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Jan Börner

Zentrum für Entwicklungsforschung

Jan Börner is Senior Researcher at Zentrum für Entwicklungsforschung (ZEF) and a Professor for Economics of Sustainable Land Use and Bioeconomy at University of Bonn. He holds a M.Sc. in International Agricultural Sciences from Humboldt University, Berlin, and a Dr. agr. from the University of Bonn. His research areas of interest are land use and food security, environmental and climate change, global development and trade, ecosystem services, sustainable use of natural resources, biodiversity, environmental impact analysis, land, water, food and energy, governance, conflicts and natural resources.
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Spatially-explicit footprints of agricultural commodities: Mapping carbon emissions embodied in Brazil's soy exports

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.

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Evaluating REDD+ at subnational level: Amazon fund impacts in Alta Floresta, Brazil

The Amazon Fund is the world's largest program to reduce emissions from deforestation and forest degradation (REDD+), funded with over US $1b donated by Norway and Germany between 2008 and 2017 to reward Brazil for prior deforestation reductions. Olhos D'Água da Amazônia is cited as a leading project success − with over one thousand small-to-medium-sized crop and livestock producers in the municipality of Alta Floresta, Mato Grosso State receiving more from the Amazon Fund than all but two other municipalities. To secure property rights, aid environmental planning, and raise farmers' productivity and output diversity, the project helped farmers register in Brazil's environmental cadaster and receive property certificates. Furthermore, Olhos D'Água supported milk and honey production and paid farmers to conserve riverine forest sites. We estimate causal effects of Olhos D'Água, versus a counterfactual estimate of what would have happened without the project, using a synthetic-control method. We build from the pool of blacklisted municipalities weighted averages (synthetic controls) that best match pre-treatment outcomes for Alta Floresta. Project effects are estimated as post-treatment differences between Alta Floresta and the synthetic controls. We find that the project increased new CAR registrations, and INCRA certifications, and may have moderately increased honey and milk production. Alta Floresta's annual forest losses remained historically low but we find no clear causal effect of the project on deforestation rates. Our results support that rigorous impact evaluation can motivate and guide project improvements.

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