Plant-level carbon accounting of China's pulp and paper industry via multimodal fusion
マルチモーダル融合による中国のパルプ・製紙産業のプラントレベル炭素会計 (AI 翻訳)
Song Hu, Hua-Zhe Qi, Zifei Wang, Xiaoyu Wu, Yulin Han, Yi Man
🤖 gxceed AI 要約
日本語
中国のパルプ・製紙産業を対象に、高解像度リモートセンシングと工場テキストデータを統合したマルチモーダルデータ融合フレームワークを提案。720工場に適用し、2022年の総排出量1.636億トンCO2を推定。5％の高排出工場が約43％を占める不均等構造を明らかにし、屋上太陽光発電で最大10.3％削減可能と示した。他産業への展開可能な手法。
English
This study proposes a multimodal data fusion framework integrating high-resolution remote sensing and plant textual data for plant-level carbon accounting in China's pulp and paper industry. Applied to 720 plants, it estimates 163.6 Mt CO2 in 2022, revealing that 5% of high-emission plants contribute ~43% of emissions. It also shows rooftop solar could reduce emissions by up to 10.3%.
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
日本でもパルプ・製紙や重工業のプラントレベル排出量把握に応用可能な手法。SSBJ対応のScope1・2算定において、リモートセンシングとテキストデータの融合が有効な事例を示す。中国固有のデータだが、日本企業の排出削減策立案に示唆を与える。
In the global GX context
This study provides a transferable blueprint for granular carbon accounting in heterogeneous heavy industries, addressing plant-level heterogeneity often masked by aggregate statistics. It demonstrates how multimodal data fusion can improve emission inventories and inform targeted mitigation policies, relevant for global disclosure frameworks like ISSB.
👥 読者別の含意
🔬研究者:Provides a novel methodology integrating remote sensing and textual data for plant-level carbon accounting, applicable to other industries.
🏢実務担当者:Offers a data-driven approach for corporates to enhance accuracy of Scope 1 and 2 emissions reporting and identify mitigation levers.
🏛政策担当者:Highlights the need for differentiated regulation based on plant-level data and shows rooftop solar potential for industrial decarbonization.
📄 Abstract（原文）
Plant-scale industrial carbon accounting is critical for developing targeted emission-reduction policies. However, most assessments of carbon-intensive sectors rely on aggregate statistics, which obscure significant heterogeneity among individual plants. China's pulp and paper industry (PPI), the largest globally, encompasses diverse production processes, raw material inputs, and emission sources. Existing accounting frameworks rely on statistical data and average emission factors within poorly defined system boundaries, which prevents differentiation at the individual plant level. Here, we propose a multimodal data fusion framework that integrates high-resolution remote-sensing imagery with plant textual data to capture structural and operational characteristics undetectable by any single data modality. Applied to 720 pulping and papermaking plants across China, the framework achieves R2 values of up to 0.96 across five plant types and estimates total sectoral carbon emissions at 163.6 million tonnes of CO2 in 2022, with pronounced regional disparities concentrated in eastern coastal provinces. Analysis of functional-zone contributions further reveals that wastewater treatment areas are a consistent cross-category emission driver, and that just 5% of high-emission plants account for approximately 43% of sectoral emissions—a skewed structure that demands differentiated regulatory intervention. Incorporating regional solar radiation data, rooftop photovoltaic deployment is projected to reduce annual PPI emissions by up to 10.3%, with primary-fiber pulp plants offering the greatest mitigation leverage. Beyond China's PPI, this scalable, data-driven approach provides a transferable blueprint for granular, plant-level carbon accounting in other heterogeneous heavy industries.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1016/j.ese.2026.100682first seen 2026-05-05 23:50:22
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。