气候变化领域集成服务门户(CCPortal): Spatially-explicit footprints of agricultural commodities: Mapping carbon emissions embodied in Brazil's soy exports

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DOI	10.1016/j.gloenvcha.2020.102067
	Spatially-explicit footprints of agricultural commodities: Mapping carbon emissions embodied in Brazil's soy exports
	Escobar N.; Tizado E.J.; zu Ermgassen E.K.H.J.; Löfgren P.; Börner J.; Godar J.
发表日期	2020
ISSN	0959-3780
卷号	62
英文摘要	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. © 2020 The Authors
关键词	Carbon footprint Deforestation Greenhouse gas International trade Life cycle assessment Supply chain
语种	英语
scopus关键词	agricultural technology; carbon emission; carbon footprint; commodity; ecological footprint; European Union; greenhouse gas; life cycle analysis; spatial analysis; trade flow; Brazil; China
来源期刊	Global Environmental Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/249611
作者单位	Institute for Food and Resource Economics (ILR), University of Bonn, Bonn, Germany; Center for Development Research (ZEF) Economics, University of Bonn, Bonn, Germany; Department of Biodiversity and Environment Management, Universidad de León, Spain; Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium; Fonds de la Recherche Scientifique F.R.S.-FNRS, Brussels, Belgium; Stockholm Environment Institute, Stockholm, Sweden
推荐引用方式 GB/T 7714	Escobar N.,Tizado E.J.,zu Ermgassen E.K.H.J.,et al. Spatially-explicit footprints of agricultural commodities: Mapping carbon emissions embodied in Brazil's soy exports[J],2020,62.
APA	Escobar N.,Tizado E.J.,zu Ermgassen E.K.H.J.,Löfgren P.,Börner J.,&Godar J..(2020).Spatially-explicit footprints of agricultural commodities: Mapping carbon emissions embodied in Brazil's soy exports.Global Environmental Change,62.
MLA	Escobar N.,et al."Spatially-explicit footprints of agricultural commodities: Mapping carbon emissions embodied in Brazil's soy exports".Global Environmental Change 62(2020).