Evaluation of land-use planning in greenbelts based on intrinsic characteristics and stakeholder values

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• 作者：John Fitzsimons^a ; ^{jfitz@uoguelph.ca} ; Craig J. Pearson^b ; ^{c.pearson@unimelb.edu.au} ; Christopher Lawson^a ; ^{jfitz@uoguelph.ca} ; Michael J. Hill^c ; ^{hillmj@aero.und.edu}
• 关键词：Greenbelts ; Land use planning ; Spatial multi-criteria modelling ; MCAS-S ; Focus groups
• 刊名：Landscape and Urban Planning
• 出版年：2012
• 期刊代码：78_01692046
• 类别：soc
• 出版时间：15 May, 2012
• 卷：106
• 期：1
• 页码：23-34
• 文件大小：1884 K

摘要

Legislation of greenbelts adjacent to cities is a common method to contain sprawl and protect land for multiple purposes e.g., agriculture, environment and recreation. However, greenbelt planning lacks an effective approach which can combine multiple intrinsic land characteristics with diverse stakeholder values to quantitatively identify a desired long term mix of land uses and the criteria upon which changes in land use can be judged. The research aimed to create a quantitative, community-engaged basis for the evolution of multiple land-uses. It evaluates a process to simultaneously develop multiple land-use designations e.g., for agriculture and environmental preservation, based on combining spatial multi-criteria modelling using MCAS-S software and stakeholder judgment. Trialling the process on a 129,000 ha portion of the Toronto Greenbelt demonstrated that it effectively identifies land that has embedded attributes or value and provides a real-time tool for assessing land-use scenarios. Maps of these attributes and focus-group scenarios did not coincide with the existing boundary of the Toronto Greenbelt. The MCAS-S composites that simultaneously address the multiple purposes of greenbelts (agriculture, environment, etc.) were likely to be helpful for policy-makers and researchers to identify land that was highly valued for several purposes and therefore likely to be contested, but they were less helpful than simple composites addressing one purpose as a basis of explanation and guiding community discussion. The method is unlikely to be scalable across large heterogeneous areas, other than to assist professionals and identify inconsistencies in land-use classification, because local knowledge is desirable for consensus on land-use scenarios.