A framework for assessing urban greenery's effects and valuing its
, Jenny Klingberg
, Bengt Gunnarsson
, Kevin Cullinane
, Marcus Hedblom
, Igor Knez
, Fredrik Lindberg
, Åsa Ode Sang
, Pontus Thorsson
Department of Earth Sciences, University of Gothenburg, Box 460, SE 405 30 G€
Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE 405 30 G€
School of Business Economics and Law, Box 610, University of Gothenburg, SE 405 30 G€
City of Gothenburg, Parks and Landscape Administration, Box 177, 401 22 G€
Department of Forest Resource Management, Landscape Analysis, Swedish University of Agricultural Sciences, Box 7044, SE 750 07 Uppsala, Sweden
Department of Social Work and Psychology, H€
ogskolan i G€
avle, SE 801 76 G€
Department of Landscape Architecture, Planning and Management, Swedish University of Agricultural Sciences, PO Box 66, SE 230 53 Alnarp, Sweden
Division of Applied Acoustics, Chalmers University of Technology, SE 412 96 Gothenburg, Sweden
Urban Climate Group, Department of Earth Sciences, University of Gothenburg, Box 460, SE 405 30 G€
Department of Architecture and Civil Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
Received 8 April 2017
Received in revised form
17 September 2017
Accepted 26 September 2017
Available online 8 October 2017
Ecosystem service effects
Ecosystem service valuation
Case study application
Ongoing urban exploitation is increasing pressure to transform urban green spaces, while there is
increasing awareness that greenery provides a range of important beneﬁts to city residents. In efforts to
help resolve associated problems we have developed a framework for integrated assessments of
ecosystem service (ES) beneﬁts and values provided by urban greenery, based on the ecosystem service
cascade model. The aim is to provide a method for assessing the contribution to, and valuing, multiple ES
provided by urban greenery that can be readily applied in routine planning processes. The framework is
unique as it recognizes that an urban greenery comprises several components and functions that can
contribute to multiple ecosystem services in one or more ways via different functional traits (e.g. foliage
characteristics) for which readily measured indicators have been identiﬁed. The framework consists of
ﬁve steps including compilation of an inventory of indicator; application of effectivity factors to rate
indicators' effectiveness; estimation of effects; estimation of beneﬁts for each ES; estimation of the total
ES value of the ecosystem. The framework was applied to assess ecosystem services provided by trees,
shrubs, herbs, birds, and bees, in green areas spanning an urban gradient in Gothenburg, Sweden. Es-
timates of perceived values of ecosystem services were obtained from interviews with the public and
workshop activities with civil servants. The framework is systematic and transparent at all stages and
appears to have potential utility in the existing spatial planning processes.
©2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
Urbanization has become one of the most extensive and per-
manent land-use changes globally, causing increasing pressure to
transform green spaces in or near cities (UN, 2014; World Bank,
2015). However, urban greenery provides a range of social and
environmental services that beneﬁt city residents and visitors
(Kabisch et al., 2015). The potential synergies and conﬂicts arising
from the beneﬁts of urban green areas and demand for their
exploitation pose challenges for sustainable urban development
and initiatives to maintain or improve human well-being. A concept
that has received increasing attention and can help efforts to
address these challenges is ecosystem services (ES) (Haase et al.,
2014; Kabisch et al., 2015; Luederitz et al., 2015). The ES concept
*Corresponding author. Swedish National Road and Transport Research Institute
(VTI), Box 8072, SE-417 55 Gothenburg, Sweden.
E-mail address: email@example.com (Y. Andersson-Sk€
Current address: Gothenburg Botanical Garden, Carl Skottsbergs gata 22A, SE-
41319 Gothenburg, Sweden.
Contents lists available at ScienceDirect
Journal of Environmental Management
journal homepage: www.elsevier.com/locate/jenvman
0301-4797/©2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Journal of Environmental Management 205 (2018) 274e285
embraces all the interlinked aspects of ecological structures with
functions that are advantageous to humans (services), and thus
contribute to human well-being (beneﬁts) (MEA, 2005; Potschin
and Haines-Young, 2011; TEEB, 2010). The ES cascade model may
also be helpful. This captures the view that a “production chain”
links biophysical structures and processes to the beneﬁts and
values of the services a considered system provides (Fig. 1). For
example, an ecosystem such as urban woodland may have the ca-
pacity (function) of slowing the passage of surface water, thereby
reducing ﬂooding in cities (service), which provides beneﬁts to
humans. The value of these beneﬁts (and, thus, preceding links in
the chain) depends on time- and place-related factors that can be
summarised as supply and demand. The cascade model also in-
cludes feedback loops, based on assumptions that services' values
will impact the ecosystem, e.g. high demand for provisional ser-
vices will result in high pressure on them. However, the pressure
imposed on ecosystems can be modiﬁed through policy actions
(Potschin and Haines-Young, 2011).
The aims of ES valuation are to unravel the complexities of
socio-ecological relationships, recognize how human decisions
impact perceived values of services, and express them in units that
allow incorporation in planning and decision-making (Mooney
et al., 2005; TEEB, 2010). Various methods can be used to esti-
mate the value of ES, all of which have limitations because of the
difﬁculties in quantifying most ES. Their values can be determined
in monetary terms, such as current market prices, e.g. market prices
for biofuel and timber, or estimates of costs that would be incurred
if the services had to be created by artiﬁcial means (TEEB, 2010).
This direct approach cannot be applied to various other ES (such as
well-being and aesthetic appreciation) that do not have any market
prices, but their monetary values can be estimated using proxies,
e.g. travel costs, or hedonic pricing methods (TEEB, 2010; Goulder
and Kennedy, 2011). Alternatively, non-monetary choice prefer-
ence methods can be used to estimate most non-market ES values.
These methods include perception ranking analysis and attitude
rating, which are regarded as useful for probing perceived values
and preferences regarding possible planning options (e.g. García-
Llorente et al., 2008; TEEB, 2010).
In addition, multi-criteria methods for assessing ES based on
Corine (Coordination of information on the environment) Land
Cover (CLC) have been recently developed. They have been applied,
for example, to estimate a region's contribution to provisioning
services as well as climate regulation, air quality, water regulation,
recreational facilities, aesthetic appeal and biodiversity, based on
stakeholder-based weighting to reﬂect the relative importance of
the investigated ES (Koschke et al., 2012). CLC data have also been
applied to estimate gradients of cooling potential, carbon seques-
tration and available recreational area in four European cities
(Larondelle and Haase, 2013).
Few studies have covered all the sequential steps related to
urban ES, including links between ecological structures, their
functions, performance and values to humans (Luederitz et al.,
2015). However, during the last decade various models have been
developed to quantify and value urban ES. One example is the i-Tree
urban forest management tool
(developed from the UFORE model,
Nowak et al., 2008) for assessing integrated beneﬁts of services
provided by urban trees (such as removal of atmospheric carbon
dioxide and storm-water reduction), valuing the services in mon-
etary terms. The results have been applied for several purposes,
such as assessing and visualizing the beneﬁts of trees and the
impact of land use changes (e.g. Nowak et al., 2014a; Hilde and
Despite the availability of models such as the i-Tree model,
further development of appropriate methods for integrated quan-
tiﬁcation of beneﬁts and valuation, including additional potential
services and urban biophysical structure components other than
trees, is still required (Haase et al., 2014; Luederitz et al., 2015).
Further, both CLC- and i-Tree-based analyses require detailed
modelling, which may hinder their use in local urban management.
To address the requirement for methods that can be more readily
applied, we present a method to integrate regulating ES (pollina-
tion, local climate regulation, air pollution control, noise reduction,
storm water management) and cultural ES, allowing inclusion of
additional ES not considered here. The method is a systematic
process involving description of urban green structure components
(trees, bushes, herbs, bees, birds) contributing to ES through
functional trait indicators and a sequence of subsequent steps
Fig. 1. The cascade model framework for ecosystem valuation modiﬁed from TEEB (2010) and Potschin and Haines-Young (2011).**.
old et al. / Journal of Environmental Management 205 (2018) 274e285 275
described in the following section.
Our aim is to provide a method for valuing multiple ES provided
by urban greenery. The method is intended to be practically
applicable in routine planning processes to enable planners to
make well-informed trade-offs. It aims, for example, to be used to
make rough assessments of effects of potential land use changes
and designs in comprehensive plans and detailed land use plans or
as a tool to increase the communication between municipal civil
servants from different disciplines and units involved in the spatial
planning process or to identify gradients and lack of ecosystem
services in urban areas.
2. Framework of the method
The method involves several “steps”designed to link measured
abundances of urban green structure components to functions
delivering ES, by means of functional traits, i.e. phenotypic char-
acteristics of groups of organisms that are considered relevant to
such organisms' responses to the environment and/or their effects
on ecosystem properties (e.g. Dıaz et al., 2013; Duncan et al., 2015).
It should be noted here that mechanistic understanding of various
species' contributions to ecosystem functions is crucial for robust
assessments of multiple ES (see e.g. Lavorel et al., 2011).
To estimate abundances of functional traits, a number of rele-
vant indicators have been identiﬁed. Some of the indicators are
directly related to functional traits (e.g. canopy cover), but others
are indirect indicators linked to the species' variation. The ES and
functional trait indicators were selected according to identiﬁed
needs for better understanding of relations between functional
traits, ES effect and ES values, and the ease of obtaining relevant
The framework of the method is based on the cascade model
presented by TEEB (2010) and Potschin and Haines-Young (2011),
taking into account the effectiveness of indicators' contributions to
considered ES. The method is intended to be generically applicable
and easy to use in planning processes, environmental impact as-
sessments (EIA), and any other cases where understanding of
ecosystems' current or potential services and values is required. It
can be applied both for individual sites and estimates of gradients
of services or values over a city or other urban area. It involves the
following ﬁve steps (Fig. 2):
1) Compilation of an inventory of indicator abundances at the
2) Application of effectivity factors to rate indicators' effectiveness
3) Estimation of the effects for each ES
4) Estimation of the beneﬁts for each ES
5) Estimation of the total ES value at the site(s)
2.1. Steps 1e3 - estimation of effects
To estimate a site's potential contribution to (investigated) ES
the abundance of each indicator, e.g. leaf area or the abundance of
birds or bee species, and their ability to contribute to the consid-
ered service(s) must be estimated. An indicator's ability to
contribute to a service is here referred to as the indicator effectivity
factor (f(i,j)). The resulting effect, E(i,j) is, calculated using the
E(i,j) is the effect (the resulting contribution) to ES j provided by
A(i) is the abundance of indicator i, and
f(i,j) is the effectivity factor, describing the ability of indicator i
to contribute to ES j
2.2. Step 4 ebeneﬁt estimation
The beneﬁtBði;jÞ;of a certain indicator, i, depends both on the
estimated effect Eði;jÞand the value of the speciﬁc ES:
where v(j) is the perceived value of ES(j).
2.3. Step 5 etotal value
The total ES value, V, of ecosystem services assessed at a site is
then the sum of the beneﬁts of the considered services:
Thus, a stepwise process based on the abundance and effectivity
of individual functional trait indicators provides a generic frame-
work that can be applied to estimate the total beneﬁt and value of
an ecosystem (Fig. 2).
3. Framework application
3.1. Study sites
To test the framework's applicability, it was used to assess the
beneﬁt and value of seven green sites in the Swedish city Goth-
E), which has approximately 540 000 in-
habitants. It is a green city, where there is a green area covering
10 ha within 300 m of homes of 58% of the population (SCB, 2010).
However, there is strong urban densiﬁcation pressure on the city's
green areas (SCB, 2010). The seven study sites (Fig. 3) span a large
gradient of urban green spaces, including a suburban forest, old
central park, allotment gardens, green yards in a residential area
and a small green area surrounded by busy roads and industrial
estates (hereafter called infrastructural green space). Detailed in-
formation about the study sites is presented in Supplementary
Table S1, and the ﬁve steps of the analysis are presented in the
3.1.1. Step 1- compilation of an inventory of indicator abundances
In the ﬁrst step, the abundance of each selected indicator is
estimated. Here, we ﬁrst describe how the indicators were identi-
ﬁed (from the literature) and selected, then how their abundances
A(i,j) were empirically estimated at each study site. Both the in-
dicators and empirical methods have been selected for applicability
to any urban biophysical structure.
126.96.36.199. Identiﬁed regulating ES indicators. Leaf area is an important
plant canopy characteristic that is widely applied in ecological
analyses and has proven importance for predicting several regu-
lating ES (Dobbs et al., 2011; Gomez-Baggethun and Barton, 2013).
These include removal of air pollutants via deposition (e.g.
Burkhard et al., 2012), wind reduction and cooling by both shading
and transpiration (e.g. Hardin and Jensen, 2007; Konarska et al.,
2014). It is also a robust predictor of water storage during and af-
ter rainfall events (Keim et al., 2006).
The amount of foliage of a vegetated surface is conventionally
old et al. / Journal of Environmental Management 205 (2018) 274e285276
Fig. 2. Framework for beneﬁt assessment and valuation of ES.
Fig. 3. Location of Gothenburg and the seven green areas selected for the study. Spatial characteristics of the study sites in terms of nearby buildings and tree canopy height are
shown. The study sites are numbered according to their ID numbers in Table S1: 1) Suburban woodland, 2) Urban woodland, 3) Urban park, 4) Allotment area, 5) Infrastructural
green space, 6) Urban park and woodland, 7) Residential area. Adapted from Klingberg et al. (2017a). (For interpretation of the references to colour in this ﬁgure legend, the reader is
referred to the web version of this article.)
old et al. / Journal of Environmental Management 205 (2018) 274e285 277
described by the leaf area index (LAI), the projected (one-sided) leaf
area per unit ground area (Monteith and Unsworth, 2008). LAI can
be an appropriate indicator of the area of foliage available for
photosynthesis, transpiration and deposition of air pollutants.
However, LAI data can be time-consuming and costly to obtain
compared to accurate, high-resolution canopy cover datasets
(Klingberg et al., 2017a), which may be acceptable substitutes in
Water retardation through storage and soil evaporation is
related to the surface permeability (EEA, 2015; Olsson et al., 2013;
Peng et al., 2016), and leaf area through transpiration (Benyon
and Doody, 2015; Zhang et al., 1999, 2001). The pollination of
plants in urban and rural areas is highly dependent on the presence
of insect pollinators, both honeybees and wild insects (Matteson
and Langellotto, 2009; Garibaldi et al., 2013). Thus, a pertinent in-
dicator is the total abundance of honeybees, bumblebees and sol-
itary bees. The identiﬁed regulating indicators (i) are presented in
188.8.131.52. Identiﬁed cultural ES indicators. Natural environments are
known to have positive health effects and stimulate physical ac-
tivities (Bell et al., 2008), but few quantitative correlations between
these effects and speciﬁc biophysical structures (trees, shrubs etc.)
have been published. In addition, physical places (e.g. urban green
spaces) and time spent in such places can anchor people's remi-
niscences by forming psychological person-place ties (Blicharska
et al., 2017; Knez, 2014). These ties are emotional and cognitive
bonds that ground our personal memories and thus our life-stories
(who we are) (Merleau-Ponty, 1945).
Recent ﬁndings, of studies focusing on the sites considered here
(Ode Sang et al., 2016) and other settings (e.g. Sandifer et al., 2015),
have shown that perceived naturalness is positively correlated with
recreational and aesthetical values including physical activities,
visual aesthetic values and self-reported well-being. Well-being is
here deﬁned broadly, including a state of physical, mental and so-
cial comfort and happiness (WHO, 1946 in Sandifer et al., 2015) and
cultural identity (MEA, 2005; Sandifer et al., 2015).
Diversity of habitats and species is known to promote abilities to
reﬂect and strengthen place-identiﬁcation (Sandifer et al., 2015;
Knez et al., submitted). Accordingly, recent studies focused on the
sites considered here have also shown that people's valuation of
green spaces is signiﬁcantly correlated with estimates of biodiver-
sity based on surveys of trees, bushes, herbs, birds and bees
(Gunnarsson et al., 2017). Moreover, the perceived aesthetic and
auditory appeal of urban greenery reportedly correlates with sub-
categories of “high, medium and low”diversity of these compo-
nents (Gunnarsson et al., 2017). In addition, urban settings are
appreciated more if bird songs are heard, especially if multiple
species are heard (Hedblom et al., 2014). It was also recently shown
that bird song and trees rustling make people feel calm, and that
visible and audible experiences of urban greenery are connected
(Hedblom et al., 2017).
184.108.40.206. Empirical measurements of indicator abundance
220.127.116.11.1. Regulating indicators. At each study site the leaf area
index (LAI) and canopy cover were estimated from aerial LiDAR
(light detection and ranging) data covering Gothenburg munici-
pality acquired in 2010. Using laser penetration metrics, high res-
olution maps of canopy cover (1 m) and LAI (10 m) were produced
following Klingberg et al. (2017a). Permeable surface (%) estimates
were acquired from map information, and soil types were esti-
mated using wet sieved soil samples collected at the study sites,
following procedures presented by Van Kleef (2017). Densities of
bees were estimated from point counts at each site (three points,
three times in each site), as detailed by Gunnarsson et al. (2017).
18.104.22.168.2. Cultural indicators. At each study site, the diversity of
birds and densities of tree, shrub and herb species were estimated,
as follows. Numbers of songbirds observed in point counts at each
site were recorded, and their diversities were estimated using
Simpson's index (1/D), as recommended by Magurran (2004).
Trees, shrubs and herbs were surveyed in circular plots with radii of
20, 10 and 0.28 m, in accordance with the National Inventory of
Landscapes in Sweden monitoring program (Ståhl et al., 2011).
Data pertaining to trees and shrubs were collected in 2e4 plots,
and data pertaining to herbs in 6e12 plots per site, depending on
the size and heterogeneity of the sites. Songbirds were counted at
two points three times in each site, as further described by
Gunnarsson et al. (2017).
We would prefer, ‘The measured abundances are shown in
supplementary material (Table S2). To allow analyses of the indi-
cator abundances with a common scale, applicable for all types of
indicators, we normalised the results by dividing the indicator
abundance (or species density) recorded at each site by the
maximum recorded across the seven sites. Thus, the possible range
of values for each indicator was 0e1.0.’
3.1.2. Step 2 eapplication of effectivity factors to rate indicators'
Contributions of the function associated with a given indicator
(functional trait indicator) to a given ES depend on the abundance
of the indicator and its effectivity, here expressed by effectivity
factors (f(i,j)) on a 3-point scale: 1 (weak), 2 (moderate) and 3
(strong). The rating is based on published evaluations. For example,
trees are well known to block solar radiation at street level
(creating shade) and to cool nearby surroundings through tran-
spiration, thereby providing effective cooling during the warmer
seasons (e.g. Ali-Toudert and Mayer, 2007; Mayer et al., 2009;
Hamada and Ohta, 2010; Lindberg and Grimmond, 2011;
Konarska et al., 2014, 2015a, 2015b). They also provide effective
wind protection (Shashua-Bar et al., 2009; Buccolieri et al., 2009),
and their leaves contribute area-dependently to removal of air
pollutants via deposition (Manes et al., 2012; Nowak et al., 2006,
om and Pleijel, 2014). However, the effectivity is
pollutant-dependent; leaf area has no detectable impact on the
quality of nearby air with respect to ground-level ozone (O3), weak
effect on nitrogen dioxide (NO
), and stronger effect on particulate
matter particles with <10
m diameter (PM10) (Janh€
Nowak et al., 2006; Grundstr€
om and Pleijel, 2014; Klingberg
et al., 2017b).
Quantiﬁcation of cultural ES effectivity factors (f(i,j)) is less
straightforward, since it involves both generic and rather complex
context-dependent components (Knez, 2014). Thus, to our knowl-
edge, ratings that can be applied are only available in the literature
for the regulative ES investigated here. Here, the effectivity factor
for cultural ES is arbitrarily set to medium (class 2). Further in-
depth analyses and method development to improve understand-
ing of the generic and context-dependent components are needed
to enable more reﬁned estimates.
The effectivity factors (f(i,j)) are summarised together with the
reasoning and citations for published evaluations in Table 1.It
should be noted that the effectivity factor ratings presented in the
table are the maximum positive contributions that the indicator
can provide. In some situations, and environments, their effects
may be negative. For example, under some conditions increasing
light availability is more widely desired than cooling, or a dense
canopy with high LAI may reduce the air quality by reducing
ventilation, thereby increasing rather than reducing local air
pollutant concentrations (Andersson-Sk€
old et al., 2015; G
Baggethun and Barton, 2013).
old et al. / Journal of Environmental Management 205 (2018) 274e285278
3.1.3. Step 3 - estimation of effects
Effects of the indicators, E(i,j), are calculated using Eq. (1) in
conjunction with the normalised abundances and effectivity factors.
The results obtained in our study (using effectivity factors listed in
Table 1) for effects of regulating ES, cultural ES (related to the
diversity of birds, trees, shrubs and herbs), and total effects are
shown in Fig. 4. The results indicate that at all sites, apart from the
residential area, regulating ES have the highest effect, both reﬂect-
ing the importance of LAI at these sites (Table 1) and that more
regulating than cultural ES currently are included in the framework.
Biophysical structure components contributing to the ES included in this study, related functions, indicators (i) included in this study and effectivity factors (f) (see next
Function Functional trait
Effectivity factor (f)ES
Bees Pollination: Bees are highly important for pollination
(Matteson and Langellotto 2009; Garibaldi et al., 2013).
Bees' abundance þ3 Success of urban
Urban trees Wind reduction: Leaves are effective wind reducers
(Shashua-Bar et al., 2009; Buccolieri et al., 2009).
)þ3 Local climate
: Leaves contribute to cooling through provision of
shade and transpiration during the warmer seasons (Ali-
Toudert and Mayer, 2007, Hardin and Jensen, 2007;
Konarska et al., 2014; Gillner et al., 2015) and the combined
cooling effect is very high (Mayer et al., 2009; Hamada and
Ohta, 2010; Konarska et al., 2015a, 2015b).
number of trees/
Air pollution mitigation
: Leaves contribute to air pollution
mitigation through deposition on leaves (Burkhard et al.,
2012; Kandziora et al., 2013; Hirabayashi et al., 2012).
) Undetectable for
, low for NO
and Pleijel, 2014;
Klingberg et al.,
2017b), stronger for
et al., 2015; Janh€
Noise scattering and absorption: Leaves contribute, but
have weaker effects than distances from sources (Kang
et al., 2011; Kim et al., 2014).
Leaf area density
tree canopy volume
þ1 Noise reduction
Water retention: Transpiration and interception (EEA,
2015; Konarska et al., 2015b; Peng et al., 2016; Zhang et al.,
1999, 2001) are highly effective during long precipitation
periods, and less effective for extreme precipitation. Also
less effective than high amounts of highly permeable
surfaces (e.g. Peng et al., 2016; Zhang et al., 2001; Van Kleef,
2017; Abda Amin, 2017).
) or the
þ2 during long
periods, þ1 for
Audial contribution to wellbeing. Rustling trees calms
people (Hedblom et al., 2017). The rustling depends on the
amount (and type of) leaves, here assumed to be correlated
to the canopy cover.
Canopy cover (m
)þ2 Recreation and
Diversity of species Audial contribution to wellbeing: Bird song by different
species contributes to calmness (Hedblom et al., 2017) and
appreciation of the urban setting (Hedblom et al., 2014).
Visual contribution to health and mental well-being (e.g.
relaxation, calmness, ability to reﬂect, self-reported well-
being and place identiﬁcation): Species diversity (Sandifer
et al., 2015; Ode Sang et al., 2016; Knez et al., submitted,
Gunnarsson et al., 2017). Here based on diversity of trees,
shrubs and herbs.
Number of tree
Number of shrub
Number of herb
Contribution to aesthetic value: Based here on subdivision
by Gunnarsson et al. (2017) of measured high, medium and
low species diversity of trees, shrubs and herbs.
Number of tree
culture, art and
Number of shrub
Number of herb
Ground cover and
Water storage and soil evaporation through permeable
surfaces and highly permeable soil (EEA, 2015; Olsson
et al., 2013; Peng et al., 2016). The effectiveness depends on
the amount of permeable surface. For large areas, it is more
effective than evaporation and transpiration, but less
effective than retardation ponds or steep slopes (Peng et al.,
2016; Van Kleef, 2017; Abda Amin, 2017).
þ2 Water regulation
Note that in some situations and environments the effectivity can be negative for this indicator.
The transpiratory cooling associated with stomatal conductance (and hence transpiration rates) is highly correlated to the permeable surface surrounding the tree,
Konarska et al., 2015b.
old et al. / Journal of Environmental Management 205 (2018) 274e285 279
However, the other indicators also contribute to the effects. For
example, effects of regulating ES are similar at sites 1 and 2, but the
diversity of trees, shrubs and herbs is higher at site 2 than at site 1,
resulting in more cultural ES and a higher total effect at site 2.
3.1.4. Step 4 - estimation of beneﬁts
A beneﬁt is something that can ‘change people's well-being’, like
health or the kinds of choices they can make. Its extent, or impor-
tance, is expressed by the value people assign to it (Potschin and
Haines-Young, 2011), expressed here as in eq. (2), i.e. B(i,j) ¼E(i,j)
*v(j), where B(i,j) is the beneﬁt and v(j) the perceived value of
ecosystem service (j).
The perceived values of the beneﬁts considered here were ob-
tained based on both the perceptions of civil servants and the
public. The aim was twofold:
1) To achieve perceived importance of individual ES
2) To compare the perceived importance of ES to other important
services in a developing city.
The perceived value of individual ES among the civil servants
was done by ranking the investigated ES at a workshop (January
2016). Eight civil servants who daily dealt with green infrastructure
and landscape planning issues in Gothenburg participated in the
workshop. Of the participants, 3 were environmental analysts
(marine, acoustics, terrestrial biology) at the Environmental
administration, 2 were project engineers at the Gothenburg Man-
agement Recycling and Water, 2 were from the Gothenburg city
planning ofﬁce (one spatial planner and one landscape architect),
one civil servant from the Gothenburg Park and nature manage-
ment, and one landscape architect and one road architect and
designer at the Swedish road and transportation agency. The
resulting rankings were compared pairwise applying the Saaty
scale (e.g. Saaty, 2001), based on the inverted sum of the re-
spondents' rankings, and the geometric mean of the pairwise
comparisons (Ishizaka and Lusti, 2006). For example, 6 respondents
ranked perceived well-being as number 1, one ranked it as number
2 and one as number 3. The resulting sum is 11 and the inverted
result is 0.09 (9%) and set equal 9 on the Saaty scale.
The resulting geometric means are shown in Fig. 5 and Table S3.
As seen in Fig. 5 and Table S3 the importance of perceived well-
being was regarded as the most important ES among the
respondents. The perceived values of the other ES were found to be
of similar importance/weight.
The perceived importance of the investigated ES in comparison
to other important services was made in pair-wise comparisons
involving two steps: 1) determination of whether the ES or other
aspect was most important, 2) then how much more important it
was on a scale from 1 (very little) to 5 (a lot more). The averaged
results of the comparisons are shown in Table S4. The result of the
comparison was normalised applying the geometric mean of a
pairwise comparison of the results (Fig. 5 and Table S3). As seen in
Fig. 5, the importance of perceived well-being was by this method
regarded more similar in importance/weight compared to the other
ES than found in the ranking estimates.
The same process was thereafter aimed to be applied among the
public. In a ﬁrst test (February 2016) it was however found that the
number and type of questions, and the consequent response time,
was perceived as far too long and demanding. Consequently, the
public was provided a questionnaire not demanding more than
maximum 10 min to respond. In the resulting questionnaire we
investigated the importance of managing storm water and well-
being associated with urban greenery (and biodiversity per se),
relative to perceived needs to increase public transport provisions,
housing, and cultural and entertainment facilities (theatres, res-
taurants etc.). The selection is based on ongoing, and planned,
signiﬁcant densiﬁcation, land use changes and infrastructure ac-
tivities in Gothenburg region initiated to facilitate population
growth, commuting, transportation of people and goods and
increasing the business in the region.
The ﬂood risk is expected to
become severe during this century due to climate change, i.e. sea
level rise and more extreme precipitation (Andersson-Sk€
Davidsson, 2016a). To yet include the ES investigated here, the
comparison was complemented with asking for spontaneous rat-
ings, on a scale ranging from 1 (very little or no importance) to 5
(very important), of regulating ES, cultural ES and biodiversity per
se (Table S5).
The measures of citizens' perceived values of the beneﬁts
considered here were obtained from face-to-face interviews, using
a questionnaire, with 111 members of the public (in FebruaryeApril
Fig. 4. Estimated effects of regulating and cultural ES, at each study site based on normalised abundances (Table S2) and the effectivity factors listed in Table 1 (assuming that every
indicator contributing to cultural ES has a medium effectivity factor). The study sites are ordered as in Fig. 2 (1 ¼suburban woodland, 2 ¼urban woodland, 3 ¼urban park,
4¼allotment area, 5 ¼infrastructural green space, 6 ¼urban park &woodland, 7 ¼residential area).
old et al. / Journal of Environmental Management 205 (2018) 274e285280
The interviews were conducted at six public places in Gothen-
burg: the central bus and rail station, a suburban indoor shopping
center, a housing area, a central university area, three central parks
(two not included in our study and the urban park included in it).
No measurable deviations were found in the ratings obtained from
interviews conducted in the six different places. Thus, the resulting
rating is regarded as representative across Gothenburg (Fig. 2).
The ratings of individual ES indicate that all the considered ESs
are highly valued (average score; 4.0 out of 5, Table S5). The ES
ranked most highly was reduction in air pollution (4.6, Table S5).
Cultural ES were also regarded as highly important (4.0e4.4,
Table S5). The lowest rating was for biological control of pests and
diseases (3.4, Table S5). The resulting relative normalised impor-
tance of the individual ES is rather similar as shown in Table S3 and
The ES investigated in the comparison were too few for esti-
mating the relative normalised importance. The results indicate
that ES were also highly rated in relation to other important aspects
in the developing city Gothenburg (Fig. 6). Both the public
(N ¼111), and civil servants participating in the workshop (N ¼8),
rated them as generally more important than the other services
considered. Among the public, only increased housing was rated as
more important than biodiversity, and ES were otherwise ranked
higher than all the other aspects they were compared to (Fig. 6).
In the responses regarding the value (importance) of the ES (in
relation to both each other and increases in public transport pro-
visions, housing, and cultural and entertainment facilities) biodi-
versity per se was highly rated generally (Table S5). This indicates
that it is important to consider biodiversity per se, in urban plan-
3.1.5. Step 5 - total value of ES at the study sites
The total value of the ES (V) provided by each investigated area,
is the sum of the beneﬁts (B(i,j)) of all the considered ES (Eq. (3)).
Among the civil servants, despite the method employed, the
most important ES was perceived well-being (Fig. 5). This was most
pronounced in the ranking. To what degree there is a variation
Fig. 5. Perceived relative importance of individual ES applying different estimation methods.
Fig. 6. Perceived values of ES and other services according to responses of participants in a workshop with civil servants (8) and interviews with the public (111). Positive and
negative values respectively indicate that both groups regarded the ES as more important and less important than the other aspect.
old et al. / Journal of Environmental Management 205 (2018) 274e285 281
between the perceived values of individual ES depends on the
method (Fig. 5). As the perceived importance's for all the other ES
considered here are high and similar, the total estimated values at
the sites follow the pattern of the total effects (Fig. 7). Also, when
applying the perceived values based on ranking the pattern re-
mains similar. The similarities in pattern can be explained by the
fact that the regulative ES are dominating. Applying the ranking,
however, result in an increased importance of the suburban
woodland (site 1 Fig. 2). This is explained by the naturalness related
to a relatively high diversity of species at this site.
To make the framework further applicable by civil servants, for
example applying GIS, or as part of integrated assessments in EIA,
the results could be classiﬁed. Color-coding could then be used to
visualize the results in maps.
In this paper, we present a framework for estimating both the ES
effects and total values of an urban ecosystem. The framework is
based on the conceptual cascade model (TEEB, 2010, Potschin and
Haines-Young, 2011) and the assumption that the functional ben-
eﬁts provided by an ecosystem depend on both the abundances of
relevant components (represented here by selected indicators) and
their ability (described by effectiveness factors) to contribute to the
considered ES. The abundances are normalised to enable assess-
ment of effects of all types of indicators on a common scale.
Alternatively, a number of classes could be used, for assessments of
a single site (but not over a green gradient or many sites as in the
illustrative application presented here). Therelevance of a common
scale can be questioned, as the relative importance of the indicators
may vary substantially, but compensatory adjustments can be
made by applying the effectivity factors, which make the effect
estimates both convenient and generically applicable as in standard
old et al., 2015).
The framework proved to be applicable, and easy to use, for the
study sites. However, the effects and values were largely inﬂuenced
by the regulating ES, at least partly because more information is
available that can be used to link regulating indicators than other
indicators to effects. To allow the inclusion of more cultural ES, and
separation of indicator abundance and effectivity from valuation of
these ES, more research to untangle the complexity of greenery
components related to cultural ES such as place-bonding is needed
(see e.g. Blicharska et al., 2017; Knez and Eliasson, 2017; Knez et al.,
As shown here for the regulating ES, it may be possible to rate
effectivity factors of indicators on a scale from 1 (low effectivity, i.e.
they make little contribution to a focal ES) to 3 (high effectivity).
However, the relations involved for the cultural ES are complex, and
there are likely to be both synergistic and conﬂicting effects, which
currently prohibit such estimates.
Furthermore, we only consider positive effects of urban green-
ery on the regulating ESs, but it may also have negative effects
(disservices), depending on its context and design. For example, it
may trap air pollutants in street canyons resulting in high local
concentrations, increase allergen exposure by releasing pollen,
cause cooling under conditions where heating is more desirable,
and dark dense foliage may reduce the feeling of safety (D'Amato,
2000; Escobedo et al., 2011; G
omez-Baggethun and Barton, 2013;
ohren and Haase, 2015). Further development of the frame-
work will allow consideration of disservices by including negative
effectivity factors, and in an even longer term, when there is more
knowledge of the complex interactions, synergetic effects could
also be included.
Clearly, ecosystems and related ESs raise highly complex issues,
and any technique for estimating associated effects and beneﬁts
can be questioned. However, the systematic approach and the
effectivity classiﬁcation scale applied in the presented framework
reduce the importance of minor uncertainties and facilitate its
application in current processes such as EIA, routine mapping and
planning procedures. Thus, it may have utility either as a stand-
alone framework or as a complement or module in more
advanced process models such as the i-Tree model (Nowak et al.,
To estimate the beneﬁts we have applied priority ranking of
individual ES and perceived relative importance of the individual
ES in comparison to other aspects important for urban develop-
ment. These methods were applied among civil servants. The
ranking based method revealed the importance of perceived well-
being in comparison to other ES (Fig. 5,Table S4). Applying rating
(public) and in comparision to other important aspects in the city
(public and civil servants) the perceived values were more even
among the different ecosystem services. In a real planning process,
a ranking valuation process is recommended to be done (at least)
among the responsible spatial planners and civil servants from
impacted sectors. W also, however, ﬁnd it important to identify the
importance relative expected changes due to planned in-
terventions. The two methods complement each other. The ranking
shall be used to estimate the internal relative weights, while the
Fig. 7. Estimated total effects and total values of ecosystem services at the study sites. The maximum values for the effect and total value are 20 and 3.0, respectively. (1 ¼suburban
woodland, 2 ¼urban woodland, 3 ¼urban park, 4 ¼allotment area, 5 ¼infrastructural green space, 6 ¼urban park &woodland, 7 ¼residential area).
old et al. / Journal of Environmental Management 205 (2018) 274e285282
comparison reveals the importance of the ecosystem services in
relation to the other aspects under consideration. Such a valuation
process would contribute to increased understanding on how
different ES are valued under different contexts and potential
spatial planning strategies. Currently there is a lack of active and
transparent valuation procedures in the municipal planning and
decision process. “Lack of time”being the main self- reported
old et al., 2013, 2016b). Therefore, simple
methods, as applied here, are recommended. The valuation method
and process must, however, be adapted to the participants and the
context. For large land use changes, it is essential to involve the
public through more site-related questionnaires, applying a more
advanced valuation methodology and/or be based on larger
The method presented here does not include non-linearities
neither in understanding the links between ecosystem functions,
indicators and effects nor regarding the valuations such as spatial
and social variations shown in needs, demands and availabilities of
ES. Greater understanding of effects, such as links to non-linearities
and the complex relations especially regarding perceived well-
being and other cultural ES (and robust ways to estimate them)
must be developed before more reﬁned methods can be applied.
However, once the links between ecosystem components, services
and effects are better understood, suitable permutations of
methods based (for example) on monetary valuation can be used.
These may include (for appropriate types of indicators and ES)
restoration values (Elmqvist et al., 2015), avoided costs and will-
ingness to pay (G
omez-Baggethun and Barton, 2013). However,
methods like those applied here are relatively quick and conve-
nient. Furthermore, the information they provide can be readily
applied and regularly updated by urban planners, and the derived
values are independent of currency rates and indices. The utility of
preference ranking methods should also be recognized in the light
of limitations of monetary methods; notably criticism by decision-
makers that economic valuation of ecosystem services is too
simplistic, insufﬁciently widely accepted, and ﬂawed (Marre et al.,
It should be noted that urban environments have many func-
tions and provide or inﬂuence many ESs not considered here. For
example, Goodness et al. (2016) suggest additional plant and bird
trait indicators for aesthetic, recreational, spiritual, religious and
heritage ESs (e.g. plant/bird size, morphology and color). Provi-
sioning services in urban environments are also becoming
increasingly important for various reasons, notably urban agricul-
ture is advocated to help make cities more self-sufﬁcient (Viljoen
and Bohn, 2014). Provisioning ESs have often been valued in rela-
tion to current market values (e.g. TEEB, 2010), but Langemeyer
(2015) shows that the major value of urban gardening, an impor-
tant part of provisioning services in cities, is entangled with cul-
tural services. Thus, valuation of urban provisioning services is also
complex and merits separate and more detailed consideration.
The effectivity factors of functional traits, synergies, inhibitory
effects and potential disservices require further investigation.
Moreover, speciﬁc ESs should be valued robustly in relation to other
relevant planning aspects in focal areas, as the values are context-
dependent. A high value of an ecosystem may result in high uti-
lisation, which may negatively affect the supply and demand. Also,
the values depend on the societal context and trends. In areas
where the greenery is threatened, the values may increase, while in
areas where other needs and social requirements, such as housing,
are more acute they may decrease.
The presented framework, based on the conceptual cascade
model presented by TEEB (2010) and Potschin and Haines-Young
(2011), can be applied to estimate both the total effect and value
of ecosystem services (ES) of a site. The framework uses a limited
number of indicators of relevant functional traits, and in contrast to
other methods, it allows for separation of both different compo-
nents (trees, shrubs, birds etc.) and the ES they contribute to.
A number of key indicators have been identiﬁed. The leaf area
index (LAI) is applicable for several of the regulative ES, and di-
versity of species for the cultural ES, included here. However, more
indicators of cultural ES are need. In step with increased knowledge
on the links between ecosystem components, services and effects
the framework can easily be updated to include more indicators as
well as additional ES.
ES provided by urban green areas included here were valued as
highly important and highly ranked in comparison to other
important aspects in cities such as improvements in public trans-
portation, housing, culture and entertainment. The relative
importance differs when ranked or rated. In addition, which ES are
most relevant differs due to different needs at different sites and
might change over time. To identify the most relevant ES ranking is
recommended, while for speciﬁc land use changes also a pair-wise
comparison of the pros and cons of expected changes in relation to
the investigated ES is thus recommended.
The framework has been developed with the idea that it should
be easy to use, systematic and transparent at all stages and to be
applied in the spatial planning processes. Once valuation of ES
becomes a routing practice and the links between ecosystem
components, services and effects especially regarding perceived
wellbeing and other cultural ES, are better understood, suitable
permutations of methods based (for example) on monetary valu-
ation can be used. Meanwhile more simple and less time
demanding methods like presented here are recommended.
The framework presented here has been developed in the
project “Valuing ESs from urban greenery”funded by the Swedish
Research Council Formas (grant 2012-3411-22602-60), the Swedish
Transport administration (Reference number 2010/11730) and
Mistra Urban Futures, who are all gratefully acknowledged. Sonja
Jonasson is acknowledged for valuable help.
Appendix A. Supplementary data
Supplementary data related to this article can be found at
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