既存産業建築物の脱炭素改修スキームの評価と優先順位付け—ティッセンクルップS工場のケーススタディ

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a robust multi-criteria decision framework (DEMATEL-TOPSIS) for retrofit prioritization, with sensitivity analysis and Monte Carlo simulation.

🏢実務担当者:Offers a practical method to rank retrofit measures based on investment, payback, and emission reduction, aiding corporate decarbonization planning.

🏛政策担当者:Highlights the importance of management-based measures for near-term industrial emission reductions, informing policy incentives for low-cost retrofits.

Existing industrial buildings represent a critical but under-addressed field for operational carbon emission reduction, as retrofit decisions are constrained by production continuity, limited investment capacity, and heterogeneous technical options. This study developed a decision support framework integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate and prioritize operational phase decarbonization retrofit schemes for existing industrial buildings. The framework was applied to the Thyssenkrupp S Plant in eastern China, where ten candidate schemes were identified through an energy audit, on-site investigation, and expert consultation. The results show that heating, ventilation, and air conditioning (HVAC) operational control and temperature set-point optimization ranked first, followed by lighting operational management and automatic control. These management-based measures offer strong near-term applicability because of their low investment, short payback periods, limited implementation disturbance, and immediate emission reduction benefits. Their sustained effectiveness, however, requires standardized procedures, staff education, energy monitoring, and appropriate automation. Rooftop photovoltaics provide the largest annual carbon reduction but have a lower short-term priority because of their high upfront investment. Expert-consistency testing and sensitivity analyses, including criterion weight perturbation, preference scenarios, and Monte Carlo simulation, support the robustness of the leading ranking pattern. The findings support staged retrofit planning that prioritizes durable management measures in the short term, equipment-level efficiency improvements in the medium term, and renewable energy deployment in the long term.

• crossref https://doi.org/10.3390/buildings16163316first seen 2026-08-21 05:19:29 · last seen 2026-09-03 06:06:30
• semanticscholar https://doi.org/10.3390/buildings16163316first seen 2026-08-24 05:21:47 · last seen 2026-09-22 05:03:26