AI & Environment Resource HubLand conversion, siting, local heat, and noise effects.
Browse all 23 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
Share of the 23 records in this view. Pathways can overlap; bars do not add to 100%.
Research papers and scholarly literature on impacts, applications, and methods.
Dargana is a fine-tuned EarthPT model that maps dynamic tree canopy cover from space with high accuracy using minimal data and compute, enabling detailed, time-sensitive land monitoring for conservation.
This study developed a high-resolution global map using satellite data and a neural network to classify the dominant drivers of forest loss from 2001 to 2022, revealing that permanent agriculture is the leading cause worldwide, and aims to support more effective policy, conservation, and supply chain monitoring at multiple scales.
This paper discusses how state legislation is dismantling local planning authority over land use and environmental review to expedite AI and data center infrastructure, concentrating power in state agencies and corporations at the expense of democratic participation and environmental protection.
Uses remote sensing data to quantify that AI data centers increase surrounding land surface temperature by 2 degrees C on average after operations begin, potentially affecting over 340 million people globally.
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.