産業用熱エネルギー供給オプション：多因子評価フレームワーク
Industrial thermal energy supply options: A multifactor evaluation framework (原題)
Tapajyoti Ghosh, Soomin Chun, Isaias Marroquin, Romain Sacchi, Jay Fuhrman, Alberta C. Carpenter, Patrick Lamers
🤖 gxceed AI 要約
日本語
米国製造業エネルギーの約50%を占める産業用熱の脱炭素化を、GCAMを初めて将来LCAに統合したLiAISONで評価。低温熱供給技術189構成を9環境影響カテゴリで2020〜2100年まで分析した。気候目標シナリオでは2050年までに全代替熱源が天然ガスとGWP同等となるが、淡水生態毒性や資源負荷など他領域への負荷移転が生じる。炭素指標のみに依存するリスクを示し、多指標の将来システム評価の必要性を強調する。
English
This study integrates GCAM into a prospective LCA framework (LiAISON) to evaluate 189 low-temperature industrial heat supply configurations across nine environmental impact categories from 2020-2100. Under a climate-target scenario, all alternatives reach GWP parity with natural gas by 2050, with heat pumps and solar thermal performing best, but trade-offs emerge in ecotoxicity, water use, and material supply chains. It argues that carbon-only metrics risk shifting burdens across domains, requiring multi-indicator systems analysis for balanced industrial decarbonization.
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
日本の製造業はScope1排出の多くを産業熱に依存し、SSBJ・有報での排出開示やSBTi目標設定において熱脱炭素の道筋が焦点となる。本論文の多指標評価は、炭素のみならず資源・水・毒性負荷を考慮した日本企業の脱炭素戦略立案に示唆を与える。
In the global GX context
As global disclosure frameworks (ISSB, CSRD) increasingly demand Scope 1 and value-chain emissions, this paper highlights that industrial heat decarbonization cannot be assessed on carbon alone. It provides a methodological template for prospective LCA that can inform transition finance and corporate target-setting beyond GWP.
👥 読者別の含意
🔬研究者:産業熱脱炭素の多因子評価手法とGCAM-LCA統合の先例として、シナリオ分析や負荷移転研究に有用。
🏢実務担当者:熱源選択時に炭素以外の環境負荷（水・毒性・資源）も考慮すべきことを示し、脱炭素投資の多面的評価に活用可能。
🏛政策担当者:産業脱炭素政策において、単一の炭素指標ではなく多指標での技術評価とシナリオ設計の必要性を裏付ける。
📄 Abstract（原文）
Industrial heat production represents approximately 50% of U.S. manufacturing sector energy needs and related emissions, yet emission reduction strategies for this sector remain underexplored relative to power and transportation. This study applies the open-source LiAISON (Life cycle Assessment Integration into Scalable Open-source Numerical models) framework, integrating for the first time the Global Change Assessment Model (GCAM) into a prospective LCA framework, to evaluate 189 unique configurations of low-temperature (100–500°C) industrial heat supply technologies across nine environmental impact categories from 2020 to 2100. Technologies evaluated include electrification (resistive heaters and heat pumps), hydrogen-based heating, biofuels, solar thermal, and carbon capture and storage (CCS)-integrated systems, assessed under both a business-as-usual (SSP2 Baseline) and a climate-target (SSP2 RCP2.6) scenario. Results demonstrate that under the climate-target scenario, all alternative heat sources reach global warming potential (GWP) parity with conventional natural gas by 2050, with heat pumps and solar thermal performing best across most impact categories. However, significant trade-offs emerge across other environmental dimensions: renewable electrification increases material supply chain burdens; hydrogen pathways involve elevated toxicity and upstream emissions; biobased heating raises marine eutrophication and water consumption concerns; and CCS-integrated systems require additional energy inputs and resources. A systems-level analysis projects cumulative GWP savings of 56.5 billion tons of CO 2 -eq. over 2030–2100 under the climate-target scenario but accompanied by a 4.5-fold increase in freshwater ecotoxicity relative to the baseline. Disability-adjusted life year (DALY) analysis reveals a short-term increase in health burdens through 2040, followed by net improvements as GWP reductions dominate. These findings underscore that relying solely on carbon metrics risks shifting environmental burdens to other domains, and that a multi-indicator, prospective systems framework is essential for identifying balanced industrial decarbonization pathways.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.jclepro.2026.149299first seen 2026-10-08 04:47:32
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