A MACHINE LEARNING APPROACH BASED ON SYNTHETIC DATA FOR ESTIMATING CARBON FOOTPRINT IN LOGISTICS PROCESSES

ロジスティクスプロセスにおける炭素フットプリント推定のための合成データに基づく機械学習アプローチ (AI 翻訳)

Bora ÖÇAL

Uluslararası İktisadi ve İdari İncelemeler Dergisi📚 査読済 / ジャーナル2026-07-31#AI×ESG経営インパクト: 調達リスク対象セクター: transport

DOI: 10.18092/ulikidince.1940624

原典: https://doi.org/10.18092/ulikidince.1940624

🤖 gxceed AI 要約

日本語

ロジスティクスプロセスにおける炭素フットプリントを機械学習で推定する研究。実データが入手困難なため、輸送距離や貨物重量などの変数から合成データを生成し、6つのアルゴリズムを比較。XGBoostがR²=0.9945で最高性能を示した。合成データによる排出量推定が持続可能な物流の意思決定に寄与しうることを示した。

English

This study estimates carbon footprints in logistics using machine learning on synthetic data generated from variables such as distance, cargo weight, and transport mode. Six algorithms were compared; XGBoost achieved the best performance (R²=0.9945). Synthetic data offers a viable alternative when real-world logistics emissions data are scarce, supporting sustainable logistics decisions.

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

物流分野の排出量算定はサプライチェーン排出量の一部として重要。日本ではSSBJ開示やScope3対応が進むが、実データ不足が課題。合成データによるML推定は、物流事業者が開示対応の初期算定を行う際の選択肢になり得る。

In the global GX context

Logistics emissions are a key component of Scope 3 and transport decarbonization. As ISSB/CSRD reporting expands, companies need practical estimation methods. This paper shows synthetic-data ML can fill data gaps and support credible carbon footprint estimates for supply-chain disclosure.

👥 読者別の含意

🔬研究者:Useful as a benchmark of ML methods (XGBoost best) for logistics carbon estimation; consider validating on real-world datasets.

🏢実務担当者:Logistics firms can adopt this synthetic-data ML approach to estimate emissions for customer disclosure requests and Scope 3 reporting.

🏛政策担当者:Highlights the need for shared methodologies and benchmark datasets for transport emissions estimation to support reporting frameworks.

📄 Abstract（原文）

This study aims to estimate the carbon footprint arising from logistics processes using machine learning methods based on synthetic data and to identify the most suitable model for this purpose. Due to data access limitations, a multidimensional synthetic dataset was generated using variables like transport distance, cargo weight, transport mode, fuel type, traffic density, weather, and vehicle characteristics. Carbon emissions were calculated via an activity-based approach. The study comparatively evaluated six algorithms: Linear Regression, Random Forest, Extra Trees, Gradient Boosting, Support Vector Regression, and XGBoost. Findings indicate that tree-based ensemble learning models outperformed classical methods in predicting emissions. Among all models, XGBoost delivered the highest performance with an R² value of 0.9945 and minimal error rates. The results demonstrate that synthetic data provides an effective alternative for estimating carbon footprints in logistics process where access to real-world data is limited, and that it can contribute to sustainable logistics decision-making processes.

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.18092/ulikidince.1940624first seen 2026-08-02 05:41:10

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