Quantifying the differences between EXIOBASE and FABIO for land use footprint studies: the case of beef consumption in six selected countries - Yeqing Zhang; Kajwan Rasul; Francesca Verones; Edgar Hertwich (Environmental Research: Food Systems, 2025)
Environmentally extended multi-regional input–output (EE-MRIO) models are widely used to address environmental issues in food system studies, but their application in policymaking is challenging because there are differences between the databases. This study compares two prominent EE-MRIO databases, EXIOBASE v3.8.2 and FABIO v1.2, by analyzing the sources of variation in per capita land use footprints of beef consumption, based on average data across six countries from 2015 to 2018. Discrepancies primarily arise from different land use accounts, both the methodological variations in estimating agricultural lands (cropland and permanent pastureland) and land use types of coverage. Further inconsistencies arise from variations in final demand boundaries, and regional and sectoral resolution. To mitigate these discrepancies, a series of modified models were developed. Aligning agricultural land use accounts significantly enhanced the consistency of footprint results, reducing the average absolute discrepancies from 76% to −29% across the selected countries. EXIOBASE’s boarder land use type coverage provides the estimation of non-agriculture land use footprint, while it may overestimate temporary pastureland use in certain advanced economies. Alignment of the final demand system boundary led to a substantial 102% relative change in EXIOBASE for land use footprint in China, while only moderate increases are observed in the other countries. Meanwhile, aligning FABIO’s higher regional and sectoral resolutions with EXIOBASE had minimal impact across the selected countries. These findings highlight how the choice of the EE-MRIO model and its land use accounts may severely affect land use footprint results. Standardizing methodologies, and data sources in EE-MRIO databases will ensure that apples are not confused for oranges, and most importantly enhance the models’ reliability for food system studies and environmental policymaking.