Fuzzy inoperability input-output analysis of mandatory biodiesel blending programs: The Philippine case - Tan, Raymond Girard R. (Animo Repository, 2014)
Biofuels are regarded as one of the major low-carbon energy options currently available for large-scale use. Many countries have implemented biofuel programs that involve blending of bioethanol with gasoline, or biodiesel with diesel, at varying proportions. These programs are designed to address pressing concerns such as climate change, environmental quality, energy security and rural development. However, recent works suggest that biofuel resources may be at risk due to aberrant climatic events that are anticipated in the near future. Potential climate-induced disruptions include changes in precipitation levels, pest infestation, plant diseases, or increased frequency of extreme weather events. The incidence of such disruptions not only affects biofuel producers, but also energy-dependent economic sectors, resulting to "ripple effects" that further increase economic losses. We apply the inoperability inputoutput model (IIM) proposed by Haimes and Jiang (2001) and later enhanced by Santos and Haimes (2004) to assess the economic effects of implementing mandatory biodiesel blending programs in the Philippines. The IIM is based on the well-established technique of input-output analysis that quantifies risk through the inoperability metric, which is a dimensionless index whose value ranges from 0 (for a system functioning normally) to 1 (for a system in a state of total failure). Using the IIM, we determine the adverse effects of climate-induced biofuel disruptions on interdependent economic sectors. We estimate the resulting crop losses using the storm damage scenario from Stromberg et al. (2011) under no blending and a proposed blending rate of 5% as considered by the Department of Energy. Different ranking strategies are evaluated to determine sector vulnerability using inoperability levels, economic losses and weighted inoperability levels. For the latter scenario, equal weights are considered. Uncertainties within the modelling framework are captured through the use of fuzzy numbers. © 2014 The Authors.