03620naa a2200457 a 450000100080000000500110000800800410001902400520006010000200011224501880013226000090032052022920032965000160262165000090263765000290264665000160267565000200269165000120271165000240272365000130274765000140276065000210277465000140279565000180280965000100282765000100283765000250284765000090287265000170288165300080289865300100290665300160291665300110293270000210294370000250296470000200298970000170300970000180302670000290304477300890307321252192020-10-01       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s11367-020-01763-32DOI1 aDONKE, A. C. G.  aIntegrating regionalized Brazilian land use change datasets into the ecoinvent databasebnew data, premises and uncertainties have large effects in the results.h[electronic resource]  c2020  aAbstract: Purpose: Land use change (LUC) is a critical process in the life cycle greenhouse gas emissions of agricultural products and Brazil is a major exporter of these. This work had the objective of integrating refined and regionalized datasets of LUC in Brazil into the ecoinvent database, to better represent its dynamics and heterogeneity. We present the adaptations needs for having it suitable for crops, pasture and forestry in state-level and impacts of modelling assumptions and uncertainties. Methods Adaptation and integration were based in ecoinvent version 3 guidelines and the database requirements to LUC modelling. BRLUC, a method for Brazilian LUC accounting, was the main data source. The workflow for the integration process consisted in identifying necessary adaptations in both sources to allow a better representation of Brazilian LUC. Four new reference products and 27 geographies were added in the database. Results and discussion A total of 566 new datasets were integrated into ecoinvent version 3.6, allowing the incorporation of LUC in Brazilian products in state, regional and national level. GHG emissions reduced, being 42.2%and 99.9%lower to soybean and sugarcane than in ecoinvent v3.5. Four improvements were the main causes: (i) state-level LUC modelling with national official data; (ii) regionalizing carbon stocks; (iii) including pasture and forestry land use categories; (iv) and considering sugarcane as a perennial crop. The way to calculate national-level results based on subnational data was an important source of difference in emissions too. Uncertainties specifically associated with land use substitution patterns were not incorporated, and they can potentially have impacts as large as the uncertainties of all the remaining processes combined. Conclusions Results showed that small changes in data sources and premises have large impacts on emissions associated with LUC in agricultural products. It also showed the large impacts of uncertainties of LUC patterns. Improving current models in better representing regional LUC patterns, regional carbon stocks and uncertainty accounting could reduce these impacts. Nonetheless, efforts in reducing the complexity of LUC accounting methods could enhance transparency and effectiveness.  aBeef cattle  aCorn  aGreenhouse gas emissions  aInventories  aLand use change  aMangoes  aPlantation forestry  aSoybeans  aSugarcane  aCana de Açúcar  aEucalipto  aGado de Corte  aManga  aMilho  aProdução Florestal  aSoja  aUso da Terra  aLUC  aMaize  aMato Grosso  aTimber1 aNOVAES, R. M. L.1 aPAZIANOTTO, R. A. A.1 aMORENO-RUIZ, E.1 aREINHARD, J.1 aPICOLI, J. F.1 aMATSUURA, M. I. da S. F.  tThe International Journal of Life Cycle Assessmentgv. 25, n. 6, p. 1027-1042, 2020.