How model paradigms affect our representation of future land-use change

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 land use models that are based on the same integrated modelling framework and utilise the same climatic and socio-economic scenarios, but which adopt fundamentally different model 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 spatial and aggregate land use change and ecosystem service supply. We find that 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 active management in large, contiguous 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 where economic and other scenario conditions prevent the normal functioning of price signals and responses. In these circumstances, agent-based modelling allows explicit consideration of behavioural processes, but in doing so provides a highly flexible account of land system development that is harder to link to underlying assumptions. We suggest that structured comparisons of parallel, transparent but paradigmatically distinct models are an important method for better understanding the potential scope and uncertainties of future land use change.

Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework

“…The main benefit would be to not only compare model outcomes with empirical observations on the individual or system level but also to quantify the relative impact of different factors determining these outcomes, such as the impact of individual behaviour compared to social networks or underlying farm characteristics (see e.g., Drechsler, 2021 for an application). The strength of ABMs such as FARMIND in this context is their ability to apply theoretical concepts (here cumulative prospect theory and social networks) to processes and conditions that empirical data and optimising models alone cannot cover (Brown et al, 2020).…”

“…In combination with machine‐learning approaches (see e.g., Storm et al, 2019), this kind of big data could be integrated into agent‐based modelling approaches such as FARMIND. Another more pragmatic approach would be to aggregate behavioural data into farm typologies (Brown et al, 2020; Malek & Verburg, 2020; Müller et al, 2020). The modular structure of FARMIND would permit the integration of such approaches and thus form the basis for testing behavioural strategies in different decision‐making contexts, thereby opening a promising path for meta‐studies or case‐study comparison (Magliocca et al, 2015; Malek et al, 2019; O’Sullivan et al, 2016).…”

Quo vadis, smallholder forest landscape? An introduction to the LPB-RAP model

“…The question if perpetuated trends of historic and recent developments are plausible for a chosen case study is another subject and should be answered externally if desired [44,45].…”

Coastal Land-Use and Land-Cover Change Trajectories: Are They Sustainable?

“…Simultaneously, advanced geospatial technology and methods (e.g., remote sensing, geographic information systems (GIS), 3D web-GIS, spatial analysis and modelling) together with free high-quality satellite imagery sources (such as Copernicus Sentinel data) or high-resolution orthoimagery that has become cheaper and more accessible enable the analysis and visualisation of the spatiotemporal dynamic of LULC change [7][8][9], expand our knowledge of complex urban systems [10,11], reduce uncertainty [12] and improve data-driven decisions. Hence, such geospatial technology and methods are becoming more important for future committed decision-making [13,14].…”