arXiv Machine Learning By Brittany V. Lancellotti, Jordan M. Malof, Aaron Davitt, Gavin McCormick, Shelby Anderson, Pol Carb\'o-Mestre, Gary Collins, Verity Crane, Zoheyr Doctor, George Ebri, Kevin Foster, Trey M. Gowdy, Michael Guzzardi, John Heal, Heather Hunter, David Kroodsma, Khandekar Mahammad Galib, Paul J. Markakis, Gavin McDonald, Daniel P. Moore, Eric D. Nguyen, Sabina Parvu, Michael Pekala, Christine D. Piatko, Amy Piscopo, Mark Powell, Krsna Raniga, Elizabeth P. Reilly, Michael Robinette, Ishan Saraswat, Patrick Sicurello, Isabella S\"oldner-Rembold, Raymond Song, Charlotte Underwood, Kyle Bradbury

Closing Gaps in Emissions Monitoring with Climate TRACE

Read the original on arXiv Machine Learning →

arXiv:2511. 19277v2 Announce Type: replace Abstract: Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers
Aug 19

Europe's Climate Ambition Under Scrutiny: Evidence from Deep Learning Emission Projections

The paper uses deep learning on high‑resolution socioeconomic and sectoral data to project EU27 CO₂ emissions through 2030. It finds that emissions will exceed the EU’s 55% reduction target by 35% (620 Mt CO₂ shortfall), with only a few countries on track. While the power sector is on target thanks to renewables, mobility remains a major source of emissions, indicating structural inertia across member states.

arXiv AI
Aug 20

Europe's Climate Ambition Under Scrutiny: Evidence from Deep Learning Emission Projections

The study uses deep learning on high‑resolution socioeconomic and sectoral data to project EU27 CO₂ emissions through 2023 and beyond. It finds that, under current trends, EU emissions will exceed the 2030 target by 35 % (620 Mt CO₂ shortfall), with only a few countries on track. While the power sector is on target thanks to renewables, mobility contributes over a third of emissions and shows little progress, indicating structural inertia across member states.

By Jacopo Ghirri, Carlos Rodriguez-Pardo, Lara Aleluia Reis, Massimo Tavoni
arXiv AI
Jun 10

Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting

arXiv:2606. 10660v1 Announce Type: cross Abstract: AI inference services -- API subscriptions, enterprise chat tools, and SaaS products with embedded AI features -- fall unambiguously within Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD), which requires disclosure for fiscal years starting January 2024.

By Guillermo Llopis (SOMA AI, Barcelona)