Eco-Translation Practice in Resisting AI's Ecological Harms: Towards a Preliminary Action Framework
Matthew Riemland · 2026-06-01
Matthew Riemland examines how translation professionals can confront the environmental costs of AI, including carbon emissions, water consumption, and rare mineral extraction, through 'eco-translation practice.' He argues these harms stem from structural power imbalances rather than individual choices, and proposes vocational and structural strategies of resistance, from supporting data center activism to demanding transparency from AI developers.
Matthew Riemland. 2026-06-01. Eco-Translation Practice in Resisting AI's Ecological Harms: Towards a Preliminary Action Framework. https://publications-prairial.fr/encounters-in-translation/index.php?id=1627 (AI & Environment Resource Hub; record paper-470; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water; Biodiversity and ecosystems; Built environment and infrastructure
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.
Nathaniel Burola
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water; Materials and critical minerals; Biodiversity and ecosystems
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; Energy and electricity; Climate and greenhouse gases; Water; Biodiversity and ecosystems; Built environment and infrastructure
This course will help you understand AI's climate implications and identify practical next steps within your organization. The course begins with demystifying the connection between AI, Large Language Models (LLMs), data centers, and energy and water demand. Then you will learn about AI's environmental footprint, the related environmental and community impacts, you will evaluate real-world applications of AI across climate adaptation, energy transition, and nature conservation, and understand the business and policy landscape shaping corporate decisions.
Coursera
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Biodiversity and ecosystems; Built environment and infrastructure
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.
