AI, Climate Change and Justice: Elements for a Normative Framework Centring the Global South
Parul Anand & Mark Coeckelbergh · 2026-02-05
This paper argues that AI's environmental impacts from energy consumption and water use to mineral extraction constitute a global climate justice concern that demands moving beyond efficiency metrics to center the unequal distribution of costs and benefits, particularly in the Global South.
Parul Anand & Mark Coeckelbergh. 2026-02-05. AI, Climate Change and Justice: Elements for a Normative Framework Centring the Global South. https://link.springer.com/article/10.1007/s43681-025-00949-5 (AI & Environment Resource Hub; record paper-427; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; Materials and critical minerals
Artificial intelligence (AI) is in the media spotlight for its potential to transform the economic and research sectors, among others. This drives funding bodies to support AI-based innovation, with for example the Horizon Europe and Digital Europe programmes run by the European Union, or France’s investment strategy France 2030 (national strategy for AI). On the other hand, the environmental impacts of AI are now better understood, and we cannot ignore the role of AI on electricity and water usage, mineral resource depletion, and greenhouse gas emissions1,2. To bring together innovation and sustainability, the French Department for the Environment (Ministère en charge de la Transition Écologique) has decided to require the use of the Green Algorithms tool for funding applications on the topic of AI and climate change. Applicants now have to include estimates of the carbon footprint and energy usage of the different development phases of the proposed AI solution. This was tested on a first funding call “Demonstrators of frugal AI for sustainable development of local communities”. The first applications were received in December 2023, with positive feedback from the different stakeholders. Applicants in particular approved of this new criterion, as they understood its necessity, found the tool easy to use, and did not consider this to slow down innovation. Following this successful implementation in a first funding call, it was decided to include the Green Algorithms tool more systematically in the application guidelines of other AI-related funding calls run by the Department. The goal of this piece is to reflect on the inclusion of environmental criteria in AI funding calls and share the lessons learned with other funding bodies internationally to promote similar initiatives across the AI ecosystem.
Loic et al.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water
AI’s rapid growth is driving an unprecedented surge in energy and water demand, raising serious environmental justice concerns, especially in marginalized communities, while sparking debate over whether climate benefits can outweigh the harms without stronger accountability and sustainable innovation.
Climate One
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water
The Environmental and Energy Study Institute (EESI) invites you to a briefing discussing the intersection of artificial intelligence (AI) and climate change in federal policy-making. While AI can aid in climate resilience and boost economic competitiveness, it is also on a trajectory to increase energy demand, greenhouse gas emissions, and water usage. This paradox presents an important opportunity for discussion on how to best minimize the negative impacts of AI on the environment and harness its powers for a sustainable future.
youtube.com
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Materials and critical minerals
Increasing investment in artificial intelligence (AI) is prompting a larger discussion around sustainability, from the huge demand for electricity to power data centers to the life cycle emissions of hardware needed to enable this technology. Globally, companies and governments are preparing for significant increases in energy use and raw material extraction to meet demand. On the other hand, evolving technology and breakthroughs in AI optimization could lead to lower emissions than previously anticipated. Join us for a discussion about opportunities to decarbonize AI and learn how leaders in the field are navigating the uncertainty.
