Author or publisher unknown · Publication date unknown
An AI-powered satellite imagery search platform built by Earth Genome that lets environmental defenders apply labels to train models that detect features anywhere on the planet — illegal mining, large-scale livestock facilities, clandestine airstrips, deforestation, waste sites, and other ecosystem disturbances — in minutes rather than months.
Author unknown. Date unknown. Earth Index. https://www.earthgenome.org/earth-index (AI & Environment Resource Hub; record atlas-83fe9760130f; collection snapshot 2026-09-15).
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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.
Encounters in Translation
Shared topics: Cross-cutting sustainability; Energy and electricity; 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; Materials and critical minerals; Biodiversity and ecosystems
This paper argues that AI's environmental footprint (energy, water, materials) is inseparably linked to social and ecological injustice, requiring analysis through multiple justice frameworks rather than environmental concerns alone.
SpringerNature
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Energy and electricity; Biodiversity and ecosystems
