Modelling the impacts of land system dynamics on human well-being: coping with data limitations

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TitleModelling the impacts of land system dynamics on human well-being: coping with data limitations
Publication TypeJournal Article
Year of Publication2012
AuthorsRobinson, D. T., Murray-Rust D., Rieser V., Milicic V., and Rounsevell M.
Journal TitleComputers, Environment and Urban Systems

The number of agent-based modelling (ABM) applications of land-use and land-cover change (LUCC) is growing at an increasing rate. This expansion has revived incipient LUCC modelling discussions of what is an appropriate application of ABM in land-use research? For many, ABM is becoming an integrative modelling approach that combines various data and representations of land systems (i.e. models). As an integrative modelling approach, ABM may represent the micro-level actors and their behaviours that aggregate to produce regional LUCC or it may represent an actor or group of actors that make similar decisions about LUCC. To cope with data limitations and to produce insight into the dynamics of LUCC for local stakeholders in the Municipality of Koper, Slovenia, we constructed an ABM (loosely defined) that integrates utility theory, logistic regression, and cellular automaton-like rules to represent the decision-making strategies of different agents. The model is used to evaluate the impact of LUCC on human well-being, as represented by the provision of highly productive agricultural soil, the extent of noise pollution, and quality-of-life measurements. Results show, under a range of model assumptions, that 1) high quality agricultural soils are disproportionately affected by urban growth, 2) aggregate resident well-being increases with a decrease in development density, 3) some drivers of residential settlement produce non-linear preferences responses, and 4) clustering industrial development had a beneficial impact on human well-being. Additional novel contributions include the incorporation of noise pollution feedbacks and an approach to empirically inform agent preferences using a conjoint analysis of social survey data

Citation Keyrobinson2012koper