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A comparison of land use change accounting methods: seeking common grounds for key modeling choices in biofuel assessments

Saez de Bikuña, Koldo, Hamelin, Lorie, Hauschild, Michael Zwicky, Pilegaard, Kim, Ibrom, Andreas
Journal of cleaner production 2018 v.177 pp. 52-61
accounting, biofuels, biorefining, business enterprises, case studies, deforestation, energy crops, feedstocks, global warming, greenhouse gas emissions, greenhouse gases, harvesting frequency, inventories, land use change, life cycle assessment, models, uncertainty
Five currently used methods to account for the global warming (GW) impact of the induced land-use change (LUC) greenhouse gas (GHG) emissions have been applied to four biofuel case studies. Two of the investigated methods attempt to avoid the need of considering a definite occupation –thus amortization– period by considering ongoing LUC trends as a dynamic baseline. This leads to the accounting of a small fraction (0.8%) of the related emissions from the assessed LUC, thus their validity is disputed. The comparison of methods and contrasting case studies illustrated the need of clearly distinguishing between the different time horizons involved in life cycle assessments (LCA) of land-demanding products like biofuels. Absent in ISO standards, and giving rise to several confusions, definitions for the following time horizons have been proposed: technological scope, inventory model, impact characterization, amortization/occupation, plantation lifetime and harvesting frequency. It is suggested that the anticipated technical lifetime of biorefineries using energy crops as feedstock stands as the best proxy for the cut-off criterion of land's occupation period and the inventory modeling period. Top-down LUC models are suggested as a gross reference benchmark to evaluate LUC results from bottom-up models, since the former represent average GHG emissions from deforestation statistics at different spatial resolutions. Reporting LUC emissions per area and implementing a corporate accounting system that ascribes deforestation emissions to responsible companies could avoid the critical uncertainty related to yield estimations.