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      The Determinants of Brownfields Redevelopment in England

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          Abstract

          This paper uses discrete choice models, supported by GIS data, to analyse the National Land Use Database, a register of more than 21,000 English brownfields—previously used sites with or without contamination that are currently unused or underused. Using spatial discrete choice models, including the first application of a spatial probit latent class model with class-specific neighbourhood effects, we find evidence of large local differences in the determinants of brownfields redevelopment in England and that the reuse decisions of adjacent sites affect the reuse of a site. We also find that sites with a history of industrial activities, large sites, and sites that are located in the poorest and bleakest areas of cities and regions of England are more difficult to redevelop. In particular, we find that the probability of reusing a brownfield increases by up to 8.5 % for a site privately owned compared to a site publicly owned and between 15 and 30 % if a site is located in London compared to the North West of England. We suggest that local tailored policies are more suitable than regional or national policies to boost the reuse of brownfield sites.

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          Theory, data, methods: developing spatially explicit economic models of land use change

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            Modeling Economics and Ecology: The Importance of a Spatial Perspective

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              Time to build, option value, and investment decisions

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                Author and article information

                Contributors
                +44(0)28 9097 2063 , a.longo@qub.ac.uk
                +44 (0)17 8646 7277 , danny.campbell@stir.ac.uk
                Journal
                Environ Resour Econ (Dordr)
                Environ Resour Econ (Dordr)
                Environmental & Resource Economics
                Springer Netherlands (Dordrecht )
                0924-6460
                1573-1502
                7 January 2016
                7 January 2016
                2017
                : 67
                : 2
                : 261-283
                Affiliations
                [1 ]ISNI 0000 0004 0374 7521, GRID grid.4777.3, Gibson Institute for Land, Food and Environment, Institute for Global Food Security, School of Biological Sciences, UKCRC Centre of Excellence for Public Health, , Queen’s University Belfast, ; Belfast, UK
                [2 ]ISNI 0000 0001 2002 0998, GRID grid.423984.0, Basque Centre for Climate Change (BC3), ; 48008 Bilbao, Spain
                [3 ]ISNI 0000 0001 2248 4331, GRID grid.11918.30, Economics Division, Stirling Management School, University of Stirling, ; Stirling, UK
                Article
                9985
                10.1007/s10640-015-9985-y
                6979648
                32025099
                a720aaee-35fa-412e-876b-8675a417c76f
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 18 December 2015
                Funding
                Funded by: UKCRC Centre of Excellence for Public Health
                Funded by: BIZKAIA TALENT 2014
                Funded by: Spanish Ministry of Economy and Competitiveness
                Award ID: ECO2014-52587-R
                Award Recipient :
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                © Springer Science+Business Media B.V. 2017

                brownfields,gis,revealed preferences,binary choice model,spatial autocorrelation,spatial probit latent class model

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