Cost assessment of urban sprawl on municipal services using hierarchical regression

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Abstract

In Europe, especially in the Valencian Community, Spain, the growth of cities in the last few decades has brought with it a major paradigm change, shifting from a compact to a sprawling urban model. Although it is known about its important environmental, social, and economic effects, there is no clear and unequivocal measurement of the impact of urban sprawl on municipal spending. The impact of the sprawling city on public finances and on the cost of local public services is clearly one of the conditioning factors that should be assessed when making urban development decisions. Based on a measurement of the sprawling city, our aim is to calculate the effect of urban sprawl on the local administration’s expenditure and particularly on the cost of basic public municipal services. These are obtained through a statistical model with cost functions that can assess the increase in spending prompted by urban sprawl for municipal current expenditure. The proposed model is novel in the field of urban planning and is based on a Bayesian hierarchical model with the ability to include modeling constraints among the expenditures variables and handle missing values accurately. With this paper, we show that urban sprawl has a significant and positive effect on the unit cost of local public services, which results in an inefficient urban growth model from the economic point of view. The effect is not transferred homogeneously to the budget. There are spending items that are more sensitive to urban sprawl like expenditure on security and public transportation and community wellbeing, which primarily covers waste collection, elimination, and treatment; sanitation, and water supply and distribution; road cleaning; and public lighting.

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Gielen, E., Riutort-Mayol, G., Miralles i Garcia, J. L., & Palencia Jiménez, J. S. (2021). Cost assessment of urban sprawl on municipal services using hierarchical regression. Environment and Planning B: Urban Analytics and City Science, 48(2), 280–297. https://doi.org/10.1177/2399808319869345

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