The Impact of Public Environmental Concern on Corporate ESG Performance

Tsun Se Cheong, Shuaiyi Liu, Ning Ma and Tingting Han ()
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Tsun Se Cheong: Department of Economics and Finance, The Hang Seng University of Hong Kong, Hong Kong 999077, China
Shuaiyi Liu: Department of Economics and Finance, The Hang Seng University of Hong Kong, Hong Kong 999077, China
Ning Ma: School of Financial Management, Hainan College of Economics and Business, Haikou 571127, China
Tingting Han: School of Financial Management, Hainan College of Economics and Business, Haikou 571127, China

JRFM, 2025, vol. 18, issue 2, 1-25

Abstract: Utilizing an advanced machine learning algorithm, particularly the Artificial Neural Network (ANN) framework, this study reveals a significant nonlinear and even cyclical relationship between public concern about environmental issues and the ESG performance of Chinese A-share listed companies, covering the period from 2004 to 2020. The findings highlight the effectiveness of the Self-Organizing Map (SOM)-ANN framework in elucidating the empirical relationship between these variables. We contend that robust public monitoring can enhance companies’ ESG initiatives, and we recommend that policymakers implement a series of measures to safeguard and promote public involvement in decision-making processes. Furthermore, our analysis of the combined effects of public concern and various performance metrics on firms’ ESG outcomes indicates that the diversity among firms is crucial for determining the most appropriate level of public participation in their sustainable development efforts. Therefore, managers and policymakers should focus on firm-specific attributes instead of adopting a “one-size-fits-all” approach to maximize the benefits of public engagement.

Keywords: ESG; public environmental concern; Chinese firms; machine learning; artificial neural network (ANN); regression by ANN with bootstrapping (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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