The Value of Trees

Abstract

The article discusses the value and importance of urban trees and factors that significantly influence public support for protecting them. These factors were identified by examining mail survey data obtained from a representative sample of 800 homeowners living in a rapidly urbanizing area within Southern Appalachia. A series of multiple regression analysis tested an Integrated Model of Urban Tree Support that combines measures of attitudes, beliefs, values, and sociodemographic variables to predict homeowner support for local urban tree protection. The findings lend support to many features of the model and revealed that homeowners who have stronger protree attitudes, have greater environmental concerns, place more importance on trees when looking for a new place to live, attribute symbolic value and meaning to them are more supportive. Women and Democrats are also more supportive. Recommendations are offered for future research and policy.

Keywords

urban tree protection environmental concern values beliefs attitudes

The United Nations officially designated 2011 as the “International Year of Forests” to increase public awareness about the vital connection between forests and humans as well as efforts to conserve the value that forests and trees represent (American Forest Foundation, 2011). Most of these efforts have focused on large forested areas in rural areas and landscapes. However, urban forests are critical components of broader vegetation configurations, which connect ecosystems and affect the movement of wildlife, people, air currents, and storm water runoff. A growing awareness of these ecological processes and others associated with global climatic change, rapid urban growth and development, should boost the visibility of urban forestry and its potential role in natural resource management in the coming years (United Nations Food and Agriculture Organization [UN-FAO], 2011; U.S. Department of Agriculture [USDA] Forest Service–Northern Research Station, 2011).

According to the USDA Forest Service (2004), urban forests are trees on “public and private land, along streets, in residential areas, parks and commercial development and other locations within a city or a metropolitan area” (p. 2).With an average tree cover of 33%, metropolitan areas collectively support nearly one quarter of the nation’s total tree canopy—some 74.4 billion trees (Dwyer, Nowak, Noble, & Sisinni, 2000; USDA Forest Service–Northern Research Station, 2011). However, studies conducted by the Urban Forest Center for the American Forest Foundation have reported that urban tree cover plummeted in all 25 metropolitan areas that it studied and that three dozen American cities have lost more than one fourth of their tree canopies since 1972 (Lin, 2007). Regretfully, this and other earlier warning signals about urban tree loss in the United States have all been drowned out as economic concerns have undermined environmental concerns (ECs; PEW Research Center for the People and the Press, 2009). Moreover, deforestation in the Amazon has been given much more media attention than the significant loss of urban tree cover in the United States (see Hansen, Stehman, & Potapov, 2010; Rice, 2010).

The major reasons for urban tree loss include rapid urban growth and development, changing weather and climate patterns, invasive species, soil compaction, tree disease, and inadequate tree maintenance, replacement, and protection (Carreiro & Zipperer, 2008; McPherson, 2006; Nowak & Walton, 2005; Scarlett, 2010). Inadequate programs and short-sighted public policies have also contributed to the problem. For example, even though integrated urban forestry programs emerged in North America in the 1960s (Konijnendijk, 2003), most American cities still do not have effective planning, regulatory, and monitoring systems to maintain and protect trees on public, residential, and commercial property. Moreover, most city officials and local government agencies promote economic, commercial, and residential growth while paying little or no attention to the harmful effects of this growth on urban tree cover and the environment. They also tend to underestimate the value residents place on street and residential trees and to the benefits urban trees provide to the public (Dwyer, McPherson, Schroeder, & Rowntree, 1992; Heynen, Perkins, & Roy, 2006; Kielbaso, 2008; Wolf, 2004).

Understanding the values and the benefits of urban trees can help spur local officials and agencies to develop tree management and protection policies that are integrated within a sustainable urban ecosystem framework. One of the major barriers to achieving this is that numerous studies have been conducted across a variety of disciplines using many different approaches and variables to try to tease out significant factors that encourage public concern and support for environment protection (Dunlap & Jones, 2002) and for tree protection (Wolf, 2006; Zhang, Hussain, Jinyang, & Letson, 2007). This epistemological and methodological diversity creates a challenge to integrate these cross-disciplinary studies to further the development of cumulative knowledge and theory across areas of social inquiry.

Studies examining public concern for the environment (and urban trees in particular) have also been driven more by the development of policy than by theory (Lorenzo et al., 2000; Sommer, Guenther, & Barker, 1990; Treiman & Gartner, 2005; Wolf, 2005; Zhang et al., 2007). For example, there are few studies that test theoretical linkages among social-psychological variables (e.g., values, beliefs, attitudes) and sociodemographic variables (e.g., age, income, education) and public support for urban tree protection. Most do not use a comprehensive model that examines public perceptions on more than a few of the many tree benefits that have been found across biophysical, economic, and social science studies.

Sasidharan and Thapa’s (1999) extensive review of urban forestry literature identified some of these weaknesses and the need for studies to link social-psychological correlates of EC with public acceptance of urban forestry programs. They concluded that a better understanding of social-psychological bases of urban and community forestry has great potential to assist urban forestry and park agencies in developing and implementing effective policies and protection strategies. One bridge to this policy goal, which would also further the advancement of theory within this area, would be the development of a model that integrates specific variables previously found related to public support for environment protection and urban tree protection. This model would help us better understand how ECs, values, and beliefs associated with trees may encourage public support for urban tree protection, thus promoting effective urban forest protection strategies. Given this goal, we review the literature on urban trees and public concern for the environment to help identify key variables to test within our Integrated Model of Urban Tree Support.

Literature Review

Urban forestry emerged largely as a response to the Dutch Elm Disease that plagued cities from the 1930s to 1960s and grew in response to urban development, loss of urban tree canopy, and rising public concern for the environment (Wolf, 2003). Research that responded to these needs has significantly increased our knowledge about the economic and ecological benefits and value of urban trees. Much of this research has been conducted or supported by the USDA Forest Service through its expanded authority granted by the Farm Bill in 1990, from support by the National Arbor Day Foundation, and by investigators in industry and academia (Johnston, 1996). Most have tried to assess the value of urban trees and tree cover through econometric modeling, photo simulations, imaging software, aerial photography, satellite imagery, and by surveys, in-depth interviews, and focus groups. Few have been national in scope and most were case studies that helped to estimate ecological and economic value only within a local context. Still, they have been able to identify a wide range of benefits and significant value associated with urban forests and trees (Barro, Gobster, Schroeder, & Bartram, 1997; Dwyer et al., 1992; Dwyer, Schroeder, & Gobster, 1991; Maller, Townsend, Pryor, Brown, & St. Leger, 2006; McPherson, 2007; Pauleit, 2003; Treiman, 2006; USDA Forest Service, 2004; Wolf, 2005, 2007).

Economic benefits associated with urban trees include increased land, property, and rental value (Anderson & Cordell, 1988; Dwyer et al., 1992; Hastie, 2003; Mansfield, Pattanayak, McDow, McDonald, & Halpin, 2005; Morales, Micha, & Weber, 1983; Orland, Vining, & Ebreo, 1992; USDA Forest Service, 2003, 2004; Wolf, 1998). Well-maintained trees and landscaped business districts have been shown to encourage consumer purchases and attract increased residential, commercial, and public investments (Wolf, 2004, 2007). Trees located in business areas may also increase worker productivity, recruitment, retention, and satisfaction (Kaplan, 1992; Kaplan & Kaplan, 1989; Wolf, 1998).

Energy benefits are in the form of reduced air conditioning, reduced heating by shading buildings, homes, and roads, absorbing sunlight, reducing ultraviolet light, cooling the air, and reducing wind speed (Coder, 1996; Hastie, 2003; Lohr, Pearson-Mims, Tarnai, & Dillman, 2004; McPherson, 1994; McPherson & Rowntree, 1993; Simpson & McPherson, 1996; Wolf, 1999). Trees and urban forests decrease soil erosion, storm water runoff, flooding, air, water and noise pollution; they provide wildlife habitats, soil nutrients, composting materials, and recycling services; and they sequester carbon dioxide (Coder, 1996; Hastie, 2003; McPherson, 1995). Overall, it is estimated that their environmental services alone bring US$400 billion in annual benefits to American cities (American Forests, 2006).

There are fewer social science studies because the benefits are harder to assess and quantify, but they suggest that trees and green spaces are part of the fabric of healthy urban communities and are integral elements of their social infrastructure (Hansen-Moller & Oustrup, 2004; Kaplan, 2001; Sorensen, Hayes, & Marina, 2000). Views of trees and natural landscapes may decrease stress, mental fatigue, length of hospital stays, medical complications, recuperation times, the need for more medication, and they may decrease the severity of attention-deficit symptoms among children (Coder, 1996; Kuo, 2003; Kuo, Bacaicoa, & Sullivan, 1998; Lohr & Pearson-Mims, 2005; Miles, Sullivan, & Kuo, 1998; Parsons, 1991; Taylor, Kuo, & Sullivan, 2001; Tennessen & Cimprich 1995; Ulrich, 1991). Trees and green landscaping also seem to promote the health of communities and social systems. Stronger ties among neighbors, more adult supervision of children in outdoor areas, more use of the neighborhood common areas, and fewer property and violent crime have been linked to the presence of trees (Kuo, 2003; Kuo et al., 1998; Kuo & Sullivan, 2001).¹

Urban trees also seem to provide visual, aesthetic, and symbolic value. They can reduce glare, screen undesirable features in the built environment, frame lighted areas, and define transportation corridors (Coder, 1996; USDA Forest Service, 2004). Trees, and especially flowering trees, provide scenic beauty while large trees can provide a sense of awe, wonder, peace, and serenity. Planting and maintaining trees provide a sense of accomplishment and responsibility, feelings of altruism, and caring and connection with nature. Trees also can provide historic, cultural, and symbolic values that connect people to the past, their childhood experiences, their community, and their cultural heritage and may provide meaning and value to our lives (Dwyer et al., 1991; Hansen-Moller & Oustrup, 2004; Kellert & Wilson, 1993; O’Brien, 2006; Rolston, 1988; Schroeder, 1988; Ulrich, 1981).

Attitude theory emphasizes the important role public concern for the environment (i.e., “environmental concern”) plays in understanding and predicting individual and collective actions to improve environmental quality (Dunlap & Jones, 2002; Routhe, Jones, & Feldman, 2005).² Although there is a large and expanding body of research that examines EC (Dunlap & Jones, 2002), there are a limited number of in-depth peer-reviewed studies that deal directly with public concern for the maintenance and protection of urban trees in the United States. Those that have, generally report that urban residents have an overall positive attitude toward street and residential trees and especially large trees (Barro et al., 1997; Dwyer, Schroeder, and Gobster, 1991; Lohr et al., 2004; Schroeder, Flannigan, & Coles, 2006; Zhang et al., 2007).

Attitudes have been found to accurately gauge individuals’ tendency to behave favorably or unfavorably to a class of objects, such as preferences for certain landscapes which may contain a varying amount of trees (Balram & Dragicevic, 2005; Kaltenborn & Bjerke, 2002; Tyrväinen, Mäkinen, and Schipperijn, 2007). Some people may prefer urban tree for economic, environmental, normative, and emotional reasons, or for historic and symbolic meanings that they provide (Hansen-Moller & Oustrup, 2004; McPherson, Simpson, Peper, & Xiao, 1999; Sasidharan & Thapa, 1999). Consequently, understanding attitudes, values, and beliefs should help us to estimate how well a particular population will embrace any collective action or policy proposal associated with urban tree protection.

Several studies suggest that attitudes toward trees and other urban landscape features are related to public concern and perceptions of the overall environment (Balram & Dragicevic, 2005). Studies of EC also suggest that factors others than attitudes influence support for collective actions and policies that can improve the environment, whereas attitude theory suggests that specific attitudes about a collective action are related to more general values, beliefs, preferences, and concerns (Dietz, Fitzgerald, & Shwom, 2005; Dunlap & Jones, 2002; Routhe et al., 2005). This means that how we view urban trees and individual and collective actions to utilize, maintain, and protect them are influenced by social values and structures, cultural and symbolic meanings about ourselves, our place in the world, the environment, and nature. (Barro et al., 1997; Carreiro & Zipperer, 2008; Dwyer et al., 1991; Rival, 1998).

Other influences on public support may include exposure to traditions of gardening, tree planting, and landscape preferences. Summit and Sommer (1998) posit that it takes direct action to raise community awareness of the benefits and value of urban trees. For example, tree-planting programs show community residents how easy it is to plant trees, demonstrate their benefits, create opportunity for people to work together, and make environmental values and behavior more appealing. Thus, it appears that knowledge and direct experience with trees may help to nurture values supporting environmental protection and sustainability (see also Chiesura, 2004; Cottrell, 2003; Miles et al., 1998).

The socioeconomic profile of a particular community, state, or region may also influence support for tree protection (Heynen et al., 2006; Jensen, Gatrell, Boulton, & Harper, 2004; Wolf, 2004). Like public support for the environment, support for tree protection may be influenced by an individual’s position in the social structure. For example, younger, the more educated, women, Democrats, urbanities, and those affiliated with environmental organizations have been linked to greater public support for environmental protection (Allen, 1997; Dickerson, Groninger, & Mangun, 2001; Dunlap & Jones, 2002; Jones & Dunlap, 1992; Jones, Fly, & Cordell, 1999; Jones, Fly, Talley, & Cordell, 2003; PEW Research Center for the People and the Press, 2009; Routhe et al., 2005; Treiman & Gartner, 2005, but see Zhang et al., 2007). However, sociodemographic variables generally explain significantly less variation in public support than attitudes, beliefs, values, and other social-psychological variables (see Cottrell, 2003; Dunlap & Jones, 2002; Ignatow, 2006; Jones & Dunlap, 1992).

Theoretical Model and Assumptions

Based on our review of the literature, we expect tree attitudes, concern for the environment, personal experience, knowledge, and beliefs associated with trees, and demographic characteristic such as age, education, gender, political, and environmental affiliation to influence public support for urban tree maintenance and protection. Perceptions of residential (Zhang et al., 2007) and symbolic tree values (Hansen-Moller & Oustrup, 2004) that have not been included in many social-psychological models of public support are incorporated in our Integrated Model of Urban Tree Support. Our model also extends attitude-behavior models that typically try to predict individual proenvironmental behaviors by examining factors that may influence collective actions such as reducing negative environmental impacts through protection of urban tree cover.

It proposes that homeowners who (a) have stronger protree attitudes, (b) have greater ECs, (c) believe trees provide many environmental benefits (EBs), (d) place more value on trees when looking for a new place to live, (e) attribute symbolic meaning to them, and (f) have more knowledge and experience about trees are generally more supportive of public policies and programs designed to better protect urban trees.

Specifically, our Integrated Model of Urban Tree Support assumes the following:

Y = β₀ + β₁x + β₂y + β₃z + β₄p + β₅q + β₆r + u, where Y is “Homeowner Tree Support” (homeowner support for local government policies that protect urban trees), β₁x is “Positive Social Impacts” (PSIs), β₂y is “Negative Social Impacts” (NSIs), β₃z is “Environmental Concern” (EC), β₄p is “Environmental Tree Benefits,” β₅q is “Residential Tree Value” (RTV), and β₆r is “Symbolic Value” (SV). Based on research on proenvironmental behavior (Hungerford & Volk, 1990; Mobley, Vagias, & DeWard, 2010), we expect that “Tree and Landscape Experience” and “Tree Knowledge” (TK) will be indirectly related to homeowner support through the independent variables in the above model. Selected sociodemographic variables that have been linked to environmental support were also examined.

Method

The research was conducted in Knox County, Tennessee. The county has a total area of 526 square miles and a population of 423,000, and its largest city, Knoxville, covers 98.1 square miles with a population of 173,890. Knox County has 174,327 acres of tree canopy (52%) and the dominant land cover in Knoxville is trees (40%), covering 25,151 acres (American Forests, 2002; U.S. Census Bureau, 2006).

Like many fast growing urban areas in the United States, Knox County has been losing its tree canopy, has few tree protection tools, and will fail to meet the minimum requirement for urban tree cover if current development practices continue (American Forests, 2002; Davis, 2011). The recognition of this problem resulted in the creation of a tree ordinance in Knoxville that protects public trees and some private trees. However, outside city limits, there are no regulations for protecting existing trees in the county except a broad statement to preserve trees “in the design of the subdivision, wherever possible” (Knoxville-Knox County Metropolitan Planning Commission, 2007).

Our research was part of an in-depth 2-year study gauging public support for the maintenance and protection of trees in Knox County, Tennessee. Data analyzed in the present study were obtained from a mail survey implemented during this larger study. Drafts of the questionnaire were tested for clarity, content, ease of administration, reliability, and validity using web-based surveys of city and county stakeholders, key informant interviews, and focus group meetings. A mail survey was administered using a four-wave mailing approach designed to improve mail survey rates (Dillman, 1999). The 976 returned questionnaires represent a 42% response rate and had an estimated sampling error of ±3.2 percentage points at the 95% level of confidence.³ The sample size was further reduced by eliminating those who did not own a single-family detached home (n = 138) and those missing ≥20 items on the survey. We focused on homeowners because they regularly make decisions that affect the urban tree canopy through management of their own homes, yards, and neighborhoods and have greater vested interest in public expenditures than renters (Barreto, Marks, & Woods, 2007; Clendenning, Field, & Kapp, 2005; Youngentob & Hostetler, 2005). The final data set contained 800 cases representing the responses of adult homeowners living in single-family detached homes in Knox County, Tennessee.⁴

Tests of construct validity and internal consistency were used to create the following scales using principal components factor analysis. Oblique rotation was used to help in data interpretation and construct differentiation for all of the variables. Internal consistency was checked for each scale using Cronbach’s alpha, and factor analysis was used to check unidimensionality.

Homeowner tree support assessed support for funding tree planting in public areas, protecting mature trees on residential property, and having local government do more to protect trees (three items, Cronbach’s α = .77, 69% of the variance in one principal component).

Seven survey items assessed beliefs about potential social impacts associated with urban trees and factor analysis reduced them into two scales. The Positive Social Impact (PSI) scale assessed beliefs that trees inspire community pride, help people feel calmer, and enhance property values (three items, Cronbach’s α = .62, 53% of the variance in one principal component). The Negative Social Impact (NSI) scale assessed beliefs that trees cannot be protected on construction sites in a cost-effective manner, should not be planted in cities because they crack sidewalks, should be replaced instead of saved when building a house or developing a commercial property, and should not be planted in business districts because they block store signs (four items, Cronbach’s α = .77, 59% of the variance in one principal component).

The Environmental Benefits (EB) scale assessed beliefs about the importance of neighborhood trees providing such things as wildlife habitats, cleaner air, shade, privacy, and wind protection (nine items, Cronbach’s α = .82, 49% of the variance explained in one principal component).

The Tree and Landscape Experience (TLE) scale assessed homeowner engagement in landscape and tree maintenance activities such as tree planting, pruning, mulching, and home gardening (six items, Cronbach’s α = .72, 40% of the variance explained in principal component).

The Tree Knowledge (TK) scale assessed homeowner knowledge about how to buy, plant, care, and prune a tree, identify diseased trees, and other activities (nine items, Cronbach’s α = .90, 57% of the variance explained in one principal component).

The EC scale assessed attitudes toward environmental protection and asked homeowners about their level of agreement that “environmental protection laws hurt the economy” and whether they “find it hard to get too concerned about environmental issues” (two items, Cronbach’s α = .67, 75% of the variance explained in one principal component).

A single-item indicator, Residential Tree Value (RTV) asked homeowners about the importance of having trees on the property when they were looking for a new place to live. Another single-item indicator, SV asked homeowners whether trees have a particular personal, symbolic, or spiritual meaning or value. Finally, all of the above items and scales were coded to reflect higher values on each theoretical construct, and the reader may obtain more information in the Online Appendix at http://eab.sagepub.com/.

Several sociodemographic variables (i.e., age, gender, political affiliation, household income, education level, length at current residence, and membership in environmental organizations) were also examined. Most have been linked to environmental support, but they tend to be undertheorized in the literature (Dunlap & Jones, 2002; Ignatow, 2006; Jones & Dunlap, 1992).

Results

We first provide details of the descriptive findings on each of the independent variables examined in the study and then present the substantive results on the correlation, multiple regression, and path analysis.

Descriptive Findings

Homeowner tree support

Responses to this three-item scale ranged from 3 to 15 and had a mean of 11.4 (see Table 1). This indicates that that the average homeowner in Knox County has fairly strong support for more public resources and regulatory oversight devoted to the maintenance and protection of local trees. Most (72%) homeowners want local government to do more to protect trees, and a majority want more local government funding allocated for planting public trees (63%) and want stronger rules to protect historical (larger and older) trees on residential properties (55%).

Table 1.

Descriptive Statistics of the Substantive Scales.

Scale	M	SD	Range	Items	α
Homeowner Tree Support (dependent variable)	11.4	2.8	3-15	3	.77
Environmental Concern	7.4	2.0	2-10	2	.67
Positive Social Impacts	9.9	1.3	5-15	3	.62
Negative Social Impacts	15.8	3.3	4-20	4	.77
Environmental Benefits	18.0	2.8	7-21	7	.82
Tree/Landscape Experience	4.3	1.7	0-6	6	.72
Tree Knowledge	16.0	4.2	7-21	9	.90

PSIs

Responses to this three-item scale ranged from 5 to 15 and had a mean of 9.9. This indicates that the average homeowner believes that city trees have several positive impacts on themselves and the community. Most homeowners think trees enhance property values (94%), inspire community pride (75%), and help people feel calmer (75%).

NSIs

Response to this four-item scale ranged from 4 to 20 and had a mean of 15.8. This suggests that the average homeowner disagrees that city trees have a negative impact on business and local development. Most (80%) do not believe that trees are problems for cities because their roots damage sidewalks or that they block signs in business areas (79%). A majority (67%) believe that cutting trees and replacing them in new residential and commercial developments is a not a good idea and that trees can be protected on new constructions sites in a cost-effective manner (55%).

EC

Responses on this two-item measure ranged from 2 to 10, and it had a mean of 7.4. This suggests the typical homeowner is concerned about and supportive of environmental protection. Two thirds (65.8%) of them are concerned about environmental issues, and a majority do not think environmental laws are hurting the economy (57%).

EBs

Responses on this six-item scale ranged from 7 to 21, and it had a mean of 18. This score indicates that the typical homeowner believes that trees provide important EBs. Most homeowners think trees are very important for improving air quality (77%), providing wildlife habitats (76%), and shade (74%). A solid majority of them think they decrease energy costs (62%) and street noise (59%), increase privacy (62%), and many (44%) think they are important for wind protection.

Tree and landscape experience

Responses on this six-item scale ranged from 0 to 6, and it had a mean of 4.3. The mean score indicates that average homeowner participated in four of the six landscape and tree care–related activities covered by this scale. Most had maintained a home garden (91%), had conversations about gardening and tree care with others (83%), pruned a tree (76%), or used tree mulch (71%). The majority of them had also planted a tree in the past 5 years (67%) or had visited an arboretum or nursery (55%).

TK

Responses on this nine-item scale ranged from 9 to 27, and it had a mean of 16. This score indicates that typical homeowner thinks he or she has some, but not a lot of, knowledge about tree care and protection. Most homeowners think that they are at least somewhat knowledgeable about planting (85%), caring (82%), trimming (77%), buying a healthy tree (72%), and selecting a suitable tree for their landscape (71%). Fewer respondents think they have enough knowledge to cut down a tree (66%), identify native trees (61%), protect trees from insects and pests (52%), or identify a diseased tree (49%).

Residential tree value

Response on this one-item indicator demonstrates that almost all of the homeowners thought it was either very important (61%) or somewhat important (32%) to have trees on the property when they were looking for a new place to live.

SV

Responses on this one-item indicator suggest that a majority (59%) of homeowners attribute personal, symbolic, or spiritual meaning and value to trees.

Substantive Findings

Before testing our model, appropriate diagnostic tests of Guass–Markov assumptions were performed to ensure the data are normally, identically, and independently distributed. Two scatterplots (residuals vs. the fitted values, and leverage against the squared residuals) revealed clustering at the right and left indicating heteroskedasticity in the data.⁵ The “Ramsey RESET test” using powers of the fitted values of the dependent variable did not indicate nonlinearity. The Breusch–Pagan and Cook–Weisberg test confirmed that the data are heteroskedastic at p < .01. Because ordinary least squares (OLS) in the presence of heteroskedasticity is not efficient and overestimates t values, we estimated variable coefficients using regression with robust standard errors.⁶ The robust correction gives consistent estimates of the standard errors if the distribution of the residuals is not normal. In addition, estimates of variance inflation factors (VIF) and tolerance were used to test for multicollinearity among the independent variables. The mean VIF is 1.32, and a mean VIF greater than unity indicates potentially problematic overlap between variables. Statistically significant correlation coefficients (p < .05) between the substantive independent variables were therefore eliminated by using their “residualized” forms in the primary multiple regression models. Consequently, multicollinearity was rigorously expurgated from the models, and the mean VIF was reduced to 1.01. We will use these residualized measures throughout to avoid multicollinearity of the independent variables.

The pairwise zero-ordered and the residualized bivariate correlation coefficients between the dependent (Homeowner Tree Support) and independent variables are shown in Table 2. The utility of using residualized values to eliminate multicollinearity is demonstrated by the nonsignificant correlations found between the independent variables. However, all of the independent variables are significantly related to Homeowner Tree Support. The correlations suggest that homeowners who have greater support for local tree maintenance and protection have stronger ECs, protree attitudes (PSI and NSI), believe trees provide a wider range of EBs, place more value on the importance of trees when looking for a new place to live (RTV), attribute symbolic meaning or value to trees (SV), and have more knowledge (TK) and experience with trees and landscaping activities (TLE).

Table 2.

Bivariate Correlations Between Homeowner Tree Support and Independent Variables.

Variable	HTS	EC	SV	PSI	NSI	RTV	EB	TLE
HTS (dependent variable)
EC	.401***
EC	.190***
SV	.380***	.286***
SV	.248***	.006
PSI	.296***	.241***	.286***
PSI	.255***	.004	.000
NSI	.360***	.467***	.207***	.135***
NSI	.237***	.000	.068	.050
RTV	.393***	.302***	.380***	.241	.368***
RTV	.333***	.009	−.050	−.008	−.021
EB	.254***	.217***	.301	.325***	.177***	.256***
EB	.041	.046	.000	.000	−.000	−.000
TLE	.087**	.212***	.127***	.084**	.283***	.214***	.100**
TLE	−.047	.063	.058	.039	−.000	.000	−.000
Tree Knowledge	.116**	.103**	.161***	.080*	.104**	.197***	.130***	.470***
Tree Knowledge	.049	−.037	.000	.043	.059	.000	.000	.000

Note: HTS = Homeowner Tree Support; EC = Environmental Concern; SV = Symbolic Value; PSI = Positive Social Impacts; NSI = Negative Social Impacts; RTV = Residential Tree Value; EB = Environmental Benefits; TLE = Tree/Landscape Experience. The independent variables are coded so that positive correlations reflect greater homeowner tree support. Figures in Row 1 are zero-ordered correlations coefficients; figures in Row 2 are residualized correlation coefficients.

p < .05. **p < .01. ***p < .001 (two-tailed).

The next step in our analysis was to perform a series of multiple regression analyses that helped us decompose and interpret the findings to obtain the most parsimonious model that would serve as a foundation for our path analysis. All the independent variables and the sociodemographic variables were included in these models (see Table 3), and they were based on listwise deletion of cases that excluded any case that had a missing value on one or more of the variables included in the analysis. This substantially decreased the sample size of 800 homeowners in each model. Model 1 includes all of the independent variables entered into the multiple regression analysis whereas Model 2 includes the sociodemographic variables. and the “Reduced Model” contains only significant variables from both groups of variables.

Table 3.

Comparison of Multivariate Models of Homeowner Tree Support for Local Urban Tree Protection.

	Standardized regression coefficients
	Model 1	Model 2	Reduced model
Independent variables
Environmental Concern (EC)	.203***		.192***
Positive Social Impact (PSI)	.251***		.246***
Negative Social Impact (NSI)	.219***		.214***
Symbolic Value (SV)	.253***		.207***
Residential Tree Value (RTV)	.335***		.313***
Environmental Benefits (EB)	.029
Tree/Landscape Experience (TLE)	−.078*		−.076*
Tree Knowledge (TK)	.035
Sociodemographic Variables
Age		.054
Education		.080
Environmental Group		.073*	.015
Gender		.179***	.087*
Income		−.031
Length of Residence		−.084
Political Identification		.201***
R ²	.342	.100	.362
Cases	577	606	527

p < .05. **p < .01. ***p < .001 based on listwise deletion of cases.

Six of the eight independent variables were statistically significant, and Model 1 accounted for 34% of the variance, F(8, 568) = 40.01, p < .001. EBs and TK were nonsignificant independent variables and were excluded in the reduced model. Three of the seven sociodemographic variables included in Model 2 were statistically significant and accounted for 10% of the variance, F(7, 598) = 10.46, p < .001. These findings are in line with other studies that report that sociodemographic variables usually explained only about 10% of the variance in public support for environmental policies (Jones & Dunlap, 1992). The reduced model included six independent and the three sociodemographic variables. Eight of the nine variables were statistically significant and accounted for 36% of the variance in the outcome variable, F(9, 517) = 32.48, p < .001.

Further multiple regression analysis was used to test the possible indirect effects of TK and EBs, on homeowner support through the significant predictor variables in the reduced model (i.e., EC, PSI, NSI, SV, RTV, TLE). Because we are interested in how well TK and EB predict the significant (residualized) predictors of Tree Support, we do not use the residualized versions of these two variables, as doing so would eliminate any correlation between them. As indicated in Table 1, the zero-ordered correlation coefficient between TK and EB is negligible, and therefore, this weak link was excluded from the analysis. These findings demonstrated that both EBs and TK had an indirect effect on homeowner support. However, except for the highly correlated (r = .470) linkage between TK and TLE, they explained little variation in each of the predictor variables and added very little explanatory power to the reduced model.

Figure 1 presents the diagram of the path analysis and the path (Beta) coefficients. Path analysis builds on the multiple regression analyses by tracing out the implications of direct and indirect effects on the dependent variable, aids in the interpretation of the findings, and helps to assess our model. Overall, it suggests that homeowners who believe that urban trees have more PSIs and less NSIs, have greater ECs, place more value on the importance of trees when looking for a new place to live (RTV), attribute symbolic meaning or value to trees (SV), and have less tree and landscape experience are more likely to support local urban tree protection policies. Women and Democrats are also more supportive than their respective counterparts. Specifically, these homeowners want local government to do more to protect local trees, more local government funding allocated for planting local public trees and want stronger rules to protect historical (larger and older) trees on residential properties. In addition, EB and TK had only an indirect effect on homeowner support.

Figure 1.

Path model of homeowner tree support with standardized path (beta) coefficients.

The path model supports most of the features of the Integrated Model of Urban Tree Support in that five of the six substantive variables (NSI, PSI, EC, RTV, SV) were confirmed to have a direct link to the dependent variable. The indirect effect of TK stipulated by our model was also confirmed. Contrary to our model, the findings show that EB did not have a direct effect; instead, it had an indirect effect on Homeowner Support through several (i.e., RTV, NSI, PSI, SV) predictor variables. Although the reduced model shows a direct effect of Tree and Landscape Experience on the dependent variable, our model assumes it has an indirect effect. Moreover, its direct effect was negative rather positive.⁷ As expected, the five independent variables in the reduced model explained most of the variation, and the sociodemographic variables explained very little variance in the dependent variable (see Table 2).

Conclusion and Discussion

The article discussed the value and importance of urban trees and identified factors that significantly influence homeowner support for collective actions designed to better manage and protect urban trees. Based on a comprehensive review of the research, it concluded that urban trees provide a wide range of benefits that represent significant value. The Integrated Model of Urban Tree Support was tested that drew mainly from the literature, but it also included two variables (RTV and SV) that have not been regularly included in survey studies examining public support for urban tree protection.

Factors that influence support for tree protection policies and actions were identified by examining mail survey data obtained from a representative sample of 800 homeowners living in a major urban area in Southern Appalachia. A series of multiple regression and a path analysis identified a model that explained 36% of the variance in homeowner tree support and supported many of the features of the model. In addition, our study effectively eliminated multicollinearity between the independent variables and performed a series of factor analyses that decreased potential theoretical concerns related to construct redundancy and potential methodological ones related to common response formats found in previous survey-based studies. Consequently, we believe that the findings based on the final (reduced) model should provide very conservative and reliable estimates of the homeowner tree support. Nevertheless, eliminating the “joint variance” shared between the independent variables in explaining the dependent variable comes at a cost because doing so ignores potentially important processes between the independent variables that may inhibit our ability to understand the richness and complexity of attitudes, beliefs, and values associated with environmental and tree protection issues (see Schoen, DeSimone, & James, 2011, pp. 674-675).

Overall, this research provides a basic way of conceptualizing and understanding public support for urban tree protection. It also provides a solid foundation for testing more complex models for understanding public support in other places and times (see Routhe et al., 2005). It demonstrates that knowledge of the social-psychological factors is essential for understanding support for urban tree protection. Knowledge of these factors can be used to help design urban tree policies and programs that are better adapted to the needs of both human and biophysical systems. Knowing these factors would also help policymakers minimize possible risk of alienating key stakeholders or potential tree supporters.

Such an adaptive management approach would acknowledge economic values and views about the role of local government and how they can potentially influence public support of urban tree protection. For example, many tree ordinances tend to focus on appeasing land developers for fear that strict rules about tree preservation would hinder economic growth. Although broad ECs may resonate with homeowners, it is understandable that some would support tree protection only to the extent that they appreciate trees in their daily lives in tandem with their ability to pay for any increase in taxes for tree protection and maintenance. Most importantly, many Americans may view tree protection less favorably if there is the possibility of devaluation of personal property or a loss of perceived freedom. No doubt, these negative perceptions and others concerning the role of government and its efficacy (Lubell, 2002) may be minimized by increasing public knowledge of the overall value of trees, especially with regard to residential preferences and relocation (see Zhang et al., 2007), and by greater community involvement in designing and implementing local tree maintenance and protection policies. Increased public concern for preserving urban cover while ensuring economic growth, environmental protection, and community cohesion will also demand greater public understanding of complex interactions between social and biophysical systems.

It is also possible that urban tree protection will not become a major environmental issue until urban growth and development significantly overshoot key ecological thresholds. But before more of the value of urban trees is lost, it will be necessary for policymakers, arborists, businesses, community groups, and other key stakeholder groups to start building partnerships that will support sustainable urban management policies that includes public education and involvement. Such a collaborative approach would appeal to local factors that play a critical role in the perceived quality of life by showing how tree protection is a small “investment” that can grow into strong and consistent returns to the community and to its future.

Such a forward-looking approach could also be used to adapt a tree ordinance to changing conditions in the future. For example, public demands for preserving tree canopy may initially increase as land gets developed, then lessen as people get used to a more highly developed landscape with less green space. This scenario would require the adaptation of a local tree ordinance to emphasize the protection of trees in singular settings rather than in stands. In regulating tree protection during increased development, policymakers must also anticipate how to appeal to new residents who may not have the same life experience with and knowledge of trees as long-term residents (Jones et al., 2003). No doubt, social-pychological models that are able to integrate these things will continue to play an important role in understanding factors influencing local urban tree support and how they may change in the future.

The United Nations campaign launched in 2006 by Nobel Peace Prize laureate and Greenbelt Movement founder Dr. Wangari Maathai (1940-2011) to plant a billion trees was a heroic effort to combat social injustice, rural poverty, and climate change. Still, many of the EBs of these trees will only begin to accrue years from now while mature trees are rapidly being cut down all over the world. Moreover, many urban areas in the United States, and especially cities in Southern Appalachia, still do not have comprehensive urban forestry programs and need to be convinced of the advantages of properly maintaining and protecting their existing trees (Ries, Reed, & Kresse, 2007). Obviously, tree planting is very important, but these and other long-term solutions to global climatic change should not detract from the immediate need to develop and implement comprehensive programs to maintain and protect existing trees.

No doubt, the protection of the urban tree canopy necessitates difficult tradeoffs. Implementing an effective management strategy is further complicated when views in a community vary about the appropriate level of legislation for meeting environmental standards. Ultimately, community and business groups, public officials, and other key stakeholders must come together to decide which mix of social, economic, symbolic, and environmental values is important when crafting a policy to protect the environment, sustain their communities, and ensure their collective future.

Footnotes

Acknowledgements

The authors thank the reviewers, Barbara B. Brown, Mary Breen, Eileen Leahy and Daniel DeNapoli for their help and guidance.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was partially funded by the State of Tennessee Department of Agriculture, Division of Forestry and the University of Tennessee Waste Management Research and Education Institute.

Notes

Author Biographies

Robert Emmet Jones is an associate professor in the Department of Sociology and a Fellow at the Institute for a Secure and Sustainable Environment and the Center for the Study of Social Justice at the University of Tennessee. He examines the human dimensions of environmental change on projects related to support for environmental protection and justice, green agriculture and genetically modified foods, and water, forests, and coal issues in Appalachia and the United States.

Kimberly L. Davis is a senior environmental engineer at the engineering consulting firm Spectra Tech, Inc. in Oak Ridge, Tennessee. She received her doctoral degree in sociology from the University of Tennessee in 2011 and specializes in using theoretically derived, agent-based behavior modeling for assessing public acceptance of ecosystem management plans for municipalities, watersheds, and across other geographic scales.

John Bradford is a visiting professor at the University of Alabama–Huntsville. He received his doctoral degree in sociology from the University of Tennessee in 2010. His current research focuses on how technological, ecological, and symbolic resources influence the formation of, and dynamic patterns exhibited by, organizational structures using computational, multiagent simulations, as well as network and communication theory.

References

Allen

B. M.

(1997). Alabama tree ordinances, protection and preservation on construction sites. Auburn, AL: Auburn University.

American Forest Foundation. (2011). The UN international year of forests 2011 kicks off. Retrieved from http://www.forestfoundation.org/-international-year-of-forests-html

American Forests. (2002). Urban ecosystem analysis, Knox County, Tennessee. Washington, DC: Author.

American Forests. (2006, June 22). Annual report: American forests 2005; The path to recovery. Washington, DC: Author.

Anderson

L. M.

Cordell

H. K.

(1988). Influence of trees on residential property-values in Athens, Georgia (USA)—A survey based on actual sales prices. Landscape and Urban Planning, 15, 153-164.

Balram

Dragicevic

(2005). Attitudes toward urban green spaces: Integrating questionnaire survey and collaborative GIS techniques to improve attitude measurements. Landscape and Urban Planning, 71, 147-162.

Barreto

M. A.

Marks

M. A.

Woods

N. D.

(2007). Homeownership—Southern California’s new political fault line? Urban Affairs Review, 42, 315-341.

Barro

S. C.

Gobster

P. H.

Schroeder

H. W.

Bartram

S. M.

(1997). What makes a big tree special? Insights from the Chicagoland treemendous trees program. Journal of Arboriculture, 23, 239-249.

Carreiro

M. M.

Zipperer

W. C.

(2008). Urban forestry and the eco-city: Today and tomorrow. In Carreiro

M. M.

Song

Y.-C.

(Eds.), Ecology, Planning, and Management of Urban Forests: International Perspectives (pp. 435-456). New York, NY: Springer.

10.

Chiesura

(2004). The role of urban parks for the sustainable city. Landscape and Urban Planning, 68, 129-138.

11.

Clendenning

Field

D. R.

Kapp

K. J.

(2005). A comparison of seasonal homeowners and permanent residents on their attitudes toward wildlife management on public lands. Human Dimensions of Wildlife, 10, 3-17.

12.

Coder

(1996). Cultural aspects of trees: Traditions and myth. Athens, GA: Cooperative Extension Service, Forest Resources Unit, University of Georgia.

13.

Connelly

Brown

T. L.

Decker

D. J.

(2003). Factors affecting response rates to natural resource-focused mail surveys: Empirical evidence of declining rates over time. Society & Natural Resources, 16, 541-549.

14.

Cottrell

S. B.

(2003). Influence of sociodemographics and environmental attitudes on general responsible environmental behavior among recreational boaters. Environment & Behavior, 35, 347-375.

15.

Davis

K. L.

(2011). The role of sense of place: A theoretical framework to aid urban forest decision-making (Doctoral dissertation). The University of Tennessee, Knoxville, Tennessee.

16.

Dickerson

S. D.

Groninger

J. W.

Mangun

J. C.

(2001). Influences of community characteristics on municipal tree ordinances in Illinois, U.S. Journal of Arboriculture, 27, 318-325.

17.

Dietz

Fitzgerald

Shwom

(2005). Environmental values. Annual Review of Environment and Resources, 30, 335-372.

18.

Dillman

D. A.

(1999). Mail and internet surveys: The tailored design method. New York, NY: John Wiley.

19.

Dunlap

R. E.

Jones

R. E.

(2002). Environmental concern: Conceptual and measurement issues. In Dunlap

R. E. W. M.

(Ed.), Handbook of environmental sociology (pp. 482-524). Westport. CN: Greenwood.

20.

Dwyer

J. F.

McPherson

E. G.

Schroeder

H. W.

Rowntree

R. A.

(1992). Assessing the benefits and costs of the urban forest. Journal of Arboriculture, 18, 227-234.

21.

Dwyer

J. F.

Nowak

D. J.

Noble

M. H.

Sisinni

S. M.

(2000). Connecting people with ecosystems in the 21st century: An assessment of our nation’s urban forests (No. A 13.88:PNW-GTR-490). Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

22.

Dwyer

J. F.

Schroeder

H. W.

Gobster

P. H.

(1991). The significance of urban trees and forests: Toward a deeper understanding of values. Journal of Arboriculture, 17, 276-284.

23.

Hansen

M. C.

Stehman

S. V.

Potapov

P. V.

(2010). Quantification of global gross forest cover loss. Proceedings of the National Academy of Scientists, PNAS Early Edition, 1-6.

24.

Hansen-Moller

Oustrup

(2004). Emotional, physical/functional and symbolic aspects of an urban forest in Denmark to nearby residents. Scandinavian Journal of Forest Research, 19, 56-64.

25.

Hastie

(2003). The benefits of urban trees. UK: Warwick District Council. Retrieved from http://www.naturewithin.info/UF/TreeBenefitsUK.pdf

26.

Heynen

Perkins

H. A.

Roy

(2006). The political ecology of uneven urban green space—The impact of political economy on race and ethnicity in producing environmental inequality in Milwaukee. Urban Affairs Review, 42, 3-25.

27.

Hungerford

H. R.

Volk

T. L.

(1990). Changing learner behavior through environmental education. Journal of Environmental Education, 21, 8-21.

28.

Ignatow

(2006). Cultural models of nature and society. Environment & Behavior, 38, 441-461.

29.

Jensen

Gatrell

Boulton

Harper

(2004). The urban environment, socioeconomic conditions, and quality of life: An alternative framework for understanding and assessing environmental justice. In Jensen

R. R.

Gatrell

J. D.

McLean

D. D.

(Eds.), Geo-spatial technologies in urban environments (pp. 63-72). New York, NY: Springer.

30.

Johnston

(1996). A brief history of urban forestry in the United States. Arboricultural Journal, 20, 257-278.

31.

Jones

R. E.

Dunlap

R. E.

(1992). The social bases of environmental concern: Have they changed over time? Rural Sociology, 57, 28-47.

32.

Jones

R. E.

Fly

J. M.

Cordell

H. K.

(1999). How green is my valley? Tracking rural and urban environmentalism in the Southern Appalachian Ecoregion. Rural Sociology, 64, 482-499.

33.

Jones

R. E.

Fly

J. M.

Talley

Cordell

H. K.

(2003). Green migration into rural America: The new frontier of environmentalism? Society & Natural Resources, 16, 221-238.

34.

Kaltenborn

B. P.

Bjerke

(2002). Associations between environmental value orientations and landscape preferences. Landscape and Urban Planning, 59, 1-11.

35.

Kaplan

(1992). Urban forestry and the workplace (No. NC-163). Chicago, IL: USDA Forest Service, North Central Forest Experiment Station.

36.

Kaplan

(2001). The nature of the view from home—Psychological benefits. Environment & Behavior, 33, 507-542.

37.

Kaplan

(1989). The experience of nature: A psychological perspective. Cambridge, UK: Cambridge University Press.

38.

Kellert

S. R.

Wilson

E. O.

(1993). The Biophilia Hypothesis. Washington, D.C.: Island Press/Shearwater Books.

39.

Kielbaso

J. J.

(2008). Management of urban forests in the United States. In Carreiro

M. M.

Song

Y.-C.

(Eds.), Ecology, planning, and management of urban forests: International perspective (pp. 240-258). New York, NY: Springer.

40.

Knoxville-Knox County Metropolitan Planning Commission. (2007). The Knox County Tree Conservation & Planting Plan. Metropolitan Planning Commission of Knoxville-Knox County. Knoxville, Tennessee.

41.

Konijnendijk

C. C.

(2003). A decade of urban forestry in Europe. Forest Policy and Economics, 5, 173-186.

42.

Krus

D. J.

Wilkinson

S. M.

(1986). Demonstration of properties of a suppressor variables. Behavioral Research Methods, Instruments, and Computers, 18, 21-24.

43.

Kuo

F. E.

(2003). The role of arboriculture in a healthy social ecology. Journal of Arboriculture, 29(3), 148-155.

44.

Kuo

F. E.

Bacaicoa

Sullivan

W. C.

(1998). Transforming inner-city landscapes—Trees, sense of safety, and preference. Environment & Behavior, 30, 28-59.

45.

Kuo

F. E.

Sullivan

W. C.

(2001). Environment and crime in the inner city: Does vegetation reduce crime? Environment & Behavior, 33, 343-365.

46.

Lin

(2007, December 21). Step away from the clippers: New laws to protect trees complicate homeowners’ lives: Saving the persimmon. The Wall Street Journal, p. W14.

47.

Lohr

V. I.

Pearson-Mims

C. H.

(2005). Children’s active and passive interactions with plants influence their attitudes and actions toward trees and gardening as adults. Horttechnology, 15, 472-476.

48.

Lohr

V. I.

Pearson-Mims

C. H.

Tarnai

Dillman

D. A.

(2004). How urban residents rate and rank the benefits and problems associated with trees in cities. Journal of Arboriculture, 30, 28-35.

49.

Lorenzo

A. B.

Blanche

C. A.

Guidry

M. M.

(2000). Assessing residents’ willingness to pay to preserve the community urban forest: A small-city case study. Journal of Arboriculture, 26, 319-325.

50.

Lubell

(2002). Environmental activism as collective action. Environment & Behavior, 34, 431-453.

51.

Maller

Townsend

Pryor

Brown

St. Leger

(2006). Healthy nature healthy people: “Contact with nature” as an upstream health promotion intervention for populations. Health Promotion International, 21, 45-54.

52.

Mansfield

Pattanayak

S. K.

McDow

McDonald

Halpin

(2005). Shades of green: Measuring the value of urban forests in the housing market. Journal of Forest Economics, 11, 177-199.

53.

McPherson

E. G.

(1994). Using urban forests for energy efficiency and carbon storage. Journal of Forestry, 92, 36-41.

54.

McPherson

E. G.

(1995). Net benefits of healthy and productive urban forests. In Bradley

G. A.

(Ed.), Urban forest landscapes: Integrating multidisciplinary perspectives. Seattle: University of Washington Press.

55.

McPherson

E. G.

(2006). Urban forestry in North America. Renewable Resources Journal, 3, 8-12.

56.

McPherson

E. G.

(2007). Benefit-based tree valuation. Journal of Arboriculture, 33, 1-11.

57.

McPherson

E. G.

Rowntree

R. A.

(1993). Energy conservation potential of urban tree planting. Journal of Arboriculture, 19, 321-331.

58.

McPherson

E. G.

Simpson

J. R.

Peper

P. J.

Xiao

(1999). Benefit-cost analysis of Modesto’s municipal urban forest. Journal of Arboriculture, 25, 235-248.

59.

Metropolitan Planning Commission of Knoxville/Knox County. (2005). The Knox County tree conservation & planting plan. Knoxville, TN: Author.

60.

Miles

Sullivan

W. C.

Kuo

F. E.

(1998). Ecological restoration volunteers: The benefits of participation. Urban Ecosystems, 2, 27-41.

61.

Mobley

Vagias

W. M.

DeWard

S. L.

(2010). Exploring additional determinants of environmentally responsible behavior: The influence of environmental literature and environmental attitudes. (2010). Environment & Behavior, 42, 420-447.

62.

Morales

D. J.

Micha

F. R.

Weber

R. L.

(1983). Two methods of valuating trees on residential sites. Journal of Arboriculture, 9, 21-24.

63.

Nowak

D. J.

Walton

J. T.

(2005). Projected urban growth and its estimated impact on the U.S. forest resource (2000-2050). Journal of Forestry, 103, 383-389.

64.

O’Brien

E. A.

(2006). A question of value: What do trees and forests mean to people in Vermont? Landscape Research, 31, 257-275.

65.

Orland

Vining

Ebreo

(1992). The effect of street trees on perceived values of residential property. Environment & Behavior, 24, 298-325.

66.

Parsons

(1991). The potential influences of environmental perception on human health. Journal of Environmental Psychology, 11, 1-23.

67.

Pauleit

(2003). Urban street tree plantings: Identifying the key requirements. Municipal Engineer, 156, 43-50.

68.

PEW Research Center for the People and the Press. (2009). On Obama’s Desk: Economy, jobs trump all other priorities. Retrieved from http://pewresearch.org/pubs/1087/economy-jobs-top-public-priorities-2009

69.

Rice

(2010). U.S. losing trees faster than other forested nations. Retrieved from http://www.usatoday.com/news/nation/environment/2010-04-27-forest27_ST_N.htm

70.

Ries

P. D.

Reed

A. S.

Kresse

S. J.

(2007). The impact of statewide urban forestry programs: A survey of cities in Oregon, U.S. Arboriculture & Urban Forestry, 33, 168-175.

71.

Rival

(1998). The social life of trees: Anthropological perspectives on tree symbolism. Oxford, UK: Berg.

72.

Rolston

(1988). Human-values and natural systems. Society & Natural Resources, 1, 271-283.

73.

Routhe

A. S.

Jones

R. E.

Feldman

D. L.

(2005). Using theory to understand public support for collective actions that impact the environment: Alleviating water supply problems in a non-arid biome. Social Science Quarterly, 86, 874-897.

74.

Sasidharan

Thapa

(1999). Ethnicity and variations in wildlife concern: Exploring the socio-structural and sociopsychological bases of wildlife values. Paper presented at the 4th International Urban Wildlife Symposium, Tucson, AZ.

75.

Scarlett

(2010). Green, Clean, and Dollar Smart Ecosystem Restoration in Cities and Countryside. New York, NY: Environmental Defense Fund.

76.

Schoen

J. L.

DeSimone

J., A

James

L. R.

(2011). Exploring joint variance between independent variables and a criterion: Meaning, effect and size. Organizational Research Methods, 14, 674-695.

77.

Schroeder

H. W.

(1988). Visual impact of hillside development: Comparison of measurements derived from aerial photographs and ground-level photographs. Landscape and Urban Planning, 15, 119-126.

78.

Schroeder

Flannigan

Coles

(2006). Residents’ attitudes toward street trees in the UK and U.S. communities. Arboriculture & Urban Forestry, 32, 236-246.

79.

Simpson

J. R.

McPherson

E. G.

(1996). Potential of tree shade for reducing residential energy use in California. Journal of Arboriculture, 22, 10-18.

80.

Sommer

Guenther

Barker

P. A.

(1990). Surveying householder response to street trees. Landscape Journal, 9, 79-85.

81.

Sorensen

Hayes

J. G.

Marina

(2000). Crime Prevention Through Environmental Design (CPTED): Workbook. Washington, DC: U.S. Department of Housing and Urban Development.

82.

Summit

Sommer

(1998). Urban tree-planting programs—A model for encouraging environmentally protective behavior. Atmospheric Environment, 32, 1-5.

83.

Taylor

A. F.

Kuo

F. E.

Sullivan

W. C.

(2001). Coping with ADD: The surprising connection to green play settings. Environment & Behavior, 33, 54-77.

84.

Tennessen

C. M.

Cimprich

(1995). Views to nature—Effects on attention. Journal of Environmental Psychology, 15, 77-85.

85.

Treiman

(2006). Are residents willing to pay for their community forests? Results of a contingent valuation survey in Missouri, USA. Urban Studies, 43, 1537-1548.

86.

Treiman

Gartner

(2005). What do people want from their community forests? Results of a public attitude survey in Missouri, U.S. Journal of Arboriculture, 31, 243-250.

87.

Tyrväinen

Mäkinen

Schipperijn

(2007). Tools for mapping social values of urban woodlands and other green areas. Landscape and Urban Planning, 79(1),5-19.

88.

Ulrich

R. S.

(1981). Natural versus urban scenes: Some psychophysiological effects. Environment & Behavior, 13, 523-556.

89.

Ulrich

R. S.

(1991). Effects of health facility interior design on wellness: Theory and recent scientific research. Journal of Health Care Design, 3, 97-109.

90.

United Nations Food and Agriculture Organization. (2011). Increased forest threat from extreme weather. Retrieved from www.fao.org/news/story/en/item/86570/icode/

91.

U.S. Census Bureau. (2006). Population and housing narrative profile: 2006-2008. Retrieved from http://factfinder.census.gov

92.

U.S. Department of Agriculture Forest Service. (2003). Benefits of urban trees: Urban and community forestry: Improving our quality of life. In Southern Region (Ed.), Urban forestry manual. Athens, GA: Author.

93.

U.S. Department of Agriculture Forest Service. (2004). Urban forestry manual—Benefits and costs of the urban forest. Athens, GA: Author.

94.

U.S. Department of Agriculture Forest Service–Northern Research Station. (2011). Urban trees canopy analysis helps Urban Planners With Tree Planting campaigns. NRS Research Review, 13(Summer), pp. 1-5.

95.

Wolf

K. L.

(1998). Enterprising landscapes: Business districts and the urban forest. In Kollin

(Ed.), Cities by nature’s design: Proceedings of the 8th national urban forest conference. Washington, DC: American Forests.

96.

Wolf

K. L.

(1999). Grow for the gold: Trees in business districts. Olympia: Washington State Department of Natural Resources.

97.

Wolf

K. L.

(2003). Introduction to urban and community forestry programs in the United States. Landscape and Planning and Horticulture (Japan), 4, 19-28.

98.

Wolf

K. L.

(2004). Economics and public value of urban forests [Special Issue on Urban and Periurban Forestry]. Urban Agriculture Magazine, 13, 31-33.

99.

Wolf

K. L.

(2005). Trees in the small city retail business district: Comparing resident and visitor perceptions. Journal of Forestry, 103, 390-395.

100.

Wolf

K. L.

(2006). Roadside urban trees: Balancing safety and community values. Arborist News, 56-57.

101.

Wolf

K. L.

(2007). City trees and property values. Arborist News, 34-36.

102.

Youngentob

Hostetler

(2005). Is a new urban development model building greener communities? Environment & Behavior, 37, 731-759.

103.

Zhang

Hussain

Jinyang

Letson

(2007). Public attitudes toward urban trees and supporting urban tree programs. Environment and Behavior, 39, 797-814.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB