Driving net-zero construction through evolutionary machine learning in Vietnam: a strategic framework for sustainable performance
ベトナムにおける進化的機械学習によるネットゼロ建設：持続可能なパフォーマンスのための戦略的枠組み (AI 翻訳)
An Thi Binh Duong, Linh Tran Khanh Do, Scott McDonald, Hiep Pham, Thinh Gia Hoang, Duy Dang-Pham, Huy Truong Truong Quang
🤖 gxceed AI 要約
日本語
本研究は、進化的機械学習（EML）を建設企業の持続可能なパフォーマンス向上に活用する戦略的枠組みを提案・検証した。ベトナムの建設企業213社のデータ分析から、EML統合戦略と業務実態・関係者の準備・革新能力の整合が、ネットゼロ建設サプライチェーンと業績向上に重要であることを示した。
English
This study develops and empirically tests a strategic framework using evolutionary machine learning (EML) to drive sustainable performance in construction firms. Analyzing 213 Vietnamese construction companies, it finds that aligning EML integration with operational realities, stakeholder readiness, and innovation capabilities is key to achieving net-zero construction supply chains and improved business performance.
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
日本の建設業界でも脱炭素化が急務だが、本論文は発展途上国ベトナムの事例を提供する。日本企業がベトナムなど東南アジアで事業展開する際の参考となり得る。また、EMLという新たなAI手法の応用は、日本の建設業におけるDXとGXの統合に示唆を与える。
In the global GX context
This paper offers a rare empirical study on AI-driven net-zero construction in a developing economy (Vietnam), contributing to the global discourse on digital transformation for decarbonization. It demonstrates how evolutionary machine learning can balance cost, environmental footprint, and economic value, which is relevant for construction firms worldwide facing similar multi-objective optimization challenges.
👥 読者別の含意
🔬研究者:Shows how evolutionary ML can be applied to construction sustainability, providing a tested framework and SEM analysis.
🏢実務担当者:Offers strategic guidance on integrating AI for net-zero construction, emphasizing alignment with operational realities.
🏛政策担当者:Provides evidence of EML's potential for promoting sustainable construction in developing economies, informing policy design.
📄 Abstract（原文）
Purpose This study investigates how evolutionary machine learning (EML), a class of adaptive and intelligent optimisation techniques, can be strategically employed to drive sustainable performance in construction firms, particularly in developing economies. Design/methodology/approach Using a business optimisation lens, this research develops and empirically tests a comprehensive framework that integrates ecological modernisation theory, adaptive structuration theory and diffusion of innovation to understand post-adoption impacts of EML-enabled technologies on carbon neutrality and organisational performance. Findings Through structural equation modeling analysis of 213 Vietnamese construction firms, the findings underscore the importance of aligning EML integration strategies with operational realities, stakeholder readiness and long-term innovation capabilities to achieve a net-zero construction supply chain and improved business performance. Research limitations/implications By framing EML as a tool for solving combinatorial and dynamic optimisation challenges, such as resource allocation, project scheduling and carbon footprint reduction, this research contributes to discourse on evolutionary computation for real-world business problems. Originality/value EML-enabled technologies can provide optimal solutions that balance multi-objective problems such as minimising cost and environmental footprint while maximising the economic value of construction that traditional machine learning cannot address. Although prior research on individual technologies and sustainability in construction supply chains has been conducted, there are limited studies centralising applications of EML-enabled optimisation in driving net-zero construction supply chains.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1108/ecam-07-2025-1096first seen 2026-07-14 05:20:29
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
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。