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

Wenli Zhong, Yang Liu, Kangyin Dong () and Guohua Ni

Energy Economics, 2024, vol. 138, issue C

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.

Keywords: Artificial intelligence; Synergistic emissions of pollutants and CO2; Mediation effects; Sustainable development; SYS-GMM technique (search for similar items in EconPapers)
JEL-codes: C33 L80 Q53 Q54 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:138:y:2024:i:c:s0140988324005371

DOI: 10.1016/j.eneco.2024.107829

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