Reliable CO2 Numbers: Data Quality, Provenance and Uncertainty in Procurement Decisions
信頼できるCO2数値：調達判断におけるデータ品質、出所、不確実性 (AI 翻訳)
Lukas Büeck, L. Ritter
🤖 gxceed AI 要約
日本語
本レビューは、調達判断に用いられるCO2排出データの品質、出所、不確実性を整理した。製造・自動車サプライチェーンにおいて、Scope 3排出量は最もデータ品質が低く、業界平均や金額ベースの推定に依存していることが多い。5つのデータ品質欠陥（境界不明、検証不足、カバレッジ不十分、相互運用性の弱さ、汎用データの使用）を特定し、一次データ共有とデジタルプラットフォームの活用が信頼性向上に寄与することを示した。
English
This review examines the quality, provenance and uncertainty of CO2 data used in procurement decisions, focusing on manufacturing and automotive supply chains. It finds that scope 3 emissions, which dominate corporate footprints, suffer from five recurring data quality deficits: unclear boundaries, limited verification, incomplete coverage, weak interoperability, and reliance on generic estimates. Uncertainty arises from input parameters, methodological scenarios, and calculation models, making a single carbon figure misleading. The paper recommends primary data sharing between trading partners supported by digital platforms and calls for procurement teams to treat carbon figures as claims with a known pedigree.
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
本論文はScope 3排出量データの品質問題を整理しており、SSBJ対応やサプライチェーン排出量の報告義務化が進む日本企業にとって、データの信頼性評価基準を考える上で有益である。
In the global GX context
This paper synthesises evidence on scope 3 data quality deficits and uncertainty, offering a framework for procurement teams to evaluate carbon numbers—relevant as ISSB/CSRD mandates push for more reliable supply-chain disclosures globally.
👥 読者別の含意
🔬研究者:Researchers examining scope 3 accounting methods should note the five data-quality deficits and the impact of primary data sharing on reliability.
🏢実務担当者:Procurement and sustainability teams can use the uncertainty and provenance framework to improve supplier carbon data evaluation.
🏛政策担当者:Standard setters should consider the recommendation to require data quality scoring in reporting frameworks.
📄 Abstract（原文）
Procurement decisions increasingly depend on the carbon footprint reported for purchased goods and services, yet the numbers that reach buyers vary widely in how they were produced. This review examined how the quality, provenance and uncertainty of emissions data shape the reliability of the carbon figures used in sourcing decisions across manufacturing and automotive supply chains. Recent open-access studies and the corporate greenhouse gas accounting standards were synthesised to trace where carbon data come from, how their quality can be judged, and how uncertainty arises and spreads. The evidence showed that supply-chain emissions dominate most corporate footprints while resting on the weakest data, since firms often replace measured supplier values with industry averages and spend-based estimates. Five recurring data-quality deficits were identified: unclear boundaries, limited verification, incomplete coverage, weak interoperability, and the use of generic instead of specific data. Uncertainty was found to enter through input parameters, methodological scenarios and the calculation models themselves, so a single value can hide a wide plausible range. Sharing primary data between trading partners, supported by digital platforms and clear governance, raised reliability, although adoption stayed uneven. The review concluded that procurement teams should treat a carbon figure as a claim with a known pedigree rather than a fixed fact, and should weight supplier comparisons by data quality. Implications for buyers, suppliers and stan154dard setters were set out. Keywords: carbon data quality, scope 3 emissions, data provenance, uncertainty, sustainable procurement, supplier selection, greenhouse gas accounting .
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.52403/ijrr.20260719first seen 2026-07-25 05:17:09
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。