食品・飲料セクターにおける製品カーボンフットプリント推定のためのガウス混合モデルベースの機械学習フレームワーク
A Gaussian Mixture Model-Based Machine Learning Framework for Product Carbon Footprint Estimation in the Food and Beverage Sector (原題)
Thing-Yuan Chang, Chien-Chih Chen, Chin-Hsien Hsu, Fong-Kin Law, Hsi-Lin Chang
🤖 gxceed AI 要約
日本語
食品サプライチェーンのCO2排出量は年間137億トンに上るが、正確なPCF推定は多くのメーカーにとって困難。本研究は、公開データを統合し、GMMクラスタリングと機械学習回帰を組み合わせたフレームワークを提案。3,933サンプルで検証し、単一モデルより高精度で、LCAに代わるスケーラブルな手法を示した。
English
The food supply chain emits 13.7 billion tons of CO2e annually, yet accurate Product Carbon Footprint (PCF) estimation is inaccessible to many manufacturers. This study proposes a machine learning framework combining Gaussian Mixture Model clustering with regression models, trained on 3,933 harmonized samples. It outperforms single-model approaches, offering a scalable, data-driven alternative to LCA for PCF transparency.
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SSBJ開示やサプライチェーン排出量算定の負担が課題。本手法は、中小企業を含む食品メーカーが低コストでPCFを推定し、Scope 3開示や取引先要求に対応する実務的な選択肢を提供する。
In the global GX context
With EU Product Environmental Footprint rules and ISSB/CSRD disclosure demands, scalable PCF estimation is critical. This ML framework offers a practical alternative to LCA, enabling manufacturers to meet disclosure requirements and support supply-chain decarbonization globally.
👥 読者別の含意
🔬研究者:Provides a novel ML approach for PCF estimation that could be extended to other sectors or integrated with LCA databases.
🏢実務担当者:Offers a cost-effective method for food and beverage companies to estimate product carbon footprints and respond to disclosure requests.
🏛政策担当者:Highlights the potential of ML-based tools to democratize carbon accounting, informing policy on data standards and support for SMEs.
📄 Abstract（原文）
The global food supply chain generates approximately 13.7 billion metric tons of CO₂-equivalents annually, yet accurate Product Carbon Footprint (PCF) estimation remains inaccessible to most manufacturers. Life Cycle Assessment (LCA), the recognized gold standard, imposes prohibitive data demands and costs, while increasingly stringent European Union (EU) Product Environmental Footprint requirements intensify the urgency for scalable alternatives. Machine learning (ML) offers a promising substitute by utilizing publicly accessible product attributes, but the structural diversity of the food and beverage sector generates heterogeneous emission profiles that undermine single-model approaches trained on limited per-category data. This study proposes a novel ML framework combining multi-source data integration with Gaussian Mixture Model (GMM)-based clustering. Seven publicly available datasets were harmonized into a unified corpus of 3,933 samples, and GMMs were employed to cluster products by underlying emission patterns rather than predetermined taxonomies. Cluster-specific Random Forest Regression, K-Nearest Neighbors Regression, and eXtreme Gradient Boosting models were trained on each subset, with unseen samples assigned at inference via K-Nearest Neighbors voting. Results from ten repetitions of stratified five-fold cross-validation confirm that the proposed framework significantly outperforms standard single-model counterparts across all major product categories. The findings demonstrate that the proposed framework offers a scalable, data-driven alternative to LCA, enhancing PCF transparency for manufacturers and supporting informed sustainability decisions across the food and beverage sector.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.20944/preprints202608.1230.v1first seen 2026-08-20 04:47:57
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