AI & Environment Resource HubMaterials, mining, critical minerals, and supply chains for computing.
Browse all 114 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
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Research papers and scholarly literature on impacts, applications, and methods.
A globally consolidated asset-level dataset for cement production, incorporating plant age and raw material sourcing, enhances emissions tracking, revealing inefficiencies in supply chains while leveraging geospatial computer vision and Large Language Models for comprehensive industry analysis.
Phi-1, a compact 1.3B parameter Transformer-based language model for code, achieves strong performance on HumanEval (50.6%) and MBPP (55.5%) despite its small scale, benefiting from high-quality web data and GPT-3.5-generated training materials.
This study applies machine learning, including deep learning with LSTM, to classify and predict Dhaka's Air Quality Index (AQI), incorporating daily temperature as a parameter and demonstrating optimal AQI forecasting performance.
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.
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.