolution and regional differences of industrial GTIE during green technology R&D, green technology commercialization, and overall GTI stages are respectively observed, and the influences from different types of ERs, command-based (CER), market-based (MER), and voluntary (VER), on industrial GTIE are analyzed. We identify China is overall experiencing relatively low but gradually increasing industrial GTIE and Industrial GTIE present gradient changes across provinces with increasingly prominent regional difference. It is found that influences of types of ERs on industrial GTIE present dynamic effect, threshold effect, lag effect and regional differences.

Keywords: industrial green technology innovation efficiency; CO 2; environmental regulation; two-stage network SBM-DEA model; panel threshold model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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