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A PSR–Entropy–TOPSIS Framework for Evaluating Low-Carbon Construction Performance of Subway Stations

地下鉄駅の低炭素建設性能評価のためのPSR–エントロピー–TOPSISフレームワーク (AI 翻訳)

Yanmei Ruan, Xu Luo, Shi Zheng, Yuan Mei, Zhonghui Wang, Hongping Lu

Buildings📚 査読済 / ジャーナル2026-05-18#エネルギー転換Origin: CN経営インパクト: コスト削減対象セクター: construction
DOI: 10.3390/buildings16101983
原典: https://doi.org/10.3390/buildings16101983

🤖 gxceed AI 要約

日本語

本研究は、地下鉄駅建設の低炭素性能を評価するためのPSR–エントロピー–TOPSISフレームワークを提案する。17の指標を用い、広州の地下鉄駅で3つの工法を比較し、逆巻き工法が最も低炭素であることを示した。電力消費とコンクリート排出が主要因であり、建設スキーム最適化の意思決定支援ツールを提供する。

English

This study proposes a PSR-entropy-TOPSIS framework to evaluate low-carbon performance of subway station construction. Using 17 indicators, it compares three construction methods for a Guangzhou station, finding reverse cover excavation most low-carbon. Electricity and concrete emissions are key factors, offering a decision-support tool for optimizing construction schemes.

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

日本の地下鉄・都市鉄道建設は膨大なCO2排出を伴い、低炭素化が急務。本フレームワークは工法選択の定量的評価を可能にし、日本の鉄道事業者が環境性能を考慮した設計・施工計画に活用できる。また、SSBJ開示や投資家対応にも資する。

In the global GX context

Globally, urban rail expansion raises construction emissions; this framework provides a transparent, data-driven method to compare construction methods for low-carbon performance. It aligns with TCFD/ISSB disclosure trends by quantifying carbon impacts, and offers a replicable model for other cities and infrastructure projects.

👥 読者別の含意

🔬研究者:Provides a novel multi-criteria decision-making framework integrating PSR, entropy, and TOPSIS for low-carbon construction evaluation.

🏢実務担当者:Offers a practical tool for selecting low-carbon construction methods in subway projects, aiding sustainability reporting.

🏛政策担当者:Highlights the importance of construction-phase emissions and provides a methodology for setting low-carbon infrastructure standards.

📄 Abstract（原文）

The rapid expansion of subway systems has led to significant carbon emissions during station construction, yet a systematic and interpretable framework for evaluating low-carbon performance across different construction methods remains underdeveloped. To address this gap, this study proposes a comprehensive evaluation model that integrates a pressure–state–response (PSR) framework with an entropy-weighted TOPSIS method. A multi-dimensional indicator system comprising 17 indicators was established, covering material and energy consumption (pressure), environmental carbon states (state), and management responses (response). The entropy weight method was employed to determine objective indicator weights, and the TOPSIS method was used to rank the overall low-carbon performance of different construction schemes. An empirical study of a subway station in Guangzhou, China, was conducted to compare three construction methods: open-cut, top-down cover excavation, and reverse cover excavation. The results demonstrate that the reverse cover excavation method achieves the highest low-carbon performance. Electricity consumption and concrete-related emissions were identified as the most influential factors, while obstacle analysis revealed key constraints for carbon reduction. The proposed PSR–entropy–TOPSIS framework offers a transparent, data-driven decision-support tool for optimizing construction schemes, contributing to the sustainable development goals of urban rail transit projects.

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