Multi-criteria efficiency assessment of international biomass supply chain pathways using Data Envelopment Analysis

Abstract

Most European countries have committed to ambitious emissions reduction goals. Energy generation in particular is responsible for more than 30% of global emissions, where significant focus has been placed on renewable energy generation, including biomass. On the one hand, there are countries, like the UK, where the biomass stock is insufficient to meet the demand; on the other hand, there are countries, like Brazil, where the stock significantly exceeds the demand. To promote a natural symbiosis, it is necessary to take on the challenge of transporting biomass through long distances in an environmentally and economically efficient manner. This paper aims to assess the efficiency of alternative pathways of international biomass supply-chains. The alternatives involve different biomass origin regions, transportation modes, export ports and processing technologies, including torrefaction. Data Envelopment Analysis (DEA) has been used for the first time to assess the efficiency of the alternative biomass supply chain pathways in a Latin American context, considering multiple-criteria relating to economic and environmental performance simultaneously, such as the biomass delivered cost, the environmental impact and the fossil energy consumption. Additionally, a sensitivity analysis was performed to analyse the robustness of the results under uncertainty in parameter values. The DEA approach presented can assist the process of planning biomass sourcing and improve decision-making under multiple decision criteria. The results can support medium- and long-term strategic decisions for decision- and policy-makers. • Efficiency assessment of biomass supply chain pathways between Brazil and the UK. • 56 alternative pathways considered with different supply chain structure. • Data Envelopment Analysis used for multi-criteria efficiency assessment. • Results can support decision and policy making. • Trade-offs between different criteria identified.