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トスカーナ各都市における気候レジリエント・インフラ設計支援のための変化係数評価の多基準フレームワーク

A multi-criteria framework for factor of change assessment to support climate-resilient infrastructure design across Tuscan cities (原題)

Giulia Pierotti, Costanza Guarducci, Filippo Landi, Pietro Croce, Masoud Haghbin, Manuel Chiachío

Environmental Research Infrastructure and Sustainability📚 査読済 / ジャーナル2026-10-21#気候リスクOrigin: EU対象セクター: construction
DOI: 10.1088/2634-4505/aeb720
原典: https://doi.org/10.1088/2634-4505/aeb720
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🤖 gxceed AI 要約

日本語

気候モデル（GCM/RCM）の地域・変数による性能差が将来の設計気候作用の不確実性を生む問題に対し、極値理論・性能指標・シャノンエントロピー・3種のMCDM・シャープレー値によるゲーム理論を組み合わせた多基準フレームワークを提案する。イタリア・トスカーナ10都市でEURO-CORDEXの12モデルをE-OBS観測と比較し、履歴再現性の高いモデルに重みを与える適応的変化係数（FC）を算出。最大気温の上昇はRCP4.5で約3.3〜4.5℃、RCP8.5で6.3〜7.4℃と推定された。

English

This study proposes a multi-criteria framework combining extreme value theory, performance indicators, Shannon entropy, three MCDM methods, and Shapley-value game theory to evaluate climate-model performance and derive an adaptive factor of change (FC) for structural design. Applied to 12 EURO-CORDEX GCM-RCM combinations across ten Tuscan cities against E-OBS observations, the adaptive FC weights models by historical skill while preserving ensemble warming. Estimated maximum-temperature increases reach ~3.3-4.5°C under RCP4.5 and 6.3-7.4°C under RCP8.5.

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

適応策・インフラ設計の物理的気候リスク評価に関わる研究で、SSBJ・TCFDの物理リスク評価や適応計画の定量基盤として日本の建設・都市インフラ実務にも示唆がある。ただし開示制度への直接的接続は限定的。

In the global GX context

Relevant to the physical climate-risk and adaptation side of TCFD/ISSB disclosure, offering a quantitative method for updating design climatic actions. It complements disclosure scholarship by strengthening the hazard/adaptation evidence base rather than addressing reporting frameworks directly.

👥 読者別の含意

🔬研究者:気候モデルの重み付けと設計用変化係数の適応的推定手法として、物理リスク評価研究に有用。

🏢実務担当者:インフラ設計・適応計画において、地域別の気候作用変化を定量化する際の参照手法となる。

🏛政策担当者:気候レジリエントなインフラ基準・気候作用マップ更新の政策的根拠として活用しうる。

📄 Abstract（原文）

Abstract Global and regional climate models (GCMs and RCMs) provide projections for climate change impact assessments. However, their performance may vary across locations and climate variables, introducing uncertainty into the estimation of future climatic actions on infrastructure. This study presents a multi-criteria framework for evaluating climate-model performance and adapting the factor of change (FC), which describes variations in characteristic values used in structural design. The framework combines extreme value theory, performance indicators, Shannon entropy, three multi-criteria decision-making (MCDM) methods, and a game-theory approach based on the Shapley value. Model performance is evaluated using complementary accuracy and trend indicators against historical observations. Shannon entropy is used to derive indicator weights, while the preference ranking organization method for enrichment evaluation (PROMETHEE-II), the technique for order of preference by similarity to ideal solution (TOPSIS), compromise programming (CP), and the Shapley value are combined to rank and weight the climate models. These weights are then used to calculate an adaptive FC that gives greater influence to models showing stronger historical performance. The methodology is applied to 12 GCM-RCM combinations from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) across ten Tuscan cities in Italy, considering maximum temperature under the Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5. Model performance is assessed against the European gridded observational dataset (E-OBS). When the maximum adaptive FC across the analysed future time windows was applied to local reference characteristic values, the estimated increases ranged from approximately 3.3 ∘ C– 4.5 ∘ C under RCP4.5 and from 6.3 ∘ C to 7.4 ∘ C under RCP8.5. The adaptive FC preserves the ensemble warming signal while giving greater influence to models that more consistently reproduce historical observations and trends. The proposed framework supports the updating of climatic action maps and climate-resilient infrastructure design and can be extended to other climate variables and cities.

🔗 Provenance — このレコードを発見したソース

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