Environmental and economic analysis of residual woody biomass transport for energetic use in Chile

Purpose: This study compares transport performance of residual biomass using different pre-treatment options. Life cycle inventory data was obtained from forestry companies in southern Chile, databases, scientific and technological literature, as well as equipment operational manuals. Methods: Three different scenarios were evaluated: residual biomass transport without pre-treatment (scenario 1), chipped residual biomass (scenario 2), and compacted residual biomass (scenario 3) transport. The truck’s loading capacity was considered as a function of the residual biomass density. Impact assessment was performed using software SimaPro 7.3.3 using the ReCiPe midpoint methodology. Moreover, an uncertainty analysis was performed using Monte Carlo simulation with a 95 % confidence. Transport costs evaluation variables considered were machine cost, machine residual value, amortization, personnel costs, fuel consumption, machine maintenance, and operational yield. All variables are based on local conditions of La Araucanía Region in Chile. Results and discussion: Regarding greenhouse gas (GHG) emissions, optimum transport distance ranges were identified for the different scenarios. For a distance up to 23 km, scenario 1 is the most favorable; for distances between 23 and 206 km, scenario 2 is the most favorable one; and for distances longer than 206 km, compacted residual biomass (scenario 3) presents the lowest GHG emissions balance. When looking the other impact categories, it was established that the benefits are not only related to GHG emission savings but also to other impact categories. Transport impacts are only relevant for large distances, while for short distances biomass pre-treatment and loading stages provoke a higher environmental load. In fact, for scenario 2 where chipped biomass is transported, only for distances longer than 120 km, the transport stage accounts for more than 50 % of the environmental load of all impact categories. For the case of scenario 3 (compacted biomass transport), this situation occurs for a distance of at least 150 km. Conclusions: Most probable optimal transport distances were determined for pre-treated and unpretreated biomass. In this sense, for determining the best transport option of residual biomass, transport distance, loading capacity, and pre-treatment processes efficiency, including chipping and compacting, as well as data uncertainty, should be taken into account. From these variables, biomass loading and pre-treatment stages account for a relevant percentage of the environmental impacts generated for transport distances of less than 100 km. In this sense, biomass loading and pre-treatment efficiency coupled with the effective supplies demand should be carefully studied in future research works.