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Global supply chains shift environmental and social impacts of consumption to remote locations. This opacity challenges many sustainability goals. To help businesses and governments realize more sustainable supply chains, new approaches are using spatial data and machine-learning techniques to connect Earth observation data to conventional economic tools.

Global consumption of farming commodities is an important driver of water demand in regions of production. This is the case in Brazil, which has emerged as one of the main producers of globally traded farming commodities. Traditional methods to assess environmental implications of this demand rely on international trade material flows at country resolution; we argue for the need of finer scales that capture spatial heterogeneity in environmental variables in the regions of production, and that account for differential sourcing within the borders of a country of production. Toillustrate this, we obtain virtual water flows from Brazilian municipalities to countries of consumption, by allocating high-resolution water footprints of sugarcane and soy production to spatially-explicit material trade flows. We found that this approach results in differences of virtual water use estimations of over 20% when compared to approaches that disregard spatial heterogeneity in sourcing patterns, for three of the main consumers of the analysed crops. This discrepancy against methods using national resolution in trade flows is determined by national heterogeneity in water resources, and differential sourcing. To illustrate the practical implications of this approach, we relate virtual water flows to water stress, identifying where global demand for water coincides with high levels of water stress. For instance, the virtual water flows for Brazilian sugarcane sourced by China were disproportionally less associated to areas with higher water stress when compared to those of the EU, due to EUs much higher reliance on sugarcane from water scarce areas in Northeast Brazil. Our findings indicate that the policy relevance of current assessments of virtual water flows that rely on trade data aggregated at the national level may be hampered, as they do not capture the spatial heterogeneity in water resources, water use and water management options. ; Funding Agencies|Swedish Institute [09208/2013]; Commonwealth Scientific and Industrial Research Organisation; Stockholm Environment Institute; Swedish Research Council Formas [2012-1401]; SIDA; Swedish EPA Naturvardsverket within PRINCE project

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.

Deforestation, the second largest source of anthropogenic greenhouse gas emissions, is largely driven by expanding forestry and agriculture. However, despite agricultural expansion being increasingly driven by foreign demand, the links between deforestation and foreign demand for agricultural commodities have only been partially mapped. Here we present a pan-tropical quantification of carbon emissions from deforestation associated with the expansion of agriculture and forest plantations, and trace embodied emissions through global supply chains to consumers. We find that in the period 2010–2014, expansion of agriculture and tree plantations into forests across the tropics was associated with net emissions of approximately 2.6 gigatonnes carbon dioxide per year. Cattle and oilseed products account for over half of these emissions. Europe and China are major importers, and for many developed countries, deforestation emissions embodied in imports rival or exceed emissions from domestic agriculture. Depending on the trade model used, 29–39% of deforestation-related emissions were driven by international trade. This is substantially higher than the share of fossil carbon emissions embodied in trade, indicating that efforts to reduce greenhouse gas emissions from land-use change need to consider the role of international demand in driving deforestation. Additionally, we find that deforestation emissions are similar to, or larger than, other emissions in the carbon footprint of key forest-risk commodities. Similarly, deforestation emissions constitute a substantial share (˜15%) of the total carbon footprint of food consumption in EU countries. This highlights the need for consumption-based accounts to include emissions from deforestation, and for the implementation of policy measures that cross these international supply-chains if deforestation emissions are to be effectively reduced. ; publishedVersion ; © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).T

Theories of frontier expansion in the last four decades have been mostly shaped by studies of state-driven smallholder colonization. Modern-day agricultural frontiers, however, are increasingly driven by capitalized corporate agriculture operating with little direct government intervention. The expansion of contemporary frontiers has been explained by the existence of spatially heterogeneous "abnormal" rents, which can be caused by cheap land and labor, technological innovation, lack of regulations, and a variety of other incentives. Here, we argue that understanding the dynamics of these frontiers requires considering the differential ability of actors to capture such rents, which depends on their access to production factors and their information, preferences, and agency. We propose a new conceptual framework drawing on neoclassical economics and political economy, which we apply to the South American Gran Chaco, a hot spot of deforestation for soy and cattle production. We divide the region into a set of distinct frontiers based on satellite data, field interviews, and expert knowledge, to review the drivers and actors of agricultural expansion in these frontiers. We show that frontier expansion in the Chaco responded to the rents created by new agricultural technologies, infrastructure, and rising producer prices but that the frontier dynamics were strongly influenced by actors' abilities to capture or influence these rents. Our findings thus highlight that understanding contemporary commodity frontiers requires analyzing the novel ways by which the agency of particular groups of actors shapes land-use outcomes.

Theories of frontier expansion in the last four decades have been mostly shaped by studies of state-driven smallholder colonization. Modern-day agricultural frontiers, however, are increasingly driven by capitalized corporate agriculture operating with little direct government intervention. The expansion of contemporary frontiers has been explained by the existence of spatially heterogeneous "abnormal" rents, which can be caused by cheap land and labor, technological innovation, lack of regulations, and a variety of other incentives. Here, we argue that understanding the dynamics of these frontiers requires considering the differential ability of actors to capture such rents, which depends on their access to production factors and their information, preferences, and agency. We propose a new conceptual framework drawing on neoclassical economics and political economy, which we apply to the South American Gran Chaco, a hot spot of deforestation for soy and cattle production. We divide the region into a set of distinct frontiers based on satellite data, field interviews, and expert knowledge, to review the drivers and actors of agricultural expansion in these frontiers. We show that frontier expansion in the Chaco responded to the rents created by new agricultural technologies, infrastructure, and rising producer prices but that the frontier dynamics were strongly influenced by actors' abilities to capture or influence these rents. Our findings thus highlight that understanding contemporary commodity frontiers requires analyzing the novel ways by which the agency of particular groups of actors shapes land-use outcomes.

Consumption of globally traded agricultural commodities like soy and palm oil is one of the primary causes of deforestation and biodiversity loss in some of the world's most species-rich ecosystems. However, the complexity of global supply chains has confounded efforts to reduce impacts. Companies and governments with sustainability commitments struggle to understand their own sourcing patterns, while the activities of more unscrupulous actors are conveniently masked by the opacity of global trade. We combine state-of-the-art material flow, economic trade, and biodiversity impact models to produce an innovative approach for understanding the impacts of trade on biodiversity loss and the roles of remote markets and actors. We do this for the production of soy in the Brazilian Cerrado, home to more than 5% of the world´s species. Distinct sourcing patterns of consumer countries and trading companies result in substantially different impacts on endemic species. Connections between individual buyers and specific hot spots explain the disproportionate impacts of some actors on endemic species and individual threatened species, such as the particular impact of European Union consumers on the recent habitat losses for the iconic giant anteater (Myrmecophaga tridactyla). In making these linkages explicit, our approach enables commodity buyers and investors to target their efforts much more closely to improve the sustainability of their supply chains in their sourcing regions while also transforming our ability to monitor the impact of such commitments over time.