AI-Driven Sustainable Infrastructure Development
AI駆動型持続可能なインフラ開発 (AI 翻訳)
P K Abhilash
🤖 gxceed AI 要約
日本語
本稿は、AI技術と持続可能なインフラ開発の融合を、エネルギー、交通、水管理、スマートシティ、気候適応などの分野にわたって分析する。機械学習や自然言語処理などの手法が効率向上や排出削減にどのように活用されているかを検討し、さらにガバナンスと倫理の課題を議論する。結論として、AIの責任ある統合が気候変動や都市格差への対応に重要であると論じている。
English
This article examines the convergence of AI and sustainable infrastructure across energy, transport, water, smart cities, and climate adaptation. It analyzes how ML, DL, CV, NLP, and optimization improve efficiency, reduce emissions, and minimize waste. It also explores governance and equity dimensions, concluding that responsible AI integration is crucial for addressing climate change and urban inequality.
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、国土強靱化やカーボンニュートラルに向けたインフラ再構築が進んでおり、本稿が論じるAIを活用した持続可能なインフラ開発は、SSBJや統合報告書での開示にも間接的に関連する可能性がある。
In the global GX context
This paper contributes to the global discourse on sustainable infrastructure, aligning with initiatives like the UN Sustainable Development Goals and climate adaptation strategies. It provides a broad overview of AI applications that can inform infrastructure planning and policy.
👥 読者別の含意
🔬研究者:Researchers can gain a comprehensive overview of AI applications in sustainable infrastructure and identify research gaps.
🏢実務担当者:Infrastructure planners and developers can learn about AI tools for efficiency and emissions reduction.
🏛政策担当者:Policymakers can understand the potential and governance needs of AI in infrastructure.
📄 Abstract（原文）
The global imperative to build and maintain infrastructure that is simultaneously resilient, economically viable, and environmentally sustainable has intensified over recent decades. As urbanization accelerates, climate change introduces unprecedented stresses on physical systems, and resource constraints tighten, traditional approaches to infrastructure planning and management are proving inadequate. Artificial intelligence (AI) has emerged as a transformative force capable of fundamentally altering how infrastructure is conceived, constructed, operated, and maintained. This article examines the convergence of AI technologies and sustainable infrastructure development across multiple domains, including energy systems, transportation networks, water management, smart cities, and climate adaptation. It analyzes the mechanisms through which machine learning, deep learning, computer vision, natural language processing, and optimization algorithms are being deployed to improve efficiency, reduce emissions, minimize waste, and enhance the adaptive capacity of built environments. The article further explores the governance, ethical, and equity dimensions of AI-driven infrastructure, arguing that technological capability alone is insufficient without robust institutional frameworks and inclusive design principles. Drawing on emerging case studies and theoretical frameworks, the article concludes that the responsible integration of AI into sustainable infrastructure development represents one of the most consequential opportunities for addressing the interconnected crises of climate change, urban inequality, and resource depletion in the twenty-first century
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20622603first seen 2026-06-30 05:10:18
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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。