Spatial Econometric Analysis of Environmental Kuznets Curves
Summary
The Environmental Kuznets Curve (EKC) posits that environmental degradation first worsens and then improves as per capita income rises, yielding an inverted U-shaped relationship. Traditional EKC studies often neglect spatial interdependence, yet pollution and policy effects transcend administrative borders. Spatial econometric analysis incorporates spatial lag, spatial error and spatial Durbin models to capture how environmental outcomes in one region are influenced by those in neighbouring areas. By embedding spatial weight matrices into panel and cross-sectional regressions, researchers can distinguish direct effects of income growth, urbanisation and technology adoption on pollution from indirect spillover effects. Empirical applications span carbon dioxide emissions, water quality, air pollutants and biodiversity risk, revealing that turning points in the EKC vary with institutional quality, land-use structure and regional integration. This approach underscores that coordinated regional policies, infrastructural planning and investment in clean technologies are essential to accelerate the descent along the EKC and to manage transboundary environmental stress.
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Spatial Econometric Analysis of Environmental Kuznets Curves publication trend
The graph below shows the total number of articles in spatial econometric analysis of environmental kuznets curves across all publications each year (not limited to Nature Index journals).
Technical terms
Environmental Kuznets Curve (EKC): Hypothesised inverted U-shaped relation between environmental degradation and per capita income.
Spatial Econometrics: Extension of econometric methods that accounts for spatial dependence and heterogeneity across geographical units.
Spatial Lag Model (SLM): Regression framework including a weighted average of neighbouring dependent-variable values to capture spillover effects.
Spatial Error Model (SEM): Specification that models spatial autocorrelation in the error term to correct for omitted spatially correlated factors.
Spatial Durbin Model (SDM): General model incorporating both spatially lagged dependent and independent variables to disentangle direct and indirect effects.
Spatial Autocorrelation: Statistical property where observations in nearby locations exhibit correlation, violating standard regression assumptions.
References
- An Introduction to Spatial Econometrics. Revue d économie industrielle (2008).
- Spatial analysis of water quality and income in Europe. Water Resources and Economics (2021).
- GHG Emissions, Economic Growth and Urbanization: A Spatial Approach. Sustainability (2016).
- Testing the Environmental Kuznets Curve Hypothesis for Biodiversity Risk in the US: A Spatial Econometric Approach. Sustainability (2011).
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