Streamlined Life Cycle Assessment of Carbon Footprint of a Tourist Food Menu Using Probabilistic Underspecification Methodology

Abstract

We proposed a methodology based on life cycle assessment streamlining techniques to estimate the carbon footprint (CF) of a meal. The methodology was applied to estimate the meal CF of twenty-four people on a 4-days Galapagos Island tour using over three hundred existing Life Cycle Analysis (LCA) results in the food industry. In spite of the abundance of food LCA studies, there were very little food CF studies on food produced in South America or Ecuador. By combining established and novel life cycle assessment streamlining techniques, we demonstrated how to (a) calculate the uncertainty associated with the use of surrogate CF data, (b) carry out a preliminary carbon footprint calculation using surrogate data to identify a subset of components that contributes the greatest CFs to the product, which we called the set of interest (SOI) and, (c) greatly reduce the uncertainty in the CF results using only exact CFs for the SOI in addition to the surrogate CFs of the other food items. In general, this methodology can systematically cut down the time and resources that are needed to collect all the emission data in the production of food in a meal, but to focus on only a small handful of food items that impact the total CF, provided that the surrogate CF database is large enough to include the true CF.