ReviewThe relationship between urban forests and income: A meta-analysis
Introduction
Traditionally, quantitative environmental justice research has been concerned with the extent to which low-income and minority communities are exposed to environmental hazards and lack access to environmental amenities. As research increasingly considers the causes of and potential remedies for environmental inequity, important questions remain about the size and scope of the problem itself. While several reviews have been conducted of the environmental hazards literature (Ringquist, 2005; Mohai, Pellow, & Roberts, 2009), little synthesis has been conducted on the distribution of environmental amenities. We conducted a systematic review and meta-analysis of an important environmental amenity, urban forest cover, and its relationship to income.
Urban forests—the land in and around areas of intensive human influence which is occupied by trees and associated natural resources (definition modified from Strom, 2007)—provide many environmental and health benefits to urban residents (Rosenzweig et al., 2006, Kuo, 2001, Donovan and Butry, 2010). Over the last several decades, studies have considered the empirical distribution of the urban forest with respect to an array of socioeconomic characteristics. Findings across studies have been mixed with respect to income; most studies find a positive relationship between urban forest cover and income (Danford et al., 2014; Heynen, Perkins, & Roy, 2006; Landry & Chakraborty, 2009; Locke & Grove,2014; Pham, Apparicio, Séguin, Landry, & Gagnon, 2012; Schwarz et al., 2015) but there are some exceptions (Pham, Apparicio, Landry, Séguin, & Gagnon, 2013; Grove, Locke, & O’Neil-Dunne, 2014).
While some evidence suggests income-based urban forest inequity exists, the magnitude of estimates varies across studies and the average magnitude is unknown. Moreover, the city-specific nature of previous research and variation in methodological choices across studies raise questions about the source of differences—does variation in results reflect real differences between study sites, or is it a product of methodological choices? Answering these questions will inform current research on the drivers of urban forest cover inequity, methodological choices in environmental justice research, and ongoing efforts to increase forest cover in cities around the world (McPherson & Young, 2010). This project aggregated information from many city-specific studies to estimate the average effect size (average relationship) between urban forest cover and income. A companion paper examined the relationship between urban forest cover and race (Watkins & Gerrish, 2017). Our analysis also examined potential explanations for variation across studies by controlling for characteristics of the original studies such as their empirical strategies and study location. By quantifying the findings from the relevant literature, meta-analysis yields a more complete summary of the state of our collective knowledge as compared to a systematic review.
Meta-analysis is particularly useful in the case of urban forest equity because it synthesizes several literatures that might not normally interact. In addition to studies that are explicitly concerned with environmental justice and inequity, there are many studies that estimate the same relationship to achieve other research objectives, including to evaluate competing theories about drivers of urban land use (Boone, Cadenasso, Grove, Schwarz, & Buckley, 2010; Grove, Cadenasso, Burch, Pickett, Schwarz, O'Neil-Dunne, et al., 2006), draw insights about the choices of private citizens (Pham et al., 2013; Grove, Locke, & O’Neil-Dunne, 2014) or public servants (Landry & Chakraborty, 2009), and improve urban forest cover measurement (Szantsoi, Escobedo, Dobbs, & Smith, 2008). This study diversity gave us a unique ability to evaluate the sensitivity of study results to methodological choices, a concern articulated by environmental justice scholars but not yet evaluated with respect to environmental amenities.
A note on terminology: scholars define and measure both urban forest cover and income in numerous ways. For example, some scholars include herbaceous cover (grass and shrubs) in their measure of urban forest cover, while others limit their measure strictly to trees or tree cover. For simplicity, we use urban forest cover as a catch-all term for our outcome of interest. We use income to describe measures of financial resources, including poverty rates or “high poverty” dichotomous indicators.
The remainder of this paper is organized as follows: the next section discusses our data collection process, including the literature search process and the inclusion criteria. We then discuss reasons that urban forest cover inequity may vary across studies according to the literature. We then discuss our coding of effect sizes and relevant covariates and the meta-analytic methods used in this analysis (forest plots and meta-regression). We report results, discuss their implications for research and urban forest policy, and conclude.
Section snippets
Literature search
We implemented this meta-analysis as outlined by Ringquist (2013) and Borenstein, Hedges, Higgins, and Rothstein (2009). First, in the scoping stage we refined and operationalized our research question and identified the focal predictors (see inclusion criteria below). Second, we populated a complete list of acceptable measures of the dependent variable (i.e. urban forest cover) and generated coding instruments.
We then conducted a systematic and exhaustive search of the existing literature to
Explaining variation in urban forest cover
The relationship between urban forests and income is likely to vary based on an array of factors. After our review of the existing literature (see detailed search methods, above), we grouped hypothesized drivers of urban forest cover into three primary categories: methodological choices, measurement of the outcome and independent variables, and characteristics of the study site. We also examined other factors important to meta-analysis, such as publication status, publication outlet, and study
Data coding and methods
Meta-analysis combines the results of numerous quantitative studies (original studies) that have previously examined a relationship of interest between a dependent variable (Y) and a focal predictor (X), otherwise known as the independent variable of interest. The unit of analysis in meta-analysis is the effect size, which is the standardized measure of each tested relationship between X and Y (often, the beta coefficient on the X variable in a regression, or a Pearson’s/Spearman’s correlation
Descriptive statistics
Descriptive statistics for the control variables are reported in Table 1. We report descriptive statistics for all studies and for studies just in the United States, which contained about 80 percent of all effect sizes. We isolated U.S. studies because of the United States’ unique history of the environmental justice movement and because some covariates (e.g. racial dissimilarity and income inequality) were only available for U.S. study sites. The mean study-site population in our sample is 2.2
Discussion
The tools of meta-analysis allowed for a more comprehensive and nuanced understanding of previous studies that have examined the distribution of the urban forest. When applied, we found positive but inconsistent evidence for income-based inequity in the distribution of the urban forest cover.
The unconditional mean effect size revealed significant income-based inequity in urban forest cover. Meta-regressions examining the impact of methodological characteristics, measurement characteristics, and
Conclusion
This meta-analysis evaluated the relationship between urban trees and vegetation (the outcome variable) and income (the focal predictor). Our literature search found 61 studies with 332 total effect sizes which quantitatively evaluated this relationship. We used the tools of meta-analysis to quantitatively accumulate original studies into standardized effects. In particular, we reported a forest plot and meta-regressions. Using meta-regression, we conditioned the observed mean effect size on a
Acknowledgements
The authors thank the authors of original studies for their contributions to the literature and for providing supplemental information about their studies. This research was supported in part by the Vincent and Elinor Ostrom Workshop in Political Theory and Policy Analysis at Indiana University; SF BUILD (Building Infrastructure Leading to Diversity) and the NIH Common Fund TL4 GM118986; and the National Cancer Institute Grant CA-113710.
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*Indicates the study was coded and included in the meta-analysis.