Linking socio-economic and physical dynamics in spatial planning

Abstract

Many of the grand challenges in spatial planning require a thorough understanding of both the socio-economic and physical dynamics that shape the land-use system. This is particularly true when addressing the many aspects related to climate change. Integrated models of land-use change can be helpful to understand the potential implications of the interplay between expected future changes in the connected socio-economic and physical systems and evaluate the impacts of potential adaptation measures on these dynamics. This presentation gives recent examples of such integrated modelling efforts that were undertaken to support spatial planning. The applications have in common that they address socio-economic processes with the Land Use Scanner modelling framework and incorporate dedicated models of specific physical processes to simulate the changing context in which future land-use developments will evolve. The first example focusses on the peaty meadow areas in the Netherlands that are characterized by relatively high levels of farming intensity, in spite of the challenges they bring in relation to soil subsidence because of drainage, flood risk, and salt intrusion. These challenges are expected to be compounded by climate change and its effects on, for instance, sea water levels, air temperatures and precipitation patterns. Indeed, when combined with continuing socio-economic growth, the cost of adaptation in dairy farming may sooner or later start to outweigh the benefits to society these regions bring. This example investigates adaptation in dairy production in peat areas in relation to the effects of climate change and changing water management policies using the Phoenix model for hydro-physical effects. It does so on the local scale of 100 x 100 meter grid cells, allowing the model to take into account the complex interactions of the highly local particularities of soil, hydrology and land use that characterizes the areas at hand. Soil subsidence and land-use change are simulated under different policy and climate scenarios. The modelling framework assumes that dairy farmers maximise utility in agricultural production systems while considering alternative production intensities. The utility that can be derived at each location is assumed to be dependent on a combination of factors that set the opportunities and constraints for different production intensity options. Production possibilities are determined by water level management in peat areas as determined by the model Phoenix (incorporating feedback effects of continued draining on land subsidence). The methodology allows for calculating costs and benefits at different scales. Combining cost-benefit analysis and geographical information systems allows for insights in costs and benefits at different geographical scales and to evaluate location-specific effects of more general policies. The second example is part of the Bangladesh Delta Plan 2100, a project for which the Government of Bangladesh requested the Government of the Netherlands for advice and recommendations on how this country should deal with increasing population growth and increasing flood hazards, in this already densely populated river delta. This example simulates future flood risk by combining four scenario-based population projections and statistical analyses on historical urbanisation patterns with a spatial allocation tool based on Land Use Scanner and the Flood Impact Assessment Tool (FIAT-Delft). It explores the possible impacts of different flood risk management strategies on limiting the population exposed to flooding. Based on our experiences in setting up the integrated modelling approaches presented in this chapter, and drawing on the discussions we had with fellow researchers and policy advisors while working on these case studies, we finalise this contribution with five lessons we have learned to generate model results that may be helpful in a planning context; ensure credibility in the modelling approach, provide transparency throughout the study, the spatial resolution should be in line with the goal of the study, being flexible with the data at hand, and keep modelling applications in spatial planning as simple as possible.