Quality-Matched Life Cycle Assessment of CCU Supply Chains for SMR Tail Gas CO2 in Industrial Parks
産業団地におけるSMRテールガスCO2のCCUサプライチェーンの品質マッチングライフサイクルアセスメント (AI 翻訳)
Jiuli Ruan, Yisong Wang, Tao Du, Lu Bai, He Jia, Yingnan Li, Peng Chen
🤖 gxceed AI 要約
日本語
本研究は、産業団地内のSMRテールガスからのCO2回収・利用(CCU)サプライチェーンに対し、品質マッチング動的LCAフレームワークを開発した。従来の一律99%純度仮定による過剰精製のエネルギー負荷を、85-90%の低純度要求に合わせることで60%以上削減可能と示した。動的電力シナリオ下では、コンクリート硬化は2031年頃にネットゼロに達し、鉱物化戦略は最終的に-0.046kg CO2-eq/kg CO2利用を達成。低純度・高循環の建材を優先する政策の科学的根拠を提供する。
English
This study develops a quality-matched dynamic LCA framework for CCU supply chains using SMR tail gas CO2 in industrial parks. By matching capture purity to endpoint requirements (85-90% instead of 99%), it reduces capture energy penalties by over 60%. Under dynamic grid decarbonization, concrete curing reaches net-zero around 2031, and mineralization achieves -0.046 kg CO2-eq per kg CO2 utilized. The findings support policies prioritizing low-purity, high-circularity building materials over carbon-intensive chemical synthesis.
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GX政策ではCCUの実装が進められているが、本論文は精製度の過剰設計によるエネルギー損失を定量化し、産業団地における低純度CO2ネットワークの設計や、鉱物化・コンクリート硬化への優先投資の合理性を科学的に裏付ける。SSBJや有報におけるカーボンニュートラル戦略の具体的な技術選択に示唆を与える。
In the global GX context
This paper provides a rigorous LCA framework that challenges the default assumption of 99% CO2 purity in CCU, relevant to global disclosure and transition finance discussions. By demonstrating that low-purity pathways (e.g., concrete curing) can achieve near-term net-zero, it offers a pragmatic pathway for industrial decarbonization. The dynamic grid model also aligns with evolving climate scenarios used in TCFD and ISSB reporting.
👥 読者別の含意
🔬研究者:Highlights the importance of quality-matching in CCU LCA and provides a dynamic framework that can be adapted to other feedstocks and regions.
🏢実務担当者:Industrial park managers can use the findings to design dual-grade CO2 pipelines and prioritize low-purity utilization pathways, reducing energy costs and emissions.
🏛政策担当者:Provides evidence for policies that incentivize low-purity CCU applications like concrete curing and mineralization over methanol synthesis, and for designing CO2 infrastructure.
📄 Abstract(原文)
Carbon capture and utilization (CCU) is imperative for industrial decarbonization. However, current life cycle assessment (LCA) methodologies often apply a static, one-size-fits-all approach, assuming a 99% CO2 purity standard for all utilization pathways. This ignores the thermodynamic limits of capture technologies and the tolerance of certain endpoints for coarse gas, leading to severe over-purification energy penalties. To bridge this gap, we developed a quality-matched dynamic LCA framework targeting steam methane reforming (SMR) tail gas in industrial parks. A superstructure matrix was constructed, coupling 16 capture configurations (spanning chemical absorption to cryogenic separation across 85–99% purities) with five utilization pathways, under a dynamic grid decarbonization model (2024–2060). The baseline scenario shows that methanol is the most carbon-intensive pathway at 16.88 kg CO2-eq per kg CO2 utilized, whereas mineralization and concrete curing remain near break-even at 0.221 and 0.010 kg CO2-eq, respectively. When low-purity demand is matched with PSA capture at 85–90% purity, the net GWP of mineralization and concrete curing decreases to 0.134 and 0.005 kg CO2-eq, corresponding to capture-stage penalty reductions exceeding 60% relative to unnecessary 99% purification. Under the dynamic electricity scenario, concrete curing reaches the net-zero tipping point around 2031, and the coupled mineralization substitution strategy ultimately achieves −0.046 kg CO2-eq per kg CO2 utilized. These findings provide a compelling scientific basis for policymakers to design dual-grade CO2 pipeline networks and prioritize low-purity, high-circularity building materials over carbon-intensive chemical synthesis in near-term industrial transitions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18105063first seen 2026-05-23 05:43:29 · last seen 2026-05-27 04:58:13
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