Elsevier

Landscape and Urban Planning

Volume 170, February 2018, Pages 195-208
Landscape and Urban Planning

Research Paper
Assessing how green space types affect ecosystem services delivery in Porto, Portugal

https://doi.org/10.1016/j.landurbplan.2017.10.007Get rights and content

Highlights

  • Ecosystems Services (ES) delivery was highly heterogeneous across the urban matrix.

  • Differences in types of urban green affect UES supply.

  • Most deprived area was the greenest but revealed lowest UES supply.

  • Composition of urban green types in socioeconomic strata influences UES delivery.

Abstract

Significant advances have been made in identifying, quantifying and valuing multiple urban ecosystem services (UES), yet this knowledge remains poorly implemented in urban planning and management. One of the reasons for this low implementation is the insufficient thematic and spatial detail in UES research to provide guidance for urban planners and managers. Acknowledging how patterns of UES delivery are related with vegetation structure and composition in urban green areas could help these stakeholders to target structural variables that increase UES provision. This investigation explored how different types of urban green spaces influence UES delivery in Porto, a Portuguese city, and how this variation is affected by a socioeconomic gradient. A stepwise approach was developed using two stratification schemes and a modelling tool to estimate urban forest structure and UES provision. This approach mapped explicit cold and hotspots of UES provision and discriminated the urban forest structural variables that influence UES at the local scale. Results revealed that different types of green spaces affect UES delivery as a direct result of the influence of structural variables of the urban forest. Furthermore, the uneven distribution of green spaces types across socioeconomic strata alters UES delivery across the city. This case study illustrates how a methodology adaptable to other geographic contexts can be used to map and analyze coupled social and ecological patterns, offering novel insights that are simple to understand and apply by urban planners and managers.

Introduction

Recent research has highlighted the capacity of urban ecosystems to provide critical benefits for human wellbeing, and the need to take them into account in urban planning (Gomez-Baggethun and Barton, 2013, Haase et al., 2014). The ecosystem services (ES) concept emerged as a holistic approach that explicitly recognizes these benefits, while integrating the management of biodiversity, natural resources and human needs (Haines-Young & Potschin, 2010). As such, various authors have adopted the ES framework in urban studies to provide relevant insights for urban planning and policy strategies (Ahern, Cilliers, & Niemelä, 2014; McPhearson, Hamstead, & Kremer, 2014). Addressing the local delivery of ES is particularly important in adaptive urban planning, as some benefits crucial for human wellbeing are locally derived, such as rainwater drainage, microclimate regulation, improvement of air quality through pollution removal, noise reduction and recreation (Bolund & Hunhammar, 1999). Urban green areas provide many of these ES, and thus their potential to contribute to human wellbeing in cities is being increasingly acknowledged (De Vries, van Dillen, Groenewegen, & Spreeuwenberg, 2013; Tzoulas et al., 2007).

Several examples illustrate how multiple urban ecosystem services (UES) have been identified, quantified and valuated to inform stakeholders and support decision-making processes (Derkzen, Teeffelen, & Verburg, 2015; Kabisch, 2015; McPhearson, Kremer, & Hamstead, 2013). However, this growing body of knowledge remains poorly implemented in actual urban planning and management (Haase et al., 2014, Kabisch, 2015, Kremer et al., 2016). One of the issues contributing to this gap is the lack of sufficient thematic and spatial detail in UES research to provide guidance for urban planning and design (Derkzen et al., 2015). Furthermore, there is a scarcity of studies aiming to analyze urban ecosystems at finer scales, addressing for example, variations in type and function of existing urban green areas (Haase et al., 2014), though some exceptions should be noted (e.g. Derkzen et al., 2015). Yet, different types of urban green areas such as public parks, domestic gardens or wasteland are heterogeneous and reflect diverse social needs and values that affect their performance in terms of UES delivery. These social needs and values are displayed through personal preferences of landowners and other stakeholders in the design and management of private green spaces, as well as strategies and policies defined by public institutions (Andersson, Barthel, & Ahrne, 2007). Selection and maintenance of vegetation in cities mirrors this human influence conspicuously, given its relevance as a major component in the design of urban green spaces (Grove et al., 2006).

Several studies have also exposed links between the spatial variability of UES delivery within the urban fabric and environmental inequity (Escobedo et al., 2006; Escobedo, Clerici, Staudhammer, & Corzo, 2015; Graça et al., 2017; Jenerette, Harlan, Stefanov, & Martin, 2011; Pedlowski, Da Silva, Adell, & Heynen, 2002), even if sometimes authors do not explicitly use the ES framework (Romero et al., 2012). To our knowledge, it remains largely unexplored how such environmental injustice can be mitigated through the proper planning of green spaces. Moreover, Luederitz et al. (2015) highlight as a key challenge for UES research the low transferability of data between contexts, especially in complex urban settings with heterogeneous socioeconomic and ecological backgrounds. This issue adds to the difficulties in providing orientations for urban planners and managers, and underlines the need to develop methodologies that can address local specific conditions and processes. Such process based knowledge is crucial to reveal unique patterns of UES delivery, as well as more generalizable trends already observed in other cross-city comparisons, both of which can contribute to effectively unravel drivers of ecosystem structure, functioning and dynamics (Kremer et al., 2016).

As a key provider of UES, vegetation holds a great potential to enhance urban resilience (Bolund & Hunhammar, 1999; Weber, 2013; Yapp, Walker, & Thackway, 2010). It is, however, necessary to better understand the ecological impacts of vegetation type and structure in cities. Previous research has shown, for example, that species assemblage and functional characteristics of vegetation affect ES provision (e.g. Lundholm, MacIvor, MacDougall, & Ranalli, 2010). In addition, structural variables of the urban forest such as tree density, size and condition impact ecosystem functions such as air pollution removal, carbon sequestration and rainfall interception, thus influencing UES supply (Nowak & Dwyer, 2007). However, trees also emit biogenic volatile organic compounds (BVOC) that can contribute to the formation of ozone (O3). Some species emit more BVOC than others and their emission rate can be further increased by higher temperatures, potentially degrading air quality especially in an urban heat island context (Calfapietra et al., 2013). Controversy persists regarding the real effect of trees in air quality (Setälä, Viippola, Rantalainen, Pennanen, & Yli-Pelkonen, 2013), supporting the need for more research. Some authors argue, for example that trees reduce air circulation in street canyons, consequently trapping pollutants and decreasing air quality (e.g. Vos, Maiheu, Vankerkom, & Janssen, 2013), while others suggest beneficial effects of trees for mitigation of air pollution (e.g. Irga, Burchett, & Torpy, 2015). Nevertheless, vegetation type and design seem to have a significant role in determining the effect in air quality (Gromke & Ruck, 2007; Janhäll, 2015).

Trees influence microclimate through evapotranspiration, shading, modified air movements and heat exchange, which also affect the urban atmosphere; moreover, urban vegetation intercepts rainfall and reduces water runoff and floods, which avoids stormwater treatment costs and damages (Nowak & Dwyer, 2007). These benefits rely on the structure and composition of vegetation, and are crucial for regulating the urban environment. Thus, acknowledging how vegetation structure and composition in urban green areas affect delivery of regulating UES could help urban planners and managers to target structural variables that enhance their provision. Adaptive design and management of urban green areas could therefore be addressed to explicitly enhance the provision of these UES and help in the implementation of the EU Strategy for Green Infrastructure (European Commission, 2013), as well as to tackle environmental inequities and to promote urban resilience.

However, few studies exist on how choices regarding vegetation use may affect the supply of regulating UES (though some exceptions should be noted, such as Hayek, Neuenschwander, Halatsch, & Grêt-Regamey, 2010; Hunter, 2011; Morani, Nowak, Hirabayashi, & Calfapietra, 2011). Likewise, comparative research concerning UES distribution within the urban fabric has not yet focused upon a full suite of designed types of urban space rather than vegetation types such as trees, shrubs and herbaceous (e.g. Derkzen et al., 2015). This paper aims to explore how different types of urban green spaces influence delivery of regulating UES in Porto, Portugal. The research was designed to answer the following questions:

  • -

    How are urban green types distributed in Porto in relation to socioeconomic patterns, and how does this distribution affect UES provision?

  • -

    Which structural variables of the vegetation differentiate the urban green types, and how do they impact UES delivery?

The purpose of the research was to assess social-ecological patterns affecting UES provision, with the central objective of identifying key variables that could be targeted through urban planning, planting design and management of green spaces to enhance UES.

Section snippets

Study area

The municipal limits of Porto, a major urban center of Portugal, were used to define the study area in this research (Fig. 1). This municipality covers 41.4 km2 with 237 591 inhabitants in 2011 (INE, 2011), and it is the nucleus of a metropolitan area comprised of 17 municipalities with 1 759 524 inhabitants in the same year (INE, 2014). Porto is bordered by the Atlantic Ocean at west, and Douro River flowing through the southern limit of the city. The climate is Mediterranean (Csb climate,

Distribution of urban green types across the city

Green areas in Porto covered about 40% of the urban area. Considering only the eight green types, the type with the highest coverage in the city was Vacant lots & wastelands (12.1%), followed by Private gardens & backyards (7.8%) and Parks, public gardens & woodlands (6.3%) (Table 2).

The urban green types were not evenly distributed throughout Porto or among the socioeconomic strata (Fig. 3).

The greenest socioeconomic stratum was CA (48.6% of the stratum area), which is the most economically

Analysis and implications of results

This research revealed that socioeconomic strata in Porto had distinct composition of urban green types, and that this strongly affected UES supply. The wealthier strata LM and AFN revealed a much better UES performance compared with the most economically deprived area (CA). LM and AFN also had by far the greatest share per hectare of managed green spaces, suggesting considerable more private and public investment than in the rest of the city. CA covers about one fifth of Porto and is home for

Conclusions

This work revealed that different types of green spaces affect UES delivery as a direct result of the influence of structural variables such as tree density, species richness, DBH, TLA and TLB. Furthermore, the uneven distribution of types of green spaces across socioeconomic strata might exacerbate this effect in some parts of the city, as observed in Porto. Urban planning can be a powerful way to address such environmental inequity, by efficiently allocating resources to the cold hotspots of

Acknowledgments

We are grateful to the Institute of Earth Sciences (University of Porto) for providing precipitation data to run i-Tree Eco v5, and to three anonymous reviewers for their valuable comments. This research was partially funded by the Portuguese Agency for Science and Technology (FCT) through PhD grant of M. Graça SFRH/BD/91028/2012 and PhD grant of J. Gonçalves SFRH/BD/90112/2012, both financed by national funds of the Ministry for Science and Education and by the European Social Fund through

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