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The increasingly serious energy crisis and environmental pollution caused by the excessive use of fossil fuels have been prompting China to aggressively seek a clean and self-sufficient energy source in the future. In the past decades, hydrogen has emerged as a promising alternative due to its advantages of cleanliness, abundance, high energy density, and high conversion efficiency. However, several challenges have to be overcome for China's successful transition to hydrogen economy. In this paper, the hydrogen supply chain is firstly described to help the readers to clearly understand the hydrogen economy. Subsequently, the feasibility of hydrogen economy is discussed by reviewing viewpoints from the literature. Finally, the challenges of China's transition to hydrogen economy are detailed summarized and discussed, and the strategies for China to develop hydrogen economy were compared with that of Japan and Australia. • Hydrogen economy is feasible with many challenges. • Hydrogen and electricity should clearly have the same strategic position in China. • Geographic differences in resources and demands should be addressed. • It is necessary to diversify the application of hydrogen. • CCS technology is significance to the medium-term development of China's hydrogen economy. [ABSTRACT FROM AUTHOR]

Hydrogen has attracted considerable attention as a clean and renewable energy carrier to potentially solve the increasingly serious problems of energy security and environmental pollution. As it can be produced from diverse feedstocks by using a variety of technologies, this study aims to develop a novel multi-criteria decision making (MCDM) mathematical framework for the sustainability assessment of multiple hydrogen production technologies (HPTs). In the proposed method, the criteria weights were determined by combining the grey relational analysis (GRA) and DEMATEL method, which can realize a relatively objective weighting process by considering the causal relationships among criteria. Moreover, the alternatives are also ranked by using the GRA approach to attain the grey correlation coefficients as indicators, which measures the closeness of the corresponding alternative with the ideal scheme. Finally, the developed MCDM method was illustrated by the sustainability prioritization of 9 HPTs. • A MCDM method was established for more objective sustainability assessment of HPTs. • An GRA-incorporated DEMATEL method was developed for determining the criteria weight. • The GRA approach was adopted for ranking the sustainability of HPTs. [ABSTRACT FROM AUTHOR]

A novel two-stage multi-criteria decision making (MCDM) method is proposed with the aim to select the most sustainable hydrogen production technology (HPT) by considering the preference information on both attributes and alternatives. In the first stage of the method, the initial sustainability ranking of the alternative HPTs was achieved by using the FBWM (Fuzzy Best-Worst Method) to determine the weights of the criteria and the fuzzy TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method to prioritize the sustainability of alternative HPTs. While, in the second stage, a novel Preference Ranking Linear Programming Method (PRLPM) was used to acquire the final sustainability ranking according to the alternative preference information by following the principle of the outranking method. The proposed method was illustrated by a case study with 8 HTPs, demonstrating that the developed two-stage MCDM method can reflect the alternative preference of the decision-maker more accurately for selecting the most preferred alternative among various HTPs. • The MCDM framework divides the sustainability decision-making into two stages. • Fuzzy best-worst method and fuzzy TOPSIS were used in the first stage. • A novel method based on preference information on alternatives is proposed. • The framework combines preference information on attributes and alternatives. [ABSTRACT FROM AUTHOR]

Hydrogen has emerged as one of the most promising players in the future energy system for a better life due to multiple advantages, it is important to identify the most sustainable technology for producing hydrogen among several alternatives. This study aims to propose a novel multi-criteria decision making (MCDM) framework for prioritizing hydrogen production technologies under data uncertainties by combining the methods of interval best-worst method (IBWM), interval entropy technique (IET), and interval best-worst projection (IBWP). In the framework, the IBWM and the IET methods were integrated to offer a comprehensive weighting result by capturing both the uncertain subjective judgments and uncertain objective information regarding the importance of multiple criteria; the IBWP approach was introduced to offer a rigorous ranking result by aggregating both the absolute performance and relative balance of the alternatives regarding the multi-criteria system in uncertain conditions. An illustrative case considering five hydrogen production technologies was studied to verify the feasibility of the proposed framework, while results comparisons and sensitivity analysis were implemented to indicate the necessities and advantages of the developed MCDM methods. • A novel framework was proposed for selecting hydrogen production technology. • Uncertainties were incorporated into the sustainability assessment framework. • The interval BWM and the interval entropy were combined for weighting the criteria. • A novel IBWP method was developed for ranking technology alternatives. [ABSTRACT FROM AUTHOR]

Abstract: Biohydrogen technology is regarded as one of the most promising ways for hydrogen production with the considerations of economic priority and environmental performance. In this study, grey model is used to predict the yield of biohydrogen under scanty data condition. An illustrative case has been studied by the proposed method, and pH, glucose and iron sulfate concentration are used as the independent variables, the yield of biohydrogen is used as dependent variable in the grey prediction model, and 9 groups of data are used as the training samples and another 2 groups of data are used as the test samples, the results show that the proposed method is feasible to predict the yield of biohydrogen under scanty data condition and the effect of the influencing factors on the yield could also be identified. According to the comparison with the results predicted by artificial neural network, it could be concluded that grey model has better predictability with scanty data. This method could be popularized to other biohydrogen systems. [Copyright &y& Elsevier]