Practice of Big Data Association Rule Algorithm in Cross Industry Product Carbon Footprint Certification Data Sharing and Tracing
ビッグデータの相関ルールアルゴリズムによる業界横断製品カーボンフットプリント認証データ共有・追跡の実践 (AI 翻訳)
Danhui Lai, Zhang Zeqi, Yingjie Li, Baifeng Ning, Ningrui Zhou
🤖 gxceed AI 要約
日本語
本論文は、ビッグデータの相関ルールアルゴリズム（FP-Growth）を製品カーボンフットプリント（CFP）認証データの共有・追跡に応用し、異なるサプライチェーン拠点間の排出関連ルールを抽出するモデルを提案。従来のApriori法と比較し、処理時間を18.6秒から7.9秒に短縮し、ルールの信頼性と向上度を改善。CFPデータの効率的な統合と排出ホットスポット特定に寄与する。
English
This paper applies the FP-Growth association rule algorithm to product carbon footprint (CFP) certification data sharing and tracing, extracting emission-related rules across supply chain nodes. Compared with the Apriori method, it reduces processing time from 18.6s to 7.9s and improves rule confidence and lift. The model enhances data integration and identifies key emission links.
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が進む中、異種データ統合と排出ホットスポット特定の効率化は実務課題。本手法はCFPデータの共有基盤やScope3算定の自動化に応用可能性があり、国内のカーボンフットプリント制度（CFP制度）や開示実務に示唆を与える。
In the global GX context
Globally, as ISSB and CSRD mandate supply-chain emissions disclosure, efficient data integration and hotspot identification are critical. This work demonstrates how association rule mining can enhance carbon data sharing and traceability, offering a scalable approach for multi-tier supply chain accounting and greenwashing detection.
👥 読者別の含意
🔬研究者:Provides a novel application of FP-Growth to carbon footprint data, with performance benchmarks against Apriori.
🏢実務担当者:Offers a method to streamline carbon data integration and identify emission hotspots across supply chains, potentially reducing Scope 3 reporting burden.
🏛政策担当者:Highlights the potential of data mining to support carbon footprint certification and traceability frameworks.
📄 Abstract（原文）
The carbon footprint management system and traceability methods face the practical needs of effectively integrating heterogeneous data from multiple sources, the important task of accurately identifying key emission links, and the objective situation of further improving cross enterprise collaboration and sharing mechanisms. In view of this, this article creatively introduces association rule algorithms in the field of big data, aiming to build an innovative model specifically for cross industry products that integrates efficient sharing of carbon footprint data and full process traceability functions. The model first performs standardized preprocessing operations on various types of data generated during the carbon footprint certification process to ensure consistency and availability of data formats, and then uses the FP-Growth algorithm to deeply explore potential carbon emission association rules between different supply chain nodes. The experimental results objectively demonstrate that compared with traditional carbon footprint data processing methods, the association rule mining method based on FP-Growth algorithm exhibits higher operational efficiency and stronger stability in the processing of cross industry carbon footprint data. Specifically, under the same minimum support condition, the average running time of the Apriori algorithm was significantly reduced from 18.6 seconds to 7.9 seconds, and the two key indicators of rule confidence and improvement were significantly improved.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.procs.2026.03.244first seen 2026-08-02 18:08:12
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