← 論文一覧に戻る

Carbon Capture Technologies for a Decarbonized Energy System –An Update of the Scenario-Reality Gap

脱炭素エネルギーシステムのための炭素回収技術―シナリオと現実のギャップに関する最新情報 (AI 翻訳)

Christian Hirschhausen, Björn Steigerwald, Erk Schaarschmidt, Frederik Schmidt

Economics of Energy and Environmental Policy📚 査読済 / ジャーナル2026-07-10#CCUSOrigin: Global対象セクター: power
DOI: 10.5547/2160-5890.15.2.chir
原典: https://doi.org/10.5547/2160-5890.15.2.chir

🤖 gxceed AI 要約

日本語

本論文では、CCTS(炭素回収・輸送・貯留)とDAC(直接空気回収)の2つの炭素回収技術について、長期気候シナリオが想定する役割と実際の展開のギャップを検証する。15年間の実証プロジェクトを調査した結果、CCTSは電力部門で大規模プロジェクトが限定的で、産業部門でも年間100万トン未満の小規模にとどまっている。DACの実績容量は2025年時点で年間0.05百万トン未満と、モデルが想定する数ギガトンには程遠い。シナリオの楽観論と現実のギャップは依然として大きく、技術展開戦略の複雑さが示唆される。

English

This paper examines the gap between the role attributed to CCTS (Carbon Capture, Transport, and Storage) and DAC (Direct Air Capture) in long-term climate scenarios and their actual deployment. Surveying global projects over 15 years, it finds that CCTS deployment remains low in the power sector and modest in industry (mostly below 1 MtCO2/year). Installed DAC capacity as of June 2025 is below 0.05 MtCO2/year, far from models projecting several gigatons by 2050. The scenario-reality gap persists, indicating that technology deployment strategies are more complex than models suggest.

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GX政策の一環としてCCUSが推進されていますが、本論文は世界的な導入の遅れを定量的に示しており、日本の実践においても現実的な目標設定と政策設計の重要性を再認識させます。SSBJや統合報告書でのシナリオ分析にも示唆を与える内容です。

In the global GX context

This paper provides a comprehensive global survey of CCTS and DAC projects, highlighting the persistent gap between ambitious model projections and real-world deployment. For global disclosure frameworks like ISSB and TCFD, it underscores the need for realistic scenario assumptions and cautious optimism when integrating carbon capture into transition plans.

👥 読者別の含意

🔬研究者:This paper offers a detailed empirical update on CCTS and DAC deployment, valuable for researchers studying carbon capture implementation barriers and scenario modelling.

🏢実務担当者:Companies considering CCUS investments can use this survey to ground expectations about technology readiness and timeline.

🏛政策担当者:Policymakers should note the persistent deployment gap when setting CCS targets and designing support mechanisms, to align scenario optimism with feasible pathways.

📄 Abstract(原文)

In this paper we explore the development of two specific carbon capture technologies, namely Carbon Capture, Transport, and Storage (CCTS) and Direct Air Capture (DAC) with respect to the role attributed to them by long-term climate scenarios. We ask whether the critical assessment in earlier literature of the gap between ambitious targets in top-down energy and climate models and the modest level of real-world implementation still persists. We provide a survey of the full set of projects on CCTS in the energy and industry sectors, as well as of all DAC projects world-wide. For CCTS, we find that although several demonstration projects have been implemented over the past 15 years, the scale of deployment remains low. In the power sector, only a few large-scale projects remain operational as of 2025; others have been delayed or cancelled. Industrial CCTS shows broader engagement, yet most projects remain below the 1 MtCO2 /year threshold. The deployment of DAC, too, has remained at very low levels: While integrated assessment models (e.g., EMF-38 and AR6 scenarios) project deployment of several gigatons per year by 2050, the actual installed DAC capacity in June 2025 remains below 0.05 MtCO2 /year. The paper concludes that while carbon capture remains a compelling field for innovation, the gap between scenario optimism and real-world progress has not closed. This is not the “fault” of the models, but these findings suggest that optimal technology deployment strategies might be more complex to implement than these models suggest.

🔗 Provenance — このレコードを発見したソース

🔔 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。