Digital Empowerment: Unlocking the 'synergy barrier' of government-enterprise-public air pollution control
Year: 2025
Type: article
Abstract: In the face of increasingly severe environmental pollution challenges, achieving efficient collaborative air pollution control has become paramount. This paper examines the impact of a collaborative air pollution control model involving the government, enterprises, and the public in reducing pollution emissions. Utilizing panel data encompassing 274 cities in China from 2009 to 2022, the study employs the panel fixed-effect model to assess the efficacy of pairwise synergies among the government, enterprises, and the public. The study also investigates how digital technology enhances information transparency and fosters technological innovation, thereby strengthening these synergies and significantly improving the overall efficiency of air pollution control. The results indicate that pairwise synergies among the government, enterprises, and the public significantly reduce pollutant emissions. However, the impact of 'tripartite coordination' on air pollution control is not statistically significant. Digital technology effectively strengthens the "pairwise synergies" mechanism and significantly improves the overall efficiency of air pollution control. This study enriches the air pollution control theory, provides a new perspective for understanding the complex mechanism of multi-subject collaborative environmental governance, and provides important implications for policymakers. • Enterprises cut SO2/PM2.5; government regulation curbs PM2.5; public supervision reduces CO2. • Government, the public, and enterprise effectively reduce SO₂ and PM₂.₅ through dyadic collaborations. • The tripartite synergy among governments, enterprises, and the public has no impact on urban air pollution.
Source: Energy Reports
Authors: Xiufan Zhang, Yingyu Tan
Institution: Zhejiang Sci-Tech University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,040
Awards: G1515243214, G2107715560
OpenAlex ID: W4409686424
