A comprehensive evaluation of machine learning regression models enhances the precision of GHG emission predictions, comparing traditional and advanced algorithms while integrating feature selection techniques like LIME to improve model interpretability and inform environmental policy.
Saïd Toumi; Abdussalam Aljadani; Hassen Toumi; Bilel Ammouri; Moez Dhiabi. 2025-01-03. AI for Climate Change: Unveiling Pathways to Sustainable Development Through GHG Predictions. https://link.springer.com/article/10.1007/s40822-024-00295-7 (AI & Environment Resource Hub; record paper-064; collection snapshot 2026-09-15).
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Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
The Environmental and Energy Study Institute (EESI) invites you to a briefing discussing the intersection of artificial intelligence (AI) and climate change in federal policy-making. While AI can aid in climate resilience and boost economic competitiveness, it is also on a trajectory to increase energy demand, greenhouse gas emissions, and water usage. This paradox presents an important opportunity for discussion on how to best minimize the negative impacts of AI on the environment and harness its powers for a sustainable future.
youtube.com
Shared topics: Cross-cutting sustainability; Governance and society; Climate and greenhouse gases; Weather, hazards, and adaptation
An integrated decision-support platform that helps governments, researchers, and citizens monitor, analyze, and act on climate vulnerability, risk, and adaptation data. It brings together diverse datasets into one unified system for easy visualization and interpretation. Users can explore regional trends, assess climate hazards, and identify suitable adaptation measures. The platform empowers evidence-based decision-making to build resilience and support sustainable development goals.
Source metadata availablefree
Shared topics: Cross-cutting sustainability; Governance and society; Climate and greenhouse gases; Weather, hazards, and adaptation
There is a growing need to prepare students to address complex environmental challenges, particularly climate change and the expanding role of artificial intelligence (AI). This article examines the adaptation of the Artificial Intelligence for Social Good (AI4SG) Ideation module, a pitch- and project-based teaching framework designed to integrate AI literacy and sustainability education.
Taylor & Francis Online
Shared topics: Cross-cutting sustainability; Governance and society; Climate and greenhouse gases; Weather, hazards, and adaptation
Himanshu Gupta and Eleonore Fournier-Tombs discuss AI’s role in climate adaptation, from predicting floods to growing resilient crops, while addressing the ethical dilemma of its high carbon footprint and environmental impact.
