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Artificial Intelligence and Urban Air Quality: The Role of Government and Public Environmental Attention

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  • Chaobo Zhou

    (College of International Economics and Trade, Ningbo University of Finance and Economics, Ningbo 315175, China
    Climate Change and Energy Economics Study Center, Wuhan University, Wuhan 430072, China
    Ningbo Philosophy and Social Science Key Research Base “Research Base on Digital Economy Innovation and Linkage with Hub Free Trade Zones”, Ningbo 315175, China)

Abstract

Artificial intelligence (AI) technology not only promotes rapid economic development but also plays an irreplaceable role in improving environmental quality. Based on the quasi-natural experiment of the National Artificial Intelligence Innovation Comprehensive Experimental Zone, this paper empirically studies the effect and mechanism of AI on urban air quality (AQ) using the multi-time difference-in-difference model. The research results showed that AI improved the AQ of cities. The mechanism analysis results indicated that there was a positive mediating effect of government environmental attention on the relationship between AI and AQ improvement. Public environmental attention can further enhance the role of AI in improving urban AQ. Further analysis revealed that the improvement effect of AI on urban AQ was mainly reflected in eastern cities and non-resource-based cities. The research conclusion of this study provides reliable empirical evidence for leveraging AI to empower urban green development and assist in air pollution prevention practices.

Suggested Citation

  • Chaobo Zhou, 2025. "Artificial Intelligence and Urban Air Quality: The Role of Government and Public Environmental Attention," Sustainability, MDPI, vol. 17(13), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:5702-:d:1683782
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    1. Ma, Hongmei & Gao, Qian & Li, Xiuzhen & Zhang, Yun, 2022. "AI development and employment skill structure: A case study of China," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 242-254.
    2. Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2022. "Artificial intelligence and industrial innovation: Evidence from German firm-level data," Research Policy, Elsevier, vol. 51(7).
    3. Yang, Chih-Hai, 2022. "How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan," Research Policy, Elsevier, vol. 51(6).
    4. Tamazian, Artur & Bhaskara Rao, B., 2010. "Do economic, financial and institutional developments matter for environmental degradation? Evidence from transitional economies," Energy Economics, Elsevier, vol. 32(1), pages 137-145, January.
    5. Friedl, Birgit & Getzner, Michael, 2003. "Determinants of CO2 emissions in a small open economy," Ecological Economics, Elsevier, vol. 45(1), pages 133-148, April.
    6. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Ulrich Wagner & Christopher Timmins, 2009. "Agglomeration Effects in Foreign Direct Investment and the Pollution Haven Hypothesis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(2), pages 231-256, June.
    8. Wolfgang Keller & Arik Levinson, 2002. "Pollution Abatement Costs and Foreign Direct Investment Inflows to U.S. States," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 691-703, November.
    9. Yu, Yunjiang & Dai, Chun & Wei, Yigang & Ren, Huiming & Zhou, Jiawen, 2022. "Air pollution prevention and control action plan substantially reduced PM2.5 concentration in China," Energy Economics, Elsevier, vol. 113(C).
    10. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Lee, Chien-Chiang & Zou, Jinyang & Chen, Pei-Fen, 2025. "The impact of artificial intelligence on the energy consumption of corporations: The role of human capital," Energy Economics, Elsevier, vol. 143(C).
    12. Hartman, Richard & Kwon, O-Sung, 2005. "Sustainable growth and the environmental Kuznets curve," Journal of Economic Dynamics and Control, Elsevier, vol. 29(10), pages 1701-1736, October.
    13. Xiao Tang & Weiwei Chen & Tian Wu, 2018. "Do Authoritarian Governments Respond to Public Opinion on the Environment? Evidence from China," IJERPH, MDPI, vol. 15(2), pages 1-15, February.
    14. Benarroch, Michael & Thille, Henry, 2001. "Transboundary pollution and the gains from trade," Journal of International Economics, Elsevier, vol. 55(1), pages 139-159, October.
    15. Guangzhi Qi & Zhibao Wang & Zhixiu Wang & Lijie Wei, 2022. "Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    16. Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    17. Zhao, Congyu & Wang, Kun & Dong, Xiucheng & Dong, Kangyin, 2022. "Is smart transportation associated with reduced carbon emissions? The case of China," Energy Economics, Elsevier, vol. 105(C).
    18. Huang, Yujie & Liu, Shucheng & Gan, Jiawu & Liu, Baoliu & Wu, Yuxi, 2024. "How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China," Energy Economics, Elsevier, vol. 140(C).
    Full references (including those not matched with items on IDEAS)

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