AI & Environment Resource HubSupporting research, data, geospatial and analytical methods; inclusion does not establish an environmental application.
Browse all 59 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
Share of the 59 records in this view. Pathways can overlap; bars do not add to 100%.
Research papers and scholarly literature on impacts, applications, and methods.
The paper argues that neurobiological principles can inform ultra-low-power AI at both hardware and software levels, and calls for long-horizon industry-academic partnerships in energy-efficient NeuroAI.
The study compares performance, energy use, processing time, and carbon emissions across dictionaries, trained classifiers, and self-hosted open LLMs for three text-analysis tasks.
A large-scale analysis of more than 500 energy-community configurations evaluates how management models and grid tariffs affect user benefits, network loading, and distribution-system cost recovery.
A review of 60 national adaptation plans submitted by developing countries evaluates plan coverage and consistency and identifies gaps in monitoring, participation and financing.
Missing metadata is a curation task, not evidence of absent research or activity. The Resource Hub does not currently contain a validated incident dataset, expert survey, or measurements of net environmental benefit.