Testing the Environmental Kuznets Curve Hypothesis for Biodiversity Risk in the US: A Spatial Econometric Approach

Abstract

This study investigates whether the environmental Kuznets curve (EKC) relationship is supported for a measure of biodiversity risk and economic development across the United States (US). Using state-level data for all 48 contiguous states, biodiversity risk is measured using a Modified Index (MODEX). This index is an adaptation of a comprehensive National Biodiversity Risk Assessment Index. The MODEX differs from other measures in that it is takes into account the impact of human activities and conservation measures. The econometric approach includes corrections for spatial autocorrelation effects, which are present in the data. Modeling estimation results do not support the EKC hypothesis for biodiversity risk in the US. This finding is robust over ordinary least squares, spatial error, and spatial lag models, where the latter is shown to be the preferred model. Results from the spatial lag regression show that a 1% increase in human population density is associated with about a 0.19% increase in biodiversity risk. Spatial dependence in this case study explains 30% of the variation, as risk in one state spills over into adjoining states. From a policy perspective, this latter result supports the need for coordinated efforts at state and federal levels to address the problem of biodiversity loss.

Decomposing the decoupling relationship between energy-related CO2 emissions and economic growth in China

“…Only for few pollutants can the typical EKC pattern be confirmed (Egli 2002;Cai 2008). Besides, many empirical results (Tevie et al 2011;Saboori and Sulaiman 2013;Lopez-Menendez et al 2014) did not support the inverted U-shaped curve hypothesis. (4) The coordination analysis.…”

Economic growth and environmental impacts: An analysis based on a composite index of environmental damage

“…Morse (2008) supposes that this might be due to the quality of the datasets used and of the individuals' selection. The geographical or spatial effects (Tevie et al, 2011) and temporal aspects (Chowdhury and Moran, 2012) can also be pointed out as possibly having an influence on the results. Brajer et al (2011) and Tevie et al (2011) mention the use of different variables to measure pollution.…”

GHG Emissions, Economic Growth and Urbanization: A Spatial Approach

“…Albu (2007) also proved the above conclusion in the spatial distribution of European space [22]. Later, the classical OLS, spatial error and spatial lag models were used to prove that the United States sample does not support the EKC curve relationship, where the spatial lag model is optimal [23]. Wang (2013) used a spatial econometric analysis to prove that the environmental indicators of the local area are affected by other regions [24].…”