低炭素コンクリート調達のためのEPD準拠カーボン・レジリエンス配分：意思決定忠実度、ルート検証、相関撹乱ストレステスト

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

🔬研究者:EPDデータとML推定の意思決定上の役割分担を実証的に検証する手法は、炭素会計研究の参照枠になる。

🏢実務担当者:建設・調達チームは、EPDを正式比較の根拠としMLを初期スクリーニングに限定する配分設計を自社調達に応用できる。

🏛政策担当者:EPDの検証済みデータと推定値の扱いを区別する規制設計の必要性を示す。

Introduction Green construction teams need a defensible way to convert product-specific concrete carbon declarations into supply decisions that recognize delivery and disruption exposure. Prediction-to-optimization workflows can obscure a critical evidence boundary: verified environmental product declaration (EPD) values can support formal supplier comparison, whereas machine-learning estimates require decision-level validation before allocation. Methods We introduce an evidence-aligned framework that separates observed EPD attributes, model-estimated screening outputs, scenario-derived A4 transport, and assumption-based disruption stress and assigns each a specific decision role. Model transportability was audited using plant, company, temporal, and geographic holdouts, followed by a direct comparison of predicted and EPD-observed rankings and allocations. Formal strength-constrained optimization minimized observed EPD A1–A3 plus scenario A4 under plant-share caps. Results After harmonization and deduplication, the analysis contained 45,739 EPD records from 663 plants. A prospective external cohort added 22 declarations from 14 plants and seven derivation-unseen companies; the frozen model reduced record-level MAE by 6.6% relative to the frozen training median, while the supplier-ranking gate retained the EPD-grounded path. Across nine city-strength cases, a 40% cap produced three-plant allocations with a mean Herfindahl–Hirschman Index of 0.36. Under a 10% independent plant-disruption stress rate, it reduced the probability of losing at least half of supply from 0.100 to 0.027; mean and median A1–A4 premiums were 14.57% and 7.76%, respectively. The benefits remained measurable across disruption probabilities of 5%–30% and pairwise correlations of 0–0.50. An OpenStreetMap routing check covered 79 plant–project pairs: median distance error was 6.39%, 17 of 18 allocation decisions were unchanged, and the remaining case had 0.026% route-evaluated objective regret. Discussion The contribution is a transparent carbon-concentration frontier with an empirical decision-fidelity gate: prediction supports early screening, whereas EPD values anchor formal allocation. The framework provides extensible planning evidence while preserving clear boundaries between comparative scenarios and operational procurement.

• openalex https://doi.org/10.3389/fmats.2026.1950801first seen 2026-10-23 05:14:33 · last seen 2026-11-06 04:49:48