Energy Geographies in the Age of GeoAI: Research Trends, Gaps, and Future Directions
ジオAI時代のエネルギー地理学：研究動向、ギャップ、将来の方向性 (AI 翻訳)
Xinming Andy Zhang, Qiusheng Wu, Yingkui Li, Jack Swab
🤖 gxceed AI 要約
日本語
本論文は、GeoAIがエネルギー研究にどのように適用されているかを文献計量分析で調査。抽出・再生可能エネルギー立地に集中する一方、エネルギー転換や社会的公正、過少代表地域への応用が不足していることを明らかにした。説明可能なGeoAI、地理的カバレッジ拡大、AIの炭素強度問題への取り組みを提案。
English
This paper uses bibliometric analysis to examine GeoAI applications in energy research. It finds heavy concentration in extraction and renewable siting, while energy transition, justice, and underrepresented regions are underserved. It calls for Explainable GeoAI, broader geographic coverage, and addressing AI's carbon intensity paradox.
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
日本でもGeoAIのエネルギー分野への応用が進むが、本論文は過少代表地域や社会的公正の欠如を指摘。日本の研究コミュニティは、技術偏重ではなく、エネルギー転換や包摂性を考慮した研究開発の方向性を検討する材料となる。
In the global GX context
This study provides a global overview of GeoAI in energy research, highlighting biases toward extraction and well-resourced countries. It is relevant for ISSB/TCFD discussions on AI transparency and the carbon footprint of AI, and for ensuring that sustainability AI tools are equitable and globally applicable.
👥 読者別の含意
🔬研究者:Provides a mapping of GeoAI applications in energy, identifying gaps in energy transition and justice research as opportunities.
🏢実務担当者:Not directly actionable for corporate sustainability teams, but raises awareness of AI biases and the need for transparent tools.
🏛政策担当者:Highlights the need for funding and data policies that support diverse geographic coverage and ethical AI in energy.
📄 Abstract（原文）
Energy Geographies has a unique position at the intersection of geospatial and social science, and it now faces a defining methodological development with the rapid rise in Geospatial Artificial Intelligence (GeoAI). This paper examines where GeoAI has and has not been applied within energy research through two bibliometric analyses using the Dimensions database. The first establishes an updated picture of energy geographies scholarship from 2020 to 2026, mapping the field’s current priorities and geographic distribution as a baseline for evaluating GeoAI’s role. The second conducts a bibliometric analysis of GeoAI-specific energy publications from 2020 to 2026, which reveals significant GeoAI Application Gaps: a heavy concentration in energy extraction and production research and in renewable energy siting and grid optimization, while energy transition, justice, and the energy problems of underrepresented regions remain substantially underserved. GeoAI energy research is also more geographically concentrated than the broader field, dominated by a small number of countries, raising questions about the applicability of these tools to the energy challenges facing the rest of the world. We argue that this gap reflects a pattern of problem selection as much as technological limitation, and that energy geographers are well positioned to redirect the development of this new field. We outline three directions for future research: developing Explainable GeoAI to ensure transparency and accountability, expanding geographic coverage to address data biases that favor a small set of well-resourced countries, and confronting the computational energy paradox of carbon-intensive AI applied to sustainability-oriented research.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18136838first seen 2026-07-28 04:59:36
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。