Evaluating Hydrogen Production Technologies for Transportation in California Using Decomposed Fuzzy Sets

Abstract

Rapid decarbonization of our economies is imperative if we want to get a chance to curb climate change. Different nations and entities have been developing strategies and plans to decarbonize. The transportation sector, especially freight, is a vital component of everyday life, and the economy is one of the largest contributors to negative externalities such as greenhouse gases and local pollutants. It has also received increased pressure to mitigate its impacts. In California, for example, different policies are promoting the use of cleaner vehicle technologies and fuels. Battery electric and fuel cell (hydrogen) trucks are among the most prominent zero-emission truck technologies for transitioning various freight vocations. Today, technologies and fuels are at different stages of market readiness for mass adoption, and research is trying to identify the best uses for each technology. However, there has been a realization that the supporting infrastructure will play a key role in determining the various options' technical, operational, and financial viability. For hydrogen, there are various production processes with different costs, environmental impacts, technical requirements, and uncertainties, and there is an urgency to invest in developing those technologies and increasing production capacity. Therefore, this paper evaluates different hydrogen production technologies (e.g., electrolysis, biomass gasification, steam methane reforming, pyrolysis, and water splitting) and their potential role in California plans. The study implements an expert-based assessment using the Decomposed Fuzzy Sets (DFS)-based Analytic Hierarchy Process (AHP) methodology to evaluate the technologies' performance against various criteria (environmental, economic, technical, transport and delivery, and social). The study's results provide additional information to address a real-life hydrogen production technologies prioritization problem and perform sensitivity analysis to test the robustness and applicability of the method.