Carbon capture and utilization (CCU) technologies capture CO 2 waste emissions and utilize them to generate new products (such as fuels, chemicals, and materials) with various environmental, economic, and social opportunities. As most of these CCU technologies are in the R&D stage, their technical and economic viability are examined with less attention to the social aspect which is an important pillar for a holistic sustainability assessment. The lack of systematic social impact research is mainly due to the difficulty of identifying and quantifying social aspects through the entire life cycle of products. We will fill this gap for CCU technologies and identify the main social indicators. A multi-criteria decision making tool: technique for order of preference by similarity to ideal solution (TOPSIS) was applied to empirically determine which indicators are more relevant for assessing the social impact of a company operating CCU activities within a European context. First, seeing that social impact categories are linked to key stakeholder groups, we considered workers, consumers, and local communities as relevant stakeholders. Second, the main social impact categories and their potential performance indicators associated to each group of stakeholders were listed using the United Nations Environment Program/Society of Environmental Toxicology and Chemistry (UNEP/SETAC) guidelines. In the third step, an online questionnaire was distributed to identify the main social categories and indicators for CCU, to which 33 European CCU experts responded. Finally, a modified TOPSIS was applied to rank the indicators based on their relevance. We found that the indicators related to "end of life responsibility" and "transparency" within a CCU company achieved the highest rank affecting the consumers group, whereas "fair salary" and "equal opportunities/discriminations" were determined as the most relevant impact categories for the workers. For the local community group, "secure living conditions" and "local employment" received the highest priority from the experts' point of view. Furthermore, "health and safety" considerations were identified as one of the most important criteria affecting all three groups of stakeholders. The ranking list of the main social indicators identified in our study provides the basis for the next steps in the social sustainability assessment of CCU technologies; that is, data collection and impact assessment. Our outcomes can also be used to inform the producers regarding the most and least relevant social aspects of CCU so that the potential social impacts caused by their production activities can be improved or prevented.
The aggregation of preferences (expressed in the form of rankings) from multiple experts is a well-studied topic in a number of fields. The Kemeny ranking problem aims at computing an aggregated ranking having minimal distance to the global consensus. However, it assumes that these rankings will be complete, i.e., all elements are explicitly ranked by the expert. This assumption may not simply hold when, for instance, an expert ranks only the top-K items of interest, thus creating a partial ranking. In this paper we formalize the weighted Kemeny ranking problem for partial rankings, an extension of the Kemeny ranking problem that is able to aggregate partial rankings from multiple experts when only a limited number of relevant elements are explicitly ranked (top-K), and this number may vary from one expert to another (top-K i). Moreover, we introduce two strategies to quantify the weight of each partial ranking. We cast this problem within the realm of combinatorial optimization and lean on the successful Ant Colony Optimization (ACO) metaheuristic algorithm to arrive at high-quality solutions. The proposed approach is evaluated through a real-world scenario and 190 synthetic datasets from www.PrefLib.org. The experimental evidence indicates that the proposed ACO-based solution is capable of significantly
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.