The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
The impact of AI on carbon emissions: evidence from 66 countries
Abstract
This study aims to address debate in previous studies on whether AI has a positive or negative effect on carbon emission reduction. We used quantile regression and PSTR models to study the diverse impacts of AI on carbon emissions in 66 countries from 1993–2019. There were three main findings in this paper. First, the impact of AI on carbon emissions varies across countries, and its effect on carbon reduction is mainly found in high-carbon emission and high-income countries. Second, the industrial structure environment of different countries affects the role of AI in carbon reduction, with its marginal effect in limiting emissions decreasing with the rise of secondary industrial structures. Third, the impact of AI varies in countries based on their different demographic structures. The marginal effect of AI on carbon emission reduction increases in places with older populations. This study offers unique insight into the heterogeneous impact of AI on CO2 emissions. Our analysis confirms the importance of industrial and demographic structures in promoting carbon emission reduction. We provide effective policy recommendations for economic development and environmental governance.
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“in Norilsk, Russia, a nickel factory released 350,000 tons of sulfur dioxide yearly, heavily polluting the city.”
From Artificial Intelligence Colonialism: Environmental Damage, Labor Exploitation, and Human Rights Crises in the Global South (2024) · cites this paper as [99]
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“In the Philippines, seventeen nickel mines were shut down due to environmental issues”
From Artificial Intelligence Colonialism: Environmental Damage, Labor Exploitation, and Human Rights Crises in the Global South (2024) · cites this paper as [99]
Citations
Cites
Robots and Jobs: Evidence from US Labor Markets
Acemoglu, D., & Restrepo, P. (2020) · Journal of Political Economy · doi:10.1086/705716 · Cited by 4,088
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203
Implications of population ageing for economic growth
Bloom, D. E., Canning, D., & Fink, G. (2010) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grq038 · Cited by 971
Zhu, H., Duan, L., Guo, Y., & Yu, K. (2016) · Economic Modelling · doi:10.1016/j.econmod.2016.05.003 · Cited by 914
Li, R., Wang, Q., Liu, Y., & Jiang, R. (2021) · Sustainable Production and Consumption · doi:10.1016/j.spc.2021.02.031 · Cited by 590
Wang, Q., Zhang, F., & Li, R. (2022) · Environmental Research · doi:10.1016/j.envres.2022.114637 · Cited by 564
The carbon impact of artificial intelligence
Dhar, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0219-9 · Cited by 509
Artificial intelligence and industrial innovation: Evidence from German firm-level data
Rammer, C., Fernández, G. P., & Czarnitzki, D. (2022) · Research Policy · doi:10.1016/j.respol.2022.104555 · Cited by 489
Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries
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Zhao, J., & Javed, M. A. (2026) · International Journal of Energy Research · doi:10.1155/er/6070715
Al-Maadid, A. A., Ali, M. S. B., & Sher, A. (2026) · Sustainable Development · doi:10.1002/sd.71686
Razmjoo, A., Asef, P., & Østergaard, P. A. (2026) · Energy Research & Social Science · doi:10.1016/j.erss.2026.105031
Governing AI for the climate: A political and economic analysis of G-20 regulatory thresholds
Hussain, P., Sabolovic, M., & Nazir, R. (2026) · International Environmental Agreements Politics Law and Economics · doi:10.1007/s10784-026-09742-z
Aydın, R., İMAMOĞLU, İ. K., & Gökçe, N. (2026) · Sustainable Development · doi:10.1002/sd.71806
Metadata
- Authors
- Zhong, J., Zhong, Y., Han, M., Yang, T., & Zhang, Q.
- Affiliations
- South China University of Technology (CN)
- Guangzhou Huashang College (CN)
- Corresponding author
- Yang, Tianjian
- Venue
- Applied Economics
- Publisher
- Informa UK Limited
- Published
- 2023-04-19
- Volume
- 56
- Issue
- 25
- Pages
- 2975-2989
- ISSN
- 0003-6846 (ISSN-L)
- 1466-4283
- OpenAlex
- W4366490174
- Cited by
- 105
- References
- 38
- Citations by year
- 2026: 38 · 2025: 41 · 2024: 20 · 2023: 6
- FWCI
- 21.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- The National Social Science Youth Fund of China (20CJY009)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Energy, Environment, Economic Growth
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Development
- Keywords
- artificial intelligence, CO2 emissions, carbon emission reduction, heterogeneous effects, industrial structure, demographic structure, population aging, high-income countries, environmental governance, economic development
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