A multi-stage data- and knowledge-coupled decision support framework for sustainable CCUS project site selection

Long Li; Jing Yang; Mengqi Yuan; Meiqi Wan; Xianfei Yin

doi:10.1016/j.seta.2026.104970

A multi-stage data- and knowledge-coupled decision support framework for sustainable CCUS project site selection

Long Li
, Jing Yang
, Mengqi Yuan
, Meiqi Wan
, Xianfei Yin^*

^*Corresponding author for this work

Department of Architecture and Civil Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

Carbon capture, utilization and storage (CCUS) is a pivotal technology for achieving carbon neutrality. CCUS site selection (CPSS) decisions are regarded as the prerequisite and critical issue for promoting CCUS deployment. This study proposes a multi-stage data- and knowledge-coupled decision support framework (DKDS) to address the sustainable CPSS decision problem. Firstly, incorporating “source-sink matching”, combining spatial data with semantic knowledge, Geographic Information Science (GIS) is used to identify the suitable basins at macro level, while the Ontology Model (OM) is employed to infer appropriate alternatives at micro level. Secondly, build a decision criteria system containing multi-dimensional sustainability factors, and use Bayesian Best-Worst Method (BBWM) to calculate criteria weight. Finally, a hybrid multi-criteria decision making (MCDM) based on IFS-TOPSIS-CPT is used to comprehensively evaluate and rank alternatives. An empirical study in China identified five suitable basins, including Junggar Basin, Songliao Basin, Ordos Basin, Bohai Bay Basin, and Northern Jiangsu Basin. Xinjiang Wucaiwan Captive Power Plant ranked as the optimal site. Furthermore, sensitivity analysis confirmed the effectiveness and robustness of the proposed method. Multi-stage DKDS provides a robust scientific basis for CCUS planning, and contributes a novel methodology for promoting energy system transformation, and expands the application prospects of complex decision research.
© 2026 Elsevier Ltd.

Original language	English
Article number	104970
Number of pages	15
Journal	Sustainable Energy Technologies and Assessments
Volume	89
Online published	11 Apr 2026
DOIs	https://doi.org/10.1016/j.seta.2026.104970
Publication status	Published - May 2026

Funding

This work was supported by the Humanities and Social Sciences Foundation of Ministry of Education of the People’s Republic of China (No. 24YJC630105), National Natural Science Foundation of China (No. 72304278), the Natural Science Foundation of Shandong Province (No. ZR2023QG100), Fundamental Research Funds for the Central Universities (No. 22CX06069A), National Natural Science Foundation of China (No. 72404233), and the Outstanding Youth Innovation Team Foundation of Shandong Province (No. 2022RW036).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action
SDG 17 Partnerships for the Goals

Research Keywords

Carbon capture utilization and storage (CCUS)
Site selection
Data- and knowledge-coupled
Sustainable development
Multi-criteria decision making

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10.1016/j.seta.2026.104970

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title = "A multi-stage data- and knowledge-coupled decision support framework for sustainable CCUS project site selection",

abstract = "Carbon capture, utilization and storage (CCUS) is a pivotal technology for achieving carbon neutrality. CCUS site selection (CPSS) decisions are regarded as the prerequisite and critical issue for promoting CCUS deployment. This study proposes a multi-stage data- and knowledge-coupled decision support framework (DKDS) to address the sustainable CPSS decision problem. Firstly, incorporating “source-sink matching”, combining spatial data with semantic knowledge, Geographic Information Science (GIS) is used to identify the suitable basins at macro level, while the Ontology Model (OM) is employed to infer appropriate alternatives at micro level. Secondly, build a decision criteria system containing multi-dimensional sustainability factors, and use Bayesian Best-Worst Method (BBWM) to calculate criteria weight. Finally, a hybrid multi-criteria decision making (MCDM) based on IFS-TOPSIS-CPT is used to comprehensively evaluate and rank alternatives. An empirical study in China identified five suitable basins, including Junggar Basin, Songliao Basin, Ordos Basin, Bohai Bay Basin, and Northern Jiangsu Basin. Xinjiang Wucaiwan Captive Power Plant ranked as the optimal site. Furthermore, sensitivity analysis confirmed the effectiveness and robustness of the proposed method. Multi-stage DKDS provides a robust scientific basis for CCUS planning, and contributes a novel methodology for promoting energy system transformation, and expands the application prospects of complex decision research. {\textcopyright} 2026 Elsevier Ltd.",

keywords = "Carbon capture utilization and storage (CCUS), Site selection, Data- and knowledge-coupled, Sustainable development, Multi-criteria decision making",

author = "Long Li and Jing Yang and Mengqi Yuan and Meiqi Wan and Xianfei Yin",

year = "2026",

month = may,

doi = "10.1016/j.seta.2026.104970",

language = "English",

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journal = "Sustainable Energy Technologies and Assessments",

issn = "2213-1388",

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T1 - A multi-stage data- and knowledge-coupled decision support framework for sustainable CCUS project site selection

AU - Li, Long

AU - Yang, Jing

AU - Yuan, Mengqi

AU - Wan, Meiqi

AU - Yin, Xianfei

PY - 2026/5

Y1 - 2026/5

N2 - Carbon capture, utilization and storage (CCUS) is a pivotal technology for achieving carbon neutrality. CCUS site selection (CPSS) decisions are regarded as the prerequisite and critical issue for promoting CCUS deployment. This study proposes a multi-stage data- and knowledge-coupled decision support framework (DKDS) to address the sustainable CPSS decision problem. Firstly, incorporating “source-sink matching”, combining spatial data with semantic knowledge, Geographic Information Science (GIS) is used to identify the suitable basins at macro level, while the Ontology Model (OM) is employed to infer appropriate alternatives at micro level. Secondly, build a decision criteria system containing multi-dimensional sustainability factors, and use Bayesian Best-Worst Method (BBWM) to calculate criteria weight. Finally, a hybrid multi-criteria decision making (MCDM) based on IFS-TOPSIS-CPT is used to comprehensively evaluate and rank alternatives. An empirical study in China identified five suitable basins, including Junggar Basin, Songliao Basin, Ordos Basin, Bohai Bay Basin, and Northern Jiangsu Basin. Xinjiang Wucaiwan Captive Power Plant ranked as the optimal site. Furthermore, sensitivity analysis confirmed the effectiveness and robustness of the proposed method. Multi-stage DKDS provides a robust scientific basis for CCUS planning, and contributes a novel methodology for promoting energy system transformation, and expands the application prospects of complex decision research. © 2026 Elsevier Ltd.

AB - Carbon capture, utilization and storage (CCUS) is a pivotal technology for achieving carbon neutrality. CCUS site selection (CPSS) decisions are regarded as the prerequisite and critical issue for promoting CCUS deployment. This study proposes a multi-stage data- and knowledge-coupled decision support framework (DKDS) to address the sustainable CPSS decision problem. Firstly, incorporating “source-sink matching”, combining spatial data with semantic knowledge, Geographic Information Science (GIS) is used to identify the suitable basins at macro level, while the Ontology Model (OM) is employed to infer appropriate alternatives at micro level. Secondly, build a decision criteria system containing multi-dimensional sustainability factors, and use Bayesian Best-Worst Method (BBWM) to calculate criteria weight. Finally, a hybrid multi-criteria decision making (MCDM) based on IFS-TOPSIS-CPT is used to comprehensively evaluate and rank alternatives. An empirical study in China identified five suitable basins, including Junggar Basin, Songliao Basin, Ordos Basin, Bohai Bay Basin, and Northern Jiangsu Basin. Xinjiang Wucaiwan Captive Power Plant ranked as the optimal site. Furthermore, sensitivity analysis confirmed the effectiveness and robustness of the proposed method. Multi-stage DKDS provides a robust scientific basis for CCUS planning, and contributes a novel methodology for promoting energy system transformation, and expands the application prospects of complex decision research. © 2026 Elsevier Ltd.

KW - Carbon capture utilization and storage (CCUS)

KW - Site selection

KW - Data- and knowledge-coupled

KW - Sustainable development

KW - Multi-criteria decision making

U2 - 10.1016/j.seta.2026.104970

DO - 10.1016/j.seta.2026.104970

M3 - RGC 21 - Publication in refereed journal

SN - 2213-1388

VL - 89

JO - Sustainable Energy Technologies and Assessments

JF - Sustainable Energy Technologies and Assessments

M1 - 104970

ER -

A multi-stage data- and knowledge-coupled decision support framework for sustainable CCUS project site selection

Abstract

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UN SDGs

Research Keywords

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