Urban Ecosystems (2018) 21:657–671 https://doi.org/10.1007/s11252-018-0760-z
Socioeconomic and ecological perceptions and barriers to urban tree distribution and reforestation programs Leaundre C. Dawes 1 & Alison E. Adams 1 & Francisco J. Escobedo 2 & José R. Soto 3 Published online: 16 April 2018 # Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract Tree planting and reforestation initiatives in urban and peri-urban areas often use tree distribution or Bgiveaway^ programs as a strategy to increase tree cover and subsequent benefits. However, the effectiveness of these programs in terms of increasing overall tree cover and providing benefits to low-income and disadvantaged communities has been little studied. We assess these programs by exploring community participation in, and barriers to, an urban tree distribution program in Fort Lauderdale, United States and the role socioeconomic background and tree functional types have on participation. We use a mixed-methods approach, panel data, choice experiments, and econometrics to quantitatively analyze respondent’s ranking of program options. High income, White respondents had the highest level of awareness and participation while low income, African Americans (AA) had the lowest level. Monetary rebates were perceived as positive and significant as the compensation value increased to US$8.00 - $12.00. Fruit-bearing and native tree functional types were more preferred than flowering or shade trees. Latinos, AA, and high income respondents preferred fruit trees, while White, high income preferred native trees. Overall, low income respondents perceived the greatest barriers towards participation. 20% of Broward County residents who participated in the survey were aware of the tree giveaway programs and 13% had previously participated. Findings indicate an adaptive governance mismatch between program objectives to equitably increase city tree cover via planting shade trees versus individual’s knowledge and preference for other tree types and functions. Results can be used for developing and evaluating reforestation initiatives to equitably increase tree cover and improve the governance of urban ecosystems. Keywords Environmental justice . Adaptive governance . Urban ecosystems . Best-worst-choice . Urban forests . Functional traits
Introduction Urban and peri-urban forests have been documented as providing people with many valuable social, environmental, and economic benefits (Soto et al. 2018). As a result, several municipalities across the globe have promoted tree planting and reforestation programs as a way to increase green * Francisco J. Escobedo
[email protected] 1
School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL 32611-0410, USA
2
Facultad de Ciencias Naturales y Matemáticas, Programa de Biología, Universidad del Rosario, Kr 26 No 63B-48, Bogotá D.C. 111221492, Colombia
3
School of Natural Resources and the Environment, The University of Arizona, 1064 E. Lowell Street - ENR2 #N216, Tucson, AZ 85721-0137, USA
infrastructure, tree cover, and overall sustainability (City Policy Associates 2008). However, urban trees and their ecological functions can also incur social, environmental, and economic costs to city residents (Escobedo et al. 2011). For example, depending on context and tree species selection and planting practices, tree mortality can negatively affect nonprofit or state-based urban tree planting programs and their associated benefits (Koeser et al. 2014; Roman et al. 2014), and removal of large trees can potentially impose a burden on municipal budgets. Green space and urban forest cover can also be inequitably distributed resulting in low income, minority, and disadvantaged communities having less vegetation and benefits (Flocks et al. 2011; Gerrish and Watkins 2018; Locke and Grove 2016; Pedlowski et al. 2002; Szantoi et al. 2012; Watkins and Gerrish 2018). Accordingly, social and political pressure is often placed on many public officials to improve their urban forest programs. Many cities have adopted tree planting and distribution programs that aim to increase urban tree cover; a key indicator
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used by scientists and policy makers to measure, monitor and evaluate the amount of vegetation in a city (Plant et al. 2017; Flocks et al. 2011). However, in some cities public trees only represent a small portion of the urban forest with a substantial portion of urban tree cover falling on private property, mainly residential areas (Nguyen et al. 2017). For example, in MiamiDade County in the United States (US) less than 15% of trees are on public lands (Zhao et al. 2010). Shrinking budgets and public participation in municipal programs and policies is one of the key factors responsible for the continued deterioration of the overall quality of life of disadvantaged communities (Faber 1998). Thus, there is a need to better understand and evaluate urban forest management programs particularly those affecting private property; in doing so, improve adaptive governance of a city’s urban ecosystem (Green et al. 2016). Tree distribution or giveaways have become a common practice and strategy to encourage tree planting on private property (Nguyen et al. 2017) so as to increase overall tree cover (Plant et al. 2017). Yet, in addition to the previously mentioned issues involving municipal urban forest programs, planting a tree can be burdensome for low-income residents who are asked to incur watering costs since residents may be asked to water the newly planted tree for a brief period during establishment (Landry and Chakraborty 2009). Additionally, renters have been shown to have low participation in many urban tree planting initiatives, even though they have lower residential tree canopy cover (Flocks et al. 2011; Landry and Chakraborty 2009; Perkins et al. 2004). Perkins et al. (2004) suggest that renters may have low participation in tree planting initiatives because they do not believe they would reap the benefits resulting from such investment. Recent research has provided insights into how different groups participate in these programs. For example, there is evidence that private landowners – as opposed to renters – are the primary participants in tree planting programs (Perkins et al. 2004; Locke et al. 2015). But other scholars have highlighted how tree-related community programs often neglect differences among community members when designing or re-designing their programs (Greene et al. 2011). Indeed, Nguyen et al. (2017) found that programs do try to account for community preferences as their publics preferred flowering and fruit trees but the study did not identify who participates in these programs and why (see also Conway 2016; Dilley and Wolf 2013; Summit and McPherson 1998). Moreover, even when these programs try to grapple with social inequalities they can inadvertently intensify other issues. For example, Watkins et al. (2017) spatially analyzed the distribution of street tree planting projects in the midwest and northeast US and found that while they might reduce Bincome-based^ inequity, they might be exacerbating Bracebased^ inequities in tree cover. Thus, in spite of the continued use of these types of urban forest programs and policies across
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the world, little is known about community preferences especially in the context of inequality and social justice. Most of the relevant studies have primarily used spatial correlations and qualitative program evaluation methods to indirectly infer community participation in, and preference for, these types of tree distribution programs (Nguyen et al. 2017; Perkins et al. 2004; Watkins et al. 2017). But, little is known about the perception of the general population and residents towards these programs, their features, and the role socioeconomics and tree functional types have on their preferences and barriers towards their participation in these programs. Stated preference surveys of residents and mixed methods, can however provide the opportunity to acquire more data and complement qualitative methods with econometrics to better explore resident perceptions towards these programs (Louviere et al. 2015). Choice experiments in particular, have recently been used by Soto et al. (2016 and 2018) to identify preferences and barriers for forest landowner participation in hypothetical carbon-offset programs, as well as to identify ecosystem service and disservice attributes from urban forests. Thus, such approaches can better add to understating community perceptions towards the incentives and barriers related to tree planning and reforestation policies and programs related to increasing tree cover, reforestation, and restoration objectives. To address this lack of information, this study aims to better understand the community participation in, and barriers to, tree distribution programs and the role that socioeconomics and tree functional types can play in incentives to increase city-wide tree cover. Specifically, we use a stated preference survey in Broward County, Florida, US to study 3 objectives. First, we identity the operational and programmatic barriers and incentives that exist for residents to participate in municipal tree distribution programs. Second, we explore what key socio-economic and demographic factors influence residents’ willingness-to-participate in these programs. Finally, we analyze which specific tree functional traits different socioeconomic groups prefer. To these ends, we use interviews, panel data, and the Best-Worst Choice (BWC) survey method to obtain more data about the respondent’s ranking of options. This method decreases survey length, exhaustion, and complexity (Soto et al. 2018). An analysis of participation and preferences for these tree distribution programs is necessary to not only improve their effectiveness, but also for adaptive governance and environmental justice issues that ensure equitable access for all urban residents. This approach can also be used to evaluate other programs such as reforestation and urban tree planting projects and policies. In turn, this may aid urban planners in increasing forest cover and providing ES access to many low-income citizens. The ultimate purpose of this study is to provide necessary policy uptake information for publicly financed tree planting initiatives to increase tree cover effectively and improve the governance of urban and peri-urban forests.
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Methods Our study focused on Broward County, Florida in the US. This is the second most populated county in Florida and is highly urbanized, has a subtropical climate with rapid vegetation growth rates, and has a diverse range of socioeconomic classes (U.S. Census Bureau 2010 and 2015), which makes it ideal for studying and evaluating the equity of urban afforestation and reforestation efforts. In 2013, the City of Fort Lauderdale, in Broward County, established a policy goal to increase urban tree cover (Feldman 2014). Urban tree cover at the start of this policy was 20.6% and 996 trees were planted in public spaces and provided to residents (Feldman 2014). The stated goal of the city’s tree distribution program is to achieve a tree cover of 23.6% and 7800 trees planted in public spaces or provided to residents over the next five years (Feldman 2014). Since 2013, tree distribution events or Bgiveaways^ hereafter, have been regularly hosted about six times a year in Broward County. These events are officially called Btree giveaways^ and are hosted by the City of Fort Lauderdale, Sustainability Division. Any Broward County resident can show up without pre-registering to a tree giveaway and receive two free trees per mailing address (City of Fort Lauderdale 2016). Proof of county residency is required in the form of a driver’s license or a utility bill (City of Fort Lauderdale 2016). Native trees, including live oak (Quercus viginiana), pigeon plum (Coccoloba diversifolia), gumbo limbo (Bursera simaruba) are given away with instructions on proper planting. These tree distribution events are located in various places in Fort Lauderdale and are advertised on the city website (City of Fort Lauderdale 2016). The urban forestry program in Fort Lauderdale keeps detailed records of all tree distribution or giveaway events; these include: tree giveaway location, number of trees distributed, tree species and the number of trees given to each address and participant. In an effort to provide incentives and increase participation, the urban forester began introducing more appealing trees such as dwarf mango, starfruit, and avocado, but, participation did not improve (G. Dempsey, Municipal Urban Forester, Personal Communication, February 21, 2015). In the case of the City of Fort Lauderdale, program participation in low-income socioeconomic areas – areas that need the most trees – is significantly lower, according to the city’s urban forester. Thus, assessing these tree distribution programs for their effectiveness and equal access is key.
Research design A mixed-method research design was implemented using interviews and surveys. First, qualitative data were gathered using informal, unstructured phone interviews that were conducted with six urban foresters and related planners/policy
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makers in the southern and central Florida area to explore perceptions, gain better understanding of the research subject, and generate or modify hypotheses (Dicicco-Bloom and Crabtree 2006). The interview instrument adopted questions from BUFRG (2014) – these were integrated with others to achieve our study objectives. We then used this information as the basis for a survey instrument that was administered to an online survey panel of 500 participants provided by the marketing firm Qualtrics. These respondents were homeowners and renters who reside in Broward County. The survey was developed and implemented following Dillman et al. (2014) and included: demographic questions, 8 BWC hypothetical program questions (see Fig. 1), along with an index (matrix table) measuring perceptions of barriers to tree giveaways. The survey also asked specific questions cornering tree giveaway awareness and participation as well as preferences for specific tree functional types. The instrument was pre-tested by 20 experts and 46 Broward County residents (Collins 2003). Data collection took place from September to November 2016. Demographic quotas were targeted to reflect a balanced sample of Broward County’s population (see Appendix 1).
Best-worst choice modeling The BWC method, Fig. 1, is a stated preference tool that combines Best-Worst Scaling (BWS) and Binary choice (Binary; Soto et al. 2016 and 2018). The method instructs participants to perform two tasks, first to select a most preferred (i.e., best) and a least preferred (i.e., worst) item, or attribute level, from a profile list of hypothetical tree giveaway institutional characteristics (i.e., BWS task). And second, to consider the entire profile as a single tree giveaway program and choose whether or not they would participate (i.e., BYes^ or BNo^; Binary task). The two tasks produce two types of data: 1) Binary - compatible with traditional economic demand (i.e., WTP), and 2) BWS – direct trade-offs related to program attributes. The BWC method has the advantage of producing more dimensions of utility from a single choice experiment question, which enables a more comprehensive analysis of the stated preference study (Flynn et al. 2007). The survey also included a hypothetical bias calibration tool called certainty scales (Morrison and Brown 2009; Poe et al. 2002). Previous studies show that survey respondents have uncertainty or bias when answering hypothetical questions (Lo and Jim 2015; Poe et al. 2002). However, certainty scales address this potential bias by asking a follow-up question after each BWC question, inquiring about the certainty of their answer (e.g., Bon a scale of 1 to 10, how certain are you about your response?^). Empirical findings have shown that respondents who are uncertain about their answers tend to answer Byes^ to a hypothetical question about paying for a service given that
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Fig. 1 Example best-worst choice question for a hypothetical tree giveaway program
Most Important
Least Important
Proof of residency required Delivery only One time $12 utility bill rebate Only native trees available Would you parcipate in this tree giveaway program?
they will not incur a cost in reality. Morrison and Brown (2009) found that calibrating surveys by switching the Byes^ answers to Bno^ answers for all respondents with certainties less than a 7 on the scale, helps correct for this type of hypothetical bias in discrete choice experimentation models. Accordingly, the first task of BWC, or the BWS task, was analyzed using a BWS Standard Score to determine the level of importance for each attribute (Louviere and Flynn 2010; Loureiro and Arcos 2012). The BWS Score (Eq. 1) is then determined by subtracting the number of times each attribute level was chosen as Bbest^ by the number of times it was chosen as Bworst,^ then divided by the number of times it was available to be chosen. Specifically, BWS Standard Score ¼
Count best −Count worst F*n
ð1Þ
where BCountbest^ is the total number of times the attribute was selected as the most preferred, BCountworst^ is the total number of times the attribute was selected as the least preferred, Bn^ is the number of participants, and BF^ is the frequency of the attribute level appearance within all of the choice sets. Then, task 2 of the BWC method, or the Binary task, was analyzed using two logistical regression models: a quantitatively coded monetary attribute and an effects coded version of this attribute. These models were used to explain the probability of participating in tree giveaways, given alternative combinations of program features. Following Soto et al. (2016), the data were adopted for analysis using STATA 14.2. The dependent variable (yes/no) was coded as follows: 1 if Byes^ and 0 if Bno^ - switching the Byes^ to Bno^ depending on previously mentioned certainty scale calibration. The independent variables were coded using effects coding (see Appendix 2). Effects coding is mean centered and typically preferred to dummy coding (Louviere et al. 2000) and where coding is the same for attributes with only two levels. Attributes with more than two levels are effects coded by embedding or omitting one level. Namely, an attribute level takes the value of 0 if absent, 1 if present, and − 1 if the embedded level is present. The embedded level then equals to the negative sum of the other levels of the given attribute. The following logistical Eq. (2), adapted from Louviere et al. (2000), depicts the typical probability equation used to infer
Yes
No
statistical relationships in Binary choice models; see Louviere et al. (2000) for further details. Pðyesjyes; noÞ ¼ exp V yes = exp V yes þ expðV no Þ
ð2Þ
Choice experiment attributes and levels There were four program attributes selected for the BWC experiment to represent the hypothetical tree giveaway program characteristics or profiles: Broward County residency (proof or no proof required), tree transport (pick-up or delivery), onetime utility rebate (US$0 to US$12), and tree functional type as a proxy for functional traits: native, shade, fruit, or flowering (Table 1). The attributes and levels closely resemble current regulations in tree giveaway programs in Broward County along with new features from similar municipal tree planting programs throughout Florida and the U.S. (City of Portland 2016; City of Orlando 2016; Chakraborty 2006). The attributes in Table 1 were used to create various hypothetical tree giveaway program profiles. To reduce survey length, profiles were created using the SAS (Statistical Analysis System) orthogonal design macro (i.e., %mktex). This resulted in sixteen profiles and questions, presented in two blocks of eight; each block was randomly assigned to each respondent - with each block retaining an equal number of responses. Additionally, summated rating indices were integrated into an index that measures perceptions of barriers to tree giveaways. First, the panel survey explained what tree giveaways were and how the city of Fort Lauderdale operates the events. Then, survey respondents were asked two questions with a yes or no answer: 1) Were you aware that the city of Fort Lauderdale has hosted Btree giveaway^ events, in which free trees are given away multiple times per year?; and 2) Have you ever participated in a tree giveaway event in Broward County? Data from these questions indicated the current levels of awareness and participation among a representative sample of Broward County residents. The summated score index was created to assess and compare respondents’ total perceptions of barriers to tree giveaways in their respective communities. Respondents were prompted to indicate the extent to which the following barriers would prevent them from attending a tree giveaway within 10 miles from their home that would
Urban Ecosyst (2018) 21:657–671 Table 1 Attributes and levels used to create choice experiment questions
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Attribute
Definition
Levels
Residency
May or may not be required to show proof for receiving free tree(s)
No Proof of Residency Proof of Residency
Tree Transport
Options for methods of receiving free tree(s)
Pick-up
Utility Rebate
One-time credit towards city water/sewer bill upon receiving free tree(s)
Delivery US$0.00 US$4.00 US$8.00 US$12.00
Tree Functional Type
Options for only a single type of tree available during a tree giveaway
Native Trees Shade Trees Fruit Trees Flowering Trees
require them to pick-up a free tree. A four-point ordinal set of response options followed each barrier item, where one corresponds to not at all and four corresponds to a great deal. The composite barrier score that was generated from the index was a sum of the rating responses to all of the items. Some of these items included Bnot enough time,^ Black of access to transportation,^ and Black of experience/skill/knowledge to care for trees.^ The selected items for the barriers index were hypothesized with help from our initial interviews.
Statistical analysis The statistical software STATA 14.2 was used to analyze all quantitative data. The Blogit^ command was used to analyze the Binary data, while basic summary statistic commands were used to develop the BWS Scores. As previously mentioned, all Binary independent variables were calibrated for hypothetical bias using certainty scales. Specifically, we adjusted participation rates to account for this hypothetical bias using the following values, participation: without certainty scales was adjusted using 35.75% for No and 64.25% for Yes and with certainty scales, was adjusted to 51.67% for No and 48.33% for Yes. As previously noted, the monetary attribute, or utility rebate, of the Binary data task was coded in two different ways: quantitatively and with effects coding, while all other independent variables remained effects coded. The quantitative coding used the following four monetary levels ($0.00, $4.00, $8.00, $12.00) and were used to better estimate the resident’s willingness-to-accept (WTA) for the other non-monetary attributes.
Results Interviews show that urban foresters tend to make a priori assumptions about different demographic groups preference
for this program and specific tree functional types. For example, one urban forester suggested that more Hispanic residents participated when fruit trees were available. Urban foresters also suggested that residents with higher education would prefer to participate when native trees were available. Also, some participants prefer to select and transport their own trees as they do not want strangers in their property or prefer to select the biggest or most healthy available tree. Additionally, according to one urban foresters, participants may be more invested into carefully planting and maintaining a free tree if they had to pick it up from a giveaway themselves since they have already invested in valuable time and effort. There was also a presumption by interviewees that shade trees have a bad reputations because they are seen as needing more space, causing more post-hurricane debris, and needing more maintenance due to their size and higher quantity of leaves and branches. The online survey returned 500 completed and verified surveys. Qualtrics sent out a total of 20,250 survey invitations, 1870 of which were started. There were 1165 respondents that were screened-out, or removed, from the survey due to either the Bspeeder^ check, where respondents took the survey in less than one-third the median survey completion time, or they did not pass an attention check, where one statement on a rating scale list indicated to please select Bmildly disagree^ for the last statement; these quality control tests ensured that participants took their time to read and follow survey instructions (Soto et al. 2018). Lastly, there were 205 survey breakoffs, meaning they left the survey before its completion. Appendix 1 shows the Broward County, Florida sample demographic characteristics as well as U.S. Census Bureau (2015) estimations for demographic characteristics in Broward County, Florida for comparison. We note that we slightly under-sampled, as compared to the Census, the following categories: residents with less formal education, those in lower income brackets, and those who fall under the highest age brackets.
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We found that 20.60% of respondents were aware of the tree giveaways being held by the city and 13.20% had participated in a tree giveaway (Table 2). Approximately 7% were aware, but had not participated previously. The respondents from Broward County zip codes located within the city of Fort Lauderdale were more aware than respondents from zip codes that were not considered located within the city of Fort Lauderdale. White/Caucasian respondents had the highest level of current awareness and participation while Black/African American respondents had the lowest level of previous participation in tree giveaways. Lastly, respondents with above average annual income (>US$50,000) were more aware and had participated more than lower average annual income (US$50,000 per year Income
