Assessment of Carbon Dioxide Removal Technologies: A Data-Driven Decision-Making Model

被引:0
|
作者
Ma, Xiaoyu [1 ]
Bai, Chunguang [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon; Soil; Oceans; Linguistics; Carbon sequestration; Rocks; Optimization; Assessment; carbon dioxide removal technology; data-driven decision making; interval type-2 fuzzy sets (IT2FSs); multiobjective optimization by ratio analysis with the full multiplicative form (MULTIMOORA); TYPE-2; FUZZY-SETS; SEQUESTRATION; SELECTION; BIOMASS;
D O I
10.1109/TEM.2023.3331034
中图分类号
F [经济];
学科分类号
02 ;
摘要
Carbon dioxide removal (CDR) technologies, as the essential tools to achieve the carbon peak and carbon neutrality goals, have received increasing attention. However, CDR technologies involve extensive expertise from different fields and lack a systematic introduction and assessment system. Therefore, this article proposes a comprehensive decision-making model to assess mainstream CDR technologies. First, we present the all-around CDR technologies and develop a criteria framework that was derived mainly from a 2021 white paper released by the World Economic Forum. Second, we design a text mining method to obtain the criteria's weights and form a fuzzy decision matrix using interval type-2 fuzzy sets and interval numbers as the quantitative tool for assessment information. Afterward, we propose the extended MULTIMOORA method to assess CDR technologies and obtain the final rankings. Results reveal that soil carbon sequestration and afforestation are the best-performing CDR technologies. Furthermore, the robustness is proved by the sensitivity analysis. Implications, conclusions, and future directions are also illustrated. The contribution of this study is that it proposes a real data-driven decision-making model, thereby offering a certain level of decision supports (suggestions) for enterprises, CDR projects' proponents, and policymakers to select which CDR technologies to prioritize and develop.
引用
收藏
页码:9726 / 9743
页数:18
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