Spatial analysis of water quality and income in Europe

Erik Brockwell,Katarina Elofsson,Sandra Nordmark,George Marbuah

doi:10.1016/j.wre.2021.100182

Abstract

The purpose of this study is to empirically investigate the environmental Kuznets curve (EKC) relationship between water quality and income within the European Union, considering spatial interdependences across countries. To this end, we apply a spatial econometrics framework using panel data, at the national level, for twenty EU countries across seventeen years, 1998 to 2014. Furthermore, we account for the role of human and livestock population size, institutional quality and economic openness for water quality. Results show that a significant EKC relationship is seen with an inverted N -shaped relationship between income and water quality. Water quality is decreasing in income for low income levels, increasing in income when GDP per capita for medium income levels, and deteriorating for high income levels. Eight out of twenty countries have income levels associated with a declining water quality. Spatial spillovers between countries are significant. Higher livestock density levels are associated with lower levels of water quality, while institutional quality and openness to trade are positively associated with water quality. • We study the linkage between water quality and income in 20 EU countries. • We use spatial econometrics to account for spillovers between countries. • Results show an inverted N-shape relationship between income and water quality. • 8 out of 20 countries have income levels associated with deteriorating water quality. • Improved governance and economic openness positively affect water quality.

Highlights

Background and hypothesesWater pollution is characterized by spatial heterogeneity, as the impact of pollution on a recipient depends on the location of the emission source
The nonspatial panel ordinary least squares (OLS) model given in Equation (1) was used as discussed in Section 3 to conduct tests for econometric method selec tion
This is followed by an estimation and analysis of results obtained using the Spatial Lag Model (SLM) given in Equation (4)

Summary

Introduction

Water pollution is characterized by spatial heterogeneity, as the impact of pollution on a recipient depends on the location of the emission source. Recipients vary in their sensitivity to pollution. Policies for water quality increasingly recognize the spatial perspective. This was an important factor in determining the design of the WFD, where it is recommended that policies are developed on the catchment level [3,14]. In the following we outline our conceptual approach, describing the linkages between water quality and income, the role of spatial spillovers, emission sources and institutions. The relationship between income and water quality

Objectives

Methods

Results