Life cycle sustainability decision-support framework for CO2 chemical conversion technologies under uncertainties

Abstract

With the emergence of numerous CO2 chemical conversion technologies to simultaneously reduce CO2 emissions and produce value-added products, it is of great importance to compare their difference and select the most sustainable routes for future development. This study quantitatively evaluated the sustainability performances of 21 alternative CO2 conversion technologies from economic, technical, and environmental perspectives and developed a novel Multi-criteria Decision-making (MCDM) model to prioritize the alternatives. To cope with the external and internal uncertainties during the decision-making, Interval-Rough Numbers (IRNs) were firstly used to deal with subjective vagueness and information incompleteness involved in the group judgements unavoidably. Secondly, DEMATEL-ANP was employed based on IRNs to specify the correlation type and degree among diverse criteria for determining the global weights accurately. Lastly, a Vector-based Algorithm method was applied to measure the alternatives’ overall performance and figure out the final ranking scores of sustainability. The results revealed that CO2 to methane, urea, methanol, dimethyl ether, and acetic acid were the top five promising conversion technologies with the highest R&D priority over the next decades from a sustainability perspective. Moreover, a detailed sensitivity analysis of criteria weights was conducted to scrutinize the effectiveness of the ranking results and to validate the reliability of the new proposed MCDM model. Furthermore, in consideration of the complexity of future technological advance, market transformation, economic and social trends, this life cycle sustainability decision-support framework for CO2 conversion technologies provides a well-informed benchmark to support the screening and selection of candidate technologies including both the existing and emerging processes, and strategically explore the development opportunities and limits under uncertainties.