Artificial Intelligence for Climate Risk, Emissions Intelligence and Planetary-Scale Environmental Decision-Making

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel framework for integrating AI into climate risk and emissions intelligence with transparent prioritization logic.

🏢実務担当者:Corporate sustainability teams can use the emissions prioritization model to inform Scope 1/2/3 reduction strategies and climate risk disclosures.

🏛政策担当者:Offers a structured, data-driven method for prioritizing mitigation actions across countries, relevant for national climate plans and global stocktake.

Abstract This study investigates how artificial intelligence can be positioned not merely as a predictive tool but as an environmental intelligence infrastructure for climate-risk assessment, emissions monitoring and planetary-scale decision support. The paper synthesises recent advances in climate analytics with a transparent, data-grounded framework that links Earth-system indicators, disaster impacts and emissions trajectories to structured policy prioritisation. Rather than relying on synthetic data or hypothetical simulations, the analysis is built from current official sources, including the World Meteorological Organization, the Global Carbon Budget and EM-DAT. The resulting framework is designed for a context in which climate governance increasingly depends on continuous observation, rapid interpretation and accountable triage under conditions of uncertainty. The empirical analysis shows that multiple planetary indicators are now moving in the same adverse direction. Atmospheric carbon dioxide reached 420.0 ppm in 2023, global mean near-surface temperature reached 1.55°C above the 1850–1900 average in 2024, the contemporary rate of ocean heat uptake is more than three times the 1960–2005 midpoint estimate, and the recent rate of sea-level rise is more than double that of the first satellite decade. In parallel, fossil CO2 emissions are projected by the Global Carbon Budget to reach a record 37.4 GtCO2 in 2024, while EM-DAT recorded 393 natural-hazard disasters and US$241.95 billion in economic losses in 2024. These observations indicate that climate governance can no longer rely on siloed sectoral monitoring; integrated, machine-assisted prioritisation is becoming a practical necessity. A mathematically explicit prioritisation model is therefore proposed to convert observed emissions size and emissions momentum into a mitigation urgency score for major emitting blocs. The resulting ranking identifies the rest of the world aggregate, China and India as the three highest-priority mitigation domains under current global conditions, while also demoneed for continued intervention because of their still-large absolute contribution. strating that declining emissions in the United States and EU27 do not eliminate the nThe study argues that a credible AI-for-climate architecture must combine transparent indicator construction, traceable data provenance, interpretable scoring logic and institutional accountability. In this form, artificial intelligence becomes a decision-enabling layer for emissions intelligence, resilience planning and planetary-scale environmental coordination rather than a black-box substitute for governance. Keywords: Artificial intelligence; climate risk; emissions intelligence; Earth-system indicators; environmental decision-making; climate governance; carbon budget; planetary analytics

• semanticscholar https://doi.org/10.62311/nesx/rp5-30032026first seen 2026-06-29 07:59:53