Decision support for sustainable building modifications to mitigate climate risk
気候リスクを軽減するための持続可能な建物改修の意思決定支援 (AI 翻訳)
John Maleyeff, Kaumudi Dande, Kunsinee Srivichaiin, David Weidman
🤖 gxceed AI 要約
日本語
本論文は、気候変動リスクを軽減するための建物改修における意思決定手法を提案する。直接・間接の費用と便益を金銭評価し、波及効果も考慮する。R-Shinyによるプロトタイプを適用し、利害関係者によるペア比較を通じて評価の頑健性を示した。
English
This paper proposes a decision-making approach for building upgrades to mitigate climate risks. It monetizes direct and indirect costs and benefits, including ripple effects of disruptions. A prototype using R-Shiny was applied to a building case, showing robustness under moderate judgment inconsistencies.
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
日本では気候災害が頻発するため、建物の強靭化は重要課題である。本手法は自治体や企業が改修優先順位を決める際の定量的根拠となる可能性がある。
In the global GX context
This methodology supports climate risk management under TCFD/ISSB frameworks by monetizing ripple effects. It is relevant for global building adaptation planning and public sector prioritization.
👥 読者別の含意
🔬研究者:Offers a structured decision analysis method that accounts for intangible costs and ripple effects.
🏢実務担当者:Provides a practical tool (R-Shiny) for prioritizing building upgrades based on climate risk.
🏛政策担当者:Can inform building regulations and adaptation policies with quantitative risk-benefit assessments.
📄 Abstract（原文）
Purpose This study aims to develop a decision-making approach that supports sustainable building upgrades or modifications where choices include a set of climate change risk mitigation options. The approach places all costs and benefits, both direct and intangible, onto a common monetary scale that includes ripple effects of potential climate-induced disruptions. Design/methodology/approach Current approaches do not include rigorous methodologies that can be implemented by practitioners, and they do not monetize disruption ripple effects. Practitioner approaches often misrepresent actual conditions because they tend to rely on ordinal transformations that have been shown to be inaccurate and abstract to decision makers. The methodology employs pairwise comparisons and willingness to pay to quantify the direct and ripple impacts of a specified climate event, with uncertainty expressed using color-coded visualizations. Findings A prototype decision support system was created using R-Shiny. It was applied to a building under consideration for upgrades that may be affected by future climate change impacts. Operating the decision support tool requires a stakeholder team to compare pairs of risk response options based on their financial consequences that account for their costs and effectiveness, including the ripple effects of climate-induced disruptions. Research limitations/implications A prototype decision support system was created using R-Shiny, and applied to a building under consideration for climate-related upgrades. A stakeholder team used the decision support tool to compare pairs of risk response options based on their effectiveness and monetary consequences, including the ripple effects of climate-induced disruptions. Results showed the method to be robust in the presence of moderate judgment inconsistencies. Originality/value The proposed methodology addresses research gaps by presenting an intuitive approach for stakeholder teams to rank risk response options while explicitly accounting for ripple effects by translating them into monetary terms. It can be used by public sector managers to prioritize building upgrade projects and by policy makers to create building regulations for facilities embedded in interconnected systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1108/ci-12-2025-0560first seen 2026-06-29 04:50:52 · last seen 2026-06-29 04:51:17
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