AI & Environment Resource HubAuthor or publisher unknown · Publication date unknown
Newly reported layoffs where AI is either explicitly cited or credibly blamed as a material factor. Reporting window starts January 1, 2025.
Source identity and required public metadata were editorially reviewed on 2026-09-15.
Author unknown. Date unknown. AI Job Loss Tracker. https://jobloss.ai/?_hsmi=24982387 (AI & Environment Resource Hub; record atlas-6a15a9eb8cde; collection snapshot 2026-09-15).
Included in the original Tool collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Selected fields checked: · 2026-09-06. Editorial review confirmed this record against its current source page/file; limited or generic machine-readable metadata prevented automatic corroboration. Verification source ↗
Source sheet: Tool · Row 232 · Original ID: Not supplied.
Inclusion does not establish effectiveness, maintenance, or endorsement. Check access and current documentation. 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: Governance and society; Energy and electricity; Materials and critical minerals
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.
Shared topics: Governance and society; Energy and electricity; 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.
Shared topics: Governance and society; Energy and electricity; Materials and critical minerals
This study models the integration of AI data centers and cryptocurrency mining with shared renewable infrastructure to enable climate-neutral digital operations, showing potential for significant CO₂ reductions and advocating for supportive global policy frameworks.
Shared topics: Governance and society; Energy and electricity; 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.