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a b s t r a c tChina faces the challenge of using limited farmland to feed more than 1.3 billion people. Accelerated urbanization has exacerbated this challenge by consuming a large quantity of high-quality farmland (HQF). It is therefore essential to assess the degree to which urban expansion has preferentially consumed HQF, and discern the mechanism behind this. We found urban areas in Beijing to expand at speeds of 48.97 km 2 /year, 21.89 km 2 /year, 62.30 km 2 /year and 20.32 km 2 /year during the periods 1986-1995, 1995-2000, 2000-2005 and 2005-2020, respectively. We developed an indicator of HQF consumption due to urban expansion, representing the ratio of HQF consumed to its proportion of overall farmland, and found its values were 2.21, 1.57, 1.99 and 1.10 for 1986-1995, 1995-2000, 2000-2005 and 2005-2020, respectively. Thus, although HQF has been overrepresented in the farmland consumed by Beijing's urbanization, this phenomenon has decreased over time. Centralized expansion has contributed greatly to consumption of HQF. Topography and distances to urban and water bodies determine the relative consumption of HQF in urbanization.

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The Land Transformation Model (LTM) is hierarchically coupled with meso-scale drivers to project urban growth across the conterminous USA. Quantity of urban growth at county and place (i.e., city) scales is simulated using population, urban density and nearest neighbor dependent attributes. We compared three meso-scale LTMs to three null models that lack meso-scale drivers. Models were developed using circa 1990-2000 data and validated using change in the 2001 and 2006 National Land Cover Databases (NLCD). LTM and null models were assessed using the mean difference in quantity between simulated and actual growth measured at multiple spatial scales. We found that LTM models performed relatively well at spatial scales as small as 450 m, and that the mean difference between the NLCD and LTM with meso-scale drivers at 900 m was 2-3%, whereas null models produced a mean difference of ∼5%. Thus, introducing meso-scale modules into large-scale LTM simulations significantly increases model accuracy.