気候レジリエントな都市のためのエージェントAI：PRISMAに基づくレビューとデジタルツインフレームワーク
Agentic AI for Climate-Resilient Cities: A PRISMA-Guided Review and Digital Twin Framework (原題)
Toqeer Ali Syed, Ali Akarma, Muhammad Tayyab Naqash, Danial Hameed, Shahid Kamal (23807824), Antonio Formisano
🤖 gxceed AI 要約
日本語
本レビューは、エージェントAIを従来の機械学習と区別し、SDG11・13への応用を体系的に分析。60件の適格研究のうち14件がエージェント性を満たし、参照アーキテクチャとデジタルツイン連携を提案。実データでは提案モデルが最良だが、ベースラインをわずかに上回るのみで、実運用には未達。
English
This PRISMA-guided review distinguishes agentic AI from conventional ML for SDG 11 and 13, analyzing 60 eligible studies (14 fully agentic). It proposes a reference architecture linking an agentic layer with urban digital twins. Real-data tests show marginal improvement over baseline, indicating weak operational evidence.
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
日本の都市計画や防災分野では、デジタルツインやAI活用が進むが、本レビューはエージェントAIの実装基準を提供し、自治体や企業の気候適応策に示唆を与える。SSBJ開示における気候リスク評価にも関連。
In the global GX context
Globally, this review addresses the gap between AI hype and validated deployment in climate-resilient cities, offering a framework for integrating agentic AI with digital twins. It informs ISSB-aligned climate risk assessments and urban resilience planning.
👥 読者別の含意
🔬研究者:Provides a taxonomy and reference architecture for agentic AI in urban climate applications, highlighting the need for stronger empirical validation.
🏢実務担当者:Offers a framework for deploying agentic AI in urban digital twins, but cautions that current models lack operational evidence.
🏛政策担当者:Suggests that AI-based urban climate solutions require rigorous testing before policy adoption, as current performance is marginal.
📄 Abstract（原文）
Cities face pressure from urban growth and climate risk, yet deployed systems stay single-domain and reactive. This PRISMA-guided rapid review applies operationalized criteria to separate Agentic AI from conventional machine learning for SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Agentic AI is defined by four properties: task-level autonomy, goal-directed planning, tool use, and multi-agent coordination; evidencing at least two marks a system as fully agentic. A two-tier search across five databases with backward citation tracking returned 896 records (2018–2026), of which 60 met the eligibility criteria and 14 satisfied the agentic threshold. The corpus is stratified by study type with a threshold sensitivity analysis. Two contributions follow: a reference architecture specifying how an agentic layer and an urban digital twin exchange state, and a real-data feasibility study on the SEVIR archive testing whether multimodal fusion improves hazard classification. On real data the proposed model is the best-ranked of four but only marginally exceeds a no-change persistence baseline, giving the assumption weak support, not operational evidence. The review reveals a field growing sharply since 2023, clustered in a few urban and climate domains, with almost no validated cross-domain deployment.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18178917first seen 2026-09-03 04:59:53
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