A strategic classification support system for brownfield redevelopment |
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| Authors: | Ye Chen Keith W. Hipel D. Marc Kilgour Yuming Zhu |
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| Affiliation: | 1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China;2. Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada;3. Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario N2L 3C5, Canada;4. School of Management, Northwestern Polytechnical University, Xi''an, Shaanxi 710072, China;1. UFZ - Helmholtz Centre for Environmental Research, Department of Economics, Permoserstr. 15, 04318 Leipzig, Germany;2. European University Viadrina, Professorship in International Environmental Economics, Postfach 1786, 15207 Frankfurt (Oder), Germany;1. Faculty of Environmental Sciences and Engineering, Babe?-Bolyai University, Cluj-Napoca, Fântânele Street, No. 30, 400294 Cluj, Romania;2. Department of Environmental Sciences, Informatics and Statistics, University Ca'' Foscari Venice, Calle Larga S. Marta 2137, 30123 Venezia, Italy;1. UFZ – Helmholtz Centre for Environmental Research, Department of Economics, Leipzig, Germany;2. German Environment Agency, Dessau-Roßlau, Germany;3. Institute of Geonics, Academy of Sciences of the Czech Republic, Brno, Czech Republic;4. University Ca'' Foscari Venice, Department of Environmental Sciences, Informatics and Statistics, Venice, Italy;5. Research Institute for the Quality of Life, Romanian Academy, Bucharest, Romania |
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| Abstract: | Brownfield redevelopment (BR) is an ongoing issue for governments, communities, and consultants around the world. It is also an increasingly popular research topic in several academic fields. Strategic decision support that is now available for BR is surveyed and assessed. Then a dominance-based rough-set approach is developed and used to classify cities facing BR issues according to the level of two characteristics, BR effectiveness and BR future needs. The data for the classification are based on the widely available results of a survey of US cities. The unique features of the method are its reduced requirement for preference information, its ability to handle missing information effectively, and the easily understood linguistic decision rules that it generates, based on a training classification provided by experts. The resulting classification should be a valuable aid to cities and governments as they plan their BR projects and budgets. |
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