Provincial-level prediction of full-chain carbon removal cost and efficiency of direct air carbon capture and storage (DACCS) in China
中国における直接空気回収・貯留(DACCS)のフルチェーン炭素除去コストと効率の省別予測 (AI 翻訳)
WANG Yuxuan, ZHANG Xian, Xueting Peng, Fan Jingli
🤖 gxceed AI 要約
日本語
中国のDACCSについて、ライフサイクル評価と学習曲線を組み合わせ、液体溶媒吸収と固体吸着の2技術の正味除去効率とコストを将来予測。全国平均の正味除去効率は液体で17.0%~69.8%、固体で77.9%~89.0%。2060年のコストは液体で1,336~1,970元/t、固体で394~1,184元/tと試算。省別の格差も明らかにし、非化石エネルギー供給の豊富な省での実証を提案。
English
This study couples life-cycle assessment with learning curves to project net removal efficiency and cost of liquid-solvent and solid-sorbent DACCS in China. National average net removal efficiency ranges from 17.0%-69.8% for liquid and 77.9%-89.0% for solid. By 2060, costs are projected at 1,336-1,970 RMB/t (liquid) and 394-1,184 RMB/t (solid). Provincial disparities suggest demonstration in provinces with abundant non-fossil energy.
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DACCSはまだ初期段階だが、CCS長期ロードマップやGX実現に向けた技術選択肢として重要。省別の評価手法は日本の地域特性に応じた導入検討に参考になる。
In the global GX context
This study provides a rigorous framework for evaluating DACCS costs and efficiency at subnational level, relevant for global net-zero strategies. It highlights the role of energy decarbonization in improving DACCS performance, informing international deployment decisions.
👥 読者別の含意
🔬研究者:Provides a comprehensive LCA-based framework for projecting DACCS costs and efficiency, useful for comparative technology assessment.
🏢実務担当者:Offers insights into cost reduction pathways and regional siting for DACCS projects, aiding investment and deployment planning.
🏛政策担当者:Informs policy on DACCS demonstration and scale-up, emphasizing the importance of non-fossil energy supply for efficiency.
📄 Abstract(原文)
Direct air capture (DAC) removes CO2 directly from air and achieves net CO2 removal when coupled with transport and geological storage, thus becoming indispensable in global net-zero emissions pathways. This study proposes to identify demonstration regions and methods for cost reduction and performance improvement of direct air carbon capture and storage (DACCS) in China. We coupled life-cycle assessment (LCA) with DAC learning curves to evaluate and project the net removal efficiency and cost of liquid-solvent absorption and solid-sorbent adsorption. The results show that the national average net removal efficiencies for liquid-based DACCS vary across scenarios, ranging from 17.0% to 69.8%, whereas solid-based technology demonstrates relative stability (77.9% ~89.0%). National average net removal costs of DACCS decline with higher learning rates and larger deployment scales. By 2060, the costs are projected to be 1 336~1 970 RMB/t for liquid-based technology and 394~1 184 RMB/t for solid-based route. There are significant provincial disparities in net removal efficiency for liquid-based technology in 2035, with only Sichuan and Yunnan provinces exceeding 70%, theoretically qualifying them as pilot demonstration. The solid-based technology, in contrast, maintains a steady efficiency of 78.6% ~84.1% across all provinces. Declining energy-related carbon emissions improve the net removal efficiency of DACCS. From 2035 to 2060, the proportion of energy-related carbon emissions drops from 77.3% to 45.7% for the liquid-based technology, and from 68.3% to 22.9% for the solid-based route, indicating that the dominant contribution of the energy supply stage to full-chain carbon emissions gradually weakens. The cost structure of the liquid route is generally energy-dominated, whereas the dominant cost component of the solid route shifts from capture-side capital expenditure to CO2 transport and storage. This study thus suggests that DACCS demonstration projects be implemented in provinces with abundant non-fossil energy supply, and deployment should be expanded through scale effects and technological improvements to steadily reduce costs and expand adoption.
🔗 Provenance — このレコードを発見したソース
- openalex https://doaj.org/article/17e1bdc382c445ffa3672eadfa1b3db3first seen 2026-08-07 05:02:27
🔔 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。