Suchergebnisse

An LCA of the cultivation of switchgrass in the Mediterranean region of Spain is carried out, based on 2010-2013 inventory data from experimental plots of two sites, Moncofar and Orihuela. Thus, a 4-year cycle is evaluated, considering different sources of variability. The functional unit is 1 t of switchgrass (dry basis) for electricity generation. Besides typical impact categories, blue and green water consumption impacts are also addressed by using watershed-specific characterization factors. In 2010, the production in Orihuela is more input-intensive than it is in Moncofar, while the biomass yield is lower. This causes greater climate change as CO2-eq. (709.1 vs. 65.0 kg t(-1)) and greater metal depletion as Fe-eq. (8.1 vs. 1.5 kg t(-1)). In the subsequent years, the yields are higher in Orihuela, and Moncofar performs worse for some specific impact categories, mainly the toxicity-related ones, and also metal depletion as Fe-eq., but only in 2011 (2.0 vs. 1.2 kg t(-1)). Due to larger irrigation doses, the blue water impact as ecosystem-eq. water is always higher in Orihuela (e.g. in 2010, 2020 vs. 390 m(3) t(-1)). On the contrary, the green water impact, also as ecosystem-eq. water, is greater in Moncofar, except for the first year (86.8 vs. 15.2 m(3) t(-1)). Switchgrass from the two locations could be eligible for bioelectricity production in the European Union in accordance with the sustainability requirements for greenhouse gas savings. Ad hoc decisions on crop management are, however, critical to the environmental impacts, evidencing the importance of taking a multi-year approach. (C) 2017 Published by Elsevier Ltd.

Reliable estimates of carbon and other environmental footprints of agricultural commodities require capturing a large diversity of conditions along global supply chains. Life Cycle Assessment (LCA) faces limitations when it comes to addressing spatial and temporal variability in production, transportation and manufacturing systems. We present a bottom-up approach for quantifying the greenhouse gas (GHG) emissions embedded in the production and trade of agricultural products with a high spatial resolution, by means of the integration of LCA principles with enhanced physical trade flow analysis. Our approach estimates the carbon footprint (as tonnes of carbon dioxide equivalents per tonne of product) of Brazilian soy exports over the period 2010–2015 based on ~90,000 individual traded flows of beans, oil and protein cake identified from the municipality of origin through international markets. Soy is the most traded agricultural commodity in the world and the main agricultural export crop in Brazil, where it is associated with significant environmental impacts. We detect an extremely large spatial variability in carbon emissions across sourcing areas, countries of import, and sub-stages throughout the supply chain. The largest carbon footprints are associated with municipalities across the MATOPIBA states and Pará, where soy is directly linked to natural vegetation loss. Importing soy from the aforementioned states entailed up to six times greater emissions per unit of product than the Brazilian average (0.69 t t−1). The European Union (EU) had the largest carbon footprint (0.77 t t−1) due to a larger share of emissions from embodied deforestation than for instance in China (0.67 t t−1), the largest soy importer. Total GHG emissions from Brazilian soy exports in 2010–2015 are estimated at 223.46 Mt, of which more than half were imported by China although the EU imported greater emissions from deforestation in absolute terms. Our approach contributes data for enhanced environmental stewardship across supply chains at the local, regional, national and international scales, while informing the debate on global responsibility for the impacts of agricultural production and trade.

Reliable estimates of carbon and other environmental footprints of agricultural commodities require capturing a large diversity of conditions along global supply chains. Life Cycle Assessment (LCA) faces limitations when it comes to addressing spatial and temporal variability in production, transportation and manufacturing systems. We present a bottom-up approach for quantifying the greenhouse gas (GHG) emissions embedded in the production and trade of agricultural products with a high spatial resolution, by means of the integration of LCA principles with enhanced physical trade flow analysis. Our approach estimates the carbon footprint (as tonnes of carbon dioxide equivalents per tonne of product) of Brazilian soy exports over the period 2010–2015 based on ~90,000 individual traded flows of beans, oil and protein cake identified from the municipality of origin through international markets. Soy is the most traded agricultural commodity in the world and the main agricultural export crop in Brazil, where it is associated with significant environmental impacts. We detect an extremely large spatial variability in carbon emissions across sourcing areas, countries of import, and sub-stages throughout the supply chain. The largest carbon footprints are associated with municipalities across the MATOPIBA states and Pará, where soy is directly linked to natural vegetation loss. Importing soy from the aforementioned states entailed up to six times greater emissions per unit of product than the Brazilian average (0.69 t t−1). The European Union (EU) had the largest carbon footprint (0.77 t t−1) due to a larger share of emissions from embodied deforestation than for instance in China (0.67 t t−1), the largest soy importer. Total GHG emissions from Brazilian soy exports in 2010–2015 are estimated at 223.46 Mt, of which more than half were imported by China although the EU imported greater emissions from deforestation in absolute terms. Our approach contributes data for enhanced environmental stewardship across supply chains at the local, regional, national and international scales, while informing the debate on global responsibility for the impacts of agricultural production and trade.