Sensitivity and Scenario Analysis to Reduce the Carbon Footprint of Polypropylene Processing Using Primary Industrial Data
一次産業データを用いたポリプロピレン加工のカーボンフットプリント削減のための感度分析とシナリオ分析 (AI 翻訳)
Chiara Antonacci, Elena Battiston, Diego Zamboni, Silvia Gross, Anna Mazzi
🤖 gxceed AI 要約
日本語
本研究は、欧州のポリプロピレン加工施設からの一次産業データを用いて、ゆりかごからゲートまでのカーボンフットプリントを定量化。感度分析とシナリオ分析を組み合わせ、リサイクルPP、プロセス効率向上、再生可能エネルギー利用により最大45.8%の排出削減が可能であることを示した。
English
This study quantifies the cradle-to-gate carbon footprint of polypropylene processing using primary industrial data from European facilities. Combining sensitivity and scenario analyses, it identifies practical priorities: recycled PP, improved efficiency, and renewable electricity can reduce emissions by up to 45.8%.
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
日本においても、プラスチック加工のカーボンフットプリント算定には一次データの重要性が認識されつつある。本手法はSSBJやサプライチェーン排出量開示への応用が期待される。
In the global GX context
Globally, this study underscores the value of primary industrial data over generic LCA databases, directly relevant to ISSB and CSRD disclosure requirements. The scenario analysis offers a replicable framework for manufacturing decarbonization.
👥 読者別の含意
🔬研究者:Provides a methodology integrating primary LCI data with sensitivity/scenario analysis for carbon footprint reduction.
🏢実務担当者:Offers actionable priorities (recycled content, efficiency, renewables) for reducing polypropylene processing emissions.
🏛政策担当者:Demonstrates the feasibility of significant emission reductions in plastics processing through targeted measures.
📄 Abstract（原文）
Life cycle assessment (LCA) studies of polypropylene (PP) processing commonly rely on generic secondary databases, while primary industrial inventories for plastic conversion processes remain scarce. This study addresses this gap by quantifying the cradle-to-gate carbon footprint of polypropylene processing using anonymised primary industrial data collected in 2024 from four European polypropylene processing facilities. Unlike previous studies relying mainly on generic secondary inventories, the proposed approach combines primary industrial data with sensitivity and scenario analyses to identify practical priorities for emission reduction. The baseline carbon footprint was estimated at 1.44 tCO2e per tonne of finished product, with material production and energy-intensive processing identified as the major emission hotspots. One-Factor-at-a-Time (OFAT) sensitivity analysis showed that polypropylene type, process efficiency, renewable electricity use, and process waste management were the most influential parameters, whereas water consumption and additive use had only a minor effect on overall emissions. Scenario analysis indicated that combining recycled polypropylene, improved process efficiency and renewable electricity reduced emissions by 45.8%, while reducing process waste and fully recycling production residues achieved a 42.2% reduction compared with the baseline. By integrating primary industrial inventory data with sensitivity and scenario analyses, this study provides a more representative assessment of real industrial polypropylene processing conditions than approaches based solely on generic databases and identifies practical priorities for industrial carbon mitigation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/polym18141760first seen 2026-07-21 04:59:59
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
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。