The globalization of artificial intelligence: consequences for the politics of environmentalism
Abstract
The globalization of artificial intelligence is supercharging the technological base of the world order. What are the consequences of the rising power of AI for environmentalism? Machine learning and intelligent robotics can advance environmental knowledge and conservation. Yet other AI technologies – from facial recognition to automated online surveillance – can enhance the power of states and corporations to suppress activism. The consequences of AI for environmentalism are highly variable, empowering some campaigns and depowering others. Still, a broad pattern is discernible. Going forward, AI technologies would seem set to advance technocratic, incremental, and moderate forms of environmentalism while hampering confrontational tactics and grassroots resistance, especially in authoritarian states. At the same time, AI is going to spur wasteful consumption, natural resource extraction, and the production of electronic waste. The globalization of AI, as these findings reveal, is set to unsettle the politics of environmentalism, doing some good, but bringing many dangers.
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?
AI involves establishing heavy energy dependency due to intensive use of innovative technologies, increasing environmental impacts, extending car traveling distance, and causing urban sprawl.
“AI involves establishing heavy energy dependency due to the intensive use of innovative technologies, increasing environmental impacts [[280], [281]], extending car traveling distance, and causing urban sprawl [249].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [281]
AI involves establishing heavy energy dependency and increasing carbon footprints as a result of externalities of its development, use, application, and disposal.
“AI involves establishing heavy energy dependency and increasing carbon footprints (Dauvergne 2021) as a result of the externalities of its development, use, application, and disposal (e.g., (Almalki et al. 2021; Kaplan and Haenlein 2019; Leal Filho et al. 2022).”
From Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review (2023) · cites this paper as Dauvergne 2021
The citation lists haven’t been fetched yet
References 57 · Cited by 72
- Authors
- Dauvergne, P.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Dauvergne, Peter
- ORCID
- Dauvergne, Peter
- Venue
- Globalizations
- Publisher
- Informa UK Limited
- Published
- 2020-06-30
- Volume
- 18
- Issue
- 2
- Pages
- 285-299
- ISSN
- 1474-7731 (ISSN-L)
- 1474-774X
- OpenAlex
- W3040571868
- Cited by
- 72
- References
- 57
- Citations by year
- 2026: 18 · 2025: 12 · 2024: 16 · 2023: 13 · 2022: 9 · 2021: 3 · 2020: 1
- FWCI
- 8.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Globalizations (publishedVersion)
- Funders
- Social Sciences and Humanities Research Council of Canada (435–2014–0115)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Innovation, Sustainability, Human-Machine Systems
- Social Sciences › Political Science and International Relations › Social Sciences and Humanities
- Keywords
- artificial intelligence, environmentalism, globalization, machine learning, robotics, environmental activism, surveillance, face recognition, authoritarian state, grassroots resistance, technocracy, electronic waste, resource extraction, environmental politics
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
