Sprawl dynamics and the development of effective smart growth policies

Abstract

Sprawling urban development is a major driving force of global environmental change. Its impact on Earth system functions is likely to increase in the future when the proportion of the world’s population living in urban areas is expected to grow dramatically. Thus, many policymakers are starting to look for ways to control sprawl through smart growth policies before it becomes unmanageable. However, the mechanisms by which sprawl takes place and the likely impact of smart growth on sprawl and on various stakeholders are not yet fully understood. Therefore, there is a need to develop a comprehensive methodology for sprawl analysis and its containment. Consequently, the goal of this dissertation was to provide a research framework and methodologies that contribute to the understanding of sprawl dynamics and its containment. It arrives at this goal through three analyses. The first analysis addresses sprawl and landscape fragmentation in Centre County, Pennsylvania through cross-tabulation, identifying the dominant and systematic land use transitions in the area and subsequently, the explanatory drivers of urban land use location through logistic regression. The second analysis projects future urban land use location in Centre County through simulation modeling using the CLUE-S modeling framework and includes validation and uncertainty analyses of the simulated products. By assessing the price elasticity of residential land demand and housing supply, the third analysis evaluates the feasibility of remedying sprawl by implementing smart growth policy through land price increases without compromising affordability of housing in Centre County. The results of these analyses demonstrate that land use transitions are predominantly from agriculture to urban land. The primary explanatory drivers of urban land use location in Centre County are soil and topographic factors. The validation of the simulation of near future urban land use location is encouraging, although sprawl projections show significant temporal decay. The output of the sprawl simulation is sensitive to decision rules on the ease of conversion to urban of other land use categories and to weights of input parameters. Price elasticity of residential land demand is relatively high, thus implying that smart growth policies that increase land price are likely to contain sprawl without increasing housing price. In sum, the analyses suggest that effective sprawl containment not only calls for a comprehensive analysis of local land use dynamics to confirm that sprawl is a problem, but also requires that policy makers are aware of the uncertainty inherent in sprawl model projections for informed and realistic application of model output in their planning policies. To avoid failure of sprawl amelioration measures, stakeholders who are liable to feel the effects of these measures and are likely to resist their implementation should be identified and incorporated in the policy process from its inception. Key words: urban sprawl, smart growth, affordable housing, explanatory drivers, simulation uncertainty, land demand