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Agent-based modelling of land use dynamics and residential quality of life for future scenarios

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)75-89
Number of pages15
JournalEnvironmental Modelling and Software
Volume46
DOIs
Publication statusPublished - 1 Aug 2013

Abstract

Current LUCC research employs scenario-based analysis to explore possible future trends and impacts by defining a coherent set of plausible future socio-economic development pathways. Typically, computational models are therein used to interpret qualitative future storylines in terms of quantitative future changes. This paper addresses these challenges and illustrates some of the advantages of a scenario-based approach using an Agent-Based Model (ABM). Storylines are shown to be useful in integrate a broad variety of knowledge sources, such as subjective expert judgement and results from other (integrative) models, which rely on a similar set of assumptions about the future. The advantages of ABMs are demonstrated for interpreting future scenarios in the context of spatial and temporal variations in socio-ecological outcomes based on heterogeneous individual behaviour. For example, ABMs are shown to enable potential hotspots of future development and LUCC to be identified. Furthermore, a procedure is presented for downscaling and interpreting storylines from general qualitative trends to local quantitative parameters within an ABM framework. This framework is applied to the Municipality of Koper, Slovenia, where the future impacts of LUCC on the loss of agricultural land and residential quality-of-life are simulated. The results are compared to a " business-as-usual" baseline and it is shown that industrial and commercial development has the greatest impact on the loss of high quality agricultural land across all scenarios. Furthermore, the model indicates an increase in inequality in the perceived quality-of-life of residential households, with new households achieving higher quality-of-life than existing residents.

    Research areas

  • Agent-based modelling, Impact assessment, Scenario downscaling, SRES scenarios

ID: 17356880