Environmental Science and Policy 102 (2019) 54–64 Contents lists available at ScienceDirect Environmental Science and Policy journal homepage: www.elsevier.com/locate/envsci Under one canopy? Assessing the distributional environmental justice T implications of street tree benefits in Barcelona Francesc Baróa,b,⁎, Amalia Calderón-Argelicha, Johannes Langemeyera,b, James J.T. Connollya,b a Institute of Environmental Science and Technology (ICTA), Universitat Autònoma de Barcelona (UAB), Edifici Z (ICTA-ICP), Carrer de les Columnes s/n, Campus de la UAB, 08193 Cerdanyola del Vallès, Spain b Hospital del Mar Medical Research Institute (IMIM), Carrer Doctor Aiguader 88, 08003 Barcelona, Spain A R T I C LE I N FO A B S T R A C T Keywords: Street trees are an important component of green infrastructure in cities, providing multiple ecosystem services Urban ecosystem services (ES) and hence contributing to urban resilience, sustainability and livability. Still, access to these benefits may Socio-environmental equity display an uneven distribution across the urban fabric, potentially leading to socio-environmental inequalities. Green infrastructure Some studies have analyzed the distributional justice implications of street tree spatial patterns, but generally Urban climate adaptation without quantifying the associated ES provision. This research estimated the amount of air purification, runoff Spatial analysis mitigation and temperature regulation provided by circa 200,000 street trees in Barcelona, Spain, using the i- Tree Eco tool. Results were aggregated at neighborhood (n = 73) and census tract (n = 1068) levels to detect associations with the distribution of five demographic variables indicating social vulnerability, namely: income, residents from the Global South, residents with low educational attainment, elderly residents, and children. Associations were evaluated using bivariate, multivariate and cluster analyses, including a spatial autoregressive model. Unlike previous studies, we found no evidence of a significant and positive association between the distribution of low income or Global South residents and a lower amount of street tree benefits in Barcelona. Rather, higher ES provision by street trees was associated with certain types of vulnerable populations, espe- cially elderly citizens. Our results also suggest that street trees can play an important redistributive role in relation to the local provision of regulating ES due to the generally uneven and patchy distribution of other urban green infrastructure components such as urban forests, parks or gardens in compact cities such as Barcelona. In the light of these findings, we contend that just green infrastructure planning should carefully consider the distributive implications associated with street tree benefits. 1. Introduction was issued by the UN through the Sustainable Development Goal (SDG) eleven1 in the framework of the 2030 Agenda for Sustainable Devel- Cities around the world are facing pressing environmental chal- opment. lenges and climate change-related risks, including air pollution, heat Urban green infrastructure (UGI) can play a pivotal role in the stress and extreme precipitation (Revi et al., 2014; Elmqvist et al., achievement of this goal by mitigating environmental burdens, con- 2018). Moreover, exposure and vulnerability to these environmental tributing to climate change adaptation, increasing public health and burdens and climate hazards are generally unequally distributed across social cohesion, and ultimately, creating more livable cities (Tzoulas neighborhoods and socioeconomic population groups (see, for instance, et al., 2007; EEA, 2011; EC, 2012; Demuzere et al., 2014). The defini- Harlan et al., 2006; Moreno-Jiménez et al., 2016; Tonne et al., 2018). tion of UGI as a “strategically planned network of green and blue spaces The notion of urban environmental justice is hence expanding beyond in urban areas, designed and managed to deliver a wide range of eco- accessibility to environmental and community resources to include the system services (ES) and other benefits at various spatial scales” local experience of climate change and how vulnerable communities (Hansen et al., 2017) reflects the idea that different green (and blue) can adapt to its impacts (Schlosberg, 2013). In this context, an inter- elements, such as urban parks, gardens, green roofs, and street trees, national call for safer, more inclusive, resilient and sustainable cities can be part of a multifunctional and interconnected ecological system ⁎ Corresponding author at: Institute of Environmental Science and Technology (ICTA), Universitat Autònoma de Barcelona (UAB), Edifici Z (ICTA-ICP), Carrer de les Columnes s/n, Campus de la UAB, 08193 Cerdanyola del Vallès, Spain. E-mail address: francesc.baro@uab.cat (F. Baró). 1 See https://sustainabledevelopment.un.org/sdg11 https://doi.org/10.1016/j.envsci.2019.08.016 Received 6 June 2019; Received in revised form 26 August 2019; Accepted 27 August 2019 Available online 12 October 2019 1462-9011/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 within the urban landscape. But, while city-scale benefits flow from this 2. Material and methods systemic aspect, the actual provision of many ES depends on being near the area of demand, i.e., where beneficiaries live and spend most of 2.1. Description of the case study area and street tree data their time (Baró et al., 2016; Kabisch et al., 2016; Larondelle and Lauf, 2016). For instance, city trees regulate temperature and mitigate the Barcelona, located in the North-East part of the Iberian Peninsula on urban heat island effect where they directly provide shade, create hu- the Mediterranean Sea, is the second largest city in Spain. The muni- midity and block wind (Bowler et al., 2010). cipality covers a total area of 10,216 ha with a population of 1.62 Recent literature has pointed towards a stronger consideration of ES million inhabitants (Barcelona City Council Statistical Yearbook, 2018). (including potential disservices and trade-offs) within the urban en- Due to its compactness and population density (greater than 400 in- vironmental justice framework for better informed decision-making in habitants per ha in some neighborhoods), Barcelona has a low ratio of cities (Ernstson, 2013; Wolch et al., 2014; Marshall and Gonzalez- green space coverage per capita compared to other large European ci- Meler, 2016). In this paper, we focus on the distributive dimension of ties (Fuller and Gaston, 2009), namely 7.0 m2 of public green space area urban environmental justice and ES, i.e., who benefits most and who per inhabitant, increasing to 17.6 m2 if the periurban natural park of remains excluded from access to the benefits of UGI in cities (Ernstson, Collserola is considered (Barcelona City Council Statistical Yearbook, 2013; Aragão et al, 2016), and more specifically, on the socio-spatial 2018). However, these figures do not include the high ratio of street inequalities related to the provision of regulating ES by street trees. trees in Barcelona (> 120 trees per 1000 inhabitants) compared to the Street trees are a critical component of UGI, providing multiple ES average range of 50–80 trees per 1000 inhabitants in other European such as runoff control, temperature regulation, air purification, noise cities (Pauleit et al., 2002). reduction and habitat for biodiversity, among others (Bowler et al., The complete inventory of Barcelona street trees was retrieved in 2010; Soares et al., 2011; Gillner et al., 2015; Livesley et al., 2016; April 2018 from the municipal open data portal2 . The database con- Salmond et al., 2016; Willis and Petrokofsky, 2017). Street trees can siders trees and palm trees located on streets, squares and promenades also provide socio-cultural benefits by contributing to the physical and (but excludes trees in parks and other green spaces). It contained in- mental health of citizens, improving cognitive development and fa- formation on 201,226 street trees, including geographic coordinates, voring a sense of belonging and aesthetic appreciation (Roy et al., 2012; species name, and several structural and tree-condition variables such Fisher et al., 2015; Nesbitt et al., 2017). An important characteristic of as total tree height or tree vitality (Fig. 1). Among the circa 300 dif- street trees is their integration with cities’ grey infrastructure, namely ferent tree species included in the inventory, the four most abundant the transport network. This is particularly important in compact cities, (> 5% of all specimens) are Platanus x acerifolia (23.2%), Celtis australis where the availability of land for the implementation of larger UGI (10.5%), Sophora japonica (5.3%) and Tipuana tipu (5.2%). Street trees components such as parks or gardens is generally limited (Jim, 2004; show a widespread though uneven distribution across the urban fabric Artmann et al., 2017). However, street trees are often overlooked in (Fig. 1). urban ES assessments, as these tend to focus on other UGI components The Master Plan for Barcelona’s Trees 2017–2037 (Barcelona City with a more defined spatial delimitation and associated land use Council, 2017a) aims to increase the city’s tree cover by 5% before (Luederitz et al., 2015). 2037 and maximize its ES provision in terms of environmental, social Several authors have analyzed urban trees’ distribution from an and economic benefits. It seeks, as well, to ensure that a substantial environmental justice perspective, mostly in the US context, with re- share of tree species (at least 40%) are adapted to future climate sults generally indicating spatial inequities associated with residential changes. However, there is no explicit reference to distributional nor segregation by factors such as race/ethnicity, education level, home- procedural justice aspects in any of the 10 strategic lines and 50 actions ownership, and income (Heynen et al., 2006; Landry and Chakraborty, proposed in the Plan. This remains a blindspot in the City’s strategy for 2009; Danford et al., 2014; Duncan et al., 2014; Schwarz et al., 2015; urban trees and points toward the urgency of our study. Further, Bar- Pham et al., 2017). However, few have explicitly considered the ES celona has recently developed an ambitious climate action plan for the provided by trees (but see Flocks et al., 2011 and Escobedo et al., 2015) period 2018–2030 (Barcelona City Council, 2018) which includes a and their role in terms of environmental (in)equalities considering the flagship adaptation measure based on UGI: increasing urban green overall UGI distribution across the urban fabric, especially in highly space by 1 m2 per inhabitant (i.e., 1.6 km2 in total). The Plan identifies compact cities. The consideration of the pattern of urban tree ES, rather four main climate change challenges for the city: rising temperatures, than simple tree distribution, is relevant in this context since ES pro- reduced availability of water, increased flooding, and shrinking beaches vision can substantially vary across tree species and tree structural due to sea level rise. Moreover, Barcelona has faced serious air quality traits (Graça et al., 2017; Vogt et al., 2017). problems in recent decades, mostly due to exceeding nitrogen dioxide Given the trajectory of the literature on urban tree distribution, the and particulate matter levels from traffic emissions (Moreno-Jiménez main goal of this study is to analyze the spatial pattern of ES provided et al., 2016). by street trees in a highly compact city and to understand their role in terms of distributional environmental justice. The specific objectives 2.2. Selection and quantification of ecosystem services are the following: 1) to quantify three locally relevant regulating ES provided by a complete inventory of street trees, namely: air purifica- The three ES quantified in this study were selected due to their tion, runoff mitigation and urban temperature regulation (following the potential contribution to mitigate the above-mentioned local urban nomenclature of urban ES by Gómez-Baggethun and Barton, 2013); 2) environmental and climate threats for Barcelona, namely air pollution, to compare the spatial distribution of these three ES in relation to five heat stress (including heatwaves) and stormwater runoff (including socio-demographic variables indicating vulnerability, namely: income, flooding) at the neighborhood or smaller spatial scale (Demuzere et al., residents from the Global South, residents with low educational at- 2014; Marshall and Gonzalez-Meler, 2016). These three ES are classi- tainment, elderly and children residents; and 3) to assess the role of fied as “in situ” or “local proximal” because their benefits are realized in street trees in terms of ES inequalities considering the overall UGI the same location of provision or their immediate surroundings distribution across the case study area. The municipality of Barcelona (Costanza, 2008; Fisher et al., 2009) and hence are among the most (Spain) was selected as case study due to the high compactness of its relevant in terms of urban distributional justice. Other regulating ES urban fabric and the availability of a large and up-to-date inventory of provided by street trees, such as carbon sequestration, were not street trees. 2 See http://opendata-ajuntament.barcelona.cat/ 55 F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 Fig. 1. Barcelona municipality displaying street tree density at the census tract level. The zoom-in map corresponds to the census tract highlighted in turquoise color in the city map (each green dot represents a street tree). The table with tree data corresponds to the street tree highlighted in turquoise color. Source: own elaboration based on Barcelona City Council datasets. considered in the assessment because their benefits and distributional from several stations. Therefore, average values were calculated from justice implications only make sense at larger spatial scales (Syrbe and seven air quality monitoring stations and three meteorological stations Walz, 2012). respectively in order to consider the heterogeneity of pollutant con- ES indicators were quantified using i-Tree Eco (version 6)3 . This centrations and precipitation across the urban fabric. Air pollutants tool contains various process-based models that estimate several ES processed by i-Tree Eco include: nitrogen dioxide (NO2), sulfur dioxide provided by UGI (Nowak et al., 2008). Despite being originally devel- (SO2), ground-level ozone (O3), carbon monoxide (CO) and particulate oped for the US context, i-Tree Eco has been adapted to support non-US matter of less than 2.5 μm (PM2.5). cases (e.g. Selmi et al., 2016; Graça et al., 2017). An i-Tree Eco as- Indicators defined by i-Tree Eco to quantify the three ES are pre- sessment of a complete tree inventory requires individual tree data sented in Table 1. Air purification is estimated from dry deposition of comprising at least scientific species name, diameter at breast height air pollutants (see Nowak et al., 2008 for more details). Runoff miti- (DBH), total tree height, crown size and crown health (% missing and % gation is estimated as the avoided runoff considering rainfall infiltra- dieback) and geographic coordinates. From the inventory of Barcelona tion, evaporation and water intercepted by the tree canopy (USDA (originally containing 201,226 street trees) some trees were discarded Forest Service, 2018). Urban temperature regulation is estimated con- due to missing location (357), species name (85), or because they were sidering tree transpiration as a proxy indicator due to its cooling effect registered as stubs, empty tree pits or trees to be removed (5387), re- on air temperature (Bowler et al., 2010). Furthermore, an aggregated sulting in a total of 195,397 street trees assessed. Furthermore, the ES index value was created to show the ES performance of each tree in a inventory did not contain the exact measurement of the required single metric. To this end, the indicators were normalized in a 0–100 structural variables, thus several assumptions were made following range using minimum and maximum values and then aggregated by a recommendations of four street tree managers from the City Council non-weighted linear summation. The resulting value was also rescaled (see Tables A1, A2 and A3 in Appendix A for all the estimates). As in a 0–100 range. additional input to the model, local hourly precipitation (in m) and air All the indicators and the ES index were estimated at the individual pollution data (in μg/m3) were collected from official public sources tree level and also aggregated at the census tract (n = 1068), neigh- (Meteorological Service of Catalonia and Public Health Agency of borhood (n = 73) and district (n = 10) levels for the sake of results Barcelona respectively). Due to limitations of i-Tree Eco, pollutant visualization and to allow the statistical analyses with the socio-de- concentrations and precipitation had to correspond to 2015 as most mographic variables. recent year and be derived from one single station or aggregated values 3 See https://www.itreetools.org/ 56 F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 Table 1 (Garbulsky et al., 2011) widely used as a proxy of UGI in urban studies ES indicators quantified by i-Tree Eco and ES index considered in the assess- (e.g., Apparicio et al., 2008; Li et al., 2015; Triguero-Mas et al., 2015). ment of street trees. Despite having some limitations as a measure of urban vegetation ES Indicators Unit complexity or structure, it currently represents the best high-resolution spatial delimitation of Barcelona’s UGI available, as it includes all green Air purification Pollution removal, as sum of: g/ha year spaces and other vegetation (both in public and private land). The NO2 removal spatial distribution of percent canopy cover at the census tract level is SO2 removal O3 removal shown in Fig. A2 (Appendix A). CO removal PM2.5 removal 2.4. Statistical analyses Runoff mitigation Avoided runoff m3/ha year Urban temperature regulation Transpiration m3/ha year ES index Rescaled sum of previous Value 0 - 100 Following the approach of other studies examining the distribu- tional equity of urban trees (Landry and Chakraborty, 2009; Duncan et al., 2014; Schwarz et al., 2015), our data was analyzed using bi- Table 2 variate and multivariate techniques considering the census tract as the Socio-demographic and UGI indicators considered in the assessment. unit of analysis (n = 1068). In addition, a cluster analysis (Raudsepp- Indicators Unit Hearne et al., 2010) was applied in order to identify groups of neigh- borhoods (n = 73) with similar socio-demographic characteristics, ES Children (residents under 14 years old) % of total population index, and overall UGI cover. As a preliminary step, we also tested the Elderly (residents over 65 years old) % of total population spatial autocorrelation of the variables using Global Moran’s I in ArcGIS Income (household disposable income index) Numerical value where 100 is the city average v. 10 (ESRI). All variables showed a significantly clustered spatial Low educational attainment (residents with % of population over 16 pattern (z-scores > 3; see Fig. A1 and Fig. A2 in Appendix A). primary education or no studies) years old As our study variables displayed a non-normal distribution and Immigrants from the Global South (residents % of total population heteroscedasticity, we first employed non-parametric Spearman corre- whose nationality is from the Global South) lations for bivariate analysis using RStudio software. As a second step, Overall UGI cover (area of canopy cover based on % of total area NDVI map) we used multivariate analysis in order to model the distribution of the street tree ES index (dependent variable) in relation to the socio-de- mographic variables (independent variables). We compared an or- 2.3. Selection and spatial distribution of social vulnerability and overall dinary least squares (OLS) linear model, a generalized linear model green infrastructure indicators (GLM) following gamma distribution and a spatial lag model (SLAG), with R squared and Akaike Information Criterion (AIC) values. Spatial The selected socio-demographic variables (see Table 2) that indicate autoregressive modeling accounts for the spatial autocorrelation of the vulnerabilities include aspects related to age (children and elders), so- variables, addressing the violation of the assumption of independent cioeconomic status (income and level of educational attainment) and observations (i.e. uncorrelated errors) generally associated with socio- risk of social exclusion (immigrants from the Global South). These or environmental data (see Anselin and Bera, 1998). We developed our similar variables have been used in previous environmental justice as- SLAG regression model using the open-source software tool GeoDa sessments in Barcelona (e.g., Moreno-Jiménez et al., 2016; Anguelovski (Anselin, 2005) and considering maximum likelihood estimation and a et al., 2018) as they indicate vulnerable social groups which are gen- first order Rook contiguity-based spatial weight matrix in order to erally assumed to have higher needs in terms of access to UGI benefits capture the highly localized clusters of data. The control variable of due to exposure to environmental burdens, health status and/or so- “percent of street area” was added to regression models to account for cioeconomic circumstances (e.g., housing condition) (Heynen et al., the space limitation associated with street tree planting. The variable 2006). All the variables were collected from the Barcelona City Council was derived from a high-resolution land use zoning dataset developed Statistical Department4 both at the census tract and neighborhood level by the Barcelona City Council (year 2016) and available online5 . The (except for the household income index which was only available at the same models were developed using the percent of UGI coverage in each latter level). We used 2015 data as the corresponding year to match tract as a dependent variable to assess the role of street trees relative to with i-Tree Eco results. The household income index is a good indicator other UGI in terms of socio-environmental inequalities. In the UGI of wealth disparities across neighborhoods since it is adjusted based on models, “total population density” (inhabitant/ha.) was used as a various socioeconomic variables such as housing prices. The index is control variable as one would expect density to limit the capacity for given relative to the total mean of Barcelona (for which the value is UGI in an area. Finally, we classified neighborhoods into clusters based 100). Following Anguelovski et al. (2018), immigrants from the Global on similar combinations of street tree ES index, UGI cover and socio- South were identified as those residents whose nationality is from all demographic variables using a k-means clustering algorithm contained African, Asian and Latin American countries, except Japan. Despite in R statistical software (R Core Team, 2018). The appropriate number their broad differences in geographical and social origins, communities of clusters was determined by interpreting the meaning of different from these countries tend to be more segregated than other social clustering outputs with the support of dendrograms and scree plots. groups, and they generally face more adverse socioeconomic and en- Each cluster was illustrated with the average standardized indicator vironmental conditions (Anguelovski, 2013; Moreno-Jiménez et al., values shown in star plots and their spatial distribution was mapped 2016; Lagonigro et al., 2018). The spatial distribution of all the socio- using ArcGIS v.10. demographic variables is shown in Fig. A1 (Appendix A). In order to assess the role of street trees in terms of ES inequalities 3. Results considering the overall UGI distribution in Barcelona, we used a pub- licly available high-resolution map of plant canopy cover based on a 3.1. Street tree benefits across the urban fabric Normalized Difference Vegetation Index (NDVI) analysis (see Barcelona Regional, 2015). NDVI is a radiometric measure of greenness Total air purification, runoff mitigation and urban temperature 4 5 See http://www.bcn.cat/estadistica/angles/index.htm See http://w20.bcn.cat/cartobcn/ 57 F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 Table 3 ES values and ES index by district (street trees of the municipality of Barcelona, year 2015). Note: neighborhoods are ranked based on the ES index. District Air purification Runoff mitigation Urban temp. regulation ES index −1 −1 −1 −1 −1 3 −1 3 −1 −1 −1 −1 3 −1 3 −1 −1 −1 −1 kg yr kg yr ha g yr tree m yr m yr ha l yr tree m yr m yr ha l yr tree Average value Eixample 4878 6.52 200 9380 12.55 385 146,383 195.79 6014 15.60 Sant Martí 5747 5.46 156 10,735 10.20 292 172,481 163.90 4686 12.04 Sants-Montjuïc 3569 1.56 150 6776 2.95 285 106,883 46.59 4494 11.62 Nou Barris 3140 3.90 147 5836 7.26 274 94,159 117.09 4414 11.33 Les Corts 2273 3.78 134 4253 7.07 251 68,163 113.27 4021 10.34 Ciutat Vella 897 2.05 126 1672 3.83 234 26,911 61.60 3771 9.68 Sant Andreu 2563 3.90 124 4772 7.27 231 76,828 117.02 3726 9.57 Horta-Guinardó 2342 1.96 115 4349 3.64 214 70,204 58.76 3457 8.87 Sarrià-Sant Gervasi 1772 0.88 112 3323 1.65 209 53,124 26.44 3343 8.60 Gràcia 842 2.01 102 1571 3.75 191 25,273 60.38 3073 7.89 Barcelona (total/*avg.) 28,023 *2.74 *143 52,668 *5.16 *270 840,408 *82.26 *4301 *10.55 Fig. 2. Spatial patterns of ES index (street trees) at the census tract level. Index values classified using the natural breaks (Jenks) method. The variable is spatially autocorrelated (z-score > 3). Source: own elaboration based on i-Tree Eco results and Barcelona City Council data. regulation by street trees in Barcelona during 2015 are estimated at almost treeless industrial park of about 600 ha, whereas in the case of 28,023 kg of removed air pollutants, 52,668 m3 of avoided runoff and Sarrià-Sant Gervasi a large portion of the district is covered by the 840,408 m3 of transpired water respectively (see Table 3). Obviously, periurban natural park of Collserola (mostly a forest area, but with few the spatial patterns of these ES (see Fig. A3 in Appendix A) largely street trees). Interestingly, Eixample and Sant Martí are also the two follow the distribution of street tree density in the city. The dense districts with the highest average ES values per tree. For example, on central district of Eixample and the nearby postindustrial district of average a street tree in Eixample almost doubles the amount of air Sant Martí (see Fig. 2) prominently show the highest provision of all ES, pollutants removed or the runoff avoided relative to a tree in Gràcia both in absolute and relative terms (i.e., ES supply normalized by area). (see Table 3). This disparity is also reflected in the ES index average On the other hand, the historical districts of Ciutat Vella and Gràcia values which are the highest in Eixample (15.60) and Sant Martí show the lowest total ES amounts, and the southern and western per- (12.04) and lowest in Gràcia (7.89) and Sarrià-Sant Gervasi (8.60). The ipheral areas of Sants-Montjuïc and Sarrià-Sant Gervasi have the lowest spatial distribution of street tree ES index is shown in Fig. 2 at the values per hectare (see Table 3). In the case of Sants-Montjuïc, this is census tract level. mainly due to the presence of “Zona Franca” within its boundaries, an 58 F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 Table 4 Spearman’s correlation results between ES index, socio-demographic variables and total UGI cover at the census tract level (n = 1068). ES index (street trees) Children Elderly Income Low education Global South Total UGI cover ES index (street trees) 1 Children **−0.11 1 Elderly **0.18 **−0.27 1 Income *−0.07 0.03 −0.04 1 Low education 0.04 −0.06 0.04 **−0.87 1 Global South 0.02 **−0.24 **−0.31 **−0.34 **0.46 1 Total UGI cover **0.37 **0.17 *0.07 **0.23 **−0.18 **−0.30 1 Note: *p-value < 0.05; **p-value < 0.01. 3.2. Bivariate and multivariate associations demographic variables except for Global South residents, where only GLM does. This difference indicates that there is a strong spatial lag Bivariate analysis (Table 4) reveals that the ES index is strongly within the UGI variable, which reduces the strength of linear models positively correlated with elderly residents and moderately negatively that do not account for this lag. correlated with children. The other vulnerability indicators do not show a significant bivariate relationship with street tree ES index. In contrast, 3.3. Cluster analysis outputs the overall UGI cover shows positive and significant relationships with children and income, but a significant negative correlation with re- The non-spatial cluster analysis reveals four meaningful groups of sidents with low educational attainment and residents from the Global neighborhoods (n = 73) based on the distributional patterns of the South. The correlation results between socio-demographic variables seven variables considered in the study (Fig. 3). All groups are also also show some significant associations. As expected, income is nega- highly spatially clustered (z-score of Moran’s I statistic = 6.82). Cluster tively correlated with low educational attainment and residents from 1 includes more than half of the neighborhoods of Barcelona (n = 40), the Global South, whereas the relationship between the two latter mostly from the districts of Eixample, Sant Martí and Nou Barris, among variables is positive and significant. Elderly population is negatively others. This cluster probably reflects the area of the city where the associated with Global South residents supporting the fact that im- benefits of street trees are more relevant since the ES index mean value migrant residents from these countries are predominantly young (their is highest, but the overall UGI cover is lowest. Cluster 1 is characterized average age is 32 years according to Barcelona Statistical Yearbook by intermediate socio-demographic values, but with a high share of 2018). Finally, children show a significant negative relationship with elderly residents and a relatively low share of children compared to elderly and Global South residents. clusters 2 and 3. Cluster 2 (n = 13) clearly contains the neighborhoods The multivariate regression results (Table 5) show that, when we of Barcelona with the most vulnerable population in terms of low in- control for all measured factors (including the added control variables), come, low educational attainment and residents from the Global South. some of the relationships seen in the bivariate analysis are altered. Most of these neighborhoods are located in the district of Nou Barris, a Diagnostic tests revealed that GLM fits better than OLS with our data historically deprived area of Barcelona (Anguelovski et al., 2018), but it and that the SLAG model provides more robust results as evidenced by also includes neighborhoods such as el Raval (Ciutat Vella) and la higher R-square and lower AIC values – as a result, we primarily in- Marina del Prat Vermell (Sants-Montjuïc) characterized by the highest terpret multivariate relationships based on SLAG results. All models percent of residents from the Global South and residents with low or no indicate significant and positive associations between the ES index and educational attainment respectively. This cluster also shows the second the age and education attainment variables, but income and Global highest mean value for percent of children in the population, but the South do not show clear relationships in the same or opposite direction. lowest for elderly. In regard to ES index and overall UGI cover, the The control variable added in the models (percent street area) is, as cluster shows intermediate values due to the fact that some neighbor- expected, strongly associated with the street tree ES index. The results hoods (mostly in Nou Barris) comprise a high amount of street trees of the multivariate analyses using total UGI cover as dependent variable and/or other green spaces (e.g., the urban park of Montjuïc and the show a different picture. This SLAG model only indicates a positive periurban park of Collserola, see Fig. A2 in Appendix A), but others association with elderly population. In contrast, the OLS and GLM (e.g., el Raval, la Marina del Prat Vermell) stand out for the opposite models show significantly positive relationships with all socio- condition. Cluster 3 (n = 9) groups the wealthiest neighborhoods of Table 5 Ordinary least squares (OLS), generalized linear model (GLM) and spatial lag model (SLAG) results for ES index (street trees) and percent UGI cover as dependent variables at the census tract level (n = 1068). ES index (street trees) Total UGI cover OLS GLM SLAG OLS GLM SLAG Children **0.39 *0.02 *0.24 **0.52 **0.02 0.12 Elderly **0.41 **0.02 **0.28 *0.19 *0.01 *0.13 Income 0.00 −0.00 0.02 **0.08 **0.00 0.02 Low education **0.20 *0.01 **0.14 **0.31 **0.01 0.08 Global South −0.01 −0.00 0.00 −0.13 *−0.01 −0.01 Street area **0.63 **0.04 **0.37 NA NA NA Population density NA NA NA **−0.02 **−0.00 **−0.02 Constant −21.32 0.54 −18.78 1.91 2.22 3.81 R-squared 0.28 0.24 0.53 0.30 0.31 0.60 Lag coeff. NA NA 0.62 NA NA 0.66 AIC 8082.9 7750.9 7730.9 8105.1 7388.7 7628.6 Note: *p-value < 0.05; **p-value < 0.01. 59 F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 Fig. 3. Spatial distribution of neighborhood clusters and standardized mean indicator values for each cluster (represented in the star plots). The number of neighborhoods per cluster is indicated with n. Source: own elaboration based on i-Tree Eco results and Barcelona City Council data. Barcelona, mainly located in the districts of Sarrià-Sant Gervasi and Les amount of air pollutants removed or the runoff avoided by a tree in Corts (Fig. 3). This cluster clearly shows the highest mean values in Gràcia, see Table 3). Trees providing more benefits have generally terms of income and the lowest for residents from the Global South and larger DBH, leaf area and crown size and present better health condi- residents with low educational attainment. It is also the cluster with the tion according to the i-Tree Eco model outputs. highest share of percent of children in the population. Interestingly, the Our results also indicate that the role of street trees in a highly cluster displays high values of overall UGI cover due to the large share compact European city such as Barcelona is substantially different in located in the periurban park of Collserola and also likely due to a high terms of distributional environmental justice than what previous re- presence of private green space, but a relatively low street tree ES index search suggested, especially in comparison with the context of the US. mean value. Finally, Cluster 4 (n = 11) is very similar to cluster 1 in In most of these studies, the findings support the inequity hypothesis, terms of socio-demographic characteristics, but the role of street trees is i.e., the proportion of street tree canopy cover is significantly lower in likely minor because the ES index mean value is lowest and the overall low-income neighborhoods or those with a higher proportion of racial/ UGI cover is highest. This cluster mostly includes neighborhoods from ethnic minority residents (Landry and Chakraborty, 2009; Flocks et al., the districts of Horta-Guinardó and Gràcia, which contain several large 2011; Danford et al., 2014; Schwarz et al., 2015). In Barcelona, we green spaces (e.g., Collserola or the urban forest of Tres Turons, see Fig. found that the provision of ES by street trees is neither significantly A2 in Appendix A), but in some cases also very compact urban forms associated with income nor with residents from the Global South. Ra- and narrow streets (especially in Gràcia). ther, the spatial regression model reveals a strong positive relationship between the street tree ES index, elderly residents and residents with low educational attainment. This shows that street trees ES do not 4. Discussion produce direct inequities for vulnerable populations – the ethnicity and income results are not significant and the elderly and low education 4.1. The role of street tree benefits in distributional environmental justice results show a tendency toward higher ES Index for these vulnerable groups. Previous environmental justice studies have called for quantifica- Moreover, cluster analysis shows that the most affluent neighbor- tion of the ES provided by urban trees beyond the mere distribution of hoods of the city (and those with a lower proportion of Global South trees (Schwarz et al., 2015). Our results show that this is a pertinent immigrants) generally display intermediate to low ES index values, appeal because the local provision of street tree benefits can be ex- whereas the most disadvantaged neighborhoods generally show inter- tremely uneven, not only at the single tree level (the most “productive” mediate to high values (with some exceptions such as El Raval in the tree in Barcelona removes almost 290 times more air pollutants or Old Town). These findings suggest that the reasons usually alleged to avoids 24 times more runoff than the least productive), but also at the explain distributional street tree inequalities might not apply in such a district level (a street tree in Eixample almost doubles, on average, the 60 F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 direct way to the case of Barcelona and possibly other similar, compact et al., 2011). While most parts of i-Tree Eco assessments rely on sample Southern European cities (see also Graça et al., 2018). Yet, this case data and hence have an associated standard error, our case study is does make clear that street tree ES are not evenly distributed and do backed by a complete municipal inventory of street trees. Cities with play an important role in shaping the local environment of vulnerable similar available information in relation to their street tree inventory residents. More empirical research is needed in order to consistently can easily adapt our methodological approach to their context thanks to compare the distributional patterns and (in)equalities of street tree the international support provided by i-Tree Eco. A key advantage of benefits across cities and the associated underlying drivers. this tool is that it can provide ES estimates at the individual tree level. The motives that drive street tree inequity in the US cases generally Thus, distributional equity assessments can be performed at any urban include incentives for wealthy residents (often homeowners) to live in spatial scale (neighborhood, census tract or even smaller units). How- (and lobby for) greener neighborhoods to increase their property values ever, the assumptions related to the tree structural data described in the (Landry and Chakraborty, 2009; Schwarz et al., 2015). In contrast, re- methods section and other assumptions directly linked to the models sidents in disadvantaged neighborhoods (usually renters) might resist (e.g., homogeneity of air pollution and precipitation distribution) entail street tree-planting programs because their perceptions of trees mainly a degree of uncertainty in the ES estimates. focus on potential negative impacts or disservices (e.g., maintenance or Probably the highest uncertainty is attached to the ES estimates of nuisance concerns, associated rising rents due to gentrification pro- air purification. The links between urban trees and air quality are ex- cesses, etc., see Carmichael and McDonough, 2019). Understanding tremely complex (Eisenman et al., 2019). In this study, we have only residents’ perceptions of street trees in Barcelona goes beyond the scope considered the air pollution removal estimates from the i-Tree Eco dry of this research, however a potential explanation for our results might deposition model, which may have a positive but very low effect on air relate to the fact that, unlike many US cases, UGI is not apparently quality and therefore on human health (Baró et al., 2014). However, associated with gentrification in several historically deprived neigh- street trees can also reduce air quality through the emission of BVOCs borhoods of the city according to a recent assessment by Anguelovski (biogenic volatile organic compounds) and allergenic pollen. Even et al. (2018). Moreover, the assumption that wealthy residents are though BVOC emissions are estimated by i-Tree Eco, the model does not homeowners and poor residents are renters is probably weakly sup- quantify the subsequent harmful O3 formation due to the complexity ported in Barcelona because a substantial share of disadvantaged associated with this chemical process (Calfapietra et al., 2013). Aller- neighborhoods are inhabited by homeowners (Barcelona Metropolitan genic pollen is also commonly highlighted as an ecosystem disservice Housing Observatory, 2019). associated with urban trees (von Döhren and Haase, 2015), even if Our results also show that percent of street area, added as a control pollen levels (and the associated health impacts) can substantially vary variable in the regression models, plays an essential role in the provi- across the urban fabric, both spatially and temporally (Weinberger sion of ES by street trees. This variable, a key aspect of urban form, is et al., 2015). Further, trees in urban street canyons can concentrate highly determined by the distinct historical processes of urban devel- local air pollution by reducing air dispersion according to various opment and expansion in European cities like Barcelona. For instance, modeling studies (Wania et al., 2012; Vos et al., 2013; Jin et al., 2014). the Eixample and Sant Martí districts were largely built following the This effect can be especially relevant in street canyons where the height square-gridded urban form designed by the 19th century urbanist of buildings at least doubles street width (i.e., aspect ratio ≥ 0.5) based Ildefons Cerdà (Neuman, 2011). Despite their compactness, the neigh- on a recent review (Abhijith et al., 2017). borhoods located in these areas (mostly from Cluster 1) are character- ized by substantially wider streets (at least 20 m) than most parts of the 4.3. Lessons for urban green infrastructure policy and practice historical neighborhoods of the city such as those in Ciutat Vella (Old Town), Gràcia or Sants-Montjuïc. This fact physically allowed the Many cities around the world are fostering city tree planting pro- planting and growth of larger street trees (mostly Platanus x acerifolia) grams and other greening initiatives (e.g., Young, 2011; Connolly et al., with a higher potential to provide ES. In contrast, the narrow street 2018), but generally the justice implications of these policies are not canyons of the historical ‘old towns’ (in some cases less than 5 m) re- carefully assessed (Haase et al., 2017). Our assessment suggests that a main as a clear barrier for street tree planting and associated ES pro- careful selection and distribution of street trees can contribute to en- vision. This physical unavailability of tree planting sites, combined with vironmental and climate justice in compact cities such as Barcelona. historical processes, has also been observed in other cities and high- Urban planners and tree managers can use street tree planting strategies lighted as a major limitation to increase the equity of urban tree cover to address certain ES mismatches (imbalance between ES provision and (Danford et al., 2014). demand) derived from the generally unequal and patchy distribution of Our findings also suggest that street trees can have a relevant re- other UGI components such as urban forests, parks or gardens (Nielsen distributive impact in the provision of regulating ES considering the et al., 2017). In Barcelona, historical legacies related to urban form whole UGI distribution in compact cities. In Barcelona, this role is have likely determined to a large extent the distribution of street trees especially relevant in neighborhoods located in Cluster 1 and, to a lesser and their ability to provide ES. However, even if the expansion of street extent, in Cluster 2 (see Fig. 3). In contrast, neighborhoods with a trees can face physical barriers in very compact neighborhoods, the higher proportion of overall UGI cover (mostly from Cluster 4 and transformation of the street network and the wider transport system can Cluster 3) have lower street tree ES index values. However, this redis- provide new opportunities for additional tree planting or at least for tributive role might not apply to other ES that have not been assessed in other greening alternatives. For example, the superilles (superblocks) this study. For example, cultural ES such as outdoor recreational op- program aims to drastically restrict car traffic within these blocks so portunities are generally associated with parks, periurban forests or street public area can be repurposed as pedestrian-friendly spaces and urban gardens and can hardly be provided by street trees alone (Cvejić green promenades (see Barcelona City Council, 2016). et al., 2015). This fact suggests potential inequalities related to the Street tree selection criteria should include ES performance, but also diversity of obtained benefits, as affluent neighborhoods (mostly from consider disservices (e.g., BVOC emissions, pollen allergenicity, damage Cluster 3) may benefit from a wider variety of UGI components and by roots), tolerance or sensitivity to current local threats (e.g., drought, derived ES, while other neighborhoods rely heavily on street trees. heat, air pollution, flooding, pests), and capacity to adapt to future conditions in a context of climate change (McPhearson et al., 2015). 4.2. Limitations and caveats Online tools and databases such as Citree6 (Vogt et al., 2017) can The advantages and limitations of i-Tree Eco models have been 6 widely discussed in the scientific literature (Nowak et al., 2008; Pataki See https://citree.de/ 61 F. Baró, et al. Environmental Science and Policy 102 (2019) 54–64 support tree officers in this challenging multicriteria decision-making following organizations: 1) Spanish Ministry of Science, Innovation and process. Management of street streets can also optimize the provision of University through the 2015-2016 BiodivERsA COFUND (project ES and minimize potential risks. For instance, Jin et al. (2014) sug- ENABLE, code PCIN-2016-002), the Juan de la Cierva Incorporación gested adequate pruning of street tree canopies to minimize their ne- Fund (IJCI-2016-31100), and the Maria de Maetzu Unit of Excellence gative effect on air pollution dispersion. grant (MDM-2015-0552); 2) the European Research Council (project Further, consideration of procedural environmental justice, i.e., the GREENLULUs; grant agreement ID: 678034); and 3) the EU’s Horizon legitimation and transparency of the process through which environ- 2020 framework program for research and innovation (project mental decisions are made and by whom (Aragão et al., 2016; Graham NATURVATION, grant agreement ID: 730243). Finally, we also thank and Barnett, 2017), has also been pointed to as an important step to- two anonymous reviewers for their helpful suggestions on an earlier wards street tree planting acceptance, especially in historically de- version of this manuscript. prived neighborhoods (Graça et al., 2018). Participatory processes re- lated to street tree planting programs or strategies would allow for a Appendix A. Supplementary data deeper understanding of how different population groups in different neighborhoods value street trees. Co-management approaches such as Supplementary data associated with this article can be found, in the the recent initiative related to the sowing of herbaceous plants in street online version, at https://doi.org/10.1016/j.envsci.2019.08.016. tree pits by citizens in Barcelona (Barcelona City Council, 2017b) can also contribute to a greater acceptance and engagement with urban re- References naturing strategies. Abhijith, K.V., Kumar, P., Gallagher, J., McNabola, A., Baldauf, R., Pilla, F., Broderick, B., 5. Conclusion Di Sabatino, S., Pulvirenti, B., 2017. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments – a review. Atmos. Environ. 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(Eds.), Handbook of dological approach based on the i-Tree Eco tool can be easily adapted to Applied Economic Statistics. Marcel Dekker, New York, pp. 237–289. other cities with available (street) tree data. Anselin, L., 2005. Exploring Spatial Data With GeoDa: a Workbook’, Spatial Analysis Unlike what previous studies suggested, we found no evidence of a Laboratory. Retrieved from:. Department of Geography, University of Illinois. http://www.sal.uiuc.edu/stuff/stuff-sum/pdf/geodaworkbook.pdf. significant and positive association between the distribution of poorer, Apparicio, P., Séguin, A.-M., Naud, D., 2008. The quality of the urban environment less educated or Global South residents and a lower amount of street around public housing buildings in montréal: an objective approach based on GIS and tree benefits in Barcelona. Rather, ES provided by street trees in multivariate statistical analysis. Soc. Indic. Res. 86, 355–380. 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