Construction of Dynamic Cost Control Model and Cost Optimization Research for Green Buildings Empowered by Low-Carbon Technology
低炭素技術を活用したグリーンビルディングの動的コスト管理モデルの構築とコスト最適化研究 (AI 翻訳)
X. Y. Xu, S. N. Sun, X. G. Zhao
🤖 gxceed AI 要約
日本語
本研究は、低炭素技術を活用したグリーンビルディングのライフサイクル全体におけるコスト管理の動的モデルを構築し、システムダイナミクスとBPニューラルネットワークを統合することで予測精度を向上させた。実証分析により、設計段階のコスト予測誤差を3.5%に低減し、ライフサイクルコストを9.8%削減、炭素排出削減量を18%増加させた。
English
This study constructs a dynamic cost control model for green buildings enabled by low-carbon technology, integrating system dynamics and BP neural networks to improve prediction accuracy. Empirical validation shows a reduction in design-stage cost prediction deviation to 3.5%, a 9.8% decrease in whole-life-cycle cost, and an 18% increase in carbon emission reduction.
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
日本の建設業界では、ZEBや省エネ基準の強化に伴い、ライフサイクルコストと炭素排出の同時最適化が重要となっている。本モデルは、SSBJ開示や投資家対応における環境性能評価の向上に寄与する可能性がある。
In the global GX context
Globally, the integration of low-carbon technologies and dynamic cost management in green buildings aligns with ISSB and CSRD requirements for sustainability-related financial disclosures. The model offers a practical approach for optimizing both cost and carbon performance, relevant for infrastructure and real estate sectors.
👥 読者別の含意
🔬研究者:Provides a novel integration of system dynamics and neural networks for cost-carbon optimization in green buildings.
🏢実務担当者:Offers a dynamic cost control model that can reduce lifecycle costs and carbon emissions, useful for construction and real estate firms.
🏛政策担当者:Demonstrates the potential of low-carbon technologies in achieving cost-effective emission reductions, informing policy on green building incentives.
📄 Abstract（原文）
To address the drawbacks of traditional cost control, such as static management and insufficient adaptability, this study focuses on low-carbon technology-enabled green buildings and constructs a dynamic cost control model to reveal the bidirectional impact mechanism of low-carbon technologies on project costs throughout the whole life cycle. As intelligent green buildings increasingly integrate electromagnetic sensing and wireless monitoring technologies for realtime operation management, dynamic cost evaluation has become an important component of sustainable infrastructure optimization. Based on whole-life-cycle theory and system dynamics, a dynamic control framework is established, and the integrated mechanism of “system dynamics (SD) + BP neural network” is developed to improve prediction accuracy. In addition, a full-life-cycle cost optimization strategy covering the design, construction, and operation stages is proposed, together with policy- and market-oriented supporting measures. Empirical validation using a university student cultural and sports center demonstrates that the model reduces the design-stage cost prediction deviation to 3.5% and achieves a 90% risk early-warning rate. After optimization, the whole-life-cycle cost is reduced by 9.8%, while carbon emission reduction increases by 18%. The proposed framework enriches the integration of low-carbon technologies and dynamic cost management, providing practical guidance for intelligent green buildings and electromagnetic-assisted infrastructure monitoring.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.7716/aem.v15i3.3670first seen 2026-08-16 04:57:48
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