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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received July 24, 2025
Revised October 13, 2025
Accepted November 10, 2025
Available online January 14, 2026
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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CCUS 부문 CO 감축 경로 추정 방법론

Method of CO2 Reduction Pathway Estimation in CCUS Sector

포스코홀딩스
POSCO Holdings 1
mdguru@posco-inc.com
Korean Chemical Engineering Research, February 2026, 64(1), 105143
https://doi.org/10.9713/kcer.2026.64.1.105143
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Abstract

우리나라는  2030년 국가 온실가스 감축목표 (NDC) 달성을 위해 부문별 감축 목표치를 제시하고 있으며, 특히  CCUS 분야에서 1,120만톤의 감축 목표를 설정하였다. 그러나 실제 목표 달성을 위한 세부 이행 경로에 대한 정량적 분석은 부족한 실정이다. 이 연구에서는  CCUS 분야의  CO2 감축경로를 추정하기 위한 체계적인 방법론을 개발하고, 개발한 방법론과 임의의 데이터를 활용하여 사례 연구를 수행하였다 . 먼저 연도별  CCUS 관련 예상 사건들을 수집하고 정량화하여 마스터데이터를 구성한 후, 이를 기반으로 감축경로를 추정하는 상향식 (bottom-up) 방법을 제안하였다. 마스터 데이터는 엑셀 기반의 데이터베이스로 구축하였으며, 파이썬 기반의 분석 툴을 개발하여 데이터 처리 및 시각화를 수행하였다 . 개발된 방법론을 통해 년도별, 포집원별, 처리방안별  CO2 흐름을 정량적으로 분석하고, 시나리오별 감축경로를 도출할 수 있다 .

South Korea has set sector-specific reduction targets to achieve its 2030 Nationally Determined Contribution (NDC), including a reduction target of 11.2 Mt-CO2 in the CCUS sector. However, the quantitative analysis of detailed implementation pathways to achieve these targets remains insufficient. This study develops a systematic methodology for estimating CO2 reduction pathways in the CCUS sector and presents case studies using the developed methodology and arbitrary data. We propose a bottom-up approach that creates master data by collecting and quantifying the expected CCUS-related events by year and estimates reduction pathways based on this data. The master data was constructed as an Excel-based database, and a Python-based analysis tool was developed for data processing and visualization. Through the developed methodology, we were able to quantitatively analyze CO2 flows by capture sources and treatment methods, and derive reduction pathways by scenarios.

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