Discontinuous Urban Development and Economic Efficiency

Ohls, James C.; Pines, David

doi:10.2307/3145087

Cited by 70 publications

(30 citation statements)

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“…The core argument is that it may be optimal to postpone development of certain parcels so that in the future they can be developed in a way that better suits contemporaneous needs (Ohls and Pines, 1975;Fujita, 1976;Mills, 1981;see Fujita, 1983, andBrueckner, 2000, for reviews). Uncertainty is particularly interesting in this context.…”

Section: Iva the Monocentric City Model And Its Generalizationsmentioning

confidence: 99%

Causes of Sprawl: A Portrait from Space

Burchfield

Overman

Puga

et al. 2006

The Quarterly Journal of Economics

575

379

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We study the extent to which us urban development is sprawling and what determines differences in sprawl across space. Using remote-sensing data to track the evolution of land use on a grid of 8.7 billion 30×30 meter cells, we measure sprawl as the amount of undeveloped land surrounding an average urban dwelling. The extent of sprawl remained roughly unchanged between 1976 and 1992, although it varied dramatically across metropolitan areas. Ground water availability, temperate climate, rugged terrain, decentralized employment, early public transport infrastructure, uncertainty about metropolitan growth, and unincorporated land in the urban fringe all increase sprawl.Key words: urban sprawl, land development, remote sensing jel classification: r14, o51 * For helpful comments and suggestions we thank three anonymous referees, William Fischel, Masahisa Fujita, John Hartwick, Vernon Henderson, John Landis, William Strange, and, in particular, Edward Glaeser. We also received helpful comments from seminar participants at the University of California Berkeley, Harvard University, Universitat Pompeu Fabra and Stanford University, and at conferences organized by the Regional Science Association International, the Centre for Economic Policy Research, the Association of Environmental and Resource Economists, and the Lincoln Institute. We are very grateful to Ferko Csillag for his advice on remote-sensing data. Also to the us Geological Survey, and in particular to Stephen Howard and James Vogelmann, for early access to preliminary versions of the 1992 data. Vernon Henderson and Jordan Rappaport kindly provided us with data on metropolitan population 1920-1990, Matthew Kahn with data on employment decentralization, and Jacob Vigdor with data on streetcar usage. While working on this project, Burchfield was an MSc student at the Department of Geography, University of Toronto. She gratefully acknowledges help from Kent Todd in preparing arc Macro Language scripts. Funding from the Social Sciences and Humanities Research Council of Canada (Puga and Turner), and the support of the Canadian Institute for Advanced Research (Puga) and the National Fellows program at the Hoover Institution (Turner) are gratefully acknowledged.† Neptis Foundation, 50 Park Road, Toronto, Ontario m4w 2n5, Canada (e-mail: mburchfield@neptis.org). ‡ Department of Geography and Environment, London School of Economics, Houghton Street, London wc2a 2ae, United Kingdom (e-mail: h.g.overman@lse.ac.uk; website: http://cep.lse.ac.uk/~overman). Also affiliated with the Centre for Economic Policy Research, and the Centre for Economic Performance at the London School of Economics.§ Department of Economics, University of Toronto, 150 Saint George Street, Toronto, Ontario m5s 3g7, Canada (e-mail:d.puga@utoronto.ca; website: http://dpuga.economics.utoronto.ca). Also affiliated with the Canadian Institute for Advanced Research and the Centre for Economic Policy Research. ¶ Department of Economics, University of Toronto, 150 Saint George Street, Toronto, On...

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Section: Iva the Monocentric City Model And Its Generalizationsmentioning

confidence: 99%

Causes of Sprawl: A Portrait from Space

Burchfield

Overman

Puga

et al. 2006

The Quarterly Journal of Economics

575

379

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“…That study used data from the 1970 Census on urbanized areas for 40 selected small to medium-sized metropolitan areas. 6 In this study, the dependent variable is the sum ofland in all of the urbanized areas in each state divided by the number of urbanized areas in the state. Essentially, the dependent variable is the average size, in square miles, of urbanized areas in Jhe state.…”

Section: Empirical Model and Data Definitionsmentioning

confidence: 99%

“…One might suggest that a less than efficient allocation can lead to less than optimal densities and increasing amounts of urban sprawl (Ohls and Pines, 1975;Peiser 1989Peiser , 1990Breslaw, 1990). An economist=s view of the issue sterns from our use of analytical models that try to show that various phenomenon, such as urban spatial size, can be analyzed as part of an orderly market process.…”

Section: Introductionmentioning

confidence: 99%

Gasoline Prices, State Gasoline Excise Taxes, And The Size Of Urban Areas

McGibany

2011

JABR

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“…Assessing the efficiency of economic and social development and their impact on the natural environment is an important issue for researchers and urban planners. Urban development efficiency has traditionally been considered an issue of economic efficiency based on economic theory [14]. Since the emergence of sustainable development, urban development efficiency has become more environmentally oriented.…”

Section: Introductionmentioning

confidence: 99%

Application of Environmental Change Efficiency to the Sustainability of Urban Development at the Neighborhood Level

Kuo

Tsou

2015

Sustainability

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This study describes a new assessment model framework, termed the driving forces-pressure-state-impact-policy and pattern (DPSIP) model, for environmental change efficiency in urban land development, based on urban sustainable development and the theory of economic efficiency evaluation. A spatial and measurable efficiency value is defined for environmental changes in urban land development, which provides a comprehensive evaluation index for the efficiency of urban development and its environmental impact. This type of urban interior sustainability is considered new within the context of global environmental changes. We identify nine important indicators to evaluate the relative efficiency of 233 neighborhoods in Tainan, Taiwan. The results indicate that the average environmental change efficiency is 89.44%, which shows clear spatial differentiation. The key indicators affecting the efficiency score are area, population density, location, mixed land uses, the floor area ratio, and the impervious ratio. In the future, urban design can reduce environmental impacts and enhance efficiency values.

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Discontinuous Urban Development and Economic Efficiency

Cited by 70 publications

References 0 publications

Causes of Sprawl: A Portrait from Space

Causes of Sprawl: A Portrait from Space

Gasoline Prices, State Gasoline Excise Taxes, And The Size Of Urban Areas

Application of Environmental Change Efficiency to the Sustainability of Urban Development at the Neighborhood Level

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