環境持続可能性と気候変動緩和のための人工知能：最近の進展、課題、将来展望
Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives (原題)
Deepmala Suryavanshi
🤖 gxceed AI 要約
日本語
本レビューは、気候予測、大気・水質監視、生物多様性保全、精密農業、再生可能エネルギー最適化、炭素排出評価、廃棄物管理、災害リスク低減など、環境・気候課題へのAI応用を批判的に検討する。透明性・エネルギー効率・説明可能性を備えたAIシステムの必要性を指摘し、学際的協働と責任あるAIガバナンス、政策支援が環境便益の最大化に不可欠と結論づける。
English
This review critically examines AI applications for environmental and climate challenges, including climate forecasting, air/water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. It identifies research gaps and calls for transparent, energy-efficient, explainable AI systems, concluding that interdisciplinary collaboration, responsible AI governance, and policy support are essential to maximize environmental benefits while minimizing AI's ecological footprint.
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
日本ではSSBJ基準や有報での気候関連開示が進む中、AIを用いた炭素排出評価や再エネ最適化は企業のScope算定・削減計画の高度化に寄与しうる。ただしESG評価や開示分析への直接的示唆は限定的で、実務への橋渡しには更なる研究が必要。
In the global GX context
Globally, this review aligns with the growing intersection of AI and climate action, supporting TCFD/ISSB disclosure through improved carbon accounting and climate risk modeling. It underscores the need for responsible AI governance, which is increasingly relevant as regulators scrutinize AI's energy footprint and transparency in sustainability reporting.
👥 読者別の含意
🔬研究者:AI×環境分野の研究動向とギャップを俯瞰でき、説明可能AIや炭素評価手法の研究方向を把握できる。
🏢実務担当者:再エネ最適化や炭素排出評価へのAI活用可能性を理解し、自社の気候対策・開示高度化のヒントを得られる。
🏛政策担当者:AIの環境便益を最大化しつつ生態学的フットプリントを抑えるための責任あるAIガバナンスと政策支援の必要性を示唆。
📄 Abstract（原文）
Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.23399627first seen 2026-11-02 04:39:50 · last seen 2026-11-06 04:36:37
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