Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Spatial Econometric Models Applied to Environmental Pollution: A Literature Review of the Socioeconomics Drivers.

Version 1 : Received: 24 April 2021 / Approved: 26 April 2021 / Online: 26 April 2021 (12:10:50 CEST)

How to cite: Cañaveral, M.; Emmendorfer, L.; Spenassato, D.; Azambuja, A. Spatial Econometric Models Applied to Environmental Pollution: A Literature Review of the Socioeconomics Drivers.. Preprints 2021, 2021040661 (doi: 10.20944/preprints202104.0661.v1). Cañaveral, M.; Emmendorfer, L.; Spenassato, D.; Azambuja, A. Spatial Econometric Models Applied to Environmental Pollution: A Literature Review of the Socioeconomics Drivers.. Preprints 2021, 2021040661 (doi: 10.20944/preprints202104.0661.v1).

Abstract

The interest in spatial analysis has been growing in recent years, mainly due to communication technology advance, economic globalization, and the development of new statistical and econometric methods. The main aim of this article is to contribute to the dissemination of spatial econometric applications by presenting some basic theoretical aspects and a literature review of articles that address the socio-economic drivers that lead to environmental pollution. Three spatial regression models are reviewed here: the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM). A literature search was conducted using specific terms of interest in eight databases, from 1996 to February 2021, where 22 articles were considered for analysis. The results showed that most articles studied environmental problems in China. The most used exploratory spatial analysis model was Moran Index and the most used explanatory spatial analysis models were SDM and SLM.

Subject Areas

spatial econometrics; literature review; socioeconomic drivers; environmental pollution.

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