2014
DOI: 10.1111/ruso.12056
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The Environmental Consequences of Rural and Urban Population Change: An Exploratory Spatial Panel Study of Forest Cover in the SouthernUnitedStates, 2001–2006

Abstract: This exploratory study examines the effects of rural and urban population change on forest cover at the local level across the southern United States. Using county‐level data from the National Land Cover Database and other U.S. government sources, we regressed the total area of forest cover on rural and urban population size in spatial panel models with two‐way fixed effects. When we controlled for several other factors, including the number of forestry operations at the county level, regression results indica… Show more

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Cited by 12 publications
(7 citation statements)
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“…The utilization of the natural logarithms for dependent and independent variables yields standardized coefficients that allow for an interpretation of model coefficients as a percent change in carbon emissions for every one-point percentage change in the predictor variable, ceteris paribus . This method is consistent with the widely used Stochoastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) ecological elasticity models (Clement, Ergas, & Greiner, 2015; Dietz & Jorgenson, 2013; York & Rosa, 2012). In investigating these relationships, we rely upon robust fixed effects regression for 126 countries from 2000 to 2010.…”
Section: Methodssupporting
confidence: 63%
“…The utilization of the natural logarithms for dependent and independent variables yields standardized coefficients that allow for an interpretation of model coefficients as a percent change in carbon emissions for every one-point percentage change in the predictor variable, ceteris paribus . This method is consistent with the widely used Stochoastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) ecological elasticity models (Clement, Ergas, & Greiner, 2015; Dietz & Jorgenson, 2013; York & Rosa, 2012). In investigating these relationships, we rely upon robust fixed effects regression for 126 countries from 2000 to 2010.…”
Section: Methodssupporting
confidence: 63%
“…It is possible that the residence time is shorter or longer and could be statistically significant, but our hydrologic dataset did not permit testing of other residence times because of data incompleteness (see Section 2.2.1.). (Jorgenson and Clark, 2011;Clement and Podowski, 2013;Clement et al, 2014), but we are not aware of any other applied study that uses spatially explicit biophysical variables to examine connectivity between hydrology and landscape change.…”
Section: Resultsmentioning
confidence: 99%
“…In order to control for both kinds of spatial autocorrelation, I included a spatial lag and a spatial error term in all regression models (Dormann et al, 2007; Golgher and Voss, 2016). Following Clement et al (2015), I used a row-standardized, first-order queen contiguity spatial weights matrix to calculate the weighted effect of the relevant values of each observation on those of its neighbors; the spatial weights matrix was generated using the Stata command ‘spmat’ (Drukker et al, 2013).…”
Section: Methodsmentioning
confidence: 99%