Abstract
Due to efforts to reduce dependence on limited fossil energy reserves and increasing GHG emissions related to fossil fuel extraction and use in transportation vehicles, renewable fuel use is growing rapidly. By adding renewable oxygenated fuel additives such as oxymethylene ether (OME) to conventional diesel, combustion GHG emissions can be reduced significantly without modifications to vehicle engines. However, life cycle sustainability assessments (LCSA) of OME production and its use with diesel are scarce. The objective of this paper is to develop an LCSA model of OME production from forest biomass to be used in vehicles as a diesel additive. This study conducts an LCSA of OME production from two types of forest biomass as feedstock, whole tree and forest residue. A framework was developed to assess environmental, economic, and social impacts of unit operations along the life cycle for a functional unit of 1 MJ of heat produced from OME. Then, PROMITHEE (Preference Ranking Organization Method for Enrichment and Evaluation) was used to rank and select the best sustainable pathway for OME production and the most sustainable OME-diesel blend based on a number of indicators. Based on the sustainability assessments results, the forest residue pathway is found to be more sustainable than the whole tree pathway. In addition, the environmental, economic, and social impact results for different OME-diesel blends show that a blend of 10% OME in 90% diesel is the most sustainable fuel mix. Assuming that the GHG emissions from biofuel combustion are offset by CO2 sequestered during plant growth, the biomass production operation contributes the highest global GHG emissions in the OME life cycle; this is due to the high energy intensity of harvesting operations for both pathways (13 gCO2eq/MJ for whole tree and 7.13 gCO2eq/MJ for forest residue). OME production costs are higher for the whole tree (1.92 $/L) than the forest residue pathway (1.71 $/L). All the social indicators (i.e., employment potential and employee wages and benefits) are more favorable in the forest residue pathway. We conducted sensitivity analyses by varying parameters such as sustainability impact weights, threshold values, and indicator impact values. We then determined the parameters’ impacts on overall ranking to verify the robustness of the model. This model can be used to assess and rank other energy technologies that integrate environmental, economic, and social sustainability impacts and thereby contribute to policy-making for the energy industry.
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