Artificial intelligence and climate change: ethical issues
Open accessAbstract
Purpose The purpose of this paper is to pinpoint and analyse ethical issues raised by the dual role of artificial intelligence (AI) in relation to climate change, that is, AI as a contributor to climate change and AI as a contributor to fighting climate change. Design/methodology/approach This paper consists of three main parts. The first part provides a short background on AI and climate change respectively, followed by a presentation of empirical findings on the contribution of AI to climate change. The second part presents proposals by various AI researchers and commentators on how AI companies may contribute to fighting climate change by reducing greenhouse gas emissions from training and use of AI and by providing AI assistance to various mitigation and adaptation measures. The final part investigates ethical issues raised by some of the options presented in the second part. Findings AI applications may lead to substantial emissions but may also play an important role in mitigation and adaptation. Given this dual role of AI, ethical considerations by AI companies and governments are of vital importance. Practical implications This paper pinpoints practical ethical issues that AI companies and governments should take into account. Social implications Given the potential impact of AI on society, it is vital that AI companies and governments take seriously the ethical issues raised by the dual role of AI in relation to climate change. Originality/value AI has been the subject of substantial ethical investigation, and even more so has climate change. However, the relationship between AI and climate change has received only limited attention from an ethical perspective. This paper provides such considerations.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Cobalt and nickel mining for AI hardware comes with severe environmental costs, including deforestation, toxic air pollution, and water contamination.
“Cobalt and nickel mining for AI hardware comes with severe environmental costs, including deforestation, toxic air pollution, and water contamination.”
From Artificial Intelligence Colonialism: Environmental Damage, Labor Exploitation, and Human Rights Crises in the Global South (2024) · cites this paper as [98]
Energy demands are often offloaded to data centers in regions with weak environmental regulations, like the Global South.
“Energy demands are often offloaded to data centers in regions with weak environmental regulations, like the Global South.”
From Artificial Intelligence Colonialism: Environmental Damage, Labor Exploitation, and Human Rights Crises in the Global South (2024) · cites this paper as [30]
By 2040, emissions from the ICT industry are expected to reach 14% of global emissions, with the majority coming from ICT infrastructure, particularly data centres and communication networks.
“By 2040, the emissions from the Information and Communications Technology (ICT) industry are expected to reach 14% of the global emissions, with the majority of those emissions coming from the ICT infrastructure, particularly data centres and communication networks [18].”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [18]
Cites
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · see citing works
On Global Electricity Usage of Communication Technology: Trends to 2030
Andrae, A., & Edler, T. (2015) · Challenges · doi:10.3390/challe6010117 · Cited by 1,334 · see citing works
The carbon impact of artificial intelligence
Dhar, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0219-9 · Cited by 509 · see citing works
Quantifying the Carbon Emissions of Machine Learning
Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019) · arXiv · doi:10.48550/arxiv.1910.09700 · Cited by 408 · cited 2× · see citing works
“For example, when using cloud computing, data centres could be moved to countries or regions providing a more fossil-free energymix (Lacoste et al., 2019).”
BackgroundMethodTowards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020) · arXiv · doi:10.48550/arxiv.2002.05651 · Cited by 393 · see citing works
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
Anthony, L. F. W., Kanding, B., & Selvan, R. (2020) · arXiv · doi:10.48550/arxiv.2007.03051 · Cited by 255 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295 · see citing works
“By 2040, the emissions from the Information and Communications Technology (ICT) industry are expected to reach 14% of the global emissions, with the majority of those emissions coming from the ICT infrastructure, particularly data centres and communication networks [18].”
Regilme, S. S. F. (2024) · SAIS Review of International Affairs · doi:10.1353/sais.2024.a950958 · Cited by 65 · see citing works
“28,29,30,31 The training of large AI models, such as OpenAI’s GPT-3, requires massive amounts of computational power.”
- Authors
- Nordgren, A.
- Affiliations
- Linköping University (SE)
- Corresponding author
- Nordgren, Anders
- ORCID
- Nordgren, Anders
- Venue
- Journal of Information, Communication and Ethics in Society
- Publisher
- Emerald
- Published
- 2022-02-10
- Volume
- 21
- Issue
- 1
- Pages
- 1-15
- ISSN
- 1477-996X (ISSN-L)
- OpenAlex
- W4211158612
- Cited by
- 94
- References
- 6
- Citations by year
- 2026: 24 · 2025: 29 · 2024: 26 · 2023: 13 · 2022: 2
- FWCI
- 20.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Information Communication and Ethics in Society (publishedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Innovation, Sustainability, Human-Machine Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- artificial intelligence, climate change, AI ethics, climate change mitigation, climate change adaptation, AI companies, government ethics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
