AI & Environment Resource HubCharlotte Ducuing and Sara Garsia · 2025-10-06
The abstract argues that policy debates about “sustainable AI” are stuck in an overly simplistic binary, AI as both an environmental threat and a tool for ecological transition, which leads to shallow regulatory approaches focused mostly on energy reporting. Drawing on law-and-technology scholarship, the authors show how technological determinism, exceptionalism, and regulatory solutionism distort the debate, and they propose reframing regulation around the socio-economic drivers and power dynamics of AI development rather than the technology itself.
Charlotte Ducuing and Sara Garsia. 2025-10-06. A Critical Re-framing of the AI(s) and Sustainability Regulation Debate through the Lens of Law and Technology. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5569738 (AI & Environment Resource Hub; record paper-411; collection snapshot 2026-09-15).
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Scientific Paper · Row 415 · Original ID: paper-411.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Biodiversity and ecosystems
AI and ML optimize optoelectronic systems for sustainability by enhancing energy efficiency, renewable energy harvesting, environmental monitoring, and smart cities while also advancing ocean optics and photonics for marine ecosystem monitoring and climate change mitigation.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Biodiversity and ecosystems
Amy Luers explores how artificial intelligence can drive the rapid and large-scale transformation needed to combat climate change and biodiversity loss, highlighting AI's role in reducing emissions, expanding renewable energy, minimizing waste, and protecting nature while emphasizing the importance of responsible AI development.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Biodiversity and ecosystems
Climate AI solutions often reinforce existing power imbalances, serving corporate interests through surveillance and commodification while neglecting social and environmental justice.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Biodiversity and ecosystems
In the Environmental Impacts of Data Centers 101 course, you will learn to analyze the environmental impacts of data centers using a life cycle assessment (LCA) perspective that goes beyond what you see in the news. This will include energy, water, land use, carbon emissions, global supply chains, e-waste concerns, ecological impacts, and environmental justice case studies. Whether you work in tech, sustainability, policy, or are just curious about how AI systems operate behind the scenes, this course gives you the clarity and frameworks to understand these impacts from end-to-end.