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

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.

The Impact of Clean Water Access on Children’s Performance in Indonesia

“…There are four econometric strategies used in this study, Propensity Score Matching (PSM), Instrumental Variable (IV), Regression Discontinuity (RD) and Double Difference (DD). Econometrics strategy used in this study has referred to some of the literature on the impact evaluation literature like [26][27][28].…”