Mismatches between ecosystem services supply and demand in urban areas: A quantitative assessment in five European cities

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Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
1
Title:
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Mismatches between ecosystem services supply and demand in urban
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areas: A quantitative assessment in five European cities
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ORIGINAL RESEARCH PAPER
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Author names and affiliations:
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Francesc Baróa, Dagmar Haaseb, c, Erik Gómez-Baggethuna, d, Niki Frantzeskakie
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aInstitute of Environmental Science and Technology (ICTA), Universitat Autònoma de
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Barcelona (UAB), Edifici Z, Carrer de les Columnes, Campus de la UAB, 08193 Cerdanyola
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del Vallès (Barcelona), Spain
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bHelmholtz Centre for Environmental Research (UFZ), Department of Computational
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Landscape Ecology, Permoser Straße 15, 04318 Leipzig, Germany
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cHumboldt University of Berlin, Department of Geography, Lab for Landscape Ecology,
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Rudower Chaussee 16, 12489 Berlin, Germany
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dNorwegian Institute for Nature Research (NINA), Gaustadalléen 21, 0349 Oslo, Norway
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eDutch Research Institute for Transitions (DRIFT), Erasmus University Rotterdam,
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Burgemeester Oudlaan 50, 3062PA Rotterdam, The Netherlands
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Corresponding author
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Francesc Baró
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E-mail address: francesc.baro@uab.cat
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Tel. (+34) 93 5868650
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Institute of Environmental Science and Technology (ICTA), Universitat Autònoma de
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Barcelona (UAB), Edifici Z, Carrer de les Columnes, Campus de la UAB, 08193 Cerdanyola
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del Vallès (Barcelona), Spain
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Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
2
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Abstract
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Assessing mismatches between ecosystem service (ES) supply and demand in urban areas can provide
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relevant insights for enhancing human well-being in cities. This paper provides a novel
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methodological approach to assess regulating ES mismatches on the basis of environmental quality
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standards and policy goals. Environmental quality standards indicate the relationship between
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environmental quality and human well-being. Thus, they can be used as a common minimum
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threshold value to determine whether the difference between ES supply and demand is problematic for
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human well-being. The methodological approach includes three main steps: (1) selection of
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environmental quality standards, (2) definition and quantification of ES supply and demand indicators,
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and (3) identification and assessment of ES mismatches on the basis of environmental quality
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standards considering certain additional criteria. While ES supply indicators estimate the flow of an
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ES actually used or delivered, ES demand indicators express the amount of regulation needed in
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relation to the standard. The approach is applied to a case study consisting of five European cities:
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Barcelona, Berlin, Stockholm, Rotterdam and Salzburg, considering three regulating ES which are
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relevant in urban areas: air purification, global climate regulation and urban temperature regulation.
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The results show that levels of ES supply and demand are highly heterogeneous across the five studied
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cities and across the environmental quality standards considered. The assessment shows that ES
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supply contributes very moderately in relation to the compliance with the EQS in most part of the
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identified mismatches. Therefore, this research suggests that regulating ES supplied by urban green
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infrastructure are expected to play only a minor or complementary role to other urban policies
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intended to abate air pollution and greenhouse gas emissions at the city scale. The approach has
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revealed to be appropriate for the regulating ES air purification and global climate regulation, for
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which well-established standards or targets are available at the city level. Yet, its applicability to the
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ES urban temperature regulation has proved more problematic due to scale and user dependent
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constraints.
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Keywords: Air purification; Assessment; Global climate regulation; Green infrastructure; Human
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well-being; Urban temperature regulation.
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60

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
3
1. Introduction
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Green infrastructure (GI) has been defined as a “network of natural and semi-natural areas with other
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environmental features designed and managed to deliver a wide range of ecosystem services (ES). It
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incorporates green spaces (or blue if aquatic ecosystems are concerned) and other physical features in
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terrestrial (including coastal) and marine areas” (EC, 2013:3). In urban areas, GI elements may include
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parks, urban forests, allotments, street trees, green roofs, etc. (Landscape Institute, 2009). Relevant ES
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delivered by GI in cities include, for instance, air purification, urban temperature regulation, runoff
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mitigation, noise reduction and recreation (Bolund and Hunhammar, 1999; Gómez-Baggethun and
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Barton, 2013; Gómez-Baggethun et al., 2013).
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An increasing body of literature highlights the contribution of GI and ES in enhancing environmental
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quality (e.g., air quality) in cities, hence fostering a better quality of life and well-being for the urban
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population (e.g., Nowak, 2006; Tzoulas et al., 2007; Escobedo et al., 2011; Pataki et al., 2011). Some
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studies even argue that urban policies based on the planning and management of GI can be comparable
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in terms of effectiveness or efficacy to other policies based on technological measures (e.g., Escobedo
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et al., 2008; 2010). Yet, the assessment of the current (and potential) contribution of urban GI through
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ES supply as a means to meeting desired or required environmental quality conditions and goals at the
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city scale remains largely unexplored.
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The main objective of the paper is hence the exploration of the possible contribution of ES supply to
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meet environmental quality standards and policy goals (hereafter referred as EQS) in urban areas. The
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underlying assumption derived from this objective is that EQS are to be met exclusively through ES
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supply. Conceptually, this hypothesis can be framed as the assessment of mismatches between ES
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supply and demand. This research argues that ES demand, defined here as the amount of service
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required or desired by society (Villamagna et al., 2013), can be expressed in relation to EQS because
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these provide a threshold value to determine whether the difference between ES supply and demand is
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problematic for human well-being. The assessment examines ES mismatches of three regulating ES
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which are relevant in urban areas (Gómez-Baggethun and Barton, 2013): air purification, urban
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temperature regulation and global climate regulation (through carbon sequestration). The
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methodological approach includes three main steps: (1) selection of EQS, (2) definition and
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quantification of ES supply and demand indicators, and (3) identification and assessment of ES
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mismatches on the basis of EQS considering certain additional criteria. While ES supply indicators
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estimate the flow or amount of an ES actually delivered (e.g., air pollutants removed by urban
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vegetation), ES demand indicators estimate the amount of inputs needing regulation (e.g., air pollutant
94

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
4
concentrations) in relation to the corresponding EQS (e.g., air quality standards). The approach is
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applied to a case study consisting of five European cities: Barcelona, Berlin, Stockholm, Rotterdam
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and Salzburg. Based on the obtained results, the actual and potential contribution of urban GI to
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address mismatches between ES supply and demand at the city scale is discussed, as well as the
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advantages and limitations of using EQS to assess these mismatches.
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101

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
5
2. Materials and methods
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2.1. Conceptual framework
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Recently developed conceptual frameworks in the ES literature call for a distinction between ES
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capacity, flow and demand as the main components of the ES delivery process (Villamagna et al.,
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2013; Burkhard et al., 2014; Schröter et al., 2012; 2014; Guerra et al., 2014). Capacity is defined as
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the ES potential (i.e., hypothetical maximum yield) and flow as the actual supply or use of ES
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experienced by people. ES demand, however, has been approached differently depending on the
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authors. Burkhard et al. (2014:5) define demand for ES as the “services currently consumed or used in
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a particular area over a given time period, not considering where ES actually are provided”.
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Alternatively, ES demand has been described as “the amount of a service required or desired by
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society” (Villamagna et al., 2013:115) or “the expression of the individual agents’ preferences for
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specific attributes of the service” (Schröter et al., 2014:541). In this paper, ES supply is conceptualized
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as ES flows (Hein et al., 2006) and ES demand as the required level of ES delivery by society
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(Villamagna et al., 2013). ES mismatches occur when the demand for ES is not totally met by the
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supply within a defined spatial and time scale. Thus, ES mismatches express the existence of an
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unsatisfied or remaining demand (Geijzendorffer et al., 2015).
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According to the framework developed by Villamagna et al. (2013), the supply of regulating ES
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contribute to the maintenance of environmental quality within socially acceptable ranges only until a
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certain level of ecological pressure (e.g., air pollution). Beyond this level, ES supply cannot sustain a
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good environmental quality and ES demand should be considered as not totally met. Under this
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approach, estimating regulating ES demand requires hence information about two main elements: (1)
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desired conditions (i.e., good environmental quality); and (2) inputs needing regulation (i.e., ecological
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pressures). In line with Paetzold et al. (2010), this paper considers that EQS can be used as a threshold
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of desired conditions in relation to the demand for regulating ES. In general terms, EQS rely on
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scientific evidence and/or expert knowledge concerning the relationship between environmental
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quality and human well-being with the underlying aim to secure or enhance the latter (e.g., EEA,
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2013a). Thus, the methodological approach considered here assumes that EQS can provide a common
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minimum threshold value to assess regulating ES mismatches across different contexts (in this case
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study, different European cities). For example, World Health Organization (WHO) air quality
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guidelines (WHO, 2005) can be used to provide a minimum threshold to assess the mismatch between
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supply and demand of the ES air purification. A city where air pollution levels exceed WHO reference
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values reflects a mismatch in which air purification demand exceeds the current local supply. Yet, this
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situation does not necessarily imply that the EQS is to be achieved solely by ES supply.
135

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
6
136
2.2. Selection of environmental quality standards
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Based on a non-exhaustive examination of European-context regulatory frameworks, relevant EQS
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were identified for the three ES assessed in this study (Table 1). EQS for ES air purification were
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derived from the European Union (EU) air quality Directive (EU, 2008) and WHO air quality
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guidelines (WHO, 2005). Reference values for ground-level concentrations of air pollutants are
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generally more stringent in the WHO standards, but only the EU standards are legally binding for the
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case study cities, hence the inclusion of both standards in the assessment was considered pertinent.
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The focus was limited to the following air pollutants: (1) particulate matter with a diameter of 10 µm
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or less (PM10); (2) nitrogen dioxide (NO2); and (3) tropospheric ozone (O3), considered three of the
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most problematic air pollutants in terms of exposure to concentrations above the EU and WHO
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reference levels in Europe for its urban population (EEA, 2013a).
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The ES global climate regulation is generally assumed to be demanded at global scale (Burkhard et al.,
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2012), yet city specific GHG emission reduction and offset targets can be considered as a desired
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condition at lower scales. Following the EU 20-20-20 targets (EC, 2008), many municipal authorities
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have signed up to the ‘Covenant of Mayors’ initiative
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, voluntarily committing themselves to reduce
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their GHG emissions by at least 20% until 2020 (see Table 1 for specific reduction targets of the case
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study cities).
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No explicit EQS were found in relation to urban temperature regulation at the European regulatory
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level, probably because human health vulnerability to temperature extremes depends on a complex
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interaction between different factors such as age, health status, socio-economic circumstances (e.g.,
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housing) and regional adaptation (Kovats and Hajat, 2008; Fischer and Schär, 2010). However,
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general critical temperature thresholds for health impacts in Europe have been estimated based on the
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spatial and temporal variance in excess mortality during recent heatwaves
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episodes (Fischer and
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Schär, 2010). According to this research, the consecutive occurrence of days with maximum
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temperature above 35ºC (‘hot days’) and nights with minimum temperature above 20ºC (‘tropical
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nights’) has been found to explain the correlation with excess mortality. These values match well with
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specific temperature thresholds officially allocated to cities like Barcelona (Tobias et al., 2012), but
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are likely overestimated for Northern cities like Stockholm (Roklöv and Forsberg, 2008) due to
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1
See www.covenantofmayors.eu
2
Fischer and Schär (2010) define a heatwave “to be a spell of at least six consecutive days with maximum temperatures
exceeding the local 90th percentile of the control period (1961-1990)”.

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
7
regional adaptation factors. In any case, the impacts of heatwaves on human health are particularly
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strong in cities, both in Northern and Southern latitudes, due to the exacerbating effect of the urban
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heat island (UHI) (EEA, 2012).
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Table 1
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EQS selected to assess mismatches between ES supply and demand
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ES
EQS
Air
purification
 EU Air Quality Directive (EU, 2008) and WHO air quality guidelines (WHO, 2005)
reference values:
Pollutant
EU
WHO
PM10
40 µg m-3 (Year)
20 µg m-3 (Year)
NO2
40 µg m-3 (Year)
40 µg m-3 (Year)
O3
120 µg m-3 (8-hour)
100 µg m-3 (8-hour)
Global
climate
regulation
 Covenant of Mayors’ GHG emission reduction targets for each case study city are:
o Barcelona: 23% by 2020 (baseline year 2008)
o Berlin: 40% by 2020 (baseline year 1990)
o Stockholm: 45% by 2020 (baseline year 1990)
o Rotterdam: 50% by 2025 (baseline year 1990)
o Salzburg: No explicit target found (assuming 20% by 2020, baseline year 1990)
Urban
temperature
regulation
 Heatwave thresholds: consecutive occurrence of hot days (T-max > 35ºC) and tropical nights
(T-min > 20 ºC) (Fischer and Schär, 2010).
Notes: Air quality policy targets correspond to the EU and WHO values set for the protection of human health (in brackets
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the averaging period applicable for each limit). EU’s reference value for O3 is subject to 25 days of allowed exceedances per
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year averaged over three years. See EEA (2013a) for more details. GHG emission reduction targets for each case study city
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are based on local Sustainable Energy Action Plans (see www.covenantofmayors.eu and Table 3).
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2.3. Defining indicators of ES supply
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ES supply was measured directly as the amount of a service delivered or experienced by people (van
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Oudenhoven et al., 2012; Villamagna et al., 2013). The indicators for ES supply were selected based
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on methods and data availability (see Table 2). For this analysis only terrestrial ecosystems were
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considered, omitting blue infrastructure elements (sea, lakes, ponds, rivers, etc.) which can also be
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important sources of ES supply in the urban context (Bolund and Hunhammar, 1999), especially in
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case study cities such as Stockholm, Rotterdam and Barcelona. The use of tools specifically designed
184

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
8
for quantifying ES delivered by terrestrial vegetation (e.g., i-Tree Eco model) prevented a more
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complete assessment of urban ecosystems (i.e., including blue infrastructure).
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The supply of the ES air purification was quantified using estimated air pollution removal of PM10,
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NO2, and O3 by urban green space. Uptake rates were quantified using the dry deposition model of i-
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Tree Eco tool (Nowak et al., 2006; 2008; Hirabayashi et al., 2012). Data required for each city
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included hourly air pollution concentration, percentage of tree canopy cover (both deciduous and
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evergreen) and meteorological data. For Barcelona and Berlin air pollution removal rates were taken
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from Baró et al. (2014) corresponding to year 2008, and Aevermann (pers. comm., 2013) for year
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2011, respectively. Air pollution concentration data from Salzburg, Stockholm and Rotterdam
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monitoring stations were obtained from the AirBase database v.7 (EEA, 2013b) for the year 2011.
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Meteorological data were retrieved from the US National Climatic Data Centre for the same year.
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Percentages of evergreen and deciduous tree canopy cover for these three cities were estimated using
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i-Tree Canopy tool
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which allows photo-interpretation of urban land covers from Google Maps aerial
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imagery using a random sampling location process. A sample of 500 survey points were photo-
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interpreted for each city based on a categorization of three cover classes: 1) deciduous tree; 2)
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evergreen tree and 3) non-tree cover. This method likely underestimates the amount of air purification
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supplied since it accounts for tree canopy but not for shrubs or herbaceous vegetation which can also
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supply this ES (Nowak et al., 2006).
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Carbon storage and annual CO2 sequestration rates performed by urban GI were used as indicators to
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measure the supply of the ES global climate regulation (Nowak and Crane, 2002; Strohbach and
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Haase, 2012; Nowak et al., 2013; Schröter et al., 2014). Barcelona’s estimates were based on the i-
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Tree Eco assessment performed in 2008 using field measurements of urban forest structure, allometric
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equations to predict above-ground biomass and adjusted urban tree growth and decomposition rates
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(Baró et al., 2014). Due to limited resources for fieldwork data collection in the other case study cities,
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carbon storage and sequestration indicators were estimated based on the assessment carried out by
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Nowak et al. (2013) using urban field data from 28 cities and 6 states in United States (US), where
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carbon storage per square meter of tree cover averaged 7.69 kg C m-2 (SE = 1.36), gross carbon
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sequestration rate averaged 0.277 kg C m-2 year-1 (SE = 0.045), and net carbon sequestration rate
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averaged 0.205 kg C m-2 year-1 (SE = 0.041). Percentage of tree canopy cover was estimated using the
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i-Tree Canopy tool as described above (for Berlin, 1,000 points were photo-interpreted due to its
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larger area). Although these rates can vary depending on variables such as tree diameter distribution or
217
3
see www.itreetools.org/canopy/index.php

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
9
species composition in each city, the indicator estimates should be accurate as they are based on local
218
tree cover values (Nowak et al., 2013). Further, empirical studies carried out in European cities
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obtained similar values (e.g., Strohbach and Haase, 2012 estimated an average carbon storage rate of
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6.82 ± 1.42 kg C m-2 of canopy cover in Leipzig, Germany). Because tree growth (and hence CO2
221
sequestration) vary depending on the local environmental conditions, sequestration rates were refined
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using the length of the growing season as a proxy, following the formula (Nowak, pers. comm., 2013):
223
224
′  
 (1)
225
226
Where
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C’ = average (gross or net) carbon sequestration rate (kg C/m2 tree cover year)
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C = US average (gross or net) carbon sequestration rate (kg C/m2 tree cover year) (Nowak et al. 2013)
229
GS = length of the growing season (days)
230
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Average length of the growing season in each case study city was based on phenological data for the
232
period 1969-1998 (Chmielewski and Rötzer, 2001). Reported trends in plant phenology in Europe and
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USA indicate a similar lengthening of the growing season in the last decades associated to global
234
warming (Linderholm, 2006), thus used lengths should be considered a first-order estimate. Carbon
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sequestration rates were converted to CO2 after applying the conversion factor 1 g C = 3.67 g CO2.
236
237
The supply of the ES urban temperature regulation by green space can provide important benefits to
238
city inhabitants by mitigating heat stress (Stone et al., 2010) and reducing UHI effects and increased
239
temperatures resulting from climate change (Gill et al., 2007). Vegetation delivers this service mainly
240
through the evapotranspiration process and the shading effect (basically from trees). Bowler et al.
241
(2010) systematically reviewed the empirical evidence of this ES showing that, on average, the
242
temperature within an urban park would be around 1 ºC cooler than a non-green site in the day. Other
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urban GI elements such as urban forests and green roofs also show evidence of lower air temperatures
244
compared to treeless areas and roofs without vegetation respectively (Oberndorfer et al., 2007; Breuste
245
et al., 2013). Tree shade area was used as a proxy indicator to quantify the supply of this service. It
246
was estimated as tree canopy cover area using i-Tree canopy tool as described above, assuming that
247
the cooling effect is provided mainly below tree canopy (Bowler et al., 2010).
248
249
250
251

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
10
Table 2
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ES supply indicators and associated quantification methods and references.
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ES
Indicators
Quantification method
Sources / References
Air
purification
PM10 removal
(kg ha-1 year-1)
i-Tree Eco dry deposition
model based on tree canopy
cover, air pollution and
meteorological data
i-Tree Canopy (www.itreetools.org)
AirBase v.7 (EEA, 2013b). Year
2011
Nowak et al. (2006); Baró et al.
(2014); Aevermann et al. (2015,
submitted)
NO2 removal
(kg ha-1 year-1)
O3 removal
(kg ha-1 year-1)
Global
climate
regulation
CO2 sequestration
(t ha-1 year-1)
Estimates from i-Tree
assessments based on tree
canopy cover and length of
growing season
i-Tree Canopy (www.itreetools.org)
Nowak et al. (2013); Baró et al.
(2014)
Carbon storage
(t ha-1)
Urban
temperature
regulation
Tree shade area (%)
Cooling effect of trees based on
empirical data and tree canopy
cover area estimates
i-Tree Canopy (www.itreetools.org)
Bowler et al. (2010); Breuste et al.
(2013)
254
2.4. Defining indicators of ES demand
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Due to the different approaches to ES demand, a variety of indicators can be defined to measure it.
256
One way is to consider population density in combination with average or desired consumption rates
257
(Burkhard et al., 2012; Kroll et al., 2012). ES demand can also be measured by the socio-cultural
258
preferences directly expressed by people in interviews and questionnaire surveys (Martín-López et al.,
259
2014) or through monetary valuation (de Groot et al., 2012). Following the conceptual framework
260
described above, in this paper ES demand indicators express the amount or concentration of inputs
261
(i.e., ecological pressures) needing regulation with regard to the corresponding EQS (i.e., the desired
262
environmental conditions which secure human well-being) (Villamagna et al., 2013; Burkhard et al.,
263
2014). Table 3 shows the selected indicators for ES demand.
264
265
Indicators for the ES air purification were estimated on the basis of air pollution levels in each city in
266
relation to the desired level expressed by air quality standards (Burkhard et al. 2014). These indicators
267
express the remaining air pollution as they already include the impact of ES supply (Guerra et al.,
268
2014 call it as “ES mitigated impact”). Annual mean concentrations for PM10 and NO2 from the
269
available traffic monitoring stations (which express the highest demand) in each case study city were
270

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
11
extracted from the AirBase database v.7 (EEA, 2013b) using values corresponding to year 2011. O3
271
levels were expressed as the twenty-sixth highest value in each city based on daily maximum 8-hour
272
averages since the current European air quality threshold includes 25 days of allowed exceedances
273
(EEA, 2013a).
274
275
Demand indicators for the ES global climate regulation were estimated on the basis of annual GHG
276
emissions as expressed in carbon dioxide equivalent (CO2-eq) per hectare and per capita (Burkhard et
277
al., 2014). Total emissions for each case study city were obtained from local Sustainable Energy
278
Action Plans (SEAPs) and other municipal policy reports (see Table 3 for references) corresponding
279
to the GHG reduction target baseline year (1990 for Berlin, Stockholm and Rotterdam, 2008 for
280
Barcelona and 2010 for Salzburg because 1990 data was not available).
281
282
Finally, demand for the ES urban temperature regulation was estimated using heatwave risk as
283
indicator. Following Fischer and Schär (2010), heatwave risk was quantified as the number of
284
combined tropical nights (> 20ºC) and hot days (>35ºC) projected for the period 2071-2100 in Europe.
285
This scenario was developed at a European scale and it does not take into account the UHI effect that
286
exacerbates heatwave risk in cities (EEA, 2012). Thus, the consideration of this future scenario can
287
roughly express a more realistic current situation of heatwave risk in the case study cities, where the
288
UHI can reach a maximum intensity of 8°C (e.g., Moreno-Garcia, 1994 for Barcelona).
289
290
291
292
293
294
295
296
297
298
299
300

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
12
Table 3
301
Demand ES indicators and associated quantification methods and references.
302
ES
Indicators
Quantification method
Sources / References
Air
purification
PM10 annual mean
concentration (µg m-3)
Statistical data review
AirBase v.7 (EEA, 2013b) - Year 2011
NO2 annual mean
concentration (µg m-3)
26th highest O3 value
based on daily max 8-
hour averages (µg m-3)
Global
climate
regulation
Annual CO2-eq
emissions per ha.
(t ha-1 year-1)
Literature review on
municipal GHG emissions
and census data
Barcelona: PECQ. 2011. The energy,
climate change and air quality plan of
Barcelona 2011-2020. Base year 2008.
Berlin: Environmental Agency of the
Senate of Berlin. Base year 1990.
Stockholm: Stockholm action plan for
climate and energy 2010–2020. Base
year 1990.
Rotterdam: CDP Cities 2012 Global
Report. Base year 1990.
Salzburg: Energiebericht 2010 Smart
City Salzburg. Base year 2010.
Annual CO2-eq
emissions per capita
(t capita-1 year-1)
Urban
temperature
regulation
Heat wave risk (# days)
Combined tropical nights
(>20ºC) and hot days
(>35ºC) expected 2071-
2100
Fischer and Schär (2010)
EEA (2012)
303
2.5. Criteria for identifying and assessing ES mismatches
304
The assessment of matches and mismatches between ES supply and demand usually requires demand
305
to be assessed in the same units as supply in order to obtain a budget or ratio indicating ES
306
undersupply, neutral balance or oversupply (Paetzold et al., 2010; Burkhard et al., 2012; Kroll et al.,
307
2012). However, because of the EQS-based approach considered in this paper, the assessment of
308
mismatches was determined by the following criteria: (1) in the case of non-compliance with the limit
309
or target values stipulated by the EQS, the demand for the corresponding ES was considered to be not
310
totally met by the current supply at the city scale, thus an ES mismatch was identified. On the
311
contrary, in the case of standard compliance, the demand was considered to be currently met by the
312
supply and no ES mismatch was expected at the city level; (2) due to the ES-based assumption
313
considered here, it was also important to assess the contribution or impact of ES supply in relation to
314
the compliance with the EQS, especially in the case of exceedance of limit or target values. In this
315

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
13
way, informed decisions can be taken on the feasibility of increasing ES supply (e.g., increase tree
316
canopy cover in the city) as an effective measure to address a given mismatch.
317
318
In the case of air purification, an ES mismatch between supply and demand was identified if, despite
319
air purification delivered by urban trees, air pollution levels exceeded EU and/or WHO air quality
320
reference values. The ES contribution to the compliance with the standards was estimated as the
321
average air quality improvement due to air purification by urban trees from i-Tree Eco dry deposition
322
model results (Nowak et al., 2006; Hirabayashi et al., 2012). The estimation of this variable involved
323
considering the mixing layer height
4
in each case city area, which was derived from radiosonde data of
324
the closest station available in the NOAA/ESRL Radiosonde Database
5
. A “substantial mismatch” was
325
identified if the ES contribution (air quality improvement) was lower than 10% in relation to the EQS
326
exceedance. A “moderate mismatch” was identified if this contribution was higher than 10%. This
327
mismatch analysis could not be done for EQS exceedances of O3 because the standards are based on
328
daily max 8-hour averages whereas air quality improvements are based on annual averages. The
329
criterion to assess an ES mismatch for the ES global climate regulation was defined as the deficit of
330
urban ecological carbon sinks to contribute substantially to CO2-eq reduction targets in each city. An
331
ES contribution lower than 10% in relation to the reduction target was considered as a “substantial
332
mismatch”. A “moderate mismatch” was identified when the contribution was higher than 10%, but
333
lower than 100%. Finally, the uncertainty and complexity related to the impact of the ES urban
334
temperature regulation supply at the wider city scale (Bowler et al., 2010) implies that the heatwave
335
risk cannot be consistently compared to the cooling effect provided by GI on the basis of the heatwave
336
thresholds at the city scale. Therefore, the mismatch assessment of this ES was excluded from the
337
analysis.
338
339
2.6. Case study cities
340
The paper builds on five case study cities distributed along a north-south and east-west gradient across
341
Europe: Barcelona, Berlin, Stockholm, Rotterdam, and Salzburg (Fig. 1). The cities vary in their
342
population size, urban form, climate patterns and socio-economic characteristics (Fig. 1, Table 4),
343
making them representative for a broad range of medium-to-large size European cities. Most of these
344
cities have ambitious strategic plans to enhance GI and ES in the coming years (e.g., Barcelona Green
345
4
The mixing height can be defined as “the height of the layer adjacent to the ground over which pollutants or any
constituents emitted within this layer or entrained into it become vertically dispersed by convection or mechanical turbulence
within a time scale of about an hour” (Seibert et al., 2000).
5
See http://esrl.noaa.gov/raobs/

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
14
Infrastructure and Biodiversity Plan 2020, Barcelona City Council, 2013). Furthermore, these are all
346
case study cities of the URBES project (Urban Biodiversity and Ecosystem Services
6
).
347
348
The spatial scope of this analysis is the municipal or core city area (Urban Audit, 2009). An intrinsic
349
limitation must be acknowledged when using administrative boundaries in urban ES assessments
350
because cities are, to a large extent, influenced by ES provided beyond these boundaries, namely from
351
the larger suburbanized and rural hinterland (Larondelle and Haase, 2013). However, the focus on the
352
administrative areas responded to the following motivations: (1) the analysis includes indicators for
353
which required datasets were only available at the administrative level; (2) urban policies related to
354
green space are usually limited to city’s municipal boundaries (e.g., Barcelona’s green infrastructure
355
and biodiversity plan 2020, Barcelona City Council, 2013), hence recommendations for future policies
356
are more likely to be applicable when addressed at this spatial scale; (3) the administrative area of the
357
case study cities corresponds well with the dense urban core of their metropolitan areas (Larondelle
358
and Haase, 2013; Larondelle et al., 2014).
359
360
Barcelona is the capital city of the region of Catalonia and Spain’s second-largest city in terms of
361
population. The city is characterized by a compact urban form together with a very high population
362
density (see Table 4). Approximately a quarter of the municipal area consists of green space (parks,
363
gardens, urban forests, etc.), most of which corresponds to the urban park of Montjuïc and the peri-
364
urban forest area of Collserola. Barcelona has also a relatively high proportion of street trees compared
365
to other European cities (Pauleit et al., 2002). Berlin is the capital city and the most populous city of
366
Germany, located at the core centre of the Berlin-Brandenburg metropolitan region. Green space
367
amounts to one third of the city’s area, including large urban parks such as Tiergarten located at the
368
city centre and larger areas of forest and water ecosystems located at the outskirts of the municipal
369
area. The former Tempelhof airport has recently been converted into an urban park, providing new
370
opportunities to benefit from green space to a large number of city inhabitants (Kabisch and Haase,
371
2014). Stockholm, awarded the first European Green Capital in 2010 by the European Commission
7
, is
372
the capital of Sweden and the country’s most populated municipality. The amount of green and blue
373
space is very relevant in Stockholm (on third of the city’s areas is covered by parks, forest and other
374
green assets and 12% by water bodies). Rotterdam is the second largest city of the Netherlands and has
375
the largest seaport of Europe in terms of cargo volume and traffic (CRRSC, 2009). Blue space covers
376
almost a quarter of the total city’s area, mainly corresponding to the lowest course of the river Nieuwe
377
Maas. The city is considered one of the greenest large cities of the Netherlands, having a total of 117
378
6
www.urbesproject.org
7
http://ec.europa.eu/environment/europeangreencapital/

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
15
public parks and 747,000 trees (Frantzeskaki and Tilie, 2014). Salzburg is the fourth largest city of
379
Austria and the capital city of the federal state of Salzburg. Almost a half of the municipal area is
380
covered by green space, including a relevant share of forest and agricultural land which is legally
381
protected by the City Council (Voigt et al., 2014).
382
383

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
16
384
Fig. 1. Location of case study cities and distribution of green space covers. Source: own elaboration based on
385
Natural Earth data (www.naturalearthdata.com) and Urban Atlas (EEA, 2010). Administrative boundaries:
386
Catalan Cartographic Institute (www.icc.cat); Senate Department for Urban Development and the Environment
387
(www.stadtentwicklung.berlin.de/ geoinformation/); Stockholm City Council (www.stockholm.se); Centraal
388
Bureau voor de Statistiek – Statistics Netherlands (www.cbs.nl); Salzburg Geoinformation System (SAGIS)
389
(www.salzburg.gv.at/sagis/).
390

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal Ecological Indicators (Elsevier). Therefore, it underwent
full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply and demand in urban areas: A quantitative assessment in
five European cities. Ecol. Indic. 55, 146–158. doi:10.1016/j.ecolind.2015.03.013
17
Table 4
391
Main characteristics of the case study cities.
392
Barcelona
Berlin
Stockholm
Rotterdam
Salzburg
Sources / References
Location in Europe
South-West
Central
North
North-West
Central
-
Physical geography
Coastal / River
delta
Inland
plains/River
Coastal/Lake
outlet
Coastal/River
delta
Inland/Foothill
of the Alps
-
Population (#)
1,615,908
3,431,675
810,120
582,951
147,169
Urban audit 2009 (reference year 2008)
Population projection
in 20501 (#)
1,672,112
3,460,046
1,648,000
621,780
161,589
Own trend calculations based on National
Census, except for Barcelona (Catalan
Statistical Institute – IDESCAT).
Total area (km2)
101.6
891.1
215.8
277.4
65.7
Municipal boundaries (various sources)
Population density
(inhab. km-2)
15,905
3,851
3,754
2,101
2,240
Urban audit 2009 (reference year 2008)
Gross Domestic
Product
(PPS inhab.-1)
30,800
24,400
41,000
36,500
38,100
Urban audit 2009 (for NUTS3 region,
reference years 2007-2010)
Green urban area
(m2 inhab.-1)
3.00
16.91
43.88
23.12
25.86
Urban Atlas (EEA, 2010); Urban audit
2009
Development of
green space 1990 –
2006 (ha)
-0.02
1,083
106
16
3
Kabisch and Haase (2013)
Number of private
cars registered
(# 100 inhab.-1)
38.13
28.56
36.98
34.13
N/A
Urban audit 2009 (reference year 2008)
Average temperature
of warmest month
(ºC)
25.5
19.5
18.5
N/A
18.6
Urban audit 2009 (reference year 2008)
1Except for Barcelona (highest population projection for 2021)
393
394

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
18
395
3. Results
396
3.1. ES supply and demand across the case study cities
397
The quantification results of ES supply and demand indicators are partly shown in Fig. 2. The
398
complete set of indicator results is presented in Table A1 (supply) and Table A2 (demand) of the
399
Appendix.
400
401
Supply of the ES air purification showed the highest values in Berlin, almost doubling the average
402
removal rate for the five case study cities when the three air pollutants are considered. The results for
403
Barcelona and Stockholm displayed comparatively intermediate values, with a total supply of nearly
404
30 kg removed air pollutants per hectare annually in both cases. Rotterdam and Salzburg were
405
characterized by the lowest values of air purification supply whatever the air pollutant considered. For
406
example, Salzburg’s O3 removal rate was negligible compared to Berlin’s (0.12 to almost 22 kg ha-1
407
year-1) even though both cities have a relevant share of green space. PM10 was the air pollutant
408
comparatively most removed in all the cities, except in Berlin where O3 removal was slightly higher.
409
Inversely, NO2 was the pollutant with lowest removal rates in all case study cities, except in Salzburg
410
where the lowest value was found for O3. Demand indicators for the ES air purification showed
411
different patterns compared to supply across the different case study cities. For example, NO2 annual
412
mean concentration levels were higher than PM10 values in all cities whereas supply indicators showed
413
the opposite condition. It must be noted that PM10 and NO2 have the same EU limit value (40 µg m-3
414
for annual mean concentration), thus demand indicators are comparable for this standard. The highest
415
values for both pollutants were found in Barcelona (32.76 µg m-3 for PM10 and 53.78 µg m-3 for NO2),
416
while PM10 was lowest in Salzburg (23.86 µg m-3) and NO2 in Stockholm (38.50 µg m-3). Results for
417
O3 were not comparable with NO2 and PM10 values because concentrations (and standards) are based
418
on daily max 8-hour averages. Berlin (with 116.14 µg m-3) and Salzburg (with 111.63 µg m-3) showed
419
the highest values for O3. In contrast, the lowest values of O3 were displayed by Rotterdam (84.74 µg
420
m-3) and Barcelona (89.60 µg m-3).
421
422
Regarding global climate regulation supply, CO2 sequestration indicators ranged from 1.05 t annually
423
sequestered per hectare in Rotterdam to 3.66 t ha-1 year-1 in Berlin. In the same way, carbon storage
424
values ranged from 9.38 t ha-1 in Rotterdam to 32.84 t ha-1 in Berlin. Although Stockholm’s average
425
growing season is the shortest compared to the other cities, net CO2 sequestration and carbon storage
426
values were second-ranked after Berlin’s. The demand side of global climate regulation showed a
427
different picture: CO2-eq emissions per hectare were remarkably highest in Rotterdam (865.2 t ha-1
428

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
19
year-1), most likely because of the impact of seaport activities on city’s GHG emissions. On the other
429
hand, the lowest value was found for Salzburg (86.6 t ha-1 year-1). However, CO2-eq emissions per
430
capita were lowest in Barcelona (2.51 t capita-1 year-1), reflecting the comparatively elevated
431
population density of the Mediterranean city. Supply and demand indicators for this ES could be
432
straightforwardly compared using annual net CO2 sequestration and CO2-eq emission rates per hectare
433
as a common unit. Results showed that demand values are approximately two orders of magnitude
434
larger than supply.
435
436
Supply indicators for urban temperature regulation revealed also a considerable heterogeneity among
437
case study cities. The highest tree cooling area values were found in Berlin (42.70%) and Stockholm
438
(37.50%). Rotterdam was distinctly the case study city with the lowest share of tree cooling area
439
(12.20%). The demand for urban temperature regulation using heatwave risk as a proxy reflected
440
clearly the different climate zones where the case study cities are located. The results for Barcelona
441
showed a very high number of expected hot days and tropical nights (> 50), while heatwave risk in
442
Stockholm is expected to be minimum (0-2 days). The values for Berlin, Rotterdam and Salzburg were
443
higher than Stockholm’s, but substantially far from Barcelona’s (2-6 days).
444
445
In summary, both supply and demand indicators differed notably among the five case study cities. In
446
most cases, Rotterdam showed the lowest supply values, followed by Barcelona or Salzburg. In
447
contrast, the results for Berlin and, to a lesser extent, Stockholm indicated a relatively high supply of
448
the three regulating ES analyzed. More heterogeneous results were found for demand indicators across
449
the different cities. Barcelona and Rotterdam were clearly characterized by a high demand for urban
450
temperature and global climate regulation respectively. Demand for air purification showed
451
comparatively minor differences across cities. See also exemplary Fig. 3 showing results for
452
Barcelona compared to case study cities averages.
453
454

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal Ecological
Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting, typesetting, pagination and
proofreading process, which may lead to differences between this version and the published version: Baró, F., Haase, D., Gómez-Baggethun,
E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply and demand in urban areas: A quantitative assessment in five
European cities. Ecol. Indic. 55, 146–158. doi:10.1016/j.ecolind.2015.03.013
20
455
456
457
Fig. 2. Quantification results of ES supply and demand indicators for the five case study cities. Notes: Air purification demand
458
values are in annual mean concentration for PM10 and NO2 and in daily max 8-hour averages for O3 (26th highest value). Urban
459
temperature regulation demand values are the maximum number of days of heatwave risk, except for the case of Barcelona
460
which is the minimum (Fischer and Schär, 2010). Supply and demand values are not directly comparable except for global
461
climate regulation.
462
463
16,4
19,0
10,9
3,7
6,9
5,4
8,4
6,3
2,2
4,1
7,2
22,0
12,7
3,0
0,1
0
5
10
15
20
25
Barcel. Berlin Stockh. Rotter. Salzb.
[kg / ha year]
(1) Air purification (Supply)
PM₁₀ removal NO₂ removal
O₃ removal
1,97
3,66
3,06
1,05
2,39
0
1
2
3
4
Barcel. Berlin Stockh. Rotter. Salzb.
[t / ha year]
(2) Global climate regulation (Supply)
Net CO₂ sequestration
29,4
42,7
37,5
12,2
28,6
0
10
20
30
40
50
Barcel. Berlin Stockh. Rotter. Salzb.
[%]
(3) Urban temperature reg. (Supply)
Tree shade area
32,8 30,1 28,5 28,5 23,9
53,8 53,4
38,5 48,7 45,2
89,6
116,1
95,1 84,7
111,6
0
20
40
60
80
100
120
140
Barcel. Berlin Stockh. Rotter. Salzb.
[µg / m3]
(4) Air purification (Demand)
[PM₁₀]
[NO₂]
[O₃]
399,0
328,8
170,0
865,2
86,6
0
200
400
600
800
1000
Barcel. Berlin Stockh. Rotter. Salzb.
[t / ha year]
(5) Global climate regulation (Demand)
CO₂ eq. emissions (baseline year)
50
626 6
0
10
20
30
40
50
Barcel. Berlin Stockh. Rotter. Salzb.
[days]
(6) Urban temperature reg. (Demand)
Heatwave risk

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal Ecological
Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting, typesetting, pagination and
proofreading process, which may lead to differences between this version and the published version: Baró, F., Haase, D., Gómez-Baggethun,
E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply and demand in urban areas: A quantitative assessment in five
European cities. Ecol. Indic. 55, 146–158. doi:10.1016/j.ecolind.2015.03.013
21
464
465
466
Fig.3. Spidergrams comparing the standardized values of ES supply and demand indicators for Barcelona with the average
467
values of the five case study cities. Supply and demand values are not directly comparable. Standardization is based on a linear
468
rescaling of values in the 0-1 range on the basis of their minimum and maximum value.
469
470
471
0,0
0,2
0,4
0,6
0,8
1,0
PM₁₀
removal
NO₂ removal
O₃ removal
Net CO₂
sequestration
Carbon
storage
Tree shade
area
(1) Supply
Barcelona Case cities average
0,0
0,2
0,4
0,6
0,8
1,0
PM₁₀ levels
NO₂ levels
O₃ levels
CO₂ eq.
emissions
(per ha.)
CO₂ eq.
emissions
(per cap.)
Heatwave
risk
(2) Demand
Barcelona Case cities average

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
22
472
3.2. Mismatches in ES supply and demand
473
Following the criteria described above, matches and mismatches between ES supply and demand were
474
identified, showing a number of cases (12) where demand was clearly not totally met by supply
475
considering the different case study cities (marked as red cells in Table 5). In only two cases ES
476
demand was not totally met by supply, but the mismatch was considered minor, suggesting that the
477
corresponding EQS could be met after the implementation of measures intended to increase ES supply
478
(marked as yellow cells). Finally, ES supply matched with demand based on the corresponding EQS in
479
almost half of the cases (14, marked as green cells).
480
481
The mismatch assessment of the ES air purification service indicated heterogeneous results across air
482
pollutants and EQS. All cities met the EU limit value for PM10 annual average concentration (40 µg m-
483
3), but none of them complied with the WHO standard (20 µg m-3). Only Stockholm met the limit
484
value for NO2 levels (set at 40 µg m-3 for both standards). Tropospheric O3 levels were below EU
485
regulation in all case cities, but above WHO’s air quality limit in Berlin and Salzburg (assuming 25
486
allowed exceedances per year as well), although the determination of the magnitude of the mismatch
487
was not possible due to data limitations. The relative contribution of the ES service supply to meet air
488
quality standards across the different case study cities is shown in Table 6. Air quality improvements
489
due to ES supply showed the lowest values in Rotterdam and the highest values in Stockholm for all
490
the analyzed pollutants, varying between 0.20% and 2.42% for PM10 levels, between 0.07% and
491
0.81% for NO2 levels and between 0.10% and 1.16% for O3 levels. According to i-Tree model results,
492
expected air quality improvements are considerably more relevant in areas with 100% tree cover (e.g.,
493
urban forests or tree-covered urban parks). However, city-scale average annual air pollution levels in a
494
hypothetic scenario without green space would not differ substantially from the current levels.
495
Therefore, the ES mismatch should be minor if realistic increases in ES supply are intended to meet
496
the standards. The results suggest that this situation only occurs for Salzburg’s PM10 levels in relation
497
to WHO limit value.
498
499
CO2 offsets by urban GI (ES supply) compared to city-based CO2 eq. emissions (corresponding to the
500
baseline year for the reduction target) were modest in all case studies, ranging from 0.12% for
501
Rotterdam to 2.75% for Salzburg. Similarly, the contribution of the ES supply in relation to CO2eq
502
reduction targets for 2020 was low in all case study cities. Salzburg was the only case where the
503
annual sequestration rate was higher than the 10% threshold contribution (13.8%), although it must be
504
noted that the city has the lowest reduction target among the case studies.
505

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
23
506
Table 5
507
Identification and assessment of mismatches in ES supply and demand across the case study cities. Red cells
508
indicate a substantial mismatch between ES supply and demand (ES contribution is lower than 10% in relation to
509
the EQS exceedance or reduction target), suggesting that the corresponding EQS can be unlikely met by increase
510
in ES. Yellow cells indicate a moderate mismatch between ES supply and demand (ES contribution is higher
511
than 10% in relation to the EQS exceedance or reduction target) suggesting that the corresponding EQS could be
512
met after the implementation of measures intended to increase ES supply. Green cells indicate that ES supply
513
matches with demand based on the corresponding EQS. Blank cells indicate that the mismatch assessment could
514
not be consistently done due to data limitations. See also subsection 2.5.
515
ES
Assessment
EQS
Barcel.
Berlin
Stockh.
Rotter.
Salzb.
Air purification
PM10 levels
EU
PM10 levels
WHO
NO2 levels
EU/WHO
O3 levels
EU
O3 levels
WHO
Global climate
regulation
Contribution to city
CO2eq reduction target
City CO2eq
reduction target
Urban temp.
regulation
N/A
Heatwave
thresholds
516
Table 6
517
Estimated air quality improvement due to air pollution removal by urban trees in case study cities (year 2011)
518
Average percent air quality
improvement at the city
scale
Average percent air quality
improvement only in areas
with 100% tree cover
Expected average annual air
pollution levels without
urban trees at the city scale
(µg m-3)
PM10
NO2
O3
PM10
NO2
O3
PM10
NO2
O3
Barcelona
0.50
0.19
0.29
1.64
0.63
0.96
32.92
53.88
39.81
Berlin
0.73
0.21
0.30
1.67
0.49
0.70
30.33
53.49
47.41
Stockholm
2.42
0.81
1.16
6.14
2.12
2.96
29.16
38.81
55.62
Rotterdam
0.20
0.07
0.10
1.57
0.57
0.81
28.51
48.69
35.93
Salzburg
1.89
0.60
0.85
6.24
2.04
2.83
24.32
45.48
41.75
519

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
24
4. Discussion
520
4.1. The contribution of ES supply to human well-being in cities
521
The impact of urban green space on air quality in cities is a subject of scientific debate. Several
522
empirical and modelling studies support that urban vegetation provides substantial air quality
523
improvements followed by associated health benefits (Nowak et al., 2006; Yin et al., 2011; Islam et
524
al., 2012; Nowak et al., 2013). However, factors such as vegetation configuration or climate conditions
525
can strongly limit the ability of vegetation to remove air pollutants, especially at the patch scale
526
(Setälä et al., 2013; Vos et al., 2013). The modelling results presented here indicate that average air
527
quality improvements due to air purification supply is relatively low at the city scale for the three
528
analyzed air pollutants in all case study cities (e.g., from 0.07% in Rotterdam to 0.81% in Stockholm
529
for NO2), although positive effects are likely to be more relevant in highly tree-covered areas such as
530
urban forests (e.g., expected air improvements are higher than 6% for PM10 in Stockholm’s and
531
Salzburg’s areas with an hypothetical 100% tree cover, see Table 6). Therefore, the average
532
contribution of ES supply in regard to the compliance with air quality standards is considered modest
533
at the local level in all case studies, suggesting a limited effectiveness to address ES mismatches by
534
increasing ES supply (e.g., implementing tree-planting programs) unless air pollution concentration
535
exceedance is minor (e.g., PM10 levels compared to WHO standard in the case of Salzburg).
536
537
A number of studies have assessed the role of urban green space as a climate change mitigation
538
strategy by offsetting city CO2 emissions (Pataki et al., 2009; Escobedo et al., 2010; Zhao et al., 2010;
539
Liu and Li, 2012). Impacts of net CO2 sequestration rates on offsetting annual city CO2 emissions vary
540
from 3.4% in Gainesville, US (Escobedo et al., 2010) to 0.26% in Shenyang, China (Liu and Li,
541
2012). As expected, similar results have been obtained for the case study cities (ranging from 0.12% in
542
Rotterdam to 2.75% in Salzburg). This paper has gone one step further by considering city-specific
543
GHG reduction targets as a desired condition at the city level. Again, results show a modest
544
contribution of ES supply (less than 15%) in all case study cities, suggesting that increases in direct
545
carbon sequestration delivered by GI (e.g., by doubling tree density) is not likely to be an effective
546
means for reaching local CO2-eq. reduction targets (in line with Pataki et al., 2011).
547
548
Previous empirical evidence on the supply of urban temperature regulation (Bowler et al., 2010)
549
revealed that the cooling effect of urban GI can be relatively relevant at the patch scale. For example, a
550
maximum of 2ºC difference relative to built-up area was observed in an urban park in Stockholm
551
(Jansson et al., 2007). However, the extension of the cooling effect of green space beyond its
552
boundaries is uncertain, especially at the wider city scale (Bowler et al., 2010). Therefore, heatwave
553

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
25
thresholds cannot be consistently balanced against the cooling effect provided by GI elements at the
554
city scale. Additional empirical research is required to assess these mismatches, especially by
555
establishing specific temperature thresholds according to each climate zone and measuring the cooling
556
impact of GI interventions at the city scale.
557
558
The findings of this research suggest that GI can only play a minor or complementary role, at least at
559
the core city level, to urban mitigation measures intended to abate air pollutant and GHG emissions at
560
the source (e.g., road traffic management or energy efficiency measures) or to adaptation policies
561
intended to cope with heat extremes (e.g., heat warning plans). Yet, there are important reasons for
562
which the current and potential supply of these ES should not be neglected in local policy decision-
563
making. First, GI can provide other important benefits to urban population due to its multifunctional
564
capacity (e.g., stormwater runoff mitigation or recreational opportunities), while technological
565
substitutes are normally designed as single-purpose. Second, although GI expansion in compact cities
566
such as those analyzed in this paper might be challenging due to lack of available land and
567
densification processes, measures for preserving existing green spaces and innovative ways to allocate
568
new ones could considerably enhance ES supply at the city level (Jim, 2004). For instance, the
569
potential of green roofs and walls to deliver a wide range of ES has been assessed in various empirical
570
studies (Oberndorfer et al., 2007; Rowe 2011).
571
572
4.2. Strengths and weaknesses of using EQS to assess ES mismatches
573
The demand side is frequently omitted or underrepresented in ES assessments which usually focus on
574
ES supply (Burkhard et al., 2014). Yet, an increasing number of studies have developed assessment
575
methods considering both the ES supply and demand in order to provide a complete picture of the ES
576
delivery process where mismatches between both sides can be identified (e.g., Van Jaarsveld et al.,
577
2005; Burkhard et al., 2012; Kroll et al., 2012; García-Nieto et al., 2013; Boithias et al., 2014; Schulp
578
et al., 2014; Geijzendorffer et al., 2015). This paper contributes to the ES research agenda (de Groot et
579
al., 2010) suggesting a novel methodological approach based on the use of EQS to assess mismatches
580
between ES supply and demand with a focus on regulating ES in core city areas. Based on the
581
assessment of ES mismatches in five European cities, strengths and weaknesses of this approach could
582
be recognized.
583
584
This approach can be especially advantageous for regulating ES assessments because of several
585
reasons: (1) demand for regulating ES usually cannot be indicated by direct market prices, unlike
586
many provisioning ES for example (De Groot et al., 2012); (2) the interactions between regulating ES
587

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
26
and human benefits are often very complex, thus ES demand is challenging to indicate (Burkhard et
588
al., 2014). EQS are generally meaningful to society and can reasonably express a common threshold to
589
assess regulating ES mismatches across different societal contexts as they provide a benchmark
590
representing the minimum desirable environmental quality conditions under which some components
591
of human well-being such as health can be secured, hence allowing comparative analyses; (3) this
592
approach allows relatively quick assessments of ES demand if data on environmental quality is
593
available at the city level. In contrast, other demand-side assessments like socio-cultural elicitation are
594
usually more time consuming and resource intensive (Martín-López et al., 2014).
595
596
However, the use of EQS in ES assessments has also drawbacks. The existence of different EQS
597
regulating the same environmental condition (or ecological pressure) can create uncertainty about
598
which thresholds are more adequate in terms of expressing a societal demand related to human needs
599
for well-being. In this paper, both WHO and EU standards for air quality have been used giving
600
different ES mismatch results for some air pollutants. Although only EU standards are legally binding
601
for case study cities, WHO standards are probably more reliable expressing a desirable or required end
602
condition of air quality (Brunekreef and Holgate, 2002). The main shortcoming of local GHG
603
emission reduction targets is that often they are not based on scientific evidence about possible climate
604
change impacts, but on political reasons. Regarding urban temperature regulation, the multiple factors
605
involved in the relationship between temperature extremes and human health vulnerability call for
606
specific temperature thresholds to properly account for varying environmental conditions and societal
607
demands at the local level.
608
609
More generally, the use of specific or local-based thresholds is possibly the most appropriate option
610
when assessing ES for which demand is strongly context/user/stakeholder dependent (Paetzold et al.,
611
2010), despite it would make cross-city comparisons less meaningful. This is clearly the case of
612
cultural ES. For example, several standards have been suggested as thresholds for assessing the
613
desirable amount of recreational opportunities delivered by green space in urban areas, normally based
614
on criteria of accessibility to green space (i.e., distance) and space size (Van Herzele and Wiedemann,
615
2003; Söderman et al., 2012; Kabisch and Haase, 2014). The former is commonly seen as the most
616
important factor related to the recreational use of urban green space and a maximum 300-400 meter
617
distance from home has been observed as a threshold after which the use decreases substantially
618
(Schipperijn et al., 2010). Some regulatory agencies have consequently recommended standards based
619
on these criteria. For example, the European Environment Agency (EEA) recommends that people
620
should have access to green space within 15 min walking distance (Stanners and Bourdeau, 1995) and
621

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
27
the English standard ANGSt (Accessible Natural Greenspace Standard, Natural England, 2010)
622
recommends that urban population should have an accessible green space no more than 300 m from
623
home (Barbosa et al., 2007). However, these standards have been criticized because they fail to
624
address issues such as green space quality or local context and needs (Pauleit et al., 2003). Still, some
625
authors claim that green space recreational standards are needed but they should be locally developed
626
according to specific social and quality criteria (Baycan-Levent and Nijkamp, 2009). Therefore, a
627
possible extension of the approach presented in this paper beyond regulating ES should be carefully
628
designed.
629
630
4.3. Spatially explicit ES mismatches
631
The spatial distribution of ES supply and demand at the city level has not been addressed in this paper.
632
Yet, for some ES such as air purification or urban temperature regulation both their supply and
633
demand can substantially vary across the urban fabric. The use of spatially explicitly indicators could
634
show the specific location of ES mismatches at the inner-urban level (or higher scales), hence
635
informing about ES deficit areas (demand is higher than supply) to urban planners and managers.
636
Several attempts of mapping ES mismatches have already been developed at different spatial scales
637
(e.g., Kroll et al., 2012; García-Nieto et al., 2013; Boithias et al., 2014; Schulp et al., 2014). However,
638
assessments at the core city scale are scarce, probably due to the lack of fine-resolution data for the
639
appropriate quantification of ES supply and demand indicators.
640
641

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
28
5. Conclusion
642
This paper provides an innovative approach for assessing mismatches in regulating ES supply and
643
demand using EQS as a common minimum threshold for determining whether the difference between
644
supply and demand is problematic in terms of human well-being. The approach has revealed to be
645
appropriate for the ES air purification, for which there is a large body of evidence on the health
646
impacts of air pollution and EQS are well-established at the international level. Similarly, local GHG
647
reduction targets can reasonably express a demand for mitigating the impacts of climate change in
648
urban areas (global climate regulation), thus the assessment of ES mismatches was also possible. The
649
application of the approach for the ES urban temperature regulation has proved more problematic. The
650
demand for urban temperature regulation is strongly context and user dependent, thus common
651
thresholds (such as heatwave thresholds) are less appropriate. Furthermore, the spatial scale to which
652
the ES is delivered is still not totally clear in terms of scientific evidence, creating uncertainties in the
653
ES mismatch assessment. In general, more empirical studies are needed to improve GI design and
654
monitor its effectiveness in meeting local or international environmental standards and goals in
655
different urban areas.
656
657
The case study of five European cities reveals mismatches between ES supply and demand in half of
658
the 28 ES/EQS/City combinations analyzed, suggesting that further protection and restoration of urban
659
GI will be required if ES are to play a more relevant role in meeting EQS to enhance human well-
660
being in cities. However, the assessment indicates that ES supply contributes very moderately in
661
relation to the compliance with the EQS in most part (12 out of 14) of the identified mismatches.
662
Results suggest that EQS could be met after the implementation of feasible measures intended to
663
increase ES supply only in two analyzed cases. Therefore, this research suggests that regulating ES
664
supplied by urban GI are expected to play only a minor or complementary role (currently and
665
potentially) to other urban policies intended to abate air pollution and GHG emissions at the city scale.
666
Urban managers and policy-makers should take into account these considerations when designing and
667
implementing GI programs, but recognizing at the same time the multiple benefits associated to GI in
668
urban contexts not addressed in this assessment (e.g., runoff mitigation, noise reduction and
669
recreational opportunities).
670
671
672

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
29
673
Acknowledgements
674
We thank two anonymous reviewers whose valuable comments have substantially improved the paper.
675
We are grateful to all the URBES team for their cooperation, especially Johannes Langemeyer
676
(Autonomous University of Barcelona) and Neele Larondelle (Humboldt University of Berlin) for
677
their support in this research. Our thanks also go to Robert E. Hoehn and David J. Nowak, from the
678
USDA Forest Service, for their assistance with i-Tree Eco model, and to Kaysara Khatun for
679
proofreading the manuscript. This research was partially funded by the ERA-Net BiodivERsA through
680
the Spanish Ministry of Economy and Competitiveness project ‘URBES’ (code PRI- PIMBDV-2011-
681
1179) and by the 7th Framework Program of the European Commission project ‘OpenNESS’ (code
682
308428).
683
684
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Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
34
Appendix. Quantification of ES supply and demand indicators
875
876
Table A1
877
ES supply indicators for the five case study cities
878
ES
Indicator
Barcel.
Berlin
Stockh.
Rotter.
Salzb.
Mean
Air purification
PM10 removal
kg ha-1 year-1
(Mg year-1)
16.42
(166.01)
18.97
(1690)
10.93
(235.77)
3.71
(101.74)
6.92
(45.46)
11.39
(447.80)
NO2 removal
kg ha-1 year-1
(Mg year-1)
5.40
(54.59)
8.36
(745)
6.29
(135.78)
2.24
(61.37)
4.12
(27.05)
5.28
(204.76)
O3 removal
kg ha-1 year-1
(Mg year-1)
7.18
(72.62)
21.96
(1,957)
12.67
(273.44)
2.99
(81.94)
0.12
(0.78)
8.98
(477.16)
Global climate
regulation
Net CO2
sequestration
t ha-1 year-1
(t year-1)
1.97
(19,986)
3.66
(325,726)
3.06
(66,131)
1.05
(29,218)
2.39
(15,673)
2.43
(91,347)
Carbon
storage
t ha-1 (Mg)
11.22
(113,437)
32.84
(2,925,924)
28.84
(622,326)
9.38
(257,071)
21.99
(144,421)
20.85
(812,636)
Urban
temperature
regulation
Tree shade
area
% (ha)
29.40
(2,973)
42.70
(38,048)
37.50
(8,093)
12.20
(3,343)
28.60
(1,878)
30.08
(10,867)
Note: see references and corresponding time-ranges in Table 2.
879
880

Important note: This is the author’s post-print version of a research paper that was accepted for publication in the journal
Ecological Indicators (Elsevier). Therefore, it underwent full peer review but has not been through the copyediting,
typesetting, pagination and proofreading process, which may lead to differences between this version and the published
version: Baró, F., Haase, D., Gómez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply
and demand in urban areas: A quantitative assessment in five European cities. Ecol. Indic. 55, 146–158.
doi:10.1016/j.ecolind.2015.03.013
35
881
Table A2
882
ES demand indicators for the five case study cities
883
ES
Indicator
Barcel.
Berlin
Stockh.
Rotter.
Salzb.
Mean
Air purification
PM10 annual mean
concentration
µg m-3
32.76
30.11
28.45
28.45
23.86
28.72
NO2 annual mean
concentration
µg m-3
53.78
53.38
38.50
48.66
45.21
47.90
26th highest O3 value
based on daily max 8-
hour averages
µg m-3
89.60
116.14
95.14
84.74
111.63
99.45
Global climate
regulation
CO2-eq. emissions per
ha.
t ha-1 year-1
398.99
214.70
128.59
1,067.35
86.59
379.25
CO2-eq. emissions per
capita
t capita-1 year-1
2.51
5.40
3.40
48.51
3.82
12.73
Urban
temperature
regulation
Heat wave risk
days
>50
2-6
0-2
2-6
2-6
N/A
Note: see references and corresponding time-ranges in Table 3.
884
885
886