Product Carbon Footprint Emission Factor Matching Algorithm Based on Large Language Models and Semantic Retrieval
大規模言語モデルと意味検索に基づく製品カーボンフットプリント排出係数マッチングアルゴリズム (AI 翻訳)
Jiawei Wen, Chengxin Pang, Yanxin Wang, Xinhua Zeng
🤖 gxceed AI 要約
日本語
本研究は、LCAデータベースを用いたPCF算定において、LLMと意味検索を組み合わせた排出係数自動マッチングアルゴリズムを提案。8産業製品でEcoinvent 3.10を用いた評価により、高精度かつ低遅延で専門家の手作業を上回る性能を示した。大規模自動PCF算定の実現可能性を高める技術的ソリューションとなる。
English
This study proposes an automated emission factor matching algorithm combining LLMs with semantic retrieval for PCF accounting. Evaluated on eight industrial products using Ecoinvent 3.10, it achieves high precision and low latency, outperforming manual expert screening. It provides a reliable technical pathway for large-scale automated PCF accounting.
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対応やサプライチェーン排出量算定の需要が高まっており、本アルゴリズムはPCF算定の自動化・効率化に寄与する。ただし、日本独自のDB（IDEA等）への適用検証が必要。
In the global GX context
As global disclosure frameworks (ISSB, CSRD, SEC) demand quantitative PCF data, this algorithm offers a scalable solution to emission factor matching, a key bottleneck. It demonstrates that LLM-based automation can reduce reliance on expert judgement, accelerating LCA-based reporting.
👥 読者別の含意
🔬研究者:Validates LLM+semantic retrieval for LCA; benchmarks against Ecoinvent and expert matching.
🏢実務担当者:Adoptable as a tool to automate PCF calculation, reducing manual effort and improving consistency.
🏛政策担当者:Shows potential for standardizing emission factor matching, supporting disclosure regulation enforcement.
📄 Abstract（原文）
Emission factor matching is the most critical step in product carbon footprint (PCF) accounting based on life cycle assessment (LCA). However, this step has long been hindered by several major challenges: a lack of standardization, overreliance on expert judgment, inconsistencies in raw data, and complex processing workflows. To address these issues, this study proposes an automated emission factor matching algorithm that combines large language models (LLMs) with semantic retrieval. The algorithm proceeds in two stages: first, an LLM identifies the reference product within the LCA database; then, an embedding model retrieves the most relevant emission factors through high-precision matching. Depending on practical requirements, the algorithm can either automatically select a single best-match factor or rank multiple best-match candidates in descending order of match precision to assist LCA experts in decision-making. We evaluate the algorithm on eight industrial products—tires, cement, ammonium phosphate, wood products, textiles, electronics and electrical appliances, steel, and lithium batteries—using the Ecoinvent 3.10 LCA database. Results demonstrate that the algorithm achieves high precision and low processing latency, significantly outperforming manual expert screening. These findings confirm that the proposed algorithm enables efficient and accurate emission factor matching, thereby providing a reliable technical solution and decision-making pathway for large-scale, automated PCF accounting.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18115444first seen 2026-06-23 05:47:47
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