Abstract
This article examines the net climate impact of artificial intelligence in the energy transition. The study is conceptual and is based on a structured narrative review of scientific literature and institutional documents published between 2020 and 2026. Drawing on the contributions reviewed, it proposes a counterfactual accounting framework that compares total emissions associated with the use of artificial intelligence with the emissions estimated for a situation in which the technology is not used. The change in emissions is decomposed into four components: emissions from digital infrastructure, changes arising from the scale and structure of production, demand rebound effects, and emissions avoided through efficiency gains. The framework is applied to Romania’s industrial sector through three sensitivity scenarios, using official data for 2024. Depending on the share of industrial electricity consumption affected by AI applications and the assumed reduction in energy use, the results indicate annual electricity savings ranging from 5.121 to 136.562 GWh and avoided operational emissions ranging from approximately 963 to 25,674 t CO₂e. These values are conditional on the scenario assumptions and do not represent savings already achieved. The results show that reducing energy use per unit of output does not guarantee a reduction in total emissions, because the net climate benefit also depends on the footprint of computing infrastructure, changes in output, and rebound effects.
Cuvinte cheie
artificial intelligence
Energy transition
energy efficiency
industry
rebound effect
net emissions.
Istoric articol
Publicat
31.08.2026
Informații autori
Citare recomandată
Irina-Elena Chirtoc, Lucia Paliu-Popa, Gabriela Bușan (2026). The Net Impact of Artificial Intelligence on the Energy Transition. Constantin Brâncuși University of Târgu Jiu Economics Series, 2(1), 222–228. https://doi.org/10.65631/jes.4.2026.21
Referințe bibliografice
Alpine, W., Geldner, N., Alpine, H., & Chepeliev, M. G. (2026). AI-driven productivity gains enable more CO₂ emissions than they avoid in a global energy–economy model. npj Climate Action, 5, Article 71. https://doi.org/10.1038/s44168-026-00411-0 European Commission. (2024). Commission Delegated Regulation (EU) 2024/1364 of 14 March 2024 on the first phase of the establishment of a common Union rating scheme for data centres. Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg_del/2024/1364/oj European Environment Agency. (2025). Greenhouse gas emission intensity of electricity generation, country level [Data set]. Retrieved August 20, 2026, from https://www.eea.europa.eu/en/analysis/indicators/greenhouse-gas-emission-intensity-of-1/greenhouse-gas-emission-intensity-of-electricity-generation Eurostat. (2026a). Simplified energy balances [Data set]. European Commission. Retrieved August 20, 2026, from https://doi.org/10.2908/NRG_BAL_S Eurostat. (2026b). Artificial intelligence by NACE Rev. 2 activity [Data set]. European Commission. Retrieved August 20, 2026, from https://doi.org/10.2908/ISOC_EB_AIN2 Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21(248), 1–43. https://www.jmlr.org/papers/v21/20-312.html International Energy Agency. (2025). Energy and AI. https://www.iea.org/reports/energy-and-ai International Energy Agency. (2026). Key questions on energy and AI. https://www.iea.org/reports/key-questions-on-energy-and-ai Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12, 518–527. https://doi.org/10.1038/s41558-022-01377-7 Lange, S., Pohl, J., & Santarius, T. (2020). Digitalization and energy consumption. Does ICT reduce energy demand? Ecological Economics, 176, Article 106760. https://doi.org/10.1016/j.ecolecon.2020.106760 Luccioni, A. S., Strubell, E., & Crawford, K. (2025). From efficiency gains to rebound effects: The problem of Jevons’ paradox in AI’s polarized environmental debate. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 76–88). Association for Computing Machinery. https://doi.org/10.1145/3715275.3732007 Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2023). Estimating the carbon footprint of BLOOM, a 176B parameter language model. Journal of Machine Learning Research, 24(253), 1–15. https://www.jmlr.org/papers/v24/23-0069.html OECD. (2022). Measuring the environmental impacts of artificial intelligence compute and applications: The AI footprint (OECD Digital Economy Papers No. 341). OECD Publishing. https://doi.org/10.1787/7babf571-en Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C., Ng, A. Y., Hassabis, D., Platt, J. C., … Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), Article 42. https://doi.org/10.1145/3485128 Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831