Life Cycle Assessment of Soybean-Based Biodiesel in Argentina for Export
Background, aim and scope. Regional specificities are a key factor when analyzing the environmental impact of a biofuel pathway through a life cycle assessment (LCA). Due to different energy mixes, transport distances, agricultural practices and land use changes, results can significantly vary from one country to another. The Republic of Argentina is the first exporter of soybean oil and meal and the third largest soybean producer in the world, and therefore, soybean-based biodiesel production is expected to significantly increase in the near future, mostly for exportation. Moreover, Argentinean biodiesel producers will need to evaluate the environmental performances of their product in order to comply with sustainability criteria being developed. However, because of regional specificities, the environmental performances of this biofuel pathway can be expected to be different from those obtained for other countries and feedstocks previously studied. This work aims at analyzing the environmental impact of soybean-based biodiesel production in Argentina for export. The relevant impact categories account for the primary non-renewable energy consumption (CED), the global warming potential (GWP), the eutrophication potential (EP), the acidification potential (AP), the terrestrial ecotoxicity (TE), the aquatic ecotoxicity (AE), the human toxicity (HT) and land use competition (LU). The paper tackles the feedstock and country specificities in biodiesel production by comparing the results of soybean-based biodiesel in Argentina with other reference cases. Emphasis is put on explaining the factors that contribute most to the final results and the regional specificities that lead to different results for each biodiesel pathway. Materials and methods. The Argentinean (AR) biodiesel pathway was modelled through an LCA and was compared with reference cases available in the ecoinvent® 2.01 database, namely, soybean-based biodiesel production in Brazil (BR) and the United States (US), rapeseed-based biodiesel production in the European Union (EU) and Switzerland (CH) and palm-oil-based biodiesel production in Malaysia (MY). In all cases, the systems were modelled from feedstock production to biodiesel use as B100 in a 28 t truck in CH. Furthermore, biodiesel pathways were compared with fossil low-sulphur diesel produced and used in CH. The LCA was performed according to the ISO standards. The life cycle inventory and the life cycle impact assessment (LCIA) were performed in Excel spreadsheets using the ecoinvent® 2.01 database. The cumulative energy demand (CED) and the GWP were estimated through the CED for fossil and nuclear energy and the IPCC 2001 (climate change) LCIA methods, respectively. Other impact categories were assessed according to CML 2001, as implemented in ecoinvent. As the product is a fuel for transportation (service), the system was defined for one vehicle kilometre (functional unit) and was divided into seven unit processes, namely, agricultural phase, soybean oil extraction and refining, transesterification, transport to port, transport to the destination country border, distribution and utilisation. Results. The Argentinean pathway results in the highest GWP, CED, AE and HT compared with the reference biofuel pathways. Compared with the fossil reference, all impact categories are higher for the AR case, except for the CED. The most significant factor that contributes to the environmental impact in the Argentinean case varies depending on the evaluated category. Land provision through deforestation for soybean cultivation is the most impacting factor of the AR biodiesel pathway for the GWP, the CED and the HT categories. Whilst nitrogen oxide emissions during the fuel use are the main cause of acidification, nitrate leaching during soybean cultivation is the main factor of eutrophication. LU is almost totally affected by arable land occupation for soybean cultivation. Cypermethrin used as pesticide in feedstock production accounts for almost the total impact on TE and AE. Discussion. The sensitivity analysis shows that an increase of 10% in the soybean yield, whilst keeping the same inputs, will reduce the total impact of the system. Avoiding deforestation is the main challenge to improve the environmental performances of soybean-based biodiesel production in AR. If the soybean expansion can be done on marginal and set-aside agricultural land, the negative impact of the system will be significantly reduced. Further implementation of crops’ successions, soybean inoculation, reduced tillage and less toxic pesticides will also improve the environmental performances. Using ethanol as alcohol in the transesterification process could significantly improve the energy balance of the Argentinean pathway. Conclusions. The main explaining factors depend on regional specificities of the system that lead to different results from those obtained in the reference cases. Significantly different results can be obtained depending on the level of detail of the input data, the use of punctual or average data and the assumptions made to build up the LCA inventory. Further improvement of the AR biodiesel pathways should be done in order to comply with international sustainability criteria on biofuel production. Recommendations and perspectives. Due to the influence of land use changes in the final results, more efforts should be made to account for land use changes others than deforestation. More data are needed to determine the part of deforestation attributable to soybean cultivation. More efforts should be done to improve modelling of interaction between variables and previous crops in the agricultural phase, future transesterification technologies and market prices evolution. In order to assess more accurately the environmental impact of soybean-based biodiesel production in Argentina, further considerations should be made to account for indirect land use changes, domestic biodiesel consumption and exportation to other regions, production scale and regional georeferenced differentiation of production systems.