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AI-Driven Regenerative Intelligence for Ecological Restoration and Climate Action

AI駆動の再生型知能：生態系回復と気候行動のために (AI 翻訳)

K. Aravinthan, Anorgul Ashirova, S. Aarthi, R. N. Ravikumar, Babamuratov Bekzod, Ulugbek Vosiqov

Advances in computational intelligence and robotics book seriesジャーナル2026-05-15#生物多様性
DOI: 10.4018/979-8-3373-9978-2.ch004
原典: https://doi.org/10.4018/979-8-3373-9978-2.ch004

🤖 gxceed AI 要約

日本語

本稿は、人工知能（AI）が炭素緩和を超えて生態系再生と気候行動に貢献する可能性を探る。AERSIフレームワークを提案し、環境センシング、分析知能、適応的意思決定、ガバナンス統合、持続可能性責任を統合する。文献レビューと分析から、AIはモニタリングや予測計画、回復決定を強化できるが、データ品質や制度的能力、地域参加、生態学的正当性に依存することを示す。

English

This chapter explores how AI can go beyond carbon mitigation to support ecological restoration and climate action. It proposes the AERSI framework integrating environmental sensing, analytical intelligence, adaptive decision support, governance integration, and sustainability responsibility. Through literature review and analytical evidence, it finds AI can enhance monitoring, predictive planning, and restoration decisions, but is conditional on data quality, institutional capacities, local involvement, and ecological legitimacy.

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、生物多様性や生態系サービスに関する開示（TNFDなど）が注目されており、本稿のAI活用フレームワークは、企業の自然関連リスク評価や再生素材管理に示唆を与える可能性がある。ただし、日本の具体的な政策や制度との連携は明示されていない。

In the global GX context

Globally, this paper contributes to the emerging discourse on using AI for ecological restoration and nature-based solutions, relevant to frameworks like TNFD and the UN Decade on Ecosystem Restoration. It offers a conceptual framework that could inform corporate and governmental strategies for integrating AI into climate and biodiversity action, though empirical validation is needed.

👥 読者別の含意

🔬研究者:The AERSI framework provides a structured approach for studying AI applications in ecological restoration and climate governance.

🏢実務担当者:Companies and organizations involved in nature-based solutions or ecological restoration can use the framework to design AI-driven monitoring and decision-support tools.

🏛政策担当者:Regulators interested in leveraging AI for environmental policy and restoration targets may find the governance and responsibility aspects relevant.

📄 Abstract（原文）

The chapter explores the way the Artificial Intelligence can go beyond carbon mitigation to ecological regeneration and restoration-oriented governance of climate action. It states that AI can be the most useful in combination with the ecological objectives, adaptive planning, governance safeguards, and long-term resilience. To reinforce this stand, the chapter presents the AERSI framework, which relates environmental sensing, analytical intelligence, adaptive decision support, integration to governance, and sustainability responsibility. The targeted literature review and analytical evidence reveal that the use of AI can reinforce the monitoring, predictive planning, and restoration decisions; yet, it is conditional to the quality of data, institutional capacities, local involvement, and ecological legitimacy. The chapter concludes that AI can only be used as restoration infrastructure through a combination of technological ability, governance responsibility, and regenerative outcomes.

🔗 Provenance — このレコードを発見したソース

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