Can Artificial Intelligence Fix Bad Environmental Regulation? Evidence from Green Total Factor Productivity in China
DOI:
https://doi.org/10.29207/joseit.v3i2.8451Keywords:
environmental regulation, green total factor productivity, artificial intelligence, digital economy, moderating effectAbstract
Environmental regulation is often assumed to work the same way regardless of how it is designed — yet this study shows that command-and-control and market-incentive instruments pull green productivity in opposite directions, and that artificial intelligence (AI) does not treat them equally either. Drawing on panel data from 281 Chinese prefecture-level cities over 2012–2024, this study finds that command-and-control regulation, measured through text analysis of local government work reports, significantly suppresses green total factor productivity (GTFP) by raising compliance costs, while market-incentive regulation, measured as the ratio of pollutant discharge fee revenue to GDP, significantly promotes it by channeling price signals into innovation. GTFP is estimated using the SBM-ML index, which incorporates labor, capital, and energy inputs together with industrial pollution as an undesirable output. Interaction-term estimates further show that local AI development attenuates the negative effect of command-and-control regulation while amplifying the positive effect of market-incentive regulation — AI acts as a “buffer” for coercive regulation and an “amplifier” for market-based regulation, rather than a uniform productivity booster. The findings suggest that AI cannot simply substitute for well-designed environmental policy, but it can make both good and bad policy design matter less — or more — than they otherwise would.
References
A. Blackman, Z. Li, and A. A. Liu, “Efficacy of command-and-control and market-based environmental regulation in developing countries,” Annual Review of Resource Economics, vol. 10, pp. 381–404, 2018.
M. E. Porter, “America’s Green Strategy,” Scientific American, no. 4, p. 168, 1991.
M. E. Porter and C. van der Linde, “Toward a new conception of the environment-competitiveness relationship,” Journal of Economic Perspectives, vol. 9, no. 4, pp. 97–118, 1995.
A. B. Jaffe and K. Palmer, “Environmental regulation and innovation: A panel data study,” Review of Economics and Statistics, vol. 79, no. 4, pp. 610–619, 1997.
M. Song and J. Qi, “The impact of environmental regulation on green total factor productivity under the digital economy: Evidence from city panel data,” China Business and Market, vol. 37, no. 6, pp. 14–26, 2023. (in Chinese)
S. Chen and D. Chen, “Haze pollution, government governance, and high-quality economic development,” Economic Research Journal, vol. 53, no. 2, pp. 20–34, 2018. (in Chinese)
D. Shi and S. Li, “Pollutant discharge permit trading system and energy utilization efficiency: Measurement and empirical evidence from prefecture-level and above cities,” China Industrial Economics, no. 9, pp. 5–23, 2020. (in Chinese)
D. Acemoglu and P. Restrepo, “Robots and jobs: Evidence from US labor markets,” Journal of Political Economy, vol. 128, no. 6, pp. 2188–2244, 2020.
Y. Liu, C. Ma, and Z. Huang, “Can the digital economy improve green total factor productivity? An empirical study based on Chinese urban data,” Mathematical Biosciences and Engineering, vol. 20, no. 4, pp. 6866–6893, 2023.
Y. Fang, H. Cao, and J. Sun, “Impact of artificial intelligence on regional green development under China’s environmental decentralization system—Based on spatial Durbin model and threshold effect,” International Journal of Environmental Research and Public Health, vol. 19, no. 22, art. 14776, 2022.
Y. Zhang and C. Zhou, “Effect of artificial intelligence on Chinese urban green total factor productivity,” Land, vol. 14, no. 3, art. 660, 2025.
K.-L. Wang, T.-T. Sun, and R.-Y. Xu, “The impact of artificial intelligence on total factor productivity: Empirical evidence from China’s manufacturing enterprises,” Economic Change and Restructuring, vol. 56, no. 2, pp. 1113–1146, 2023.
S. Shao, L. Ge, and J. Zhu, “How can humans and nature coexist harmoniously: Environmental regulation and environmental welfare performance from a geographic perspective,” Management World, vol. 40, no. 8, pp. 119–146, 2024. (in Chinese)
P. Song, Q. Zhu, and H. Zhang, “Interactive enforcement of environmental regulation and pollution governance in urban agglomerations,” China Population, Resources and Environment, no. 3, pp. 49–61, 2022. (in Chinese)
W. Zhang, J. Dong, H. Zhang, et al., “Mechanisms, dilemmas, and countermeasures for environmental policy to enhance green total factor productivity in China: An innovation-mode-choice perspective,” Economic Review, no. 1, pp. 126–143, 2023. (in Chinese)
K. Tone, “A slacks-based measure of efficiency in data envelopment analysis,” European Journal of Operational Research, vol. 130, no. 3, pp. 498–509, 2001.
B. Arabi, S. Munisamy, and A. Emrouznejad, “A new slacks-based measure of Malmquist-Luenberger index in the presence of undesirable outputs,” Omega, vol. 51, pp. 29–37, 2015.
J. Xiao, G. Li, B. Zhu, et al., “Evaluating the impact of carbon emissions trading scheme on Chinese firms’ total factor productivity,” Journal of Cleaner Production, vol. 306, art. 127104, 2021.
Q. Wu and Y. Wang, “How does carbon emission price stimulate enterprises’ total factor productivity? Insights from China’s emission trading scheme pilots,” Energy Economics, vol. 109, art. 105990, 2022.
C. Li, et al., “Do carbon ETS pilots improve cities’ green total factor productivity? Evidence from a quasi-natural experiment in China,” Energy Economics, vol. 108, art. 105931, 2022.
S. Wang, G. Chen, and X. Han, “An analysis of the impact of the emissions trading system on the green total factor productivity based on the spatial difference-in-differences approach: The case of China,” International Journal of Environmental Research and Public Health, vol. 18, no. 17, art. 9040, 2021.
S. Albrizio, T. Kozluk, and V. Zipperer, “Environmental policies and productivity growth: Evidence across industries and firms,” Journal of Environmental Economics and Management, vol. 81, pp. 209–226, 2017.
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