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Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China

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  • Zhong, Wenli
  • Liu, Yang
  • Dong, Kangyin
  • Ni, Guohua

Abstract

Artificial intelligence (AI) has become a key driver in the latest wave of scientific and technological advancement, and its rapid development, proliferation, and environmental impacts cannot be ignored. China and numerous emerging economies are confronted with the dual challenges of environmental degradation and climate change. Hence, it is imperative to assess whether the advancement of AI can contribute to a synergistic reduction in pollutant and CO2 emissions. This paper utilizes the system-generalized method of moments (SYS-GMM) to study the synergistic effect of artificial intelligence on mitigating pollutant and carbon emissions. The following three main conclusions are drawn: (1) AI plays a major role in synergistically decreasing pollutant and CO2 emissions; (2) AI indirectly helps lower pollutant and CO2 emissions by fostering technological advancements and enhancing industrial structures. Although it contributes to an increase in emissions by expanding production scale, its suppression effect dominates overall; (3) The impact of AI applications is particularly vital in cities with strict environmental controls, especially in the central and eastern regions. Finally, we suggest some policy measures to augment the influence of AI in reducing emissions and attaining sustainable development.

Suggested Citation

  • Zhong, Wenli & Liu, Yang & Dong, Kangyin & Ni, Guohua, 2024. "Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China," Energy Economics, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:eneeco:v:138:y:2024:i:c:s0140988324005371
    DOI: 10.1016/j.eneco.2024.107829
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    as
    1. Li, Zhen & Wu, Baijun & Wang, Danyang & Tang, Maogang, 2022. "Government mandatory energy-biased technological progress and enterprises' environmental performance: Evidence from a quasi-natural experiment of cleaner production standards in China," Energy Policy, Elsevier, vol. 162(C).
    2. Jiaxiong Yao & Mr. Yunhui Zhao, 2022. "Structural Breaks in Carbon Emissions: A Machine Learning Analysis," IMF Working Papers 2022/009, International Monetary Fund.
    3. Ma, Qiang & Murshed, Muntasir & Khan, Zeeshan, 2021. "The nexuses between energy investments, technological innovations, emission taxes, and carbon emissions in China," Energy Policy, Elsevier, vol. 155(C).
    4. Chang, Lei & Taghizadeh-Hesary, Farhad & Mohsin, Muhammad, 2023. "Role of artificial intelligence on green economic development: Joint determinates of natural resources and green total factor productivity," Resources Policy, Elsevier, vol. 82(C).
    5. Carol Corrado & Jonathan Haskel & Cecilia Jona-Lasinio, 2021. "Artificial intelligence and productivity: an intangible assets approach," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 435-458.
    6. Selden Thomas M. & Song Daqing, 1994. "Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions?," Journal of Environmental Economics and Management, Elsevier, vol. 27(2), pages 147-162, September.
    7. Gloria Cicerone & Alessandra Faggian & Sandro Montresor & Francesco Rentocchini, 2023. "Regional artificial intelligence and the geography of environmental technologies: does local AI knowledge help regional green-tech specialization?," Regional Studies, Taylor & Francis Journals, vol. 57(2), pages 330-343, February.
    8. Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    9. Li, Xuehui & Xu, Yangyang & Yao, Xin, 2021. "Effects of industrial agglomeration on haze pollution: A Chinese city-level study," Energy Policy, Elsevier, vol. 148(PA).
    10. Shahbaz, Muhammad & Loganathan, Nanthakumar & Muzaffar, Ahmed Taneem & Ahmed, Khalid & Ali Jabran, Muhammad, 2016. "How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 83-93.
    11. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    12. Mohammad Hossein Rezaei & Milad Sadeghzadeh & Mohammad Alhuyi Nazari & Mohammad Hossein Ahmadi & Fatemeh Razi Astaraei, 2018. "Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 13(3), pages 266-271.
    13. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).
    14. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    15. Pan, Yuling & Dong, Feng, 2023. "Factor substitution and development path of the new energy market in the BRICS countries under carbon neutrality: Inspirations from developed European countries," Applied Energy, Elsevier, vol. 331(C).
    16. He, Jie, 2006. "Pollution haven hypothesis and environmental impacts of foreign direct investment: The case of industrial emission of sulfur dioxide (SO2) in Chinese provinces," Ecological Economics, Elsevier, vol. 60(1), pages 228-245, November.
    17. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    18. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    19. Agrawal, Ajay & Gans, Joshua S. & Goldfarb, Avi, 2019. "Exploring the impact of artificial Intelligence: Prediction versus judgment," Information Economics and Policy, Elsevier, vol. 47(C), pages 1-6.
    20. Song, Yan & Zhu, Jing & Yue, Qian & Zhang, Ming & Wang, Longke, 2023. "Industrial agglomeration, technological innovation and air pollution: Empirical evidence from 277 prefecture-level cities in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 240-252.
    21. Benzidia, Smail & Makaoui, Naouel & Bentahar, Omar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    22. Leng, Jiewu & Ruan, Guolei & Jiang, Pingyu & Xu, Kailin & Liu, Qiang & Zhou, Xueliang & Liu, Chao, 2020. "Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    23. Yan, Bingqian & Xia, Yan & Jiang, Xuemei, 2023. "Carbon productivity and value-added generations: Regional heterogeneity along global value chain," Structural Change and Economic Dynamics, Elsevier, vol. 65(C), pages 111-125.
    24. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    25. Aisen, Ari & Veiga, Francisco José, 2013. "How does political instability affect economic growth?," European Journal of Political Economy, Elsevier, vol. 29(C), pages 151-167.
    26. Liddle, Brantley, 2014. "Impact of population, age structure, and urbanization on carbon emissions/energy consumption: Evidence from macro-level, cross-country analyses," MPRA Paper 61306, University Library of Munich, Germany.
    27. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    28. Zhao, Chuan & Guo, Qidong & Jia, Rongwen & Dong, Kangyin & Wang, Kun, 2023. "How does clean energy transition promote original design manufacturers? A three-party evolutionary game analysis," Energy Economics, Elsevier, vol. 126(C).
    29. Xinshu Zhao & John G. Lynch & Qimei Chen, 2010. "Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 197-206, August.
    30. Nicola Jones, 2018. "How to stop data centres from gobbling up the world’s electricity," Nature, Nature, vol. 561(7722), pages 163-166, September.
    31. Xing, Peixue & Wang, Yanan & Ye, Tao & Sun, Ying & Li, Qiao & Li, Xiaoyan & Li, Meng & Chen, Wei, 2024. "Carbon emission efficiency of 284 cities in China based on machine learning approach: Driving factors and regional heterogeneity," Energy Economics, Elsevier, vol. 129(C).
    32. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    33. 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).
    34. Smaïl Benzidia & Naouel Makaoui & Omar Bentahar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Post-Print hal-03028127, HAL.
    35. Yan, Ran & Ma, Minda & Zhou, Nan & Feng, Wei & Xiang, Xiwang & Mao, Chao, 2023. "Towards COP27: Decarbonization patterns of residential building in China and India," Applied Energy, Elsevier, vol. 352(C).
    36. Ali Komeili Birjandi & Morteza Fahim Alavi & Mohamed Salem & Mamdouh El Haj Assad & Natarajan Prabaharan, 2022. "Modeling carbon dioxide emission of countries in southeast of Asia by applying artificial neural network [Energy and exergy analyses of single flash geothermal power plant at optimum separator temperature]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 321-326.
    37. Li, Ke & Lin, Boqiang, 2014. "The nonlinear impacts of industrial structure on China's energy intensity," Energy, Elsevier, vol. 69(C), pages 258-265.
    38. Liu, Yang & Dong, Xiucheng & Dong, Kangyin, 2023. "Pathway to prosperity? The impact of low-carbon energy transition on China's common prosperity," Energy Economics, Elsevier, vol. 124(C).
    39. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    40. Jiang, Hong-Dian & Purohit, Pallav & Liang, Qiao-Mei & Liu, Li-Jing & Zhang, Yu-Fei, 2023. "Improving the regional deployment of carbon mitigation efforts by incorporating air-quality co-benefits: A multi-provincial analysis of China," Ecological Economics, Elsevier, vol. 204(PA).
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    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • L80 - Industrial Organization - - Industry Studies: Services - - - General
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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