Quantifying parametric uncertainty in future food demand in GCAM

Abstract. Projecting food demand is important for understanding the future of land, water, and energy in the integrated human-Earth system and also has implications for health and human well-being. Uncertainty in demand has many drivers, including model structure, technological change, socio-economic development, trade, and policies. An under-studied driver is the uncertainty in model parameters estimated on historical data. We use the Global Change Analysis Model (GCAM), an integrated model of land, water, energy, and economy interactions, to investigate the parametric uncertainty in projected food demand. We modify GCAM’s demand functions to better represent regional variation in consumption patterns, disaggregate consumption within regions across income deciles, and re-estimate uncertain parameters. To more efficiently characterize uncertainty, we use the demand functions as an emulator of the full food system in the more complex model and project a large ensemble of demand for staples and non-staples in a GCAM reference scenario. We find that parametric uncertainty in demand, as well as in the response of demand to price changes, is a substantial source of uncertainty in future outcomes and is especially large in low-income deciles. Using scenario discovery techniques, we identify five sets of parameters that effectively span the range of uncertainty across regions and deciles in both demand and its response to price changes. These parameter sets allow GCAM users to capture parametric uncertainty in a small number of scenarios. This uncertainty in demand can substantially affect uncertainty in cropland, water withdrawals, and biomass production in some regions.