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Building an Environmental Quality Index for a big city: a spatial interpolation approach with DP2

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  • Montero, José María
  • Larraz, Beatriz
  • Chasco, Coro

Abstract

The elaboration of Environmental Quality Indexes (EQI) for big cities is one of the main topics in regional and environmental economics. One of the usual methodological paths consists of generating a single measure as a linear combination of several air contaminants applying Principal Component Analysis (PCA). Then, as a final step, a spatial interpolation is carried out to determine the level of contamination across the city in order to point out the so-called ‘hot points’. In this article, we propose an alternative approach to build an EQI introducing some methodological and practical novelties. From the point of view of the selection of the variables, first we will consider noise -joint to air pollution- as a relevant environmental variable. We also propose to add ‘subjective’ data -available at the census tracts level- to the group of ‘objective’ environmental variables, which are only available at a number of environmental monitoring stations. This combination leads to a mixed environmental index (MEQI), which is more complete and adequate in a socioeconomic context. From the point of view of the computation process, we use kriging to match the monitoring stations registers to the Census data. We follow an inverse process as usual, since it leads to better estimates. In a first step, we krige the environmental variables to the complete surface and finally, we elaborate the environmental index. At last, in order to build the final synthetic index, we do not use Principal Components Analysis -as it is usual in this kind of exercises- but a better one, the Pena Distance method (DP2).

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 10736.

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Date of creation: 24 Sep 2008
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Handle: RePEc:pra:mprapa:10736

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Keywords: Environmental index; Air pollution; Noise; Subjecive expectations; Kriging; Distance indicators;

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  1. V. Smith & Ju Huang, 1993. "Hedonic models and air pollution: Twenty-five years and counting," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 3(4), pages 381-394, August.
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  9. Spence, Jeffrey S. & Carmack, Patrick S. & Gunst, Richard F. & Schucany, William R. & Woodward, Wayne A. & Haley, Robert W., 2007. "Accounting for Spatial Dependence in the Analysis of SPECT Brain Imaging Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 464-473, June.
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Cited by:
  1. PHOLO BALA, Alain & PEETERS, Dominique & THOMAS, Isabelle, 2013. "Spatial issues on a hedonic estimation of rents in Brussels," CORE Discussion Papers 2013036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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