AI & Environment Resource HubMobility, freight, transport, and logistics systems.
Browse all 182 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
Share of the 182 records in this view. Pathways can overlap; bars do not add to 100%.
Research papers and scholarly literature on impacts, applications, and methods.
Artificial neural networks (ANN) outperform other models in forecasting transportation-related CO2 emissions, highlighting their reliability for policy evaluation, with scenario analysis showing significant emission reductions under the “30@30” policy by 2030.
Including current feedback in high-resolution atmosphere-ocean model simulations over the Gulf Stream extension alters upper ocean heat transport, aligning temperature patterns with vorticity and reducing submesoscale vertical heat flux, impacting ocean-atmosphere energy transfer and stratification.
A randomized controlled trial demonstrates that per-flight contrail avoidance in commercial aviation is feasible, reducing contrail formation by 64% using altitude adjustments based on machine learning and physics-based predictions, though with a 2% increase in fuel consumption.
A deep learning model with explainable AI predicts vehicle CO2 emissions, aiding strategies for reducing transportation-related environmental impacts.
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.