1.5°Cの惑星境界を超えて：AIによる世界の疾病負荷と健康格差の予測
Beyond the 1.5°C planetary boundary: AI-forecast of Global Disease Burden and Health inequities (原題)
Hanif Abdul Rahman, Hein Minn Tun
🤖 gxceed AI 要約
日本語
気候変動の惑星境界（1.5°C、2.0°C、2.5°C）を超えた場合の世界の疾病負荷を、AI（Random Forest、XGBoost、SHAP）を用いて定量化。温暖化レベルに応じて熱関連死亡と心血管疾患負荷が指数関数的に増加し、低資源地域の脆弱な集団に不均衡な影響を与えることを示した。2.0°C未満に抑えることで何百万人もの早期死亡を防げる可能性がある。
English
This study uses AI (Random Forest, XGBoost, SHAP) to quantify global disease burden under climate warming scenarios of 1.5°C, 2.0°C, and 2.5°C. Heat-related deaths and cardiovascular disease burden escalate exponentially with warming, disproportionately affecting vulnerable populations in low-resource settings. Limiting warming below 2.0°C could prevent millions of premature deaths.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の気候変動適応策や健康影響評価に示唆を与える。特に、熱中症対策や健康リスク評価におけるAI活用の可能性を示しており、自治体や企業の適応計画に参考になる。
In the global GX context
This paper contributes to global climate risk assessment by quantifying health impacts of crossing planetary boundaries. It provides evidence for policymakers to strengthen climate mitigation and adaptation, aligning with the goals of the Paris Agreement and informing health-related climate policies.
👥 読者別の含意
🔬研究者:AIと気候・健康データの統合手法の参考になる。
🏢実務担当者:気候変動リスク評価や適応計画の策定に活用できる。
🏛政策担当者:温暖化目標の健康影響を考慮した政策決定に有用。
📄 Abstract（原文）
BACKGROUND: The planetary boundary for climate change-initially set at global mean temperature increase of 1.5°C above pre-industrial levels-has been significantly transgressed. Understanding the mechanistic pathways through which climate warming impacts disease burden is critical for evidence-based policy interventions. OBJECTIVE: This study aims to quantify and explain the global burden of disease when planetary climate boundaries are crossed at 1.5°C, 2.0°C, and 2.5°C warming thresholds. METHODS: We applied a dual-phase framework: (1) Exploratory AI using Random Forest and XGBoost algorithms to identify non-linear relationships and threshold effects between temperature increase and disease burden; (2) Explanatory AI employing SHAP (SHapley Additive exPlanations) values and causal inference models to mechanistically explain pathways linking climate change to health outcomes. RESULTS: At 1.5°C warming, global heat-related deaths increase from baseline (2019) of 0.21 million to 0.35 million (+66.7%), with corresponding heat-related disability-adjusted life-years (DALYs) rising from 4.77 million to 8.20 million (+71.9%). Cardiovascular disease burden escalates from 24.18 million DALYs to 29.50 million (+22.0%). At 2.0°C warming, these impacts intensify dramatically: heat-related deaths reach 0.52 million (+147.6%) and DALYs 12.50 million (+162.1%), while CVD DALYs increase to 35.80 million (+48.1%). At 2.5°C warming, heat-related deaths surge to 0.78 million (+271.4%) with DALYs of 18.90 million (+296.4%), and CVD DALYs reach 44.20 million (+82.8%). CONCLUSIONS: Crossing planetary climate boundaries triggers exponential escalation of global disease burden through multiple, synergistic pathways disproportionately affecting vulnerable populations in low-resource settings. Urgent climate mitigation to limit warming below 2.0°C could prevent millions of premature deaths and substantial DALY losses globally. Health system adaptation strategies must prioritize vulnerable populations and integrate climate resilience into health policy frameworks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1371/journal.pone.0354159first seen 2026-08-31 04:55:15
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