Artificial Intelligence Policy and Urban Carbon Emission Performance: Evidence from the Yangtze River Delta, China
References
- Carbon Emission Accounts and Datasets (CEADs). China City-Level CO₂ Emission Inventory (1997-2019) [DB/OL]. Published 2022-10-11. Available from: https://www.ceads.net/data/carbon-inventory/city-level/.
- Meng F, Hu H, Sun Y, Zhang L, Hou J, Zhang Z, et al. Full-scope carbon dioxide emission dataset for Chinese cities in 2023. Scientific Data. 2025;12:1672. DOI: 10.1038/s41597-025-05949-y.
- National Bureau of Statistics of China and relevant ministerial statistical yearbooks. China Statistical Yearbook, China City Statistical Yearbook, China Energy Statistical Yearbook, China Fiscal Yearbook, China Education Statistical Yearbook, China Financial Yearbook and related statistical yearbooks (various years, 2011-2024) [DB/OL]. Beijing: China Statistics Press. Available from: https://www.stats.gov.cn/.
- International Federation of Robotics (IFR). World Robotics 2025: Industrial Robots [R/OL]. Frankfurt: International Federation of Robotics, 2025. Available from: https://ifr.org/worldrobotics/report-2025.
- Mischos S, Dalagdi E, Vrakas D. Intelligent energy management systems: a review. Artificial Intelligence Rev. 2023;56(10):11635-74. DOI: 10.1007/s10462-023-10441-3.
- Ma N, Deng Y. Synergistic governance of digital economy development and carbon reduction: Evidence from Chinese cities. Ecol Chem Eng S. 2024;31(2):203-13. DOI: 10.2478/eces-2024-0014.
- Zhou P, Ang BW, Poh KL. Measuring environmental performance under different environmental DEA technologies. Energy Economics. 2008;30(1):1-14. DOI: 10.1016/j.eneco.2006.05.001.
- Zhou P, Ang BW. Decomposition of aggregate CO₂ emissions: A production-theoretical approach. Energy Economics. 2008;30(3):1054-67. DOI: 10.1016/j.eneco.2007.10.005.
- Zhou P, Ang BW, Han JY. Total factor carbon emission performance: A Malmquist index analysis. Energy Economics. 2010;32(1):194-201. DOI: 10.1016/j.eneco.2009.10.003.
- Vinuesa R, Azizpour H, Leite I, Balaam M, Dignum V, Domisch S, et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Commun. 2020;11:233. DOI: 10.1038/s41467-019-14108-y.
- Rolnick D, Donti PL, Kaack LH, Kochanski K, Lacoste A, Sankaran K, et al. Tackling climate change with machine learning. ACM Computing Surveys. 2022;55(2):Article 42. DOI: 10.1145/3485128.
- Kaack LH, Donti PL, Strubell E, Kamiya G, Creutzig F, Rolnick D. Aligning artificial intelligence with climate change mitigation. Nature Climate Change. 2022;12:518-27. DOI: 10.1038/s41558-022-01377-7.
- Zhang K, Kou ZX, Zhu PH, Qian XY, Yang YZ. How does AI affect urban carbon emissions? Quasi-experimental evidence from China’s AI innovation and development pilot zones. Economic Analysis Policy. 2025;85:426-47. DOI: 10.1016/j.eap.2024.12.013.
- Yu L, Wang Y, Wei X, Zeng C. Towards low-carbon development: The role of industrial robots in decarbonization in Chinese cities. J Environ Manage. 2023;330:117216. DOI: 10.1016/j.jenvman.2023.117216.
- Feng L, Qi J, Zheng Y. How can AI reduce carbon emissions? Insights from a quasi-natural experiment using generalized random forest. Energy Economics. 2025;141:108040. DOI: 10.1016/j.eneco.2024.108040.
- He H, Wu S, Wang X. Carbon reduction effect of digital transformation in Chinese steel enterprises. Environ Eng Manage J. 2025;24(1):105-15. DOI: 10.30638/eemj.2025.009.
- Ren B, Qiu Z, Liu B. Supply chain decarbonisation effects of artificial intelligence: Evidence from China. Int Rev Economics Finance. 2025;101:104198. DOI: 10.1016/j.iref.2025.104198.
- Wang L, Wang H, Cao Z, He Y, Dong Z, Wang S. Can industrial intellectualisation reduce carbon emissions? - Empirical evidence from the perspective of carbon total factor productivity in China. Technol Forecasting Social Change. 2022;184:121969. DOI: 10.1016/j.techfore.2022.121969.
- Chu H, Li D, Qie X, Ning Y. Research on the impact of digital economy on pollution and carbon reduction in the Yangtze River Delta urban agglomeration. Sustainability. 2026;18(8):4090. DOI: 10.3390/su18084090.
- Haefner N, Wincent J, Parida V, Gassmann O. Artificial intelligence and innovation management: A review, framework, and research agenda. Technol Forecasting Social Change. 2021;162:120392. DOI: 10.1016/j.techfore.2020.120392.
- Chen J, Yang L, Su H. Green transformation in the era of intelligence: How artificial intelligence affects disruptive green innovation in firms. Energy Economics. 2026;153:109093. DOI: 10.1016/j.eneco.2025.109093.
- Lee CC, Fang Y, Quan S, Li X. Leveraging the power of artificial intelligence toward the energy transition: The key role of the digital economy. Energy Economics. 2024;135:107654. DOI: 10.1016/j.eneco.2024.107654.
- Zhou W, Zhuang Y, Chen Y. How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology. Energy Economics. 2024;131:107355. DOI: 10.1016/j.eneco.2024.107355.
- Han Y. Digital empowerment and enterprise carbon risk management: evidence from manufacturing sector. Ecol Chem Eng S. 2024;31(2):193-202. DOI: 10.2478/eces-2024-0013.
- Wang Y, Zhao W, Ma X. The spatial spillover impact of artificial intelligence on energy efficiency: Empirical evidence from 278 Chinese cities. Energy. 2024;312:133497. DOI: 10.1016/j.energy.2024.133497.
- Xia L, Han Q, Yu S. Industrial intelligence and industrial structure change: Effect and mechanism. Int Rev Economics Finance. 2024;93:1494-506. DOI: 10.1016/j.iref.2024.04.002.
- Strubell E, Ganesh A, McCallum A. Energy and policy considerations for deep learning in NLP [C]. Proc 57th Annual Meeting Assoc Computational Linguistics. Florence: Assoc Computational Linguistics. 2019:3645-50. DOI: 10.18653/v1/P19-1355.
- Schwartz R, Dodge J, Smith NA, Etzioni O. Green AI. Commun ACM. 2020;63(12):54-63. DOI: 10.1145/3381831.
- Lannelongue L, Grealey J, Inouye M. Green algorithms: Quantifying the carbon footprint of computation. Adv Sci. 2021;8(12):2100707. DOI: 10.1002/advs.202100707.
- van Wynsberghe A. Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics. 2021;1:213-8. DOI: 10.1007/s43681-021-00043-6.
- De Vries A. The growing energy footprint of artificial intelligence. Joule. 2023;7(10):2191-4. DOI: 10.1016/j.joule.2023.09.004.
- Luccioni AS, Jernite Y, Strubell E. Power hungry processing: Watts driving the cost of AI deployment? [C]. Proc 2024 ACM Conf Fairness, Accountability Transparency. New York: Assoc Computing Machinery. 2024:85-99. DOI: 10.1145/3630106.3658542.
- International Energy Agency (IEA). Energy and AI [R/OL]. Paris: International Energy Agency, 2025. Available from: https://www.iea.org/reports/energy-and-ai.
- International Energy Agency (IEA). Key Questions on Energy and AI [R/OL]. Paris: International Energy Agency, 2026. Available from: https://www.iea.org/reports/key-questions-on-energy-and-ai.
- Sorrell S. Jevons’ Paradox revisited: The evidence for backfire from improved energy efficiency. Energy Policy. 2009;37(4):1456-69. DOI: 10.1016/j.enpol.2008.12.003.
- Gillingham K, Rapson D, Wagner G. The rebound effect and energy efficiency policy. Rev Environ Economics Policy. 2016;10(1):68-88. DOI: 10.1093/reep/rev017.
- Lin JY, Wang X. Endowment, technology choice, and industrial upgrading. Structural Change Economic Dynamics. 2023;65:364-81. DOI: 10.1016/j.strueco.2023.03.002.
- Zou W, Xiong Y. Does artificial intelligence promote industrial upgrading? Evidence from China. Economic Res - Ekonomska Istraživanja. 2023;36(1):1666-87. DOI: 10.1080/1331677X.2022.2092168.
- Chen H, Liang M, Wang J, Shi B. Environmental regulation, R&D investment and industrial transformation and upgrading. Environ Eng Manage J. 2025;24(3):449-60. DOI: 10.30638/eemj.2025.036.
- Dabuo FT, Xu L, Madzikanda B, Appiah-Twum F. Analyzing the impact of eco-innovation and environmental regulation on green development efficiency. Environ Eng Manage J. 2024;23(11):2247-66. DOI: 10.30638/eemj.2024.181.
- Yang Y. Green development synergies and regional disparities in the Yangtze River Delta urban agglomeration. Ecol Chem Eng S. 2025;32(2):189-212. DOI: 10.2478/eces-2025-0010.
- Zhao Y, Qiao Y, Zhang L, Fan Q. Green innovation spillover vs. competition in Chinese urban agglomerations: A comparative study of green total factor productivity before and after the 2015 Environmental Protection Law revision. Environ Eng Manage J. 2024;23(12):2593-605. DOI: 10.30638/eemj.2024.208.
- Zhao Y. Carbon emission reduction effects of heterogeneous environmental regulation: evidence from the firm level. Ecol Chem Eng S. 2024;31(2):243-52. DOI: 10.2478/eces-2024-0017.
- Sun L, Abraham S. Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. J Econometrics. 2021;225(2):175-99. DOI: 10.1016/j.jeconom.2020.09.006.
- State Council of the People’s Republic of China. New Generation Artificial Intelligence Development Plan [EB/OL]. State Council Document No. 35 [2017], 2017-07-20. Available from: https://app.www.gov.cn/govdata/gov/201707/20/408540/article.html.
- China Research Data Services Platform (CNRDS). Green Patent Research Database (GPRD) [DB/OL]. Database accessed in 2026. Available from: https://www.cnrds.com/ (accessed 12 August 2026); China National Intellectual Property Administration (CNIPA). Patent Search and Analysis System [DB/OL]. 2023. Available from: https://pss-system.cponline.cnipa.gov.cn.
- Callaway B, Sant’Anna PHC. Difference-in-Differences with multiple time periods. J Econometrics. 2021;225(2):200-30. DOI: 10.1016/j.jeconom.2020.12.001.
- Borusyak K, Jaravel X, Spiess J. Revisiting event-study designs: Robust and efficient estimation. Rev Economic Stud. 2024;91(6):3253-85. DOI: 10.1093/restud/rdae007.
- Acharya A, Blackwell M, Sen M. Explaining causal findings without bias: Detecting and assessing direct effects. Amer Political Sci Rev. 2016;110(3):512-29. DOI: 10.1017/S0003055416000216.
- Gelbach JB. When do covariates matter? And which ones, and how much? J Labor Economics. 2016;34(2):509-43. DOI: 10.1086/683668.
- Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychological Methods. 2010;15(4):309-34. DOI: 10.1037/a0020761.
- Conley TG, Taber CR. Inference with “Difference in Differences” with a small number of policy changes. Rev Economics Statistics. 2011;93(1):113-25. DOI: 10.1162/REST_a_00049.
- Ferman B, Pinto C. Inference in Differences-in-Differences with few treated groups and heteroskedasticity. Rev Economics Statistics. 2019;101(3):452-67. DOI: 10.1162/rest_a_00759.
- MacKinnon JG, Webb MD. The wild bootstrap for few (treated) clusters. Econometrics J. 2018;21(2):114-35. DOI: 10.1111/ectj.12107.
- Roth J. Pretest with caution: Event-study estimates after testing for parallel trends. Amer Economic Rev: Insights. 2022;4(3):305-22. DOI: 10.1257/aeri.20210236.
- Rambachan A, Roth J. A more credible approach to parallel trends. Rev Economic Stud. 2023;90(5):2555-91. DOI: 10.1093/restud/rdad018.
DOI: https://doi.org/10.2478/eces-2026-0020 | Journal eISSN: 2084-4549 (formerly 1898-6196) | Journal ISSN: 1898-6196
Language: English
Page range: 375 - 398
Published online: Oct 11, 2026
Published in print: Sep 1, 2026
Published by: Society of Ecological Chemistry and Engineering
In partnership with: Paradigm Publishing Services
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© 2026 Zirui Guo, Hanyu Lu, published by Society of Ecological Chemistry and Engineering
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