Over the last five decades, there have been a few phases of interest in the so-called hydrogen economy, stemming from the need for either energy security enhancement or climate change mitigation. None of these phases has been successful in a major market development mainly due to the lack of cost competitiveness and partially due to technology readiness challenges. Nevertheless, a new phase has begun very recently, which despite holding original objectives has a new motivation to be fully green, based on renewable energy. This new movement has already initiated bipartisan cooperation of some energy importing countries and those with abundant renewable energy resources and supporting infrastructure. For example, the abundance of renewable resources and a stable economy of Australia can attract investments in building these green value chains with countries such as Singapore, South Korea, Japan, and those even further distant like in Europe. One key challenge in this context is the diversity of pathways for the (national and international) export of non-electricity renewable energy. This poses another challenge, i.e., the need for an agnostic tool for comparing various supply chain pathways fairly while considering various techno-economic factors such as renewable energy sources, hydrogen production and conversion technologies, transport, and destination markets, along with all associated uncertainties. This paper addresses the above challenge by introducing a probabilistic decision analysis cycle methodology for evaluating various renewable energy supply chain pathways based on the hydrogen vector. The decision support tool is generic and can accommodate any kind of renewable chemical and fuel supply chain option. As a case study, we have investigated eight supply chain options composed of two electrolysers (alkaline and membrane) and four carrier options (compressed hydrogen, liquefied hydrogen, methanol, and ammonia) for export from Australian ports to three destinations in Singapore, Japan, and Germany. The results clearly show the complexity of decision making induced by multiple factors. For the case study, under the given input parameters, the methanol combination with alkaline electrolysers becomes the least-cost supply chain option for Singapore, Japan, and Germany with expected levelised costs of hydrogen (ELCOH) of 6.53, 6.61, and 6.93 $/kgH2, respectively. However, the second-best choices are not the same for all countries. Ammonia (with alkaline electrolysers) becomes the second-best option for Singapore ($7.98/kgH2) and Japan ($8.20/kgH2) destinations, while methanol (this time with PEM electrolysers) proves to be the second-best supply chain option for German destinations ($8.62/kgH2)
