気候変動と持続可能な開発への人工知能統合：包括的書誌レビュー
Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review (原題)
H. B. T. P. Jayathilaka, Shiyan Zhai, Yuke Feng
🤖 gxceed AI 要約
日本語
2007〜2025年のAI・気候変動・持続可能な開発に関する3291件の文献をBibliometrixとVOSviewerで分析した書誌レビュー。年間38.29%の成長、中国と米国が主導し、AI駆動型デジタル化・地理空間モニタリング・予測モデリング・スマート農業の4領域を特定。低所得国研究の41%が高所得国主導という地理的非対称性を指摘し、SDGs13・17を軸に政策提言を行う。
English
A bibliometric review of 3,291 publications (2007–2025) on AI, climate change, and sustainable development, analyzed via Bibliometrix and VOSviewer. It finds 38.29% annual growth, China/US leadership, and four research clusters: AI-driven digitalization, geospatial/ML environmental monitoring, predictive modeling, and smart agriculture. It flags geographic asymmetries—41% of low-income-country publications are led by high-income countries—and offers policy recommendations tied to SDGs 13 and 17.
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
日本企業のGX開示実務への直接示唆は薄いが、AIを活用した気候・SD研究の全体像と研究ギャップを把握する基礎資料として、SSBJ対応や気候リスク分析の手法選定に間接的に寄与しうる。
In the global GX context
While not a disclosure-framework paper, it maps the AI-for-climate research landscape that increasingly underpins TCFD/ISSB scenario analysis and climate-risk modeling. Its finding of geographic research asymmetry is relevant to global discussions on equitable transition finance and capacity building.
👥 読者別の含意
🔬研究者:AI×気候×SD研究の潮流と未開拓領域（低所得国・データギャップ）を俯瞰する出発点として有用。
🏢実務担当者:AIを気候リスク評価やサステナビリティ分析に活用する際の手法分類と適用領域の参考になる。
🏛政策担当者:研究能力の地理的非対称性を踏まえ、気候脆弱国へのAI・データ基盤支援の政策設計に示唆を与える。
📄 Abstract（原文）
The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.sftr.2026.102145first seen 2026-09-25 04:48:05
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