ANALYZING OPEN SPACE DISTRIBUTIONS in the Context of the …gis.geog.uconn.edu/personal/paper1/journal paper/57 2014... · 2015. 5. 26. · Bentley: Analyzing Open Space Distributions

Bentley: Analyzing Open Space Distributions in the Context of the EKC

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ANALYZING OPEN SPACE DISTRIBUTIONS in the Context of the Environmental Kuznets Curve:

An Example from the Northeastern United States

George C. BentleyFramingham State University

Dean M. Hanink

University of Connecticut, Storrs

Robert G. CromleyUniversity of Connecticut, Storrs

Chuanrong ZhangUniversity of Connecticut, Storrs

Daniel L. CivcoUniversity of Connecticut, Storrs

Introduction

In the early 1950s Simon Kuznets described a curvilinear relationship between economic inequality and income growth (Kuznets 1955). The general shape of the Kuznets curve is an inverse U-shape indicative of an inequality-income polynomial relationship, with initial increases in income corresponding to increases in economic inequality until an inflection point is reached. Thereafter further increases in income correspond to decreasing economic inequality. Decades later, environmental economists and other analysts adapted the Kuznets

ABSTRACTEnvironmental Kuznets’ curves (EKCs) are often used to specify variations in en-vironmental characteristics as covariates of per capita income. Based on a stylized model that links open space to spatial variations in per capita income, this paper uses both global and local regression analyses to test the conformity of income and open space cover to an EKC across counties in the northeastern United States. Glob-al results indicate only weak EKC correspondence between per capita open space and per capita income in the study area, but strong EKC correspondence between open space as a percentage of county area and per capita income. Local results show strong correspondence between the land cover-income EKCs and the stylized model. Keywords: Open Space; Environmental Kuznets Curve; Spatial Modeling; Northeast-ern United States.

©2014 by the New England-St. Lawrence Valley Geographical Society. All rights reserved.

The Northeastern Geographer Vol. 6 2014

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curve to the study of the relationship between environmental characteristics and per capita incomes. The environmental Kuznets curve (EKC) has become a leading empirical model for exploring the causal relationship between economic growth and environmental characteristics (Costantini and Martini 2006). Increasing levels of environmental degradation correspond to increases in per capita incomes in an EKC until an inflection point is reached, then further increases in per capita incomes correspond to decreasing levels of environmental degradation (Figure 1). Alternatively, decreasing levels of positive environmental characteristics, such as clean air or forest cover, correspond to initial increases in per capita incomes until an inflection point is reached where further increases in per capita incomes correspond to increasing levels of such environmental goods (Figure 2). As opposed to a polynomial relationship, a strictly linear relationship between environmental degradation or environmental goods and per capita income typically would be expected to show that as per capita incomes increase environmental degradation would continually increase and environmental goods would continually decrease, respectively.

There is a large volume of EKC research that has investigated the relationship between environmental degradation, particularly air pollution and water pollution, or environmental quality and income (Stern (2004), Aslanidis (2009), and Carson (2010) provide thorough reviews.) Land-use analyses employing EKCs have largely focused on forests. Deforestation, an environmental “bad”, and forest cover, an environmental “good”, have been shown to consistently follow an EKC relationship. Deforestation analyses are more common, with multiple findings of an inverse U-shaped curve describing the association of deforestation and per capita income (Cropper and Griffiths 1994; Bhattarai and Hammig 2001; Ehrhardt-Martinez, Crenshaw, and Jenkins 2002; Panayotou 2003). Forest cover, an environmental good, has been less commonly analyzed, but has been found to follow a U-shaped curve by Bentley et al. (2013). Other land cover/use types are less frequently studied in the context of EKCs, but cropped land cover, pasture land cover, and agricultural land cover have each been shown to conform to an EKC ( James 1999; Kumar and Aggarwal 2003).

The purpose of this paper is to extend EKC-related research both empirically and theoretically by building on the work by Bentley et al. (2013). The empirical extension is made by analyzing the distribution of open space in the context of an EKC. The extension is timely given the ongoing concern about urban sprawl in many places. The study area for the empirical analyses is an area of the northeastern United States. Global regression analysis is used to determine the area-wide existence of an EKC, and local regressions are used to explore likely variations in the spatial distribution of the EKC effect. The theoretical extension is made in the form of a conceptual model that links open space distributions to the spatial distribution of per capita income in a way that conforms to the land-cover and income association expressed in the EKC.

EKC Background

EKC analysis is theoretically controversial. For example, it runs counter to the widely accepted IPAT (Impact = Population +Affluence+ Technology) identity that has both intuitive


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Figure 1. An environmental Kuznets curve for environmental degradation.

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Figure 2. An environmental Kuznets curve for environmental goods.

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appeal and widespread empirical verification (Waggoner and Ausubel 2002). Instead of viewing economic growth and the coincident rise in incomes as the cause of environmental degradation, Beckerman (1992, 495) argued that “The strong correlation between incomes and the extent to which environmental protection measures are adopted demonstrate that in the longer run the surest way to improve your environment is to become rich.” Panayotou (1993) coined the term EKC to describe the circumstances where initial increases in incomes correspond to increasing environmental degradation until an inflection point at which further increases correspond to falling environmental degradation. Initial empirical analyses using EKCs were typically conducted at the international scale, with countries as units of observation. They often focused on variations in air and water quality using GEMS (Global Environmental Monitoring System) data (Grossman and Krueger 1991, 1995; Shafik 1994; Panayotou 1997). The topic of scale in early empirical EKC analyses is addressed by Mazzanti, Montini, and Zoboli (2007). They argue in favor of regional and local analyses over international analyses since the more micro-based analyses better address statistical and policy aims (Mazzanti, Montini, and Zoboli 2007). Examples of finer spatial scale EKC analyses include Rupasingha et al. (2004) and Bentley et al. (2013), with both utilizing counties as the units of analysis.

EKCs are often explained terms of the supply and demand sides of environmental economics. In short, the demand side of the literature examines the need for environmental quality while the supply side looks at structural features of the economy as the main catalyst for increasing environmental protection (Costantini and Martini 2006).

The demand side of environmental economics treats the environment as an income elastic commodity where demand for environmental quality does not occur until a threshold income or standard of living is reached (Galeotti 2007; Bimonte 2002). The generalized notion presented by Dinda (2004) holds that the poor have little interest in the quality of their surrounding environment. It is not until economic development with coinciding increases in income and higher standards of living that the population begins to place more importance on surrounding environmental quality (Galeotti 2007). At higher levels of income, populations exert political pressure toward environmental remediation and also put greater pressure on firms to implement pollution abating technologies. Increased political pressure and willingness to pay for environmental quality forces the curve to its inflection point where further growth in income leads to decreased environmental degradation. The leading supply side argument for the EKC concerns the change in economic or structural composition that occurs as per capita income increases. As an economy develops its economic structure moves from the primary and secondary sectors, dominated by agriculture and manufacturing, respectively, to the tertiary and quaternary sector, dominated by services and information, respectively. As economies transition from manufacturing to services a corresponding shift from “dirty” to “clean” industry occurs (Grossman and Kreuger 1995).

One of the earliest critiques leveled at EKC theory focused on the failure of some of the early literature to consider that sustainability of economic activity depends on the resiliency of ecosystems (Arrow et al.1995). The absence of feedback with increased pollution levels harming production levels can lead to the notion that maximizing economic growth maximizes environmental quality, although in reality it may be unsustainable in the long run (Stern,


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Common, and Barbier 1996). Another criticism focuses on the fact that trade may be responsible for driving the relationship between the environment and economy in countries. Rothman (1998) argues that wealthy countries have the ability to relocate heavily polluting industries outside their borders and still consume the products as imports. In terms of the EKC, the distancing effect or pollution exporting process could be causing the curve to reach the transition point and further growth in per capita income appears to correspond to a decrease in environmental degradation, but the effect is misleading (Dinda 2004; Halkos 2011). In spite of recent literature (see Chowdury 2012; Chowdury and Moran 2012; Dietz, Rosa, and York 2012; Franklin and Ruth 2012; Günerlap and Seto 2012; Walker 2012) critiquing the EKC for reasons including the challenge of assembling appropriate cross-sectional and panel data and challenging the common statistical-econometric estimations of EKC models, analyses based on the EKC continues (see Bentley et al. 2013; Robalino-Lopez et al. 2014; Shafiei and Salim 2014) primarily using its functional-form as a benchmark to investigate environmental variation (for example Caviglia-Harris, Chambers, and Kahn 2009; Clay and Troesken 2010; Gassebner, Lamla, and Sturm 2006).

Land Cover/Use EKC Research

Shafik (1994) found an EKC for both total deforestation in seventy-seven countries and annual deforestation in sixty-six countries over a twenty-four year period. Cropper and Griffiths (1994) examined the relationship between tropical deforestation and income in sixty-four developing countries in Africa, Asia, and Latin America over a thirty year period and found an EKC. Panayatou (1995) found EKC conformity between deforestation and income levels, noting that tropical countries and densely populated countries experience greater levels of deforestation. Mather, Needle, and Fairbairn (1999) also found EKC relationships for forest trends and reforestation trends in developing nations. Ehrhardt-Martinez, Crenshaw, and Jenkins (2002) found a deforestation EKC for seventy-four less developed countries in an analysis using 1980 and 1995 forest stock information. Bhattarai and Hammig (2001) discovered a strong EKC relationship between deforestation and income using deforestation rates calculated from 1972 and 1991 forest stock levels for sixty-six countries in Latin America, Africa, and Asia. Instead of using deforestation rates in their EKC analysis Bentley et al. (2013) use forest cover measures at a finer spatial scale than used in most EKC research, the U.S. county. Regression analysis indicated conformity to an EKC for three different measures of forest cover: total forest cover, per capita forest area, and percentage forest cover of the total county area.

Non-forest land cover types have been examined less often. James (1999), utilizing data from the United Nations Food and Agriculture Organization for 127 countries, examined the relationship between three land covers/uses: crop land, pasture land, and total agricultural land (an aggregation of both crop land and pasture land). All three of these land uses were treated by James (1999) as environmental bads because of the loss of biodiversity that results from land being converted to crop land or pasture land. He found an EKC for all three land uses. La Peyre et al. (2001) tested the relationship between both wetland program efforts and wetland protection efforts and economic capital across ninety countries during the late 1990s.


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Ultimately, no EKC conformity between either wetland program efforts or wetland protection efforts and economic capital was observed. Kumar and Aggarwal (2003) examined crop cover and pasture cover and their relationship to per capita incomes in nineteen major states of India using time series data spanning from 1963-1964 to 1995-1996. They treat crop cover as an environmental bad because its expansion requires the conversion of forest land and pasture land, negatively impacting the biodiversity of the area. As such, the EKC they found follows an inverse U-shaped curve.

To follow the logic of the original Kuznets curve, an EKC ideally should trace the environment-income relationship over a long period of time in a single place. However, EKC analysis is often plagued by a lack of sufficient temporal data. The so-called Kuznets-Chenery controversy was a dispute in the literature in which the former argued that temporal data are required for such analyses while the latter argued that cross-sectional (geographical) data are sufficient to demonstrate whether or not the inequality-economic growth association was curvilinear in the hypothesized way (Gregory and Griffin 1974). If, in fact, cross-sectional data are used in an EKC analysis most of the theoretical foundations linking environment and income described above are easily modified so that they generally still apply. In a cross sectional analysis where an EKC exists for an environmental bad the lowest and highest income places will have the lowest levels of environmental degradation while the places with intermediate incomes will have the highest levels of environmental degradation. As described below, however, the land cover/use EKC developed in this paper is based on a model that specifically concerns cross-sectional analysis in a particular geographical context.

Open space is effectively defined here as any land not considered developed. It is considered to be an environmental good for several reasons including carbon storage (in forests and fields), flood prevention (in wetlands), and outdoor recreational opportunities, among others. Given the widespread benefits, it is not surprising that every state in the United States, and a large majority of more local jurisdictions, have policies supporting open space preservation (Zinn 2004; Press and Nakagawa 2009).

Deller (2009) has suggested that categorizing land as either rural or urban is too simplistic and that it is useful to consider two non-urban environments. Instead of only rural areas that are dominated by commercial and extractive use of the natural landscape, there are also non-urban exurban areas within commuting distance of larger centers. These higher income places maintain open space without significant primary sector activity (Power 1996). These areas tend to be rich in amenities, particularly natural ones that are considered to be important factors for their residential attractiveness (Mulligan and Carruthers 2011). Schmidt and Courant (2006) also note proximity to natural amenities is an important factor in the choice of residential location. While some of the studies outlined above treated agriculture as an environmental bad, agricultural land may be positively valued for its scenic views over a pastoral landscape in exurban places (Lopez, Shah, and Altobello 1994). An EKC for open space with respect to spatial income variations may occur due to the stylized relationship described in Figure 3. That figure indicates that open space increases (almost axiomatically) with the transition from urban to rural space. Population density, not illustrated in the figure, would decrease along the same spatial trajectory. Income variation is not so straightforward. The stylized depiction is


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that average income increases in the transition from urban to exurban, but then decreases in the transition from exurban to rural.EKC Analysis of Open Space in the Northeastern United States

The study region for the empirical analysis is in the northeastern United States and consists of nine states: Maine, New Hampshire, Vermont, Massachusetts, Connecticut, Rhode Island,

New York, New Jersey, and Delaware. The unit of analysis within the study area is the county; there are a total of 153 counties within the study area (Figure 4). Land cover data are from the National Oceanographic and Atmospheric Administration’s Coastal Change Analysis Program (National Oceanographic and Atmospheric Administration (NOAA) 2011). This is a nationally standardized database of land cover data derived from remotely sensed imagery (NOAA 2011). The study area was chosen as the largest contiguous multistate set available. The data are classified using a constant scheme of twenty-eight unique land cover types and are available for three time periods: 1996, 2001, and 2005/2006 (NOAA 2011). Due to the short 10 year window of data availability for the aforementioned states a meaningful time series analysis cannot be conducted. Cross-sectional analyses for 1996 and for 2006 were conducted yielding very similar results. Only the results for 2006 are reported in this paper.

The data are developed by NOAA to meet an overall target accuracy of 85 percent; however, the target accuracy can vary by geography and date (NOAA 2011). The states within

Figure 3. Stylized relationship of open space and income.

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the study region fall within five NOAA zones: zone 60, zone 63, zone 64, zone 65, and zone 66. In 2006 the overall accuracy percentage and accuracy variation for zone 60, zone 63, zone 64, zone 64, and zone 66 are 71 percent ± 3.13 percent, 62.0 percent ± 0.61 percent, 62.0 percent ± 0.51 percent, 85.1 percent ± 0.49 percent, and 85.3 percent ± 1.43 percent, respectively (NOAA 2011). ArcGIS’ ArcMap program was used to create a single raster image from the individual raster images. A zonal function was run on the single raster image to calculate areas of each land cover type falling within the 153 counties. For analysis purposes the detailed land cover types were aggregated to create simplified land cover categories (Table 1).

Note that there are two measures of open space, one defined by the absence of three developed land covers ( Open Space (3)) and the other by the

absence of four developed land covers (Open Space (4)). In the first case open space includes forest, agricultural, and wetland covers, in the second case it includes those covers and also the cover labeled developed open space, which includes such features as parks, golf courses, and cemeteries. Two operational measures of Open Space (3) and Open Space (4) are used in the analysis: the logit of the percentage of county area in open space, and county per capita area in open space. The measures are only weakly correlated, with a Pearson correlation of 0.231 for Open Space (3) and 0. 257 for Open Space (4).

In addition to NOAA’s Coastal Change Analysis Program (CCAP) data we also use per capita income data available from the U.S. Bureau of Economic Analysis Regional Economic Accounts (U.S. BEA 2011). Per capita income and per capita income squared constitute the income polynomial and are the two key explanatory variables in the regression analyses described below. Additional explanatory variables: county area, county population, and a topography index value are used as controls. County area controls for variation caused by the

Figure 4. Study area.

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differing sizes of the counties and is calculated using ArcGIS’ ArcMap program (Environmental System Research Institute (ESRI) 2009). County population controls for variation in differing sizes of population between counties and is available from the U.S. BEA (U.S. BEA 2011). Lastly, the topography index controls for the variation in terrain between counties (U.S. Department of Agriculture, Economic Research Service (USDA ERS 2012). Preliminary step-wise regression analyses were conducted using additional explanatory variables measuring employment structure and specialization, but they were not found to be significant.

Across all counties in the study region the Open Space (3) category has an average value of approximately 87 percent of land and an average of close to 3.7 hectares per person (Table 2). The coefficients of variation indicate that the per capita measure of Open Space (3) cover is more heavily concentrated than the percentage measure of Open Space (3) cover, with values of 2.518 and 0.226 respectively (Table 2). The percent Open Space (3) cover measure indicates a greater degree of spatial autocorrelation than does the per capita Open Space (3) measure, with Moran’s I statistics of close to 0.71 and about 0.18, respectively (Table 2.2). The highest values of per capita Open Space (3) are located primarily in the northern part of the study region, particularly northern New York, northeastern Vermont, northern New Hampshire, and most of the counties in Maine except for those in the southwestern portion of the state. The lowest values are in southwestern Connecticut, southern New York including counties on Long Island, and central New Jersey. The highest values of percent Open Space (3) are located in northern New York, southwestern and northern New Hampshire, and northern Maine. The lowest values are in eastern Massachusetts, southern New York including Long Island, and central New Jersey.

The Open Space (4) categories have the largest mean values of percent cover and per capita cover with a value of approximately 88.2 percent of land falling in this category and close to five hectares per person (Table 2). The coefficients of variation indicate per capita Open Space (4) cover is more concentrated across the counties with a value of about 1.9, while percent Open Space (4) cover has a value of 0.182 (Table 2). Moran’s I statistics indicate a greater level of spatial autocorrelation for the percent Open Space (4) cover measure, with a value of 0.714, than for the per capita Open Space (4) cover measure, with a value of about 0.22. The highest values of per capita Open Space (4) are located in the northern portion of the study

Table 1. Land Cover Aggregation Scheme.

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area, particularly the northern counties of New York, New Hampshire, and most counties of Maine except for those in its southwest. The lowest values are in eastern Massachusetts, southwestern Connecticut, southern New York including Long Island, and central New Jersey. The highest values of the Open Space (4) measure are northern, western, and central New York, northern New Hampshire, and northern Maine. The lowest values are in eastern Massachusetts, southwestern Connecticut, southern New York including Long Island, and central New Jersey.

Per capita income, and the control variables of county area, county population, and county topography, all exhibit varying degrees of concentration and spatial autocorrelation across the counties (Table 2). In 2006 the county average per capita income was slightly greater than $37,500. Distribution of per capita income is not heavily concentrated or spatially autocorrelated, with a coefficient of variation of 0.315 and a Moran’s I statistic of 0.419, respectively (Table 2). The highest per capita incomes are mainly associated with the major metropolitan areas in the region and the counties that comprise them; this is the case for New York City and its surrounding suburban counties located in New York, New Jersey, and Connecticut. Additionally the counties comprising metropolitan Boston in Massachusetts and Hartford in Connecticut are locations of high per capita income in the study region. Spatially,

Sources: Land cover types were calculated using data from NOAA (2011). Population and income data are from U.S. BEA (2011). County topography data are from USDA ERS (2012). Per capita areas are in hectares.

Table 2. Selected Summary Statistics for Study Region Counties (2006).

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population follows a pattern similar to that of per capita income. However, the coefficient of variation of 1.018 and Moran’s I statistic of 0.723 indicate a greater level of concentration across the counties and greater spatial autocorrelation as compared to per capita income (Table 2). County area exhibits concentration across the measures, with a coefficient of variation of 1.443, and moderate levels of spatial autocorrelation, with a Moran’s I statistic of 0.673. The largest county areas are found predominately in the northern tier of the study region and the smallest counties in the southern portion of the study region, particularly in southern New York and New Jersey. The county topography measure is not heavily concentrated but is moderately spatially autocorrelated, with a coefficient of variation of 0.376 and a Moran’s I statistic of 0.713 (Table 2). The highest values occur in New York and New Hampshire, primarily in the Catskills and White Mountains, respectively. The lowest values are in Massachusetts, New York, New Jersey, and Delaware, particularly for those counties along the coastline.

Regression Models Background

Two issues frequently arise in EKC regression analysis. One concerns polynomial expansion of explanatory variables and affects both temporal and cross-sectional modeling. The other, spatial autocorrelation of residuals, is of concern only in cross-sectional analyses. The polynomial expansion of an explanatory regression variable, per capita income in the case of an EKC, can generate a greater likelihood of an ill-conditioned matrix that cannot be inverted for the purpose of calculating regression parameters (Bradley and Srivastava 1979). If matrix inversion is successful there is also the problem of high multicollinearity between the original variable and its expanded form. High multicollinearity can cause the parameter estimates for the two terms to be redundant. In light of expected collinearity problems for the polynomial terms, the parameters are often evaluated as a suite where instead of individually evaluating the statistical significance of each parameter, the parameters are interpreted jointly with a focus on expected sign conformity.

There have been several cross-sectional EKC analyses which directly address interdependence across spatial observations, or issues of spatial autocorrelation, by the application of spatial econometric techniques. One of the earliest analyzed the relationship between county level income and toxic release inventory data (Rupasingha, et al. 2004). Models with a spatial error term and without a spatial error term were evaluated. The model without a spatial error term indicated EKC conformity, however spatially autocorrelated residuals opened the possibility that the estimated parameters were inefficient (Rupasingha et al. 2004). The model with a spatial error term, considered to be more appropriate given statistically significant levels of spatial autocorrelation, also indicated EKC conformity. McPherson and Nieswiadomy (2005) conducted an EKC analysis using a spatial lag model to examine the relationship between threatened bird and mammal species and country-level per capita income in 2000. The significant coefficients of the spatial lag variables indicate the presence of spatial spillovers that are common when political boundaries delineate the observations of environmental variables.


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Maddison’s (2006) EKC analysis examined the relationship between atmospheric pollutants, including sulfur dioxide, nitrogen oxide, volatile organic compounds, and carbon monoxide, and per capita incomes for 135 countries. He tested for the presence of spatial lag dependence and spatial error dependence and constructs two sets of models, one to adjust for spatial lag dependence and spatial error dependence. His results indicate not only EKC conformity, but also the presence of significant spatial effects, particularly for the lags. Mills and Waite (2009) addressed the issues of heteroskedasticity and spatial autocorrelation in their EKC analysis of the relationship between the proportions of species conserved and gross domestic product per capita for thirty-five countries by using quantile regression and spatial filtering. Using a cross-sectional data set, Bentley et al. (2013) tested for EKC conformity in the forest cover - per capita income association in the counties of the northeastern United States. They used both a global spatial error model (SEM) and geographically weighted regression (GWR) to account for spatial effects in their models. Their global model results indicate EKC conformity with significant spatial errors parameters, but results of their GWR analysis indicate significant spatial variation in conformity at the local county scale. We also use both a global SEM and GWR in assessing the EKC for Open Space, as described below.

Specification

OLS regression models of the open space EKC yielded residuals with high levels of spatial autocorrelation, as expected given the cross-sectional nature of the data. To account for that spatial dependence, we used an SEM model, which has the additional benefit of decreasing problems of omitted variable effects if those omitted variables are spatially variable (Cohen and Coughlin 2008). The SEM is:

OS = β0 + β1lnPCI + β2lnPCI2 + β3lnAREA + β4lnPOP + β5lnTOPO + γ + μ (1)

where OS is the open space measure (either ln per capita open space or the logit of percent open space), β0 is the intercept term, β1-5 are parameters to be estimated, PCI is per capita income, PCI2 is per capita income squared, AREA is county area, POP is the county population, TOPO is the topography index, γ is the spatial error parameter, and μ is the random error term.

In addition to the global regression model, a local regression model, GWR, is used to define the associations of the variables within specific counties. GWR is a form of weighted least squares in which each data point within a study region is weighted by its distance to the regression point. The weights decrease as distance between the regression point and data points increase (Fotheringham, Brunsdon, and Charlton 2002). The GWR form is:

OS(ui,vi) = β0(ui,vi) + β1lnPCI(ui,vi) + β1lnPCI2(ui,vi) + β1lnAREA(ui,vi) + (2) β1lnPOP(ui,vi) + β1lnTOPO(ui,vi) + ε(ui,vi)


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where (ui,vi) refers to the coordinates of a data point within the study area, in this case the (ui, vi) values refer to the centroid of the counties in the study area. Variables and parameters are defined as above, but in GWR each county has an individual parameter estimate for each variable.

There are issues that may arise with the use of GWR. The first is that GWR estimates are mildly biased due to the response to locational weights used in their estimation. The local parameters of an observation are based on the values of those spatially proximal observations that fall within a defined spatial window or bandwidth, with nearby observations generating more influence. The mild bias is offset by reduced standard errors of the estimates as long as the spatial bandwidth contains a sufficient sample size (Fotheringham, Brunsdon, and Charlton 2002). Wheeler and Tiefelsdorf (2005) caution that local GWR coefficients can be correlated and suffer from multicollinearity even when the explanatory variables are uncorrelated. If this condition exists, it does not invalidate the interpretation of parameters as a suite, it simply means that caution should be practiced if individual GWR parameter estimates are to be used in formal hypothesis testing. Using the same dataset as Fotheringham, Brunsdon, and Charlton (2002), Griffith (2008) showed that while GWR accounts for a portion of spatial autocorrelation by transferring it to the estimated spatially varying coefficients, positive spatial autocorrelation remained in the residuals. Páez, Farber, and Wheeler (2011) have shown that small sample sizes can often lead to misleading results in GWR due to leveraging by anomalous observations, so it is best used for exploratory purposes rather than confirmatory ones.

Global SEM Results

The SEM estimated parameter signs for the per capita income coefficient (negative) and per capita income squared coefficient (positive) conform to a EKC relationship between per capita Open Space (3) cover and per capita income, but neither of the parameters is significant in the EKC income polynomial (Table 3). Instead, it appears that per capita Open Space (3) land cover is better characterized as simply increasing in income, as indicated in the results of the SEM linear-in-income specification (Table 3). As expected, Open Space (3) cover is significantly increasing in county area and decreasing in population across the region, but topography has no statistical effect. The results of those control variables are consistent across the polynomial and linear specifications.

SEM parameter estimates of percent Open Space (3) cover indicate an EKC, with a negative and significant coefficient for the per capita income and positive and significant coefficient for per capita income squared (Table 3). The results indicate that an upward inflection in percent Open Space (3) cover occurs at a per capita income of about $38,000. Support for the EKC is provided by the linear specification, which indicates that percent Open Space (3) cover is not linearly associated with per capita income in a significant way (Table 3). The polynomial specification in general has a much better fit than does the linear specification, given that the former’s AIC statistic is much lower than the latter’s. As in the per capita cover model, the control variables of county area (positive) and county population (negative) have


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a significant effect with respect to percent Open Space (3) cover, but in this case so does topography, which has a significantly positive effect.

The results of the global Open Space (4) cover models are very similar to those for the Open Space (3) cover models. As in the case of Open Space (3), the signs of the estimated income coefficients are correct for an EKC, but while the coefficient for per capita income is significant, the one for per capita income squared is not (Table 4). Despite that lack of

significance, however, the polynomial EKC model is a marginally better fit to the data than is the linear model. An EKC is fully apparent for percent Open Space (4) cover just as in the case of percent Open Space (3) cover. The estimated coefficient for per capita income is negative and significant and the estimated coefficient for per capita income squared is positive and significant, indicating an upward inflection in percent Open Space (4) cover at a per capita income of about $36,000. In this case, however, the linear model indicates a significant income association as well, with a negative and significant income coefficient. The EKC has an AIC of about 54, however, while the linear model has an AIC of about 79, indicating the former has a much better fit to the data. As in the case of percent Open Space (3) cover, county area and increasing topographic variation have a positive effect on percent Open Space (4) cover while county population has a decreasing effect. Finally, it is important to note that while

Table 3. Global Regression Model Results for Open Space (3) Cover.

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forest cover makes up the great majority of land in open space in the region, the EKC is specific to the total category for both Open Space (3) and (4). An independent global SEM analysis of percent forest cover in the study area does not yield an EKC, but is better modeled as a linear relationship with percent forest cover significantly increasing in per capita income after accounting for the negative effects of population and the positive effects of area and topographic variation.

Local GWR Results

In the case of per capita 3 Open Space (3) cover, 19 neighbors is the optimum GWR bandwidth for minimizing the AIC statistic (Table 5). The range of values across the first and third quartiles indicate a possible EKC with a negative sign on the per capita income parameter at the first quartile and a positive sign on the per capita income squared parameter at the third quartile. A Monte Carlo test indicated the lack of significant spatial variation (α ≤ 0.05) in the local parameter estimates of the per capita income variable and the per capita income squared variable. The errors exhibit low levels of spatial autocorrelation, as indicated by the Moran’s I coefficient. The signs on the area control variable and population control variable are consistent across the first to third quartile, positive and negative, respectively. The sign of the topography

Table 4. Global Regression Model Results for Open Space (4) Cover.

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parameter is negative at the first quartile, then positive at the median and above. There are fifty-seven counties conforming to an EKC with respect to sign (Figure 5). There are spatial clusters around major metropolitan areas, including Philadelphia (southern New Jersey and Delaware), Boston, New York City, and Hartford that effectively fit the stylized model illustrated in Figure 3. There are additional spatial clusters in central and northern New York State.

For percent 3 Open Space (3) cover, 53 neighbors is the optimum number of neighbors for GWR (Table 5). The signs on the three control variables’ parameters are consistent across the first to third quartiles with a positive sign on that for county area, a negative sign on that for population, and a positive sign on that for topography. While the parameter signs on the

control variables are consistent across the first to third quartiles, the signs of the per capita income variables’ parameters vary. The sign on the per capita income parameter is negative at the first quartile and median, while the sign on the per capita income squared variable is positive at the median and the third quartile. A Monte Carlo test indicated the presence of significant spatial variation (α ≤ 0.05) in the local parameter estimates of the per capita income variable and the per capita income squared variable. There is very weak spatial autocorrelation of the errors, as indicated by the Moran’s I coefficient. There are 107 counties conforming to an EKC with respect to sign (Figure 6). Nearly all of the counties in the more populated southern part of the region exhibit an EKC with respect to sign, again fitting the stylized model well. Additionally most of northern New York state and parts of Vermont and northern Maine also exhibit sign conformity to an EKC for percent Open Space (3) and per capita income.

As with the global models, the GWR results for Open Space (3) measures and Open Space

Table 5. Geographically Weighted Regression Model Results for Open Space (3) Cover.

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(4) measures are quite similar. In the case of 2006 per capita Open Space (4) cover, 36 is the optimum number of neighbors for GWR (Table 6). The signs on the three control variables’ parameters are consistent across the first to third quartiles with a positive sign on that for county area, a negative sign on that for population, and a positive sign on the topography parameter. While the parameter signs on the control variables’ parameters are consistent across the first to third quartiles, the signs on the per capita income variables vary. The signs conform to the EKC income polynomial at the first quartile of the per capita income parameter and at the third quartile of the per capita income squared parameter. A Monte Carlo test indicated a lack of significant spatial variation (α ≤ 0.05) in the local parameter estimates of the per capita income variable and the per capita income squared variable. There is a very low level of spatial autocorrelation in the errors, as indicated by the Moran’s I coefficient. There are 57 counties that conform to an EKC with respect to sign (Figure 7). Their cluster forms a coherent band from southern New Hampshire along the coast through metropolitan conforms well to the stylized model of open space and per capita income illustrated in Figure 3.For percent Open Space (4) cover, 61 is the optimum number of neighbors for GWR (Table 6). The signs on the three control variables’ param-eters are consistent across the first to third quartiles with a positive sign on that for county area, negative sign on that for population, and positive sign on the topography parameter. The signs on the EKC income poly-nomial parameters reveal EKC conformity with a negative sign at the first quartile and median of the per capita income term and positive signs at both the median and third quartile of the per capita income squared term. The Monte Carlo test indicates

Figure 5. Local sign conformity of 2006 per capita open space (3) cover to the environmental Kuznets curve.

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the presence of significant spatial variation (α ≤ 0.05) in the local parameter estimates of the per capita income variable and the per capita income squared variable. The errors exhibit very weak levels of spatial autocorrelation, as indicated by the Moran’s I coefficient. There are 112 counties conforming to an EKC with respect to sign (Figure 8). Their cluster is contiguous, including all the major metropolitan centers in the study area and excluding most of its nonmetropolitan, northern, part.

Discussion

The results of the empirical analysis indicate the presence of an EKC relationship between open space cover and per capita income in the northeastern United States. Additionally, the stylized model presenting income associations with place types holds true for Open Space (3) and Open Space (4). As discussed earlier, the only difference between Open Space (3) and Open Space (4) is Open Space (4) includes developed open space land cover. Naturally, in all counties the percentage of land in Open Space (4) is slightly higher than the percentage of land in Open Space (3). Therefore, only the income associations with place types will be explicitly discussed for Open Space (3). For Open Space (3) the counties containing the largest cities in the study region contained lower amounts of open space and have, with some exception, medium incomes. The five counties of New York City: New York County, Kings County, Bronx County, Richmond County, and Queens County, consisted in 2006 of 15.74 percent Open Space (3), 12.64 percent Open Space (3),

Figure 6. Local sign conformity of 2006 percent open space (3) cover to the environmental Kuznets curve.

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22.66 percent Open Space (3), 39.59 percent Open Space (3), and 14.20 percent Open Space (3), respectively (NOAA 2011). The per capita incomes for New York County, Kings County, Bronx County, Richmond County, and Queens County in 2006 were $109,707, $30,802, $25,720, $40,566, $34,163, respectively (U.S.BEA2011). Suffolk County contains Boston and in 2006 had 16.95 percent Open Space (3) and a per capita income of $48,963 (NOAA 2011; U.S. BEA2011). Exurban counties, those in close proximity to the major cities of the region but within commuting distance, had higher per capita incomes and higher percentages of Open Space (3) Cover. For example, exurban counties tied to New York City such as Westchester County in 2006 consisted of 79.66 percent Open Space (3) cover and a per capita income of $70,518 (NOAA 2011; U.S. BEA 2011). Additionally, Nassau County in 2006 consisted of 40.66% Open Space (3) cover and had a per capita income of $59,827 (NOAA 2011; U.S. BEA 2011). Rural counties such as Herkimer County and Washington County in New York, as well as Essex County in Vermont, have high amounts Open Space (3) cover and lower per capita incomes. In 2006 Herkimer County and Washington County had 98.97 percent Open Space (3) cover and 97.87 percent Open Space (3) cover, respectively (NOAA 2011; U.S. BEA 2011). In 2006 Herkimer County and Washington County had per capita incomes of $27,055 and $26,800, respectively (NOAA 2011; U.S. BEA 2011). Additionally, in 2006 Essex County in Vermont consisted of 99.43 percent Open Space (3) cover and had a per capita income of $22,288 (NOAA 2011; U.S. BEA2011). All of these results are described with respect to the extreme values of income and open space, but they drive the model. The results for the middle income counties are transitional, as expected for the EKC in the context of the study area and

Table 6. Geographically Weighted Regression Model Results for Open Space (4) Cover.

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the stylized model in Figure 3. The results of the empirical

analysis indicate the presence of an EKC for open space in the northeastern United States and reveal income associations between place types when open space is analyzed in the spatial cross-section. While the analysis is cross-sectional, it is important to discuss elements of the land-cover history of the northeastern United States to contextualize the results. The land-cover history of the study region is marked by periods of transition. Prior to the mid-nineteenth century forest cover was cleared primarily for agricultural purposes, while more recently there has been a general trend towards agricultural abandonment. Scrub or shrub cover growth in the initial years following agricultural clearing leads to forest regrowth. Farmland abandonment in the northeastern United States occurs for a variety of reasons, including growth of urban areas, and relatively unproductive soils, among others (Hart 1968).

There are limitations to the research meriting discussion. The use of the county as the unit of analysis, while a finer spatial scale than most EKC research, limits the application of findings to policy given the non-existent or weak county governments in part of the study area. Many open-space decisions are made at the town/municipal level or state level. The use of sub-county units of analysis was precluded by data availability issues, as the previously mentioned employment structure and specialization variables are not available at this finer spatial scale. The use of the state as the unit of analysis would have introduced small sample size problems for the regression techniques utilized in this study, GWR in particular. Additionally, this research treats all land cover types aggregated to calculate Open Space (3) and Open Space (4) as offering an equal

Figure 7. Local sign conformity of 2006 per capita open space (4) cover to the environmental Kuznets curve.

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amount of environmental quality. Future research will quantify environmental quality differences between land-cover types, focusing specifically on agricultural land to determine how the value of a pastoral view is offset by the environmental costs of pesticide and fertilizer application. Lastly, it was noted in the literature review that proximity to natural amenities is an important factor in decisions concerning residential location. The cross-sectional analysis only indicates that income and land-use co-vary in a statistically significant way. Whether that covariance is the result of mobility on the part of people selecting land-covers in their residential decisions or the result of land-use and land-cover decisions made after the fact remains for future research. As more land cover data become available, a time-series model will be used to evaluate changes in the economic profiles of exurban counties in the context of temporal income change and migration patterns.

Conclusion

The environmental Kuznets curve (EKC) is a leading empirical model for exploring the statistical relationship between economic growth and environmental degradation. The EKC is a curvilinear relationship, as opposed to a linear relationship, between environmental degradation or environmental goods and per capita income. For environmental goods such as open space, initial increases in per capita incomes correspond to decreasing levels until an inflection point is reached where further increases in per capita incomes correspond to increasing levels. A linear relationship between environmental goods and per capita incomes would show that as per capita incomes increase environmental goods would continually decrease.

This paper extends EKC-related research both empirically and theoretically. The empirical

Figure 8. Local sign conformity of 2006 percent open space (4) cover to the environmental Kuznets curve.

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extension is made in the analysis of open space as an environmental good. The theoretical extension is made in the form of a conceptual model linking open space to per capita income in a way that conforms to an EKC. While a global EKC for per capita open space is not strongly supported in this paper, there is strong empirical support for a global EKC for open space as a percentage of county area in the northeastern United States. There is support for EKCs for both per capita open space and percent open space in the local analyses. Those findings, in particular, conform to the conceptual model of open space-income relationships by county typology variations over space. Open space is at its greatest extent in higher income and lower income places and is intermediate in percent area in middle income counties. In general, the lower income counties are rural places that can be expected by definition to have large endowments of open space, so the more meaningful result concerns the middle income counties with low levels of open space and the high income counties also enjoying high levels of open space. If open space is, in fact, an environmental good, then its distribution violates principles of environmental justice if it is biased by income. The findings presented in this paper are certainly not final, but rather preliminary in that regard, especially given the variation between per capita and percentage measures. They do, however, identify an interesting area of future research that links land cover, environmental quality, income, and the morphology of metropolitan areas with respect to the impacts of sprawl and other factors both contributing and resulting from changing economy and land use._____________________

george c. bentley is an Assistant Professor of Geography at Framingham State University. His research interests are environmental-economic geography, GIS, and the use of geospatial technology to track changes in the built environments of post-industrial cities. Email: [email protected]

dean m. hanink is a Professor of Geography at the University of Connecticut. His research interests are in regional economic-environmental change and economic geography. Email: [email protected]

robert g. cromley is a Professor of Geography at the University of Connecticut. His research interests are in GIScience and economic geography. Email: [email protected]

chuanrong zhang is an Associate Professor of Geography at the University of Connecticut. Her research interests are in GIS, geostatistics, and the applications of geostatistics in natural resource management and envi-ronmental evaluation. Email: [email protected]

daniel l. civco is a Professor of Geomatics in the Department of Natural Resources and the Environment at the University of Connecticut. His research interests are in remote sensing and GIS applications. Email: [email protected]

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