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Monetary Valuation of Urban Forest Attributes in Highly Developed Urban Environments: An Experimental Study Using a Conjoint Choice Model

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It is important to integrate user preferences and demands into the design, planning, and management of urban forests. This is particularly important in highly urbanized areas where land is extremely limited. Based on a survey with 600 participants selected by quota sampling in Seoul, Korea, we developed a conjoint choice model for determining the preferences of urban dwellers on urban forest attributes, the levels of attributes, and the preferences for particular attributes. Then, the preferences were transformed into monetary values. The results indicated that urban dwellers preferred broadleaved forests over coniferous forests, soil-type pavement materials over porous elastic pavement materials on trails, and relatively flat trails over trails with steep slopes. The model indicated that participants were willing to pay an additional 11.42 USD to change coniferous forest to broadleaved forest, 15.09 USD to alter porous elastic pavement materials on trails to soil-type pavement materials on trails, and 23.8 USD to modify steeply sloping trails to relatively flat trails. As previously reported, considerable distance decay effects have been observed in the user preferences for urban forests. We also found a significant difference in the amount of the mean marginal willingness to pay among sociodemographic subgroups. In particular, there were significant positive responses from the male group to changes in urban forest attributes and their levels in terms of their willingness to pay additional funds. By contrast, the elderly group had the opposite response. In this study, we were not able to integrate locality and spatial variation in user preferences for urban forests derived from locational characteristics. In future studies, the role of limiting factors in user preferences for urban forests and their attributes should be considered.
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sustainability
Article
Monetary Valuation of Urban Forest Attributes in
Highly Developed Urban Environments:
An Experimental Study Using a Conjoint
Choice Model
Sung-Kwon Hong 1, Ju-Mi Kim 2, Hyun-Kil Jo 3and Sang-Woo Lee 1,*
1
Department of Forestry and Landscape Architecture, Konkuk University, Gwangjin-gu, Seoul 05029, Korea;
skhong@konkuk.ac.kr
2Graduate Program, Department of Environmental Science, Konkuk University, Gwangjin-gu, Seoul 05029,
Korea; realk9@konkuk.ac.kr
3
Department of Ecological Landscape Architecture Design, Kangwon National University, Chuncheon 24341,
Korea; jhk@kangwon.ac.kr
*Correspondence: swl7311@konkuk.ac.kr
Received: 26 June 2018; Accepted: 12 July 2018; Published: 13 July 2018


Abstract:
It is important to integrate user preferences and demands into the design, planning,
and management of urban forests. This is particularly important in highly urbanized areas where
land is extremely limited. Based on a survey with 600 participants selected by quota sampling in
Seoul, Korea, we developed a conjoint choice model for determining the preferences of urban dwellers
on urban forest attributes, the levels of attributes, and the preferences for particular attributes. Then,
the preferences were transformed into monetary values. The results indicated that urban dwellers
preferred broadleaved forests over coniferous forests, soil-type pavement materials over porous
elastic pavement materials on trails, and relatively flat trails over trails with steep slopes. The model
indicated that participants were willing to pay an additional 11.42 USD to change coniferous forest
to broadleaved forest, 15.09 USD to alter porous elastic pavement materials on trails to soil-type
pavement materials on trails, and 23.8 USD to modify steeply sloping trails to relatively flat trails.
As previously reported, considerable distance decay effects have been observed in the user preferences
for urban forests. We also found a significant difference in the amount of the mean marginal
willingness to pay among sociodemographic subgroups. In particular, there were significant positive
responses from the male group to changes in urban forest attributes and their levels in terms of
their willingness to pay additional funds. By contrast, the elderly group had the opposite response.
In this study, we were not able to integrate locality and spatial variation in user preferences for urban
forests derived from locational characteristics. In future studies, the role of limiting factors in user
preferences for urban forests and their attributes should be considered.
Keywords:
urban forest; willingness to pay; forest attributes; attribute level; urban park; conjoint
choice model
1. Introduction
Urban forests are valuable and integral components of the urban environment. In previous
studies, urban forests have been shown to provide numerous ecological and environmental benefits
such as enhancing water quality, improving air quality, conserving energy, carbon storage, reducing
storm water runoff, and enhancing biodiversity (e.g., [
1
8
]). At the same time, it has been well
documented that urban forests and parks increase property values and housing prices in major
Sustainability 2018,10, 2461; doi:10.3390/su10072461 www.mdpi.com/journal/sustainability
Sustainability 2018,10, 2461 2 of 22
cities worldwide [
9
14
] and moderate the adverse effects of chemical facilities in cities on property
values [
15
]. In these studies, forests in urban settings enhance the aesthetic appeal of houses
and neighborhoods, which has positive effects on housing prices and property values. In many
studies, urban forests have also been shown to have psychological effects. Interestingly, urban forests
increase neighborhood satisfaction [
16
,
17
] and enhance social interactions among residents in the
community [
18
]. Recent studies on the benefits of urban forests have paid more attention to the
wellbeing and health of urban dwellers, which is critical for securing sustainability of urban ecosystem
in where human systems and eco-systems are strongly tied. For example, Tyrväinen (2001) [
14
]
investigated the influence of urban green environments on stress relief in a study with 95 participants
in Helsinki, Finland, and reported that natural settings may differ in terms of their restorative quality.
Their results also suggested that remaining longer than 15 min in a natural environment was necessary
to enhance the feelings of vitality. In addition, many recent studies have reported significant positive
relationships of urban forests with mortality, physical conditions, mental indicators, and the level of
physical activity undertaken by urban residents [1924].
Despite the numerous positive effects of urban forests, most cities around the world have
experienced a loss of forested area due to urban sprawl, population growth, and infrastructure
developments [
25
]. It is very common to neglect the value of urban forests during land use decision
making and the planning process, because policy makers and planners tend to undervalue urban
forests compared to other land uses such as commercial, residential, and industrial land uses. For these
reasons, researchers and scientists have attempted to assess the value of urban forests in monetary
terms [
26
]. Many studies have demonstrated that the extent of urban forest or forest views in urbanized
areas has significant economic value (e.g., [
13
15
,
27
29
]). With such knowledge of the value of urban
forests, decision makers, planners, and stakeholders would be better equipped to argue for the
preservation of forested areas in cities during the decision making and planning processes. In turn,
this might secure the quality of urban environments, reduce the adverse effects of urbanization,
and enhance the wellbeing of urban dwellers.
However, most previous studies have mainly focused on the extent of urban forest, forest views,
or time spent in forests when considering the environmental, ecological, socio-economic, psychological,
and physiological benefits of urban forests. Although such knowledge of the positive effects of urban
forests and forest views might provide significant theoretical insights into the decision making and
land use planning processes, it is not sufficient for urban forest designers and managers in practice.
Designers and managers inevitably require more detailed information and knowledge of how urban
dwellers respond to different forest attributes and how the different levels of forest attributes differ in
terms of monetary value. A number of studies have investigated user preferences on the attributes of
urban forests from a recreational perspective (e.g., [
30
32
]). It is difficult to utilize user preferences for
particular attributes and the levels of attributes in practice, because the measurements of preferences
made in previous studies have mostly been conceptual.
We assessed the preferences of urban dwellers for urban forests and estimated the relative
monetary values of the general attribute levels of urban forests. In this study, we used the term ‘urban
forests’ in a broad sense. Specifically, we considered urban forests to be all areas with trees such as
small mountains, riparian buffers, neighborhood parks, and small urban parks that urban dwellers can
easily access in their daily life. The underlying assumption was that people selected an urban forest
that was equipped with the most preferable attributes and attribute levels among given alternative
destinations to maximize their utility when visiting urban forests. We assumed that the differences in
preference for particular attributes and their levels could be transformed into a monetary value.
Interpreting the preferences of urban dwellers as a monetary value could provide the relative
importance of the attributes and attribute levels of urban forests. Often, it is very difficult to justify
preserving urban forests or creating new urban forests in densely urbanized areas. According to
many previous studies that have used choice models to select recreational sites in urban settings,
the attributes of urban forests have a significant impact on the selection of recreational destinations by
Sustainability 2018,10, 2461 3 of 22
urban dwellers (e.g., [
33
35
]), and the preferred attributes of urban forests can be estimated in monetary
terms [
33
,
36
,
37
]. Such knowledge and information could play a significant role in communicating
with stakeholders during the decision-making process for securing sustainable urban systems. In this
regard, it is a serious challenge for planners, designers, and forest managers to understand how to
accommodate user preferences in practice.
2. Methods and Materials
2.1. A Conjoint Choice Model Approach
The approaches to valuing environmental amenities can be classified into either direct or indirect
approaches. Typically, direct approaches ask urban dwellers how much they would willingly pay for
an improvement in environmental amenities, whereas indirect approaches use the actual choices made
by participants to develop a choice model [
38
]. A choice experiment (CE) is an indirect approach rooted
in conjoint analyses, in which participants choose among multi-attribute goods [
39
]. The underlying
theories of the CE approach are the characteristics theory of value [
40
] and random utility theory [
41
,
42
].
In the CE approach, participants are asked to choose among different bundles of environmental goods
described in terms of their attributes, characteristics, and levels [
39
]. Compared to other approaches,
the CE has several advantages. In particular, it is easy for investigators to estimate the value of the
individual attributes, which is important because many management and planning decisions are
concerned with changing attribute levels rather than losing or gaining attribute levels. CE is able to
identify the marginal values of attributes that may be difficult to identify using the preference data
that is revealed because of co-linearity or a lack of variation. The repeated sampling approach of CE
also allows for internal consistency [
39
,
43
,
44
]. Because of these known benefits, we adopted the CE
approach to investigate the preferences of urban dwellers for the attributes of urban forests. In fact,
this approach has been effectively used in investigating preferences or values of non-market public
goods (e.g., urban forests, water, ecosystem services, and environmental diversity) in urban forest
studies or related areas [25,30,3539,44].
In the general hypothesis of a CE, individuals attempt to integrate information about a number
of variables (i.e., attributes) characterizing the given choice alternatives using their own rules. Of all
of the variables, only information about the particular salient attributes is used to select alternatives
due to the available information, limited information processing capabilities of humans, limited time,
and/or individual differences. It is assumed that the utility of an individual after selecting a particular
alternative can be represented as a combination of deterministic components and error terms [
45
]
(Equation (1)).
Ua=Va+εa(1)
The utility of an alternative (
Ua
) can be estimated, with the deterministic components (
Va
) and
random error (
εa)
reflecting the measurement error and omitted explanatory variables, respectively.
The linear function of the deterministic component depends on the way in which the part-worth
utilities are combined; then Equation (1) can be transformed to Equation (2).
Va={+βkXk(a=1, . . . , K), (2)
where
{= alternative specific constant (ASC);
β= parameter vector of the attributes; and
X= vector of k attributes from a choice set.
However, it is impossible to include socioeconomic variables and taste variables directly into
the utility function because they are invariant across the alternatives in a choice set. Alternatively,
Sustainability 2018,10, 2461 4 of 22
these socioeconomic and taste variables can be included as interactions, with the ASC used for
alternatives [46,47], as in Equation (3).
Va={+{Sh+βkXk+εa(h=1, . . . , H), (3)
where
Sh= socioeconomic or taste variables.
Based on the random utility theory, the CE assumes that choices are made in a utility-maximization
process in which individuals tend to select the option maximizing utility on the basis of their
circumstances. The probability that an individual will choose alternative a over option j is given
by Equation (4), and the probability of choosing alternative a among a complete set of alternatives set
can be estimated by Equation (5).
Pa
{i=P(Va+εa>Vj+εj), all j{i(4)
Pa
{i=exp(Va)
j{iexpVj, (5)
where
Pa
{i= probability of choosing alternative a from {i(i= 1, 2, . . . , n);
{i= given complete alternative sets; and
Va= utility of alternative.
Then Equation (5) is estimated by means of a multi-nomial logit regression because the choices
are affected not only by the levels of variables comprising alternatives but also by various surrounding
conditions, and individuals may choose different alternatives, although the choice alternatives remain
constant [
48
]. Therefore, a CE can directly predict choices in the form of the logit model by calculating
the part-worth of attribute levels (or values) obtained from choice-type data rather than preference
data. Eventually, it is possible to estimate the economic value of an attribute’s level by linking a price or
cost factor with the levels (or values) of attributes, based on the choices made by urban dwellers. In this
study, we developed a conjoint choice model based on Equation (5) with selected salient attributes.
The larger the coefficient of levels of a certain attribute the larger the effects of the different levels of
the attribute and the larger the difference in the preferences for the alternatives resulting from the
different levels. If participants do not differ in their preferences for urban forests by attribute level,
the estimated parameters are not significantly different from zero.
2.2. Determination of Salient Attributes and Their Levels
The recreational destination selections made by urban dwellers are significantly affected by the
salient attributes of urban forests, and these attributes can be estimated to be a monetary value allowing
planners and decision makers to compare the relative importance or degree of relative preference held
by an urban dweller for a specific salient attribute of urban forests. However, it is difficult to
specify which salient attributes are applicable for all urban forests because the characteristics of the
forests and demands of visitors could vary greatly among countries, regions, and sites. For example,
Atmi¸s et al. (2017) [
33
] recently reported that the decision factors in urban forest management in
Turkey were forest versatility (e.g., variety in tree species), management intensity (e.g., well-staffed
administrative unit), visitor services (e.g., general and information services), tranquility (e.g., lack
of sport facilities and variety in number of animal species), and forest activities (e.g., sport facilities
and terrace viewing platforms). In European cases, key attributes affecting forest selection were
the phase of tree development, management intensity, and tree species type [
31
]. In South Korea,
Koo et al. (2013) [
36
] investigated the relative importance attributes (e.g., trail length, biodiversity,
Sustainability 2018,10, 2461 5 of 22
accessibility, environmental education programs, and slope) of urban forests that affect the use of
urban forests by urban dwellers using an “entrance fee.” The results of this study revealed differences
in the marginal willingness to pay (MWTP) among the various attributes and user groups. It was
found that the majority of respondents were willing to pay an additional 3.29 USD per visit (i.e.,
an additional entrance fee) to enter an urban forest with a rich level of biodiversity compared
to an urban forest with a low level of biodiversity. These results supported the findings of other
studies reporting positive correlations between the level of biodiversity and willingness to pay (WTP)
(e.g., [
49
52
]). Interestingly, the preferences of urban dwellers for particular attributes of urban forests
were different for the different major activity types that visitors wanted to undertake in the urban
forest [
53
]. Andrada II et al. (2015) [
54
] reported similar results indicating that the preferences of
urban dwellers for particular attributes of urban forests could vary depending on the reasons for
the visit, age, sex, education level, visiting season, and frequency of visit. Despite the differences in
the preferences of urban dwellers for particular attributes of urban parks among sub-user groups,
the overall preferences were for high levels of plant variety, a scattered planting pattern, a variety
of plant colors, and a managed plant distribution [
54
]. Edwards et al. (2012) [
31
] also reported that
user preferences were positively correlated with tree size, mixed stand type, and the presence of
broadleaved species, but no relationships were found with type and number of tree species in urban
forests in Europe. A preference for broadleaves over coniferous forests was also found in a study of
Korean forests [
55
]. The importance of the salient attributes of urban forests was consistent in small
urban parks. According to Nordh (2011) [
35
], physical elements such as the amounts of grass, trees,
and visitors in small urban parks were the most influential factors on the choices made by urban
dwellers among park alternatives in urban settings. Urban dwellers were likely to select an urban park
with large grass areas, many trees, and less crowded conditions among given alternative parks.
From previous studies, we were able to extract a few salient attributes of urban forests that affected
the recreational destination selections made by urban dwellers in urbanized areas. However, it was
not appropriate to include all of the potential salient attributes in the hypothetical model, because a
number of studies have suggested that optimal conditions may not include more than seven attributes
and four levels for each attribute in a choice experimental model [
56
,
57
]. The refining process (i.e.,
a literature review, preliminary analyses, and discussions with practitioners) resulted in five salient
attribute types, each of which was assigned three or four levels. The selected salient attributes of
urban forests were forest tree type (broadleaved trees, mixed, coniferous), paving material (soil-type,
wooden deck, porous elastic) topography (hilly, flat, mountainous), walking time (
1 h, between 1–2 h,
2 h), and travel time from home (
15 min, 15–30 min, 30–60 min,
60 min). A “fund” value was
included as an urban forest attribute to calculate the MWTP. The levels of attribute types were coded
in a dummy format (i.e., select: 1, not select: 0) for model estimation. All attributes except “fund” were
also coded as dummy variables to allow an estimation of the change from the baseline case (Table 1).
Table 1.
Hypothetical attribute types and levels of urban forests. Six salient attributes were selected
from the literature and a preliminary study.
Attribute Attribute Level c
Forest tree type Broadleaved forest, Mixed forest, (Coniferous forest)
Paving material of trail Soil-type pavement, Wooden deck, (Porous elastic pavement)
Topography Hilly, Flat, Mountainous slope
Walking time Less than 1 h walk, between 1 and 2 h walk, (more than 2 h walk)
Travel time aLess than 15 min, between 15 and 30 min, between 30 and 60 min, (more than 60 min)
Fund b5000 KRW, 20,000 KRW, 30,000 KRW, (50,000 KRW)
a
Time to urban forest from home;
b
Amount of fund per person (per visit);
c
Attribute levels in parenthesis indicate
the base level.
Despite the forest development stage (i.e., tree age or tree size) being a significant variable
affecting user preferences for urban forests in previous studies (e.g., [
31
]), it was not included in this
study because tree age was almost invariant across the country. In Korea, most forests in urbanized
Sustainability 2018,10, 2461 6 of 22
areas and natural areas were simultaneously created and managed by the Korean Forest Services
after the Korean War. Thus, we only included tree type in the model. Walking is the most common
activity in urban forests [
58
,
59
], and the characteristics of trails for walking are key factors in a user’s
preferences for urban forests. In previous studies, people showed a preference for non-hardened paths
over hardened paths [
60
,
61
]. In a previous study, preferences for trail length (or walking time) in urban
forests were dependent on the user group, as classified by the frequency of visits to urban forests.
The higher-frequency group preferred a longer trail than the lower frequency group [
62
]. In general,
people prefer hilly topography over flat topography in urban forest areas (e.g., [
61
]), but the relative
importance of topography is relatively low (e.g., [
36
,
60
]). However, we included topography of trails
in the model because topography (i.e., slope) is critical for elder users in terms of accessibility. It has
been widely accepted that there is a distance decay effect in the frequency of visits to urban forests or
preferences for a particular urban forest. In England, more than 70% of respondents preferred an urban
forest that was only a 5 min walk from their home [
63
]. In the United States, about 80% of metropolitan
local trail users were people living within a distance of 5 miles from the trail [
60
]. In a Chinese study,
the mean Euclidean distance to most urban parks was between 1 and 3 km [
64
], which was shorter
than the distance studied in the United States.
2.3. MWTP and Bid Amount
MWTP is the additional amount of money for which individuals are willing to pay for a particular
attribute’s level in a certain product. In other words, MWTP indicates how much individuals are ready
to pay for an upgrade from level A to level B for an attribute (i.e., extra price above the current price
they are already paying). The essential characteristics of the MWTP are the marginal rate of substitution
among levels, prices, or costs of an alternative, and thus can be estimated by determining the ratio
between the estimated parameter for an alternative k and an estimated parameter of price [
32
,
65
]
(Equation (6)).
MWTP =βk
βprice
(6)
where
βk= coefficient of non-monetary attribute k; and
βprice = coefficient of price.
Many previous studies have adopted “tax” or “entrance fee” as the payment vehicle to calculate
the MWTP (e.g., [
37
,
39
,
66
]). However, we used “fund” as the payment vehicle to compute the MWTP
because not all residents in our study area visited urban forests, and additional tax payments for
non-user groups would make it difficult to determine the exact MWTP. At the same time, there is no
entrance fee for urban forests in Korea, and adopting “entrance fee” as the payment vehicle might result
in negative answers from participants, resulting in a strategically biased WTP. Compared to the other
types of payment, the use of fund did not cause a serious strategic bias [67]. For the analyses, the bid
amount was decided on the basis of previous studies (e.g., [
55
,
68
]) and the results of a preliminary
study which was performed from 1 to 6 June 2017 with 115 college students at Konkuk University
in Seoul, Korea. Based on the results of the preliminary study, we selected 5000 KRW (4.62 USD),
10,000 KRW (9.24 USD), 30,000 KRW (27.72 USD), and 50,000 KRW (46.2 USD) as the bid amounts
(Table 2).
Sustainability 2018,10, 2461 7 of 22
Table 2.
Frequencies of the bid amounts resulting from the preliminary study with college students.
About 69.6% of participants preferred to pay one of 5000 KRW (4.62 USD), 10,000 KRW (9.24 USD),
30,000 KRW (27.72 USD), and 50,000 KRW (46.2 USD) for changes in the levels of the attributes.
FWTP a(USD b)Frequency Percent (%) FWTP a(USD b)Frequency Percent (%)
0 (0) 7 6.1 30,000 (27.72) c11 9.6
5000 (4.62) a12 10.4 50,000 (46.2) c20 17.4
8000 (7.39) 1 0.9 100,000 (92.4) 11 9.6
10,000 (9.24) c37 32.2 200,000 (184.8) 1 0.9
15,000 (13.86) 3 2.6 1,000,000 (924) 2 1.7
20,000 (18.48) 10 8.7
aamount of funds willing to pay; b10,000 KRW 9.24 USD; cBid amount used in the study.
2.4. Characteristics of the Study Area and Sampling
The study was conducted in the City of Seoul, the capital of South Korea, in 2016. The Han River
crosses the city from east to west and separates the old districts in the north from the new districts in the
south. According to government statistics [
69
], the City of Seoul has a population of about 9.8 million,
and the city boundary encompasses about 605.21 km
2
including 25 sub-districts. About 169.7 km
2
(28.0%) of the city is forested, encompassing various land use zones, such as urban natural forests,
neighborhood parks, pocket parks, and children’s parks. The city is surrounded by densely forested
mountains, except on the west side, while various neighborhood parks, small urban parks, and children’s
parks are scattered throughout the city, with most located in central areas (Figure 1).
Sustainability 2018, 10, x FOR PEER REVIEW 7 of 23
km2 (28.0%) of the city is forested, encompassing various land use zones, such as urban natural
forests, neighborhood parks, pocket parks, and children’s parks. The city is surrounded by densely
forested mountains, except on the west side, while various neighborhood parks, small urban parks,
and children’s parks are scattered throughout the city, with most located in central areas (Figure 1).
Figure 1. Location of the study area (City of Seoul). The total area is about 605.21 km2 and the
population of the city is 9.8 million. The Han River separates old districts in the north and new
districts in the south. Relatively large urban forests are located in both the northern and southern
boundary areas, while neighborhood and small urban pocket parks are scattered throughout the city
(modified map provided by the City of Seoul).
2.5. Constructing Hypothetical Choice Sets and Data Collection
Hypothetical alternatives for urban forest preferences were constructed using the six levels of
attributes provided in Table 1, and two 64 choice set profiles were generated through a double fractional
factorial design. The 64 choice sets were generated by randomly selecting one hypothetical alternative
out of each of the two 64 profiles and adding the base alternative “I would not select both of them”. To
secure the validity of responses, we tried to enhance the participants’ understanding of the choice
attributes by providing example images for some questions. We designed the survey method so that
each choice set had one base alternative and two alternatives, which were constructed by changing the
levels of the attribute. It was demanding in terms of time for one respondent to evaluate all 64 choice
sets, and participants might not be able to maintain their attention during the evaluation. To make the
evaluation easier, each respondent was provided with only eight choice sets that were randomly
selected from the 64 choice sets. Therefore, eight participants were needed to evaluate all 64 choice sets.
A web survey of residents of Seoul was conducted by a polling agency (see Figure 2 for an example of
the questionnaire used in the survey). Using quota sampling, based on the population of five
geographical regions, ages, and sex ratio, a total of 600 residents were selected during 3 to 13 August
2017 (Table 3).
Figure 1.
Location of the study area (City of Seoul). The total area is about 605.21 km
2
and the
population of the city is 9.8 million. The Han River separates old districts in the north and new districts
in the south. Relatively large urban forests are located in both the northern and southern boundary
areas, while neighborhood and small urban pocket parks are scattered throughout the city (modified
map provided by the City of Seoul).
2.5. Constructing Hypothetical Choice Sets and Data Collection
Hypothetical alternatives for urban forest preferences were constructed using the six levels of
attributes provided in Table 1, and two 64 choice set profiles were generated through a double fractional
factorial design. The 64 choice sets were generated by randomly selecting one hypothetical alternative
Sustainability 2018,10, 2461 8 of 22
out of each of the two 64 profiles and adding the base alternative “I would not select both of them”.
To secure the validity of responses, we tried to enhance the participants’ understanding of the choice
attributes by providing example images for some questions. We designed the survey method so that
each choice set had one base alternative and two alternatives, which were constructed by changing the
levels of the attribute. It was demanding in terms of time for one respondent to evaluate all 64 choice
sets, and participants might not be able to maintain their attention during the evaluation. To make
the evaluation easier, each respondent was provided with only eight choice sets that were randomly
selected from the 64 choice sets. Therefore, eight participants were needed to evaluate all 64 choice
sets. A web survey of residents of Seoul was conducted by a polling agency (see Figure 2for an
example of the questionnaire used in the survey). Using quota sampling, based on the population
of five geographical regions, ages, and sex ratio, a total of 600 residents were selected during 3 to
13 August 2017 (Table 3).
Figure 2.
Example of two attributes used in the survey. Some images were provided to enhance
respondents’ understanding of the attributes and their levels. * Modified from [36].
Sustainability 2018,10, 2461 9 of 22
Table 3.
Stratified sampling design (quota sampling design). The study areas were grouped into five
regions based on the geographical locations. The allocation of sampling size was determined by the
population size of the regions. We attempted to evenly distribute the sampling size for age groups and
gender groups.
Region Sample Size (%) Age Group Sample Size (%) Sex Group Sample Size (%)
Central region 31 (5.2) 20s 138 (23.0) Male 300 (50.0)
East-south region 124 (20.7) 30s 156 (26.0) Female 300 (50.0)
East-north region 188 (31.3) 40s 156 (26.0)
West-south region 186 (31.0) 50s 150 (25.0)
West-north region 71 (11.8)
Total 600 (100) 600 (100) 600 (100)
3. Results and Discussion
3.1. Profiles of Participants and Visiting Characteristics
The total number of participants was 600, and Table 4describes the overall characteristics of
participants in this study. The majority of participants had graduated from high school (12.3%), were
attending college (8.5%), or had graduated from college (79.0%). The largest group of participants
was office workers (42.5%), and the second largest group was homemakers (12.0%). The other
occupation groups included students (8.7%), managers (8.0%), and the self-employed (8.0%). The ratio
of monthly household income of participants within groups was relatively similar among regional
groups. The largest income group was 5,000,000–6,000,000 KRW (4620–5544 USD) and the second
largest income group was 4,000,000–4,500,000 KRW (3696–4158 USD). More than 50% of participants
were married, and most had no children (elementary school age) at home (78.9%). Due to the survey
design, there were equal numbers of male and female participants. There was also an almost even age
distribution, with ages ranging from 20 to 60 years classified into four groups. The visiting frequency
analyses indicated that 339 (56.5%) participants visited urban forests at least once a month. More than
61% (371 participants) stayed in the urban forest at least more than 1 h, with the highest frequency of
time spent in the urban forest of 1–2 h (45.0%). The majority of participants usually visited the urban
forest on weekends (272 participants, 50.4%), and in the afternoon (1–6 PM) (241 participants, 44.64%).
Most participants visited the urban forest with family members (268 participants, 49.6%) and accessed
the forests by walking (275 participants, 50.9%) or car (129 participants, 23.9%). The survey results
also indicated that most participants (482 participants, 89.2%) could access an urban forest within 1 h.
To summarize, most participants in this study visited the urban forests more than once a month in
the afternoon on weekends with family members and stayed more than 1 h. They also visited urban
forests that were accessible within 1 h by walking or driving. The main purposes of visiting urban
forest were walking, resting, exercise, being away from home, spending time with family members,
and enjoying the natural environment.
Sustainability 2018,10, 2461 10 of 22
Table 4.
Characteristics of participants. Most participants had graduated from college, and most were
office workers. Interestingly, the monthly income of participants was relatively evenly distributed.
Variables Frequency (%)
Education level Middle school: 1 (0.2) High school: 74 (12.3)
College student: 51 (8.5) Above college level: 474 (79.0)
Occupation
Office worker: 255 (42.5) Homemaker: 72 (12.0)
Student: 52 (8.7) Manager: 48 (8.0)
Self-employed: 48 (8.0) Professional: 33 (5.5)
Sales: 23 (3.8) Retired: 21 (3.5)
Service: 18 (3.0) Simple labor: 6 (1.0)
Technician: 5 (0.8) Government employee: 2 (0.3)
Others: 14 (2.3)
Monthly household income *
(10,000 KRW)
Less than 200: 37 (6.2) 200–250: 35 (5.8)
250–300: 42 (7.0) 300–350: 56 (9.3)
350–400: 60 (10.0) 400–450: 64 (10.7)
450–500: 53 (8.8) 500–600: 81 (13.5)
600–700: 56 (9.3) 700–800: 46 (7.7)
800–900: 24 (4.0) 900–1000: 17 (2.8)
More than 1000: 29 (4.8)
Marriage status Married: 335 (55.8) Not married: 249 (41.5)
Divorced/separated: 16 (2.7)
Children ** Yes: 74 (21.1) No: 277 (78.9)
Sex Male: 300 (50) Female: 300 (50)
Age 20–30 years: 138 (23.0) 30–40 years: 156 (26.0)
40–50 years: 156 (25.0) 50–60 years: 150 (25.0)
n = 600; * 10,000 KRW (Korean Won) 9.24 USD; ** Excluding 249 not married participants.
3.2. Estimated Model and Preferred Urban Forest Attributes
The estimated choice experiment model, which was composed of hypothetical salient urban forest
attributes and their levels, is shown in Table 5. The likelihood ratio index (indicator of the overall
goodness-of-fit,
ρ2
) was 0.1823, which was very close to the extremely good fit range (0.2–0.4) [
70
]. In a
general sense, the constant of the model (i.e., ASC value) indicated the difference in utility between
the changes in urban forests and the base alternative of no urban forest visit. In a practical sense,
a positive coefficient of the constant indicated a user’s WTP a certain amount of money for visiting
their preferred urban forest [
71
,
72
]. The constant of the estimated model had a negative sign but was
not significant, which suggests that there were no significant differences between the probabilities of
visiting and not visiting any of the given urban forest alternatives. The insignificance of the constant in
the estimated model might indicate that participants were not very willing to change or enhance the
attributes of urban forests. One reason for the insignificance of the constant could be the relatively high
proportion of participants that were none- and low-frequency urban forest visitors (less than three
times a year) in the sample (43.3%). The estimated model also indicated significant differences in
the preferences of the participants for all attribute levels except trial length. The model suggested
that participants significantly preferred broadleaved (b = 0.4361) or mixed forests (b = 0.3390) over
coniferous forests (i.e., base level of forest tree type). Urban dweller utility could be increased by
converting coniferous forests into mixed or broadleaved forests. This enhancement of the utility of an
urban dweller would be higher if coniferous forests were converted into broadleaved forests rather
than mixed forests. Urban dwellers preferred natural trails with a wooden deck (b = 0.5066) or soil-type
pavement (b = 0.5765) rather than a porous elastic pavement. The positive coefficients of a wooden
deck or soil-type pavement indicated that simply changing the trail pavement materials to a wooden
deck or soil-type material would increase visitor utility when visiting an urban forest. The positive
parameters of the topography attribute levels indicated that utility was increased with flat (b = 0.9091)
Sustainability 2018,10, 2461 11 of 22
or slightly hilly trails (b = 0.7824). Likewise, the preference and utility of an urban dweller for visiting
an urban forest were increased with a shorter travel time from home to the forest. Compared to the
base walking time on trails (more than 3 h), other walking times were not significant in the model.
As expected, an increased amount of funds spent to enhance urban forest attributes might reduce the
preference and utility of an urban dweller when visiting urban forests (b = 0.0353).
Table 5. The estimated conjoint choice model with five salient attributes and attribute levels affecting
the urban forest of urban residents. A positive coefficient indicates that participants had a preference
for that particular attribute level.
Attribute Attribute Level Coefficient S.D. pof t
Forest type
a. Broadleaved forest 0.4361 0.1558 0.0051
b. Mixed forest 0.3390 0.1586 0.0326
c. Coniferous forest (base level) 0.0
Paving material of trail
a. Soil-type pavement 0.5765 0.1849 0.0018
b. Wooden deck 0.5066 0.1630 0.0019
c. Porous elastic pavement (base level) 0.0
Topography
a. Flat 0.9091 0.1796 0.0000
b. Hilly slope 0.7824 0.1691 0.0000
c. Mountainous slope (base level) 0.0
Walking time
a. Less than 1 h 0.0326 0.1572 0.8355
b. Between 1 and 2 h 0.0419 0.1868 0.8226
c. More than 2 h (base level) 0.0
Travel time
a. Less than 15 min 0.6888 0.1782 0.0001
b. Between 15 and 30 min 0.6824 0.1882 0.0003
c. Between 30 and 60 min 0.4921 0.1894 0.0094
d. More than 60 min (base level) 0.0
Amounts of fund - 0.0353 0.0041 0.0000
ASC (constant) - 0.0074 0.2741 0.9784
LL-value: 539.0109, ρ2= 0.1823.
3.3. Estimated Values of Attribute Levels
On the basis of Equation (6) and the estimated model in Table 5, we transformed the preferences
of participants for the attribute levels of urban forests into monetary values (US Dollars). It should be
noted that the monetary values given in Table 6were not the absolute amount of funds participants
were willing to pay, but rather the additional funds they were willing to pay to enhance the base
level to an alternative level of an attribute. Levels of “walking time” in urban forests did not have
a significant coefficient in the estimated model (Table 5), and the monetary value needed to change
the level of the attribute was not calculated. Overall, the values needed to change attribute levels
were positive for the change in all attribute levels. Altering the topography of a trail had the highest
value. Participants were willing to pay additional funds for changing the trail topography from a
mountainous slope to a hilly slope (20.48 USD) and a flat trail (23.8 USD). Shortening the travel time
from home to an urban forest was also a critical factor in determining the amount of additional funds
that participants would provide. If the travel time could be shortened from more than 1 h to less
than 15 min, urban dwellers were willing to pay an additional 18.03 USD. Similarly, the values for
altering paving materials from porous elastic materials to a wooden deck and soil-type materials were
13.26 6and 15.09, respectively. Participants were prepared to commit relatively smaller amounts to
change forest type from coniferous to mixed forest (8.87 USD) or broadleaved forest (11.42 USD).
Sustainability 2018,10, 2461 12 of 22
Table 6.
The additional funds that urban dwellers would be willing to pay to enhance each attribute
from the base level. Altering the trail topography resulted in the highest amount of additional funds
and altering forest tree types from coniferous forests into mixed forest resulted in the lowest amount of
additional fund.
Attribute Attribute Level FWTP aKRW b(USD c)Computation
Forest tree type
a. Broadleaved tree forest 12,354 (11.42) 0.4361
0.0353
b. Mixed forest 9603 (8.87) 0.3390
0.0353
c. Coniferous forest (base level) - -
Paving material of trails
a. Soil- type pavement 16,331 (15.09) 0.5765
0.0353
b. Wooden deck 14, 351 (13.26) 0.5066
0.0353
c. Porous elastic pavement (base level) - -
Topography
a. Flat 25,753 (23.8) 0.7824
0.0353
b. Hilly slope 22,164 (20.48) 0.9091
0.0353
c. Mountainous slope (base level) - -
Travel time
a. Less than 15 min 19,512 (18.03) 0.6888
0.0353
b. Between 15 and 30 min 19,331 (17.86) 0.6824
0.0353
c. Between 30 and 60 min 13,940 (12.88) 0.4921
0.0353
d. More than 60 min (base level) - -
a
FWTP = amount of additional funds willingness to pay (one-time payment per person);
b
Korean Won;
cTransformed into USD (10,000 Korean Won 9.24 USD).
4. Discussions
4.1. Effects of Salient Attributes on Preferences
Urban dwellers preferred mixed or broadleaved forests over coniferous forests. This might be
associated with the various characteristics of trees such as tree shape, foliage color, foliage shape,
bark color, seasonal variation, and the sense of safety. The results of this study were consistent with
the findings of previous studies that reported a preference for a spreading shape over more conical
forms [
30
,
73
78
], coarse foliage (i.e., broadleaved trees) over fine foliage (i.e., conifers) [
61
,
78
], bright
bark color over dark bark color [
78
], variety of colors in a forest over simple colors [
54
,
79
81
] and
seasonal variation over no variation [
77
,
82
,
83
]. Often, people perceive conifer forests in relation to the
terms “artificial”, “man-made”, “darkness”, “impermeable”, “repelling”, “uniform”, “monotony”,
“young trees,” and “mushrooms,” whereas broadleaved forests are perceived in relation to the terms
“native”, “natural,” “light diverse colors”, “permeable”, “inviting”, “individual”, “diversity”, “old
trees”, and “flowers” [
84
]. It was noteworthy that user preferences for a particular tree type were
partially influenced by cultural, regional, contextual, and subjective expectations [
85
]. The most
common coniferous trees in Korea are Korean red pine (Pinus densiflora), Korea pine (P. koraiensis), Pitch
pine (P. rigida), and needle fir (Abies holophylla), and these trees can be characterized as having a conical
form, fine foliage, dark bark color, similar leaf color, and being evergreen, which are all characteristics
that are undesirable in most previous studies. Another possible explanation for peoples’ preferences
for broadleaved forest over coniferous forest is the “sense of safety” with broadleaved forest associated
with “visual penetration”, “visibility”, and “openness” [
31
,
86
91
]. Many studies have reported that
the perception of safety is strongly related to landscape preferences (e.g., [
30
,
92
96
]). The thick foliage
of coniferous trees could obstruct view (i.e., low visual penetration) and evoke fear or a sense of being
unsafe in visitors to urban forests (e.g., [
93
,
94
]). We found that participants were willing to pay an
additional 11.42 and 8.87 USD to convert coniferous forest to mixed or broadleaved forest, respectively.
This study also found a greater preference for soil-type pavement or wooden decking over porous
elastic pavement on trails in urban forests. Considering that the main purpose of visiting an urban forest
was to take a walk, rest, be away from home, and to enjoy a natural environment, the lack of preference
Sustainability 2018,10, 2461 13 of 22
for porous elastic pavement materials was likely because elastic pavement materials are not natural and
might restrict the sense of being in a natural environment when walking on a trail. It has been shown
that walking in an urban forest is one of the most popular and important leisure activities among urban
dwellers in terms of their physical and psychological benefits [
97
101
]. For these reasons, trails are
considered to be the most preferred area of urban forests among visitors [
58
], with one of the critical
factors affecting preferences on trails being the pavement material [
102
]. Gobster (1995) [
60
] suggested
that poor trail conditions and poorly maintained trail environments were perceived as being too wild
or overgrown and were not desirable. We found that urban dwellers preferred wooden decking or
soil-type pavement materials over porous elastic materials and were willing to pay 13.26 USD for
wooden decking and 15.09 USD for soil-type pavements if porous elastic materials were to be replaced.
From a design and planning perspective, it is not easy to determine which materials should be placed
on trails on urban forests. There is strong evidence that a heavy concentration of visitors on trails has
adverse impacts on fauna, flora, and soil properties (e.g., [
103
105
]). To minimize the adverse impacts
of trail use on ecological communities by visitors in urban forests, the use of soil-type materials on
trails simply because of visitor preferences might not be the best way to apply the study results into
design and planning practices. A recent study estimated trail demand model for bicyclist, pedestrian,
and mixed-mode traffic in multiple states in the US and reported that trail demands of bicyclists and
pedestrians were significantly associated with different variables [
106
]. Interestingly enough, they
also reported that bicyclists and pedestrians responded differently to variations in specific weather
variables such as temperature and precipitation [
107
]. With respect great difference in spatial extent
between these studies and our study, the result of these recent studies suggested that users’ preferences
on trails in urban forests might be greatly different due to types of trail uses (i.e., bicycling, walking, or
others). The type of paving materials used in recreational trails should be determined on the basis
of a number of critical factors including type of use, amount of use, slope, soil type, and aesthetic
quality [
108
]. The study results could be applied by dividing urban forests into zones on the basis of
these critical factors and using different pavement materials in the different zones.
The preference of participants for a flat terrain on trails was consistent with the results of Koo et al.
(2013) [
36
], who reported that people preferred to walk on comfortable flat trails. However, a number
of studies have reported the opposite results, which suggests a greater preference of people for sloping
trails in Spain (e.g., [
30
]), Denmark (e.g., [
109
]), and elsewhere (e.g., [
61
,
63
]). Thus, the preference for
flat trails identified in this study should not be overgeneralized. A preference for a sloping trail could
depend on the purpose of the visit or the sociodemographic characteristics of visitors. For example,
outdoor activity enthusiasts (i.e., climbing or hiking), cyclists, and all-terrain vehicle users might
prefer the complex, dynamic, and challenging topography of trails in forests located in areas remote
from cities rather than urban forests [
110
]. In contrasts, elders might prefer gently sloping trails in
urban forests over steeper trails in remote areas [
111
,
112
]. Furthermore, various attributes of trails,
socio-demographic variables, and their interactions might also influence the preferences of urban
dwellers and their demand for trails (for more details see [
113
]). Our study revealed that participants
were willing to pay 23.8 USD to alter trail topography from mountainous slopes to flat trails.
Spatial and temporal accessibility were preferred, which was consistent with the findings of many
previous studies that have reported higher utility for forests or parks located close to user’s homes
rather than remotely located forests (e.g., [
114
121
]). By contrast, a recent study reported that people
in Korea were not sensitive to the spatial accessibility of urban forests when they were located in
urbanized areas and public transportation systems were effective [
36
]. The attribute levels of travel
time were very short (5, 10, and 15 min), and this short time difference among travel time attributes did
not significantly change the utility. It was assumed that people were more sensitive to the accessibility
of an urban forest rather than the time required to travel to the forest.
It was not possible to specify the journey time or distance at which people became sensitive in
this study. In European cases, a number of studies have reported slightly different preferred distances
(or travel time) between their place of residence and an urban forest. In previous European studies,
Sustainability 2018,10, 2461 14 of 22
the preferred maximum travel distance was 10–25 km in Belgium [
61
], 8.6 km in Ireland [
122
124
],
and 1 km in Sweden [125]. In central Europe and Great Britain, the distance declared by respondents
was approximately 10 km, while a Scandinavian study reported a substantially shorter distance of
approximately 1 km [
126
]. The great difference in preferred maximum distance might be associated
with the demand for accessing green spaces. People living in highly urbanized areas have limited
access to green areas in a city, a greater demand for natural areas, and are more willing to travel
longer distances for recreational purposes [
126
]. People living close to forests or parks have a higher
likelihood of visiting forests or parks than those living further away [
121
,
127
]. From a planning
standpoint, the results of this and previous studies provide a rational justification for why more urban
forests and parks should be provided to maximize the socio-cultural and environmental benefits for
urban dwellers and urban ecosystems [
128
]. In our study, urban dwellers were willing to pay an
additional 18.03 USD to shorten the journey time from home to an urban forest from more than 1 h to
less than 15 min.
As discussed earlier, walking on a trail was the main reason for visiting an urban forest, and trail
condition was a critical factor affecting the preferences for urban forests [
58
,
60
,
97
102
,
129
]. Our results
and the urban forest literature suggest that management conditions, topography, and the paving
materials used to construct trails are more significant variables determining user preferences than
walking time. Walking time was not a significant variable in this study. The provision of multiple
trail routes with different lengths, topography, and forest types could provide a diverse range of
choices that would attract various sociodemographic groups and visitors, with different purposes for
their visit.
4.2. Effects of Sociodemographic Characteristics and Use Patterns
There is much evidence that sociodemographic characteristics and the patterns of use by
visitors have a significant impact on a user’s preferences for urban forests (e.g., [
36
,
59
,
130
133
]).
Based on the estimated base model (Table 5), we added a number of variables into the estimated
model and re-estimated the comprehensive model to investigate the effects of sociodemographic
characteristics and the patterns of use by visitors on the WTP additional funds. The
ρ2
of the
comprehensive model was 0.2115 (Table 7), which was considerably greater than the attribute only
model (
ρ2
= 0.1823) in Table 5. On a general level, the comprehensive model better explained the true
nature of a user’s preferences for urban forest. The estimated comprehensive model revealed that the
sociodemographic characteristics of participants and their patterns of use had significant impacts on
user preferences and the selection of urban forests. Respondents that were frequent visitors to urban
forests were more actively prepared to pay additional funds to enhance their preferred urban forest
than those who less frequently visited urban forests. This result reinforced previous findings that
active forest users would be willing to improve the attributes of urban forests [
39
,
132
,
134
]. We also
identified two participant groups that were classified by the travel mode taken to reach the urban
forest. The estimated comprehensive model indicated that car users were more actively interested in
enhancing urban forest attributes than those who accessed urban forests by walking, bicycle, taxi, or
public transport (e.g., subway or bus). In our study, about 41.1% of respondents used a car to visit
urban forests, which was similar to a European case study conducted in Belgium [
61
], which found
that most urban forest visitors used private vehicles. Travel mode was associated with travel distance,
with about 70% of study participants indicating that they travelled up to 10 km. In Sweden, people
preferred to walk up to 2 km to visit urban forests and take a car to visit forests if the travel distance
was greater than 2 km [
125
]. In general, personal car users can travel farther to visit high quality
forests than visitors who travel on foot [
135
]. In transportation science, the selection of travel mode
depends on various environmental, sociocultural, and land use variables such as distance, quality of
road systems, weather conditions, number of children, income, education, and age (e.g., [
136
138
]).
However, we were not able to find published studies of the relationships between car users and
Sustainability 2018,10, 2461 15 of 22
preferences for forest attributes. Thus, it was not possible to directly compare our results with previous
studies, and further studies are needed to verify our findings.
Table 7.
The re-estimated conjoint choice model with salient attributes, sociodemographic
characteristics, and patterns of use. The attributes of forests and their levels had similar coefficients to
those of the estimated model without sociodemographic characteristics and use patterns.
Attribute Attribute Level Coefficient S.D. p. of t
Forest tree type
a. Broadleaved forest 0.4530 0.1573 0.0040
b. Mixed forest 0.3818 0.1615 0.0181
c. Coniferous forest (base level) 0.0
Paving material of trail
a. Soil-type pavement 0.6046 0.1885 0.0013
b. Wooden deck 0.5189 0.1658 0.0018
c. Porous elastic pavement (base level) 0.0
Topography
a. Flat 0.9122 0.1819 0.0000
b. Hilly slope 0.8061 0.1715 0.0000
c. Mountainous slope (base level) 0.0
Walking time aa. Less than 1 h 0.0392 0.1590 0.8053
b. Between 1 and 2 h 0.0287 0.1888 0.8790
c. More than 2 h (base level) 0.0
Travel time
a. Less than 15 min 0.6786 0.1794 0.0002
b. Between 15 and 30 min 0.6991 0.1896 0.0002
c. Between 30 and 60 min 0.4985 0.1908 0.0090
d. More than 60 min (base level) 0.0
Visit frequency aa. Low frequency visitors 0.2882 0.1173 0.0140
b. High frequency visitors (base level) 0.0
Access mode a. Cars 0.5141 0.1414 0.0003
b. Others (base level) 0.0
Sex a. Male 0.7652 0.2535 0.0025
b. Female (base level) 0.0
Income level - 0.0618 0.0392 0.1146
Age - 0.2606 0.1290 0.0434
Fund - 0.0358 0.0041 0.0000
ASC (constant) - 0.0975 0.9455 0.9187
LL-value: 519.7783, ρ2= 0.2115; aMore than three times per year.
Interestingly, male and younger participants exhibited a WTP to enhance urban forest attributes.
However, as discussed by Koo et al. (2013) [
36
], there were great differences in the preferences for
urban forest attributes among sociodemographic groups. Specifically, male and younger participants
were willing to enhance urban forest attributes by paying additional funds compared to female and
elder participants. A possible explanation for this is that the main household income in Korea depends
on a male’s income, and males are more aggressive in changing environments than females [
55
].
McAlister and Pessemier (1982) [
139
] reported that younger people have a stronger tendency to seek
variety than elderly people. Regarding the role of sex in preferences of urban forest attributes, we found
slightly different results in the urban forest literature. For example, there is no difference between
males and females in the WTP for enhancing forest attributes [
36
]. In addition, the WTP of females
was higher than that of males in a nature-based forest study [
32
]. We did not observe any significant
difference in the WTP among income groups in our study. The reasons for the inconsistency of the sex
effect between this study and previous studies and insignificant income effects in our estimated model
were not clear.
Sustainability 2018,10, 2461 16 of 22
5. Conclusions
For planners, decision makers, and the designers of urban forests, it is an important but difficult
task to respond to user’s preferences and demands. This is particularly critical in highly urbanized
areas where land values are extremely high and the land available for urban forests is extremely
limited, because every planning, management, and design practice is required to be justified. Using a
conjoint choice model, this study assessed user preferences for the attributes of urban forests and
estimated the monetary values of attribute levels in Seoul, Korea, which is a highly urbanized
environment. Based on a survey with 600 respondents, we were successfully able to estimate a
conjoint choice model and transform user preferences for attributes and attribute levels into monetary
values. The results indicated that urban dwellers in our study area preferred broadleaved forest
(11.42 USD) over coniferous forest, soil-type pavement materials (15.09 USD) over porous elastic
pavement materials on trails, relatively flat trails (23.8 USD) over trails with steep slopes and a short
travel time (18.03 USD for less than 15 min) to the urban forest from home. A comparison of the
monetary values of the preferences estimated in the topography of the trail had the highest preference
value, while forest type had the lowest preference value. The sociodemographic characteristics (i.e., age,
sex, and access mode) of visitors also had a significant impact on the attributes of urban forests and their
levels. From a practical perspective, our study results suggest that the preferences of urban dwellers
can be enhanced by providing more broadleaved trees, paving trails with natural materials and
appropriate maintenance, and providing gently sloping or flat trails (or various slope trail alternatives).
Both previous studies and our study results strongly indicate that the distance to urban parks is a
critical factor in user’s preferences and their frequency of visits. However, the provision of large new
urban forests is not an option in most large cities. Instead, providing urban forests in neighborhood
parks or small urban parks could be a practical alternative, with consideration of the sociodemographic
characteristics of the surrounding area.
Despite our study providing useful insights into urban forest planning, design, and management,
the results of this study raise some practical questions. For example, we estimated a conjoint choice
model for the entire city. However, there could be differences in people’s preferences for urban forests
depending on the location of the forest within the city. In the urban edge area, residents may have many
alternative forests to visit while the residents of central areas do not. In this case, the demand for urban
forests and the user preferences could be considerably different among the residents of different parts
of the city. To address this issue, we adopted a quota sampling method. However, we were not able to
verify that this sampling method neutralized the spatial and locality issues sufficiently, with such an
analysis being beyond the scope of the study. Alternatively, a segregated survey or geographically
weighted regression (GWR) methods might be useful to integrate our study results with locality and
spatial dimension issues. This study was not able to integrate a number of important salient attributes
such as infrastructure (e.g., information boards, open play areas) [
61
] and perceived safety into our
model because there was a limit on the maximum number of salient attributes. The potential demand
and preferences of visitors for particular urban forests could vary across a city, and it may be necessary
to adopt different attributes in a CE, corresponding to the different demands and preferences in a
particular location within a city on the basis of the results of our study.
Considering that the decision to visit an urban forest is often made based on the limiting factors
rather than the opportunity, an investigation of the limiting factors preventing visits to urban forests
might be theoretically meaningful to verify the results of this study. Most previous studies have
investigated user preferences for urban forests using attributes revealed by visitor statements. However,
“why do people not visit urban forests?” is also a critical question from a non-user perspective.
According to prospect theory, people are more sensitive to “what to lose” than “what to gain” [
140
].
This theory suggests that people are not willing to visit an urban forest if they think the expected
utility of not-visiting the forest (i.e., loss) is greater than the expected utility (i.e., gain) of visiting the
forest. Visiting an urban forest requires time, cost, and effort, and identifying the attributes greatly
affecting “loss” factors in a visitor’s perception can significantly contribute to urban forest planning and
Sustainability 2018,10, 2461 17 of 22
management in urban areas, where available land for forests is extremely limited. In addition, it would
be a quite interesting and fresh perspective to view urban forests as local economic opportunity [
141
].
Using hedonic price model, many studies reported that amounts of urban forests and proximation to
urban forests increase property values or housing price. However, these previous studies often neglect
attributes and attribute levels of urban forests. Conjoint choice modeling used in this study may help
to elaborate hedonic price model used housing market research.
Author Contributions:
All authors significantly contributed to this paper. S.-K.H. and H.-K.J. were responsible
for conceiving the study, the study design, and the basic statistical analyses. J.-M.K. made a unique contribution
to the survey and data coding. S.-W.L. performed additional statistical analyses and wrote the manuscript.
Acknowledgments:
This study was conducted with the support of the “R&D Program for Forest Science
Technology (Project No. 2017043B10-1819-BB01)” provided by the Korea Forest Service (Korea Forestry Promotion
Institute).
Conflicts of Interest: The authors declare no conflict of interest.
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... In a study among students in Manila, Lagbas (2019) found positive WTP for the services provided by urban parks, mainly their perceived capability of regulating air quality and temperature. WTP for different attributes of urban forests (the type of trees, gradient, and type of paving for walking trails) in Seoul were also computed by Hong et al. (2018) using a DCE application. Preferences and values for urban forests in Korea, and their potential to reduce the urban heat island were also analyzed by Kim et al. (2016). ...
... We could not find any equivalent finding in the literature, but these figures can be usefully placed next to others produced by the previous studies in the region, to give elements to understand people's preferences for various aspects of urban vegetation. For example, Hong et al. (2018) found that respondents were willing to pay US $11 for a change from coniferous to broad leaves tree forests in urban parks in Seoul. In the same city, Koo et al. (2013) computed a willingness to pay of approximately US $3 per visit for a park with higher biodiversity. ...
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Streets are an essential element of cities, and their design has a profound impact on their functionality to the movement of people and their well-being. This paper investigates preferences for and economic values of several street design characteristics, encompassing greenspaces (ground vegetation, trees, flowers), and walking and cycling infrastructure. A discrete choice experiment on a single case study street in Taipei, Taiwan, has revealed positive preferences for ground vegetation (and a willingness to pay-WTP-between $2.8 and $4 per year for a 1% increase in coverage), separated cycling infrastructure (with a WTP between $60 and $100 for cycleways separated from traffic), pedestrian access to road islands (WTP of $55), and the (reduced) amount of space dedicated to motor vehicles (WTP of $29 to avoid any increase). Flowers were also deemed important, but a mixed picture was obtained with respect to preference for street trees. The analysis is exploratory, on a relatively small sample of street users, but contributes to the literature on the importance of urban vegetation and walking and cycling infrastructure when designing streets and be used to draw lessons for other similarly dense urban areas in the country and wider region.
... Particularly, it is an urgent issue for highly urbanized regions and countries. For example, in Korea, where approximately 90% of the population resides in urbanized areas, the Korean government and local authorities have been continuously trying to provide more urban green spaces for years to meet the increasing demand [95][96][97]. ...
... To acquire such specific information of UGS attributes, the conjoint choice model may be beneficial because it is able to make direct predictions on choices of respondents in the form of a logit model by calculating the part-worth of attribute levels obtained from choice-type data [144]. For example, Hong et al. [97] found that the primary salient attributes affecting whether people visit urban forests were forest type, paving material of trail, topography, and travel time from home. At the same time, they were able to specify attribute levels for each forest attribute preferred by forest visitors. ...
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Exposure to green spaces can reduce the negative effects of stress. This study examines how frequency of visits and time spent in urban green spaces (UGS) affect urban dwellers’ subjective well-being. We also investigated the numbers of respondents visiting UGS, their primary motivation, and constraints on their ability to visit. Using quota sampling, an online survey was conducted of 400 residents of Daejeon City, South Korea. ANOVA results indicated no significant interactions between visit frequency and time spent in UGS. Respondents who had visited UGS within the past two weeks expressed higher positive and lower negative emotions than did non-visitors, regardless of visit frequency, and regular visitors showed higher general life satisfaction levels. These positive effects were confirmed by estimated structural equation models. However, the time spent in UGS did not affect emotions or life satisfaction in general. Heavy users mostly visited UGS to walk, and light/non-users cited the lack of urban green spaces near their home as the major constraint on visiting UGS. The estimated structural equation models clearly show positive effects from motivation and negative effects of constraints and access time to UGS on visit frequency. To improve urban dwellers’ subjective well-being, UGS should prioritize good walking environments and accessibility.
... Aesthetic considerations also play a role in trail construction. Trail use studies have shown variations in preferences in relation to setting and activity types, with asphalt and gravel trails and flatter surfaces preferred by urban trail walkers [194,218] and dirt trails by day hikers [244]. Study findings are not always consistent with each other, however, and preferences for trail construction criteria can depend on the issue being addressed by the investigator. ...
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Forest therapy is an emerging holistic health practice that uses multisensory immersive engagements in forest settings to achieve health and wellbeing outcomes. Many forest therapy engagements take place via slow walks along a trail to optimally experience the array of sensory phenomena afforded along the route, yet surprisingly few forest therapy studies to date have investigated the characteristics of forest sites and trails that give rise to healthful experiences. In this research, we employ a hybrid approach to understand the conditions and features that contribute to a good forest therapy trail, using interviews with forest therapy guides to identify and highlight concepts for further refinement and structuring via a broad, integrative review of the relevant research and planning literature. Through this iterative approach, we identify and describe three site-related criteria (landscape character and quality, tranquility, and accessibility) and two trail-related criteria (design and construction and key features and qualities), each with a number of sub-criteria detailing specific conditions and considerations. This effort helps build a conceptual foundation and evidence base for assessment procedures that can be used to identify existing trails and design new ones that meet the needs of forest planners, managers, guides, and participants for the growing international practice of forest therapy.
... Most of the studies show preference for mixed forest but then deciduous forest usually takes precedence over coniferous. Hong et al. [48] and Abildtrup et al. [21] have determined a significant preference for broadleaved and mixed forests over coniferous forests among the urban population of Seoul (Korea) and France. In the study review by Ciesielski and Stereńczak [24], central Europeans expressed preference for mixed forests with a higher share of coniferous trees. ...
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By providing ecosystem services, urban forests contribute significantly to the well-being of urban populations. Urban forests, along with other urban green spaces, are often the closest natural environment in the city where a child can play. The majority of pre-school children spend a large part of the day in kindergarten, which means that forest visits should have a prominent place in the kindergarten curriculum. Therefore, this study focuses on making the forest more suitable and thus more accessible for visits with children. The first goal of the research is to identify teachers’ preferences for the forest environment they visit with a group of pre-school children. The second goal is to present a forest suitability model for a visit with kindergarten children based on the teachers’ preferences. Based on the research survey conducted among the teachers in Slovenian public kindergartens, we formed and evaluated the criteria for the construction of a model of forest suitability for a visit with children. As the most important requirement for visiting a forest, the teachers note its proximity. They prefer a mature, mixed forest, with a bit of undergrowth, dead wood, and a presence of water and a meadow. Based on the identified criteria, we used the multi-criteria evaluation method in the GIS-environment in order to build a model of urban forest suitability for a visit with kindergarten groups of children in the study area of the City of Ljubljana, Slovenia. The results are useful in urban forest planning and management to ensure better forest suitability and accessibility for visits by children. Suitability maps can be used as one of the spatial foundations necessary for an integrated urban forest planning with emphasis on social functions. The model can be adapted beyond Slovenia to different spatial and social requirements and contexts.
... Forests and green areas greatly contribute to the adaptation of urban areas to climate changes, [13][14][15][16][17][18], as well as to the protection of biodiversity [16,19,20]. They also provide economic benefits in the form of increased real property prices [21][22][23][24] and a number of socio-cultural services [16,25,26] essential for the well-being of humans [27]. Forests also improve physical health and mental well-being [28][29][30][31]. ...
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Knowledge about urban forests in Poland is still limited, as it is primarily based on aggregate, formal data relating to the general area, ignoring the spatial dimension and informal green areas. This article describes and analyses spatio-temporal changes in the actual urban forest resources in Wrocław in 1944–2017, which covers the first period of the city’s rebuilding after its destruction during World War II and its development during the nationalised, centrally-planned socialist economy, as well as the second period of intensive and only partly controlled growth under conditions of market economy. The study is based on current and historical orthophotomaps, which were confronted with cartographic data, as well as planning documents. We found that between 1944 and 2017, the percentage contribution of informal woodlands increased tenfold (from 0.5 to 4.9% of the present total area of the city). The area occupied by such forests has grown particularly during the most recent years of the city’s intensive development. However, the forests have been increasingly fragmented. During the first period, new forest areas were also created in the immediate vicinity of the city centre, while during the second one, only in its peripheral sections. The post-war plans regarding the urban green spaces (UGS), including the current plan, are very conservative in nature. On the one hand, this means no interference with the oldest, biggest, and most valuable forest complexes, but on the other hand, insufficient consideration of the intensive built-up area expansion on former agriculture areas. Only to a limited extent did the above-mentioned plans take into account the informal woodlands, which provide an opportunity for strengthening the functional connectivity of landscape.
... (6) Urban forests also provide various ecosystem services such as microclimate improvement, atmospheric cleanup, (7) energy saving, (8) rainwater collection and storage, the improvement of biological diversification, (9) and the enhancement of soil fertilization; (10) ecological benefits such as noise reduction, landscape beauty and amenity, and recreation and learning; (11) esthetic/welfare benefits such as health improvement; (12) and socioeconomic benefits such as real estate and property value increase. (13) The ecosystem service that is extremely important for our lives is carbon storage and uptake by vegetation; this plays a particularly important role in the alleviation of climate change, the severity of which has been realized only in recent decades. (14,15) Climate change has come to the fore as the most serious environmental issue globally. ...
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The residential forest provides many amenities to communities, including aesthetics, air quality improvement, and higher property values. However, the residential forest may also contribute human-perceived problems or disamenities including allergens, leaf debris, infrastructure damage, and maintenance costs. Vegetation management by utility companies along power lines is one process that shapes the residential forest. Property owners’ decisions to consent or object to utility vegetation management may be influenced by perceived tree amenities and disamenities. To explore this decision-making process, we conducted 32 one-on-one semi-structured qualitative interviews with resident-homeowners who consented or objected to a utility company tree removal on their property between 2014 and 2017. The study area included several towns in eastern Connecticut, USA, representing urban, suburban, and exurban residential areas. We applied the means-end chain theory as a theoretical framework, and used laddering interviews to explore the tree amenities, disamenities, and values associated with trees. Attractiveness, shade, and privacy were the most frequently identified amenities of trees; risk to power lines, trees being dead or diseased, and risk to people were the most frequently identified disamenities. Amenities and disamenities were connected to such values as happiness and enjoyment, closeness to nature, comfort, pride in one’s home, aesthetics, life, avoiding harm to others, and time or money for other priorities. Participants who objected to utility tree removals primarily identified tree amenities as reasons to retain the trees, whereas participants who allowed tree removals primarily identified disamenities as reasons for their decision. The most common reason for objecting to removal was uncertainty about the need for removal. Participants had diverse perceptions of how tree amenities and disamenities affected their potential consent to utility vegetation management, illustrating that the priorities and concerns of individual residents are important considerations for forest managers and arborists engaged in vegetation management on private property.
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The aim of this paper is to find drivers behind visitor’s participation in the use of urban forests and to explain the differences in co-operation in urban forest management with the help of game theoretic modeling. For this purpose, data regarding public urban forests of Turkey were collected and analyzed by various statistical methods. According to the principal component analysis, leading factors affecting the use of urban forest were, ordered from the most important to the least important: (1) forest versatility, (2) management intensity, (3) visitor services, (4) forest tranquility, and (5) forest activities. These five factors accounted for 71% of the total variance among the variables. Furthermore, multiple regression analyses showed that, especially in cities with an abundance of forests, the use of urban forests was not widespread, whereas urban forests were visited more in the settlements having a high number of young population and a large family size. The estimated game theoretic model on participation indicated that the availability of forest services among visitors was generally harmonious. It could be concluded that urban forestry has to focus, not only on increasing the number and size of urban forests, but also on educating all relevant social groups in society on how to use urban forests in a sustainable and responsible manner.
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Background Previous studies revealed a relationship between residential green space availability and health, especially mental health. Studies on blue space are scarcer and results less conclusive. Aims To investigate the hypotheses that green and blue space availability are negatively associated with anxiety and mood disorders, and positively associated with self-reported mental and general health. Method Health data were derived from a nationally representative survey (NEMESIS-2, n =6621), using a diagnostic interview to assess disorders. Green and blue space availability were expressed as percentages of the area within 1 km from one’s home. Results The hypotheses were confirmed, except for green space and mood disorders. Associations were generally stronger for blue space than for green space, with ORs up to 0.74 for a 10%-point increase. Conclusions Despite the different survey design and health measures, the results largely replicate those of previous studies on green space. Blue space availability deserves more systematic attention. Declaration of interest None. Copyright and usage © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.
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This study presents new trail demand models based on data collected between January 1, 2014 and February 16, 2016 at 32 locations in the seven major climatic regions in the continental U.S. We contribute fourfold to the literature on analysis of trail traffic demand. First, we develop a set of econometric models to predict average daily pedestrians (ADP), average daily bicyclists (ADB), and average daily mixed-mode traffic (ADM) using the 5 D’s of the built environment (i.e., density, diversity, design, distance to transit, and destination accessibility), and socio-economic characteristics. Second, we test the performance of trail demand models in predicting ADB, ADP, and ADM using the leave-one-out cross-validation technique and compare the relative accuracy of the models. Third, we assess the performance of separate bicycle and pedestrian demand models in predicting mixed-mode travel demand. Fourth, we introduce a post-validation technique to advance the prediction accuracy of trail traffic demand models. The results indicate: (1) with only a few exceptions, ADP and ADB are correlated with different variables, and the magnitude of effects of variables that are the same varies significantly between the two modes; (2) The mean relative percentage error (MRPE) for bicyclist, pedestrian, and mixed-mode models equals 65.4%, 85.3%, and 45.9%; (3) Although using separate but integrated sensors to monitor bicycle and pedestrian traffic enables us to juxtapose the bicyclist demand with pedestrian demand, there is not a significant improvement in predicting total demand using these more expensive sensors; (4) A new post-validation procedure improved the demand models, reducing the MRPE of bicyclist, pedestrian, and mixed-mode models by 27.2%, 32.1%, and 14.1%. Overall, our models confirm that different variables are correlated with bicycle and pedestrian traffic volumes and that these modes need to be modeled separately. Our models can be used in practical applications such as selection of trail corridors and prioritization of investments where order-of-magnitude estimates suffice.
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As demographic changes abound, landscape planners should increase their understanding of both elderly people’s preferences concerning nature-based recreation and approaches to consider those preferences in planning. This study aims to synthesize existing knowledge about elderly people’s preferences, namely, how they interact with green spaces, what landscape characteristics they prefer or dislike, and how practitioners can improve planning to better meet elderly people’s needs. A systematic literature review based on the PRISMA method was conducted, including an in-depth analysis of 44 peer-reviewed journal articles. We find that published studies focus primarily on elderly people’s recreational activities in urban parks. Across different contexts, elderly people seem to have common preferences: landscape features that are natural, aesthetic, comprehensible, and diverse, with accessible and well-maintained infrastructure and facilities. Moreover, interactions between people and nature may affect the relative importance levels of the preferences. We recommend that landscape planning practitioners consider both scientific evidence and local conditions that could affect elderly people’s preferences, and explore the degree to which design options may fulfill these preferences. Further research is needed to explore differences in preferences between urban and rural dwellers, to quantify preferences, and to enhance understanding of elderly people’s emotional ties with nature.
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