The use of fossil resources has lead to great increase in concentration of carbon dioxide (CO2) in the atmosphere beyond sustainable limits, which causes environmental issues such as greenhouse gas effect, climate change and extreme weather events and threats the human life. Thus, several researches have been focused on mitigate this problem. Possible strategies involve implementing technologies of carbon capture storage and utilization. Among them, integrated processes for carbon dioxide capture and its conversion into value-added products have gained attention. Carbon dioxide hydrogenation is among the most developed technologies for its conversion, but requires an external hydrogen (H2) source. Since the conversion of carbon dioxide is highly energy-demanding, assessing its overall process sustainability requires a comprehensive study on the whole system, including its raw material sources (carbon dioxide and hydrogen). Thus, this work proposes a multi-criteria framework to select suitable sources of carbon dioxide and hydrogen to be used in the conversion of carbon dioxide. Potential sources of carbon dioxide (from power plants to ethanol fermentation) and hydrogen (from dedicated production to by-product hydrogen) were evaluated considering environmental, economic, and technical aspects associated with the usage of each source. The Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is the multi-criteria decision analysis method used to aggregate the criteria and to rank each source individually and further in a pair-wise assessment to identify potential synergic combinations between carbon dioxide and hydrogen sources. Results suggested that using carbon dioxide from natural gas steam reforming, iron and steel industries, ethylene oxide and other high concentration point sources may be the ideal choice for sustainability. The analysis also indicated that hydrogen may be more sustainable if it is a process by-product or is produced by low-cost wind-powered electrolysis. It is important to consider that the analysis is based on several specific data inputs and assumptions, and that a lower score does not mean that the source is not worth investing in.
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Lopes et al. (2021) studied this question.
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