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Forest Cover Change in the Northeastern U.S.: A Spatial Assessment in the Context of an Environmental Kuznets Curve

Forest Cover Change in the Northeastern U.S.: A Spatial Assessment in the Context of an Environmental Kuznets Curve

George C. Bentley, Robert G. Cromley, Dean M. Hanink, C. Patrick Heidkamp
Copyright: © 2013 |Volume: 4 |Issue: 3 |Pages: 18
ISSN: 1947-9654|EISSN: 1947-9662|EISBN13: 9781466633735|DOI: 10.4018/jagr.2013070101
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MLA

Bentley, George C., et al. "Forest Cover Change in the Northeastern U.S.: A Spatial Assessment in the Context of an Environmental Kuznets Curve." IJAGR vol.4, no.3 2013: pp.1-18. http://doi.org/10.4018/jagr.2013070101

APA

Bentley, G. C., Cromley, R. G., Hanink, D. M., & Heidkamp, C. P. (2013). Forest Cover Change in the Northeastern U.S.: A Spatial Assessment in the Context of an Environmental Kuznets Curve. International Journal of Applied Geospatial Research (IJAGR), 4(3), 1-18. http://doi.org/10.4018/jagr.2013070101

Chicago

Bentley, George C., et al. "Forest Cover Change in the Northeastern U.S.: A Spatial Assessment in the Context of an Environmental Kuznets Curve," International Journal of Applied Geospatial Research (IJAGR) 4, no.3: 1-18. http://doi.org/10.4018/jagr.2013070101

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Abstract

An analysis of the association of forest cover, treated as an environmental good, and income at the county scale in the Northeastern United States was conducted for 2006. Global analysis using a spatial error regression model indicates an environmental Kuznets curve (EKC) type of relationship, with total forest cover, percent forest cover, and forest cover per capita is better associated with per capita income and is better specified as a polynomial rather than in linear terms. Local analysis, using geographically weighted regression, indicates that sub-regional effects are pronounced, and that conformity to an EKC varies spatially and by forest cover measure. The findings should be interpreted strictly within their context of a cross-sectional analysis and within certain statistical limitations, primarily engendered by multicollinearity of the explanatory variables in the regression models.

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