ネットゼロ建設のための人工知能：AEC産業全体における研究景観、テーマクラスター、新興方向性の書誌計量マッピング
Artificial intelligence for net-zero construction: a bibliometric mapping of research landscape, thematic clusters and emerging directions across the AEC industry (原題)
Marcela Pincay-Pilay, Dayana Michelle Castro-Chilán, Marlon Alexander González Regalado, Diego Sornoza-Parrales
🤖 gxceed AI 要約
日本語
AEC産業は世界のCO₂の約39%を排出し、脱炭素化の主要対象である。本論文はScopusとWeb of Scienceから2017〜2026年の517件をPRISMA準拠で収集し、AIとネットゼロ建設の交差領域を書誌計量分析した。年間成長率54.45%、2023〜2026年に87.4%が集中し、生成AIとデジタルツインが新興フロンティアとして特定された。中国・米国・英国が研究を主導している。
English
The AEC industry emits ~39% of global CO2, making it central to decarbonization. This bibliometric study analyzes 517 Scopus/WoS articles (2017-2026) on AI and net-zero construction, finding 54.45% annual growth with 87.4% published in 2023-2026. Two thematic clusters emerge—materials/optimization/carbon quantification and AI/sustainability/digital integration—with generative AI and digital twins as emerging frontiers. China, USA, and UK lead output.
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
建設業は日本でも主要排出源であり、SSBJ開示やScope3算定において施工段階の排出把握が課題となる。AIによる炭素定量化やデジタルツインは、建設企業の開示データ整備や削減目標管理に資する可能性がある。
In the global GX context
Construction decarbonization is a priority under TCFD/ISSB and CSRD, where Scope 3 category 1-2 emissions from materials and construction are material. This mapping highlights AI's role in carbon quantification and digital twins, offering a research agenda relevant to disclosure infrastructure and transition planning globally.
👥 読者別の含意
🔬研究者:AIと建設脱炭素の研究動向を俯瞰し、生成AIやデジタルツインなど新興領域の研究課題を特定するのに有用。
🏢実務担当者:建設・不動産企業のサステナビリティ担当が、AIを活用した炭素排出量算定や施工最適化の技術動向を把握する参考になる。
🏛政策担当者:建設分野の脱炭素政策において、AI技術の活用可能性と研究開発支援の方向性を検討する材料となる。
📄 Abstract（原文）
The architecture, engineering, and construction (AEC) industry accounts for approximately 39% of global CO₂ emissions and 40% of worldwide energy consumption, positioning it as a primary target for decarbonization. Artificial intelligence (AI) has emerged as a transformative enabler for net-zero construction strategies; however, no comprehensive bibliometric mapping of the intersection of AI, net-zero construction, and the full AEC industry has yet been published. This study addresses that gap through a bibliometric and science mapping analysis based on a systematic, transparent document identification and selection protocol inspired by PRISMA reporting principles. A corpus of 517 peer-reviewed articles and reviews was assembled from Scopus and Web of Science (2017–2026) following PRISMA guidelines, with data retrieved on 26 May 2026. Results reveal an annual growth rate of 54.45%, with 87.4% of all publications concentrated in 2023–2026, reflecting a research explosion driven primarily by the maturation of machine learning and deep learning applications for net-zero targets, within which the emergence of large language models (LLMs) and generative AI from 2023 onwards constitutes a fast-growing but still peripheral thread. China, the USA, and the UK lead scientific output. Two densely connected thematic clusters were identified — materials, optimisation, and carbon quantification; and AI, sustainability, and digital integration — alongside a long tail of peripheral, still-emerging themes and a clear three-phase paradigmatic evolution. The Thematic Map identifies generative AI and digital twins as high-priority emerging fronts. Building on prior reviews, this study presents a bibliometric review covering the entire AEC industry for net-zero construction using dual databases and proposes a research agenda for the field.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2026.1958541/pdffirst seen 2026-10-03 05:17:20 · last seen 2026-10-07 05:19:02
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