Abstract
Abstract. Land use models operating at regional to global scales are almost exclusively based on the single paradigm of economic optimisation. Models based on different paradigms are known to produce very different results, but these are not always equivalent or attributable to particular assumptions. In this study, we compare two pan-European integrated land use models that utilise the same climatic and socio-economic scenarios but which adopt fundamentally different modelling paradigms. One of these is a constrained optimising economic-equilibrium model, and the other is a stochastic agent-based model. We run both models for a range of scenario combinations and compare their projections of spatially aggregate and disaggregate land use changes and ecosystem service supply levels in food, forest and associated environmental systems. We find that the models produce very different results in some scenarios, with simulated food production varying by up to half of total demand and the extent of intensive agriculture varying by up to 25 % of the EU land area. The agent-based model projects more multifunctional and heterogeneous landscapes in most scenarios, providing a wider range of ecosystem services at landscape scales, as agents make individual, time-dependent decisions that reflect economic and non-economic motivations. This tendency also results in food shortages under certain scenario conditions. The optimisation model, in contrast, maintains food supply through intensification of agricultural production in the most profitable areas, sometimes at the expense of land abandonment in large parts of Europe. We relate the principal differences observed to underlying model assumptions and hypothesise that optimisation may be appropriate in scenarios that allow for coherent political and economic control of land systems, but not in scenarios in which economic and other scenario conditions prevent the changes in prices and responses required to approach economic equilibrium. In these circumstances, agent-based modelling allows explicit consideration of behavioural processes, but in doing so it provides a highly flexible account of land system development that is harder to link to underlying assumptions. We suggest that structured comparisons of parallel and transparent but paradigmatically distinct models are an important method for better understanding the potential scope and uncertainties of future land use change, particularly given the substantive differences that currently exist in the outcomes of such models.
Highlights
Computational models of the land system make essential contributions to the exploration of environmental and socioeconomic changes, supporting efforts to limit climate change and reverse biodiversity loss (Harrison et al, 2018; Rogelj et al, 2018)
In all simulations with very low climate change (RCP2.6), CRAFTY produces an undersupply of food and both models produce an undersupply of timber; these shortfalls are reduced under intermediate climate change (RCP4.5), whereby productivity is slightly higher (Fig. 3)
The extent of agricultural abandonment is greatest in the Integrated Assessment Platform (IAP) under intermediate climate change (RCP4.5), whereby increased yields in some areas reduce the relative competitiveness of agricultural land in less productive areas
Summary
Computational models of the land system make essential contributions to the exploration of environmental and socioeconomic changes, supporting efforts to limit climate change and reverse biodiversity loss (Harrison et al, 2018; Rogelj et al, 2018). Because comparable alternative findings are rare, model results often go unchallenged and may be misinterpreted as predictions of how the future will develop rather than projections dependent upon underlying assumptions (Low and Schäfer, 2020). This could be misleading in social systems such as those underpinning human land use, wherein no universal laws or predictable patterns exist to guide the representation of human behaviour in models. Modellers must choose between a range of contested theoretical foundations, practical designs and evaluation strategies (Brown et al, 2016; Meyfroidt et al, 2018; Verburg et al, 2019)
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