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A demand index for recreational ecosystem services associated with urban parks in Beijing, China
Journal of Environmental Management 251 (2019) 109612
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Journal of Environmental Management journal homepage: http://www.elsevier.com/locate/jenvman
Research article
A demand index for recreational ecosystem services associated with urban parks in Beijing, China Ranhao Sun a, *, Fen Li a, Liding Chen a, b, ** a b
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China University of Chinese Academy of Sciences, Beijing, 100049, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Urban park Ecosystem service Landscape planning Recreation Urbanization Beijing
Good planning for urban parks requires an analysis of the quantitative relationship between the distribution of an urban population and the demand for recreational ecosystem services (RES). A barrier to RES quantification is the lack of connections between survey materials and spatial data. This study developed a logistic regression model for the demand for RES associated with urban parks based on the characteristics of individual visitor and their willingness to visit parks. The model was fitted by a questionnaire survey completed by 4096 park visitors and was used to predict the RES demand in 317 sub-districts of Beijing. Results showed that: (1) park visitors rated sightseeing as the most important, followed by jogging, boating, partying, cycling, and fishing in Beijing’s parks; (2) high-income and older residents had higher willingness to visit the parks than did low-income and younger park visitors; (3) the fringe areas between the urban and rural regions showed a relatively low demand index for RES. This study exhibits a feasible method to predict RES demand based on surveys and statistical data. Our research suggests that improving park planning necessitates developing a diverse recreational infrastructure, a tradeoff among different stakeholders, and spatial optimization for sustainable urban development. The results provide a potential tool that can be used to assess the balance of RES in a scenario of urbanization and population growth.
1. Introduction Ecosystems provide a range of services that benefit human wellbeing, health, and livelihoods (De Groot et al., 2002; Costanza et al., 2014). Ecosystem services consist of flows of materials, energy, and information from natural capital stocks that improve human welfare (Costanza et al., 1997). The supply of ecosystem services is defined as an ecosystem’s potential to deliver services; meanwhile, the demand for ecosystem services is the extent to which a service is required or desired €gner et al., by society (Crossman et al., 2013; Plieninger et al., 2013; Scha � et al., 2013; Villamagna et al., 2013; Serna-Chavez et al., 2014; Baro 2015, 2016; Darvill and Lindo, 2015; Geijzendorffer et al., 2015; Vigl et al., 2016). Some well-known studies that have made this determina tion include: The Millennium Ecosystem Assessment (MA, 2005), the Economics of Ecosystems and Biodiversity (TEEB, 2011), and the Intergovernmental Platform on Biodiversity and Ecosystem Services (Pascual et al., 2017). A better understanding of the role of ecosystem services emphasizes having a good match between the supply, flow, and
demand for ecosystem services (Kroll et al., 2012; Barό et al., 2016; Hamel and Bryant, 2017). Human society is increasingly urban while the urban residents are being increasingly disconnected from nature. Cities are dependent on variety of ecosystems beyond the city limits, but also benefit from in ternal urban ecosystems. Recreational ecosystem services (RES) repre sent the recreational pleasures derived from natural or cultivated ecosystems (TEEB, 2011; Bertram and Rehdanz, 2015). The RES asso ciated with urban parks have become especially beneficial in urban re gions (Chiesura, 2004; Barbosa et al., 2007; Shan, 2014a; Gong et al., 2015; Soga and Gaston, 2016). The RES can provide direct and indirect benefits including benefits to ecological protection, human health, social and cultural education, as well as market economic value (Sikorska et al., 2017). Understanding the degree of match between the distribu tion of an urban population and the RES demand has been regarded as one important way to improve park planning and land management as part of an effort to better integrate environmental issues (De Groot et al., 2010; Bagstad et al., 2013; von Haaren et al., 2014; Guerry et al., 2015).
* Corresponding author. ** Corresponding author. University of Chinese Academy of Sciences, Beijing, 100049, China. E-mail addresses: [email protected] (R. Sun), [email protected] (L. Chen). https://doi.org/10.1016/j.jenvman.2019.109612 Received 28 April 2019; Received in revised form 13 August 2019; Accepted 19 September 2019 Available online 25 September 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.
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Journal of Environmental Management 251 (2019) 109612
Most past studies of RES have focused on their capacity and supply (Burkhard et al., 2012; Sch€ agner et al., 2013; Lawler et al., 2014) and economic value (De Groot et al., 2010; Bagstad et al., 2013; Costanza et al., 2014; Kremer et al., 2016). Two main difficulties arise when the quantification of RES is directed towards the demand for RES. First, the quantification of RES demand is affected by specific cultural and social contexts. The demand for RES is influenced by individual characteristics of park visitors (Shan, 2014b; He et al., 2016; Xiao et al., 2017), such as gender, age, marital status, education, income, and ethnicity (Neuvonen et al., 2010; Bertram and Rehdanz, 2015; Mensah et al., 2017). Recent comparative studies have investigated how RES activities differ under diverse cultural and social contexts (Wang et al., 2015; Swapan et al., 2017). Second, the spatial mapping of RES demand has rarely been done because of a lack of spatial data associated with these individual char � acteristics (Maes et al., 2012; Geijzendorffer and Roche, 2014; Baro ~ a et al., 2015; Mensah et al., 2017). Some of these in et al., 2015; Pen dividual characteristics are hard to map spatially and explicitly based on data availability (Bastian et al., 2012; Geijzendorffer et al., 2015; Fagerholm et al., 2016). Therefore, the quantification of RES demand is essential to assess the degree of match between the distributions of a
population and urban parks. Research on the RES demand has been conducted in large Chinese cities, such as Beijing (Liu et al., 2017a), Shanghai (Xiao et al., 2017), Guangzhou (Shan, 2014b), Wuhan (Liu et al., 2017b), and Chengdu (Swapan et al., 2017). We selected the city of Beijing as an example because it has the largest population of any city in China and features various types of urban parks. The Beijing government is trying to meet the growing demand for RES of the residents (Gong et al., 2015). Therefore, the present study determined that Beijing would be an ideal site to examine spatial heterogeneity and potential drivers of the de mand for RES. Most past studies focused on the factors influencing RES demand based on questionnaire data. The present study investigated the spatial distribution of RES demand by combing questionnaire and spatial data. The study aimed to: (1) quantify the factors that influencing RES demand by using questionnaire data; (2) investigate the spatial distribution of RES demand based on demographic data; and (3) assess the degree of match between the distribution of the urban population and demand for RES in Beijing.
Fig. 1. The selected 20 urban parks in Beijing analyzed in the present study. 2
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2. Materials and methods
2.3. Logistic regressions of RES demand
2.1. Study area
The willingness of visitors to use parks repeatedly to meet RES de mand is a function of different percentages of individual groups. The hypothesis of the logistic regression model was based on the relationship between the willingness of visitors to use park repeatedly to meet RES demand and individual characteristics. The dependent variable was designated as the binary probability for the willingness (0 and 1). The individual characteristics were classified as categorical explanatory variables, including gender, age, education, and income. The gender of each visitors was designated as 1 (male) and 0 (female). The visitor age was designated as a dummy variable (age <30), 1 (30–50), and 2 (>50). Each visitor’s education was designated as a dummy variable (undercollege), 1 (some college education), and 2 (college graduate). The average monthly income was designated as a dummy variable (<3,000 Yuan), 1 (3,000–10,000 Yuan), and 2 (>10,000 Yuan). The predictors were selected based on the probability of RES demand defined by a logistic function:
In northern China, Beijing covers an area of 16,808 km2 and ranges between 39� 280 N – 41� 250 N and 115� 250 E 117� 300 E. Beijing lies within a warm temperature zone and has a typical continental monsoon climate. As the capital of China, the city has experienced rapid urbani zation in the last three decades. The gross domestic product of Beijing grew from 10.8 billion Renminbi (RMB, Yuan; about 1.57 billion US dollars) to 2,490 billion RMB (about 362 billion US dollars). The total population exceeded 21.7 million by the end of 2016 (BSIN, 2017). The number of urban parks increased from 122 in 2000 to 259 in 2010 with the park area increasing from 77.4 km2 to 129.3 km2 during this period (Mao et al., 2010). Gaining a better understanding of the demand for RES is very important in the management of Beijing’s urban parks. Beijing is divided into 16 districts in which 317 sub-districts have been organized. We used sub-districts as analytical units to assess the demand for RES in this study.
p¼
2.2. Questionnaire survey
eβ0 þβ1 X1 þβ2 X2 þβ3 X3 þβ4 X4 1 þ eβ0 þβ1 X1 þβ2 X2 þβ3 X3 þβ4 X4 �
We selected 20 urban parks to conduct questionnaire survey asso ciated with the demand for RES for two reasons (Fig. 1). First, these parks have large land areas and many visitors. Second, they have high proportions of water-bodies or wetlands which provide various types of RES for visitors. A pilot survey was conducted in four parks in 2010, in which 322 visitors were randomly selected to practice face-to-face in terviews and to test the questionnaire. All questions were designed to be short, concise and easy to understand so that more residents would enjoy participating in the questionnaire. Then, a revised questionnaire was employed at each of the 20 urban parks to record the individual char acteristics of park visitors and the recreational activities they chose to engage in. The questionnaire survey was conducted by three Ph. D students and six assistants. The Ph. D students were responsible for all the arrangements of the interviews. All the interviewers were trained during the pilot survey in four parks. Finally, to gather an adequate number of samples, we conducted >100 interviews in each park. Therefore, the interview process lasted for one year in 2010 and covered different seasons. These interviews were also assigned to represent different events and time periods including weekdays, weekends, and holidays. This study was designed to investigate the demand for RES in rela tion to the residents of Beijing. Therefore, park visitors from other countries or other administrative regions of China were excluded from the survey. The visitors were selected randomly as they entered a park. We tried to keep the interview results correct by designing multiple questions for each specific objective. In addition, we abandoned any incomplete questionnaires acquired during the interviews. Ultimately, a total of 4096 questionnaires were collected from local park visitors. The population profiles of participants are shown in Supplementary Table 1. No significant differences were observed in the gender, age, education, and income of visitors when comparing between the different parks. The recreational activities that visitors engaged in were determined through face-to-face interviews. To explain the underlying drivers of park visits, the participants were asked about their individual characteristics, including gender, age, education, and income. The willingness of visi tors to use parks repeatedly to meet RES demand was inquired about and quantified during the interviews. The willingness was designated as 1 if the participant had visited multiple parks or visited one park more than one time in one year. Otherwise, the willingness was defined as 0 if the visit of participant was visiting the park for the first time and would never visit again.
logitðpÞ ¼ loge
1
(1)
�
p p
¼ β 0 þ β 1 X1 þ β 2 X2 þ β 3 X3 þ β 4 X4
(2)
where p is the probability of RES willingness, β0 is a constant to be estimated, and βi is the coefficient to be estimated for each explanatory variable Xi. Eq. (1) can be transformed into the linear function displayed in Eq. (2). The results are presented as an Odds Ratios (OR) with 95% confidence intervals. The chi-square model is the difference between the likelihood for the best fitting model and likelihood for the null hy pothesis being correct. Goodness-of-fit of the models was assessed by the Hosmer-Lemeshow test. Statistical analyses were performed using SPSS 19 (IBM SPSS Inc., USA). The significance (p < 0.05) of each explanatory variable was used to select the key predictors. The coefficients of the regression model were refitted by these key predictors. Then, the regression model was used to predict the spatial pattern of RES demand in different regions. The de mographic data were derived from China’s 6th national population census data (NBSC, 2010). The population density (Rk, capita per km2) of urban residents in different groups of gender, age, education, and income was calculated in each sub-district k for a new prediction. The population density of the RES demand (Ek) was calculated as Ek ¼ logit (p)k in each sub-district k. Thus, the demand index of RES (Ik) was developed based on the population density between the total residents and the population associated with RES demand. Ik ¼
Ek β 0 þ β 1 X1 þ β 2 X2 þ β 3 X3 þ β 4 X4 ¼ Rk Rk
(3)
3. Results 3.1. Different recreational activities in urban parks We analyzed the main types of recreational activities enjoyed by visitors in urban parks based on questionnaire survey that included 4096 participants. The activities included sightseeing, jogging, boating, partying, cycling, fishing, and other types. Based on the questionnaire data, we found that each visitor usually participated in different types of recreational activities in a park when he or she made a return visit. Multiple choices in the questionnaire allowed researchers to ask about different types of recreation enjoyed by visitors. Results showed that approximately 82% of all participants visited the parks for sightseeing, following by jogging (56%), boating (33%), partying (26%), cycling (8%), fishing (6%), and other types of recreation (17%) (Fig. 2). The participants who came to parks for sightseeing had similar proportions in different parks while the percentages of those interested in boating 3
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Fig. 2. Main types of recreational activities enjoyed by visitors in urban parks. The dash-line represents the average percentage of each recreational activity.
and fishing were different in different parks.
visitors (age > 50) had higher demand for RES than the middle-aged visitors. Comparatively, the middle-income visitors (monthly income from 3,000 to 10,000 RMB) had similar demand for the RES when compared with the high-income visitors (>10,000 RMB). Other indi vidual characteristics, including gender and education level, were not significant factors in the logistic regression.
3.2. Key factors of RES demand A total of 2162 participants showed a high willingness to use parks repeatedly to meet RES demand which means they had made multiple visits to parks or were willing to visit again. The factors influencing RES demand were identified by the logistic regression (Table 1). A high chisquare value indicated that high RES demand was less expected under the null hypothesis than in the full regression model. Four predictors were found to be significant (p < 0.05) for model prediction. The older
3.3. Spatial pattern of RES demand Based on the above key factors and the related parameters, we developed a model to predict the RES demand in different sub-districts
Table 1 Statistical analysis of the predictors obtained by logistical regression. Predictor Age** Income**
Al (Age < 30) Am (Age 30–50) * Ah (Age >50) ** Il (<3,000 Yuan) Im (3,000–10,000 Yuan) ** Ih (>10,000 Yuan) *
Constant Chi squared ¼ 126.319** Hosmer-Lemeshow ¼ 7.192 (sig ¼ 0.516)
β
SE
Wald
Sig.
OR
95%CI
0 0.182 1.053 0 0.329 0.350 0.401
– 0.076 0.113 – 0.110 0.153 0.122
– 5.780 86.992 – 8.933 5.230 10.744
– 0.016 0.000 – 0.003 0.022 0.001
1 1.200 2.866 1 1.390 1.419 0.669
– 1.034–0.392 2.297–3.575 – 1.120–1.725 1.051–1.915 –
Note: *p < 0.05, **p < 0.01, β is the regression coefficient, SE is the standard error, Wald is the value of a Wald test, Sig. (p value) is the significance of each explanatory variable, OR is Odds Ratios, and 95%CI represents 95% confidence interval. 4
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from 16 to 40,566 capita per km2, with an average of 8068 capita per km2 (Fig. 3b). The RES demand index represented the degree of imbalance between RES demand and population density. The demand index ranged from 0.36 to 0.7, with an average of 0.54 (Fig. 3c). The population density of urban residents and RES demand decreased gradually from the urban to the rural regions. However, the spatial pattern of RES demand index was different from the distributions of total residents and high-income residents (Fig. 3d). The fringe areas between the urban and rural regions showed a relatively low demand index for the RES.
as seen in Eq. (4): Ek ¼ logitðpÞk ¼
0:401 þ 0:182 � Am þ 1:053 � Ah þ 0:329 � Im þ 0:35 � Ih (4)
where Ek is the demand index of each sub-district k, Am and Ah are the respondents from middle- and high-aged populations, respectively, while Im and Ih are the respondents from middle- and high-income populations, respectively. The RES demand was predicted by using demographic data in each sub-district (Fig. 3a). The population density of RES demand ranged from 10 to 23,863 capita per km2, with an average of 4288 capita per km2. Comparatively, the population density of urban residents ranged
Fig. 3. Spatial distribution of (a) population density for recreational ecosystem services (RES) demand, (b) urban population density, (c) demand index of RES, (d) high-income population density in 317 sub-districts of Beijing, China. 5
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4. Discussion
visit the parks were classified into multiple levels. Third, the area of the sub-districts varied substantially from 0.9 to 382 km2; therefore, the spatial heterogeneity of the RES demand in the large sub-districts needs to be considered in a future study. Last, this study focused on the de mand for RES by Beijing’s residents without considering the RES de mand of non-local visitors. This question is particularly important when the prediction model is used in relation to certain holidays.
4.1. Implications for urban planning The quantification of RES demand is particularly important for landscape planning in urban regions. Studies focused only on RES supply can provide one-sided information and may lead to inappropriate de cisions by management. For example, the relocation of urban parks has been regarded as a mitigation method for restoring and preserving ecosystem services (Ruhl and Salzman, 2006; BenDor, 2009). Although the balance of park acreage can preserve the supply of RES, it will result in a “loss” of RES because of the redistribution of different stakeholders. Clearly a need exists for assessing the rational distribution of urban parks that target the social expectations of users (Salvo and Signorello, 2015). The present study provides a model that can be used to predict and map the demand for RES. The demand of RES of different population groups should be considered in the future to guarantee an appropriate arrangement of urban parks. The method has several advantages and implications for the urban planning. First, the mapping of the demand for RES is helpful for spatial tradeoffs related to RES. Previous studies are mainly focused on the spatial pattern of the RES supply (Burkhard et al., 2012; Sch€ agner et al., 2013; Lawler et al., 2014). After overlying the RES demand, we can obtain the hot-points where the RES supply and demand are mis matched. Current urban park planning is usually based on the distri bution of urban residents. This study shows that the core city and rural regions of Beijing need more parks than the urban fringes although the rural regions have a low population density. Second, the mapping of RES demand indicates that urban planning will require more specific mea sures based on the population characteristics. The results of this study indicate that park planning should be made based on the age and income level of urban residents. This result supports similar findings in previous studies (Neuvonen et al., 2010; Rossi et al., 2015). However, the result is not consistent with a study in South Africa which suggests that age, gender, employment status, and economic situations have no significant influence on the perceived importance of cultural ES (Mensah et al., 2017). Therefore, the demand for the RES is city-specific and should be quantified prior to the implementation of management measures, including the recreational activities and social contexts. The rapidly aging population of Beijing will increase the demand for RES in the future. Park construction should be based on future scenarios of the economy and of population increases. Third, although the demand for RES is important for park arrangement, the quantification of RES supply and flow are also essential for urban planning. This study quantified the proportions of recreational activities enjoyed in different parks. For example, the recreational activities of jogging, boating, and partying are more popular in Beijing’s parks while fishing and cycling are less pop ular. The total proportions of recreational activities in Qinglonghu Park are almost twice than those in Chaoyang Park. The diverse types of recreational activities can provide more attractions in a park which in creases the ability of the RES supply. However, we note that travel time is an important factor which affects the RES flow. Travel time is deter mined by the road conditions and traffic tools. In the future, the RES supply and flow should be quantified based on other supplementary data, such as the big data from the internet and remote sensing images.
5. Conclusions The present study investigated the types and drivers of RES demand associated with urban parks in Beijing. The questionnaire survey shows that high income and older residents have a relatively higher willingness to visit the parks than did low income and younger visitors. The analysis of RES demand based on logistic regression produced a flexible and easily applicable model. The spatial mapping of RES demand at subdistrict levels shows that the fringe areas between the urban and rural regions had a relatively low demand index for the RES. While this study could be improved with more detailed information and data, the results expand our understanding of the heterogeneity of RES and the under lying drivers of RES demand. Our research suggests that the park plan ners should provide more diverse infrastructure for RES and optimize parks spatially to accommodate more urban residents. These results have the potential to inform decision makers on the status of spatial equality and availability of RES so that they may be prepared for different urbanization and population growth scenarios. Acknowledgements The work was financed by the National Natural Science Foundation of China (41590841; 41922007). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jenvman.2019.109612. References Bagstad, K.J., Semmens, D.J., Waage, S., Winthrop, R., 2013. A comparative assessment of decision-support tools for ecosystem services quantification and valuation. Ecosyst. Serv. 5, 27–39. Barbosa, O., Tratalos, J., Armsworth, P., Davies, R., Fuller, R., Johnson, P., Gaston, K., 2007. Who benefits from access to green space? A case study from Sheffield, UK. Landsc. Urban Plan. 83 (2–3), 187–195. Bar� o, F., Haase, D., G� omez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply and demand in urban areas: a quantitative assessment in five European cities. Ecol. Indicat. 55, 146–158. Barό, F., Palomo, I., Zulian, G., Vizcaino, P., Haase, D., Gόmez-Baggethun, E., 2016. Mapping ecosystem service capacity, flow and demand for landscape and urban planning: a case study in the Barcelona metropolitan region. Land Use Policy 57, 405–417. Bastian, O., Haase, D., Grunewald, K., 2012. Ecosystem properties, potentials and services – the EPPS conceptual framework and an urban application example. Ecol. Indicat. 21, 7–16. Beijing Statistical Information Net (BSIN), 2017. Beijing statistical yearbook 2016. http://www.bjstats.gov.cn/nj/main/2016-tjnj/zk/indexee.htm. BenDor, T., 2009. A dynamic analysis of the wetland mitigation process and its effects on no net loss policy. Landsc. Urban Plan. 89, 17–27. Bertram, C., Rehdanz, K., 2015. Preferences for cultural urban ecosystem services: comparing attitudes, perception, and use. Ecosyst. Serv. 12, 187–199. Burkhard, B., Kroll, F., Nedlkov, S., Müller, F., 2012. Mapping ecosystem service supply, demand and budgets. Ecol. Indicat. 21, 17–29. Chiesura, A., 2004. The role of urban parks for the sustainable city. Landsc. Urban Plan. 68 (1), 129–138. Costanza, R., D’Arge, R., De Groot, R., Farber, S., Grasso, M., Hannon, B., Limberg, L., Naeem, S., O’neill, R., Paruelo, J., Raskin, R.G., Sutton, P., Belt, M., 1997. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260. Costanza, R., De Groot, R.S., Sutton, P., Ploeg, S., Anderson, S.J., Kubiszewski, I., Farber, S., Turner, R.K., 2014. Changes in the global value of ecosystem services. Glob. Environ. Chang. 26, 152–158. Crossman, N.D., Burkhard, B., Nedkov, S., Willemen, L., Petz, L., Palomo, I., et al., 2013. A blue print for mapping and modelling ecosystem services. Ecosyst. Serv. 4, 4–14.
4.2. Limitations of this study The prediction model of the RES demand in this study was mainly based on the demographic characteristics of urban residents which were obtained from national population census data. The prediction method has some limitations. First, the application of questionnaires involves some uncertainties, such as the interview time, location, participants, and psychology. Second, the dependent variable of the RES demand is designated as the high and low willingness to visit the parks. The modelling results could be more reliable if the residents’ willingness to 6
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Darvill, R., Lindo, Z., 2015. Quantifying and mapping ecosystem service use across stakeholder groups: implications for conservation with priorities for cultural values. Ecosyst. Serv. 13, 153–161. De Groot, R.S., Alkemade, R., Braat, L., Hein, L., Willemen, L., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7 (3), 260–272. De Groot, R.S., Wilson, M.A., Boumans, R.M.J., 2002. A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol. Econ. 41, 393–408. Fagerholm, N., Oteros-Rozas, E., Raymond, C.M., Torralba, M., Moreno, G., Plieninger, T., 2016. Assessing linkages between ecosystem services, land-use and wellbeing in an agroforestry landscape using public participation GIS. Appl. Geogr. 74, 30–46. Geijzendorffer, I.R., Martín-L� opez, B., Roche, P.K., 2015. Improving the identification of mismatches in ecosystem services assessments. Ecol. Indicat. 52, 320–331. Geijzendorffer, I.R., Roche, P.K., 2014. The relevant scales of ecosystem services demand. Ecosyst. Serv. 10, 49–51. Gong, L., Mao, B., Qi, Y.D., Xu, C.Y., 2015. A satisfaction analysis of the infrastructure of country parks in Beijing. Urban For. Urban Green. 14, 480–489. Guerry, A.D., Polasky, S., Lubchenco, J., Chaplin-Kramer, R., Daily, G.C., Griffin, R., et al., 2015. Natural capital and ecosystem services informing decisions: from promise to practice. P. Natl. Acad. Sci. USA 112 (24), 7348–7355. Hamel, P., Bryant, B.P., 2017. Uncertainty assessment in ecosystem services analyses: seven challenges and practical responses. Ecosyst. Serv. 24, 1–15. He, J.L., Yi, H.M., Liu, J., 2016. Urban green space recreational service assessment and management: a conceptual model based on the service generation process. Ecol. Econ. 124, 59–68. Kremer, P., Hamstead, Z.A., McPhearson, T., 2016. The value of urban ecosystem services in New York City: a spatially explicit multicriteria analysis of landscape scale valuation scenarios. Environ. Sci. Policy 62, 57–68. Kroll, F., Müller, F., Haase, D., Fohrer, N., 2012. Rural–urban gradient analysis of ecosystem services supply and demand dynamics. Land Use Policy 29, 521–535. Lawler, J.J., Lewis, D.J., Nelson, E., Plantinga, A.J., Polasky, S., Withey, J.C., Helmers, D. P., Martinuzzi, S., Pennington, D., Radeloff, V.C., 2014. Projected land-use change impacts on ecosystem services in the United States. P. Natl. Acad. Sci. USA 111 (20), 7492–7497. Liu, W.P., Chen, W.J., Dong, C.L., 2017. Spatial decay of recreational services of urban parks: characteristics and influencing factors. Urban For. Urban Green. 25, 130–138. Liu, H.X., Li, F., Li, J.Y., Zhang, Y.Y., 2017. The relationships between urban parks, residents’ physical activity, and mental health benefits: a case study from Beijing, China. J. Environ. Manag. 190, 223–230. MA, 2005. Ecosystems and Human Well-Being: Current State and Trends. The Millennium Ecosystem Assessment, Washington, DC. Maes, J., Egoh, B., Willemen, L., Liquete, C., Vihervaara, P., et al., 2012. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39. Mao, X., Song, J., Yang, H., Zhao, J., 2010. Changes of the spatial pattern of Beijing city parks from 2000 to 2010. Prog. Geogr. 31, 1295–1306 (Chinese with English Abstract). Mensah, S., Veldtman, R., Assogbadjo, A.E., Ham, C., Kakaï, G., Seifert, T., 2017. Ecosystem service importance and use vary with socio-environmental factors: a study from household-surveys in local communities of South Africa. Ecosyst. Serv. 23, 1–8.
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Research article
A demand index for recreational ecosystem services associated with urban parks in Beijing, China Ranhao Sun a, *, Fen Li a, Liding Chen a, b, ** a b
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China University of Chinese Academy of Sciences, Beijing, 100049, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Urban park Ecosystem service Landscape planning Recreation Urbanization Beijing
Good planning for urban parks requires an analysis of the quantitative relationship between the distribution of an urban population and the demand for recreational ecosystem services (RES). A barrier to RES quantification is the lack of connections between survey materials and spatial data. This study developed a logistic regression model for the demand for RES associated with urban parks based on the characteristics of individual visitor and their willingness to visit parks. The model was fitted by a questionnaire survey completed by 4096 park visitors and was used to predict the RES demand in 317 sub-districts of Beijing. Results showed that: (1) park visitors rated sightseeing as the most important, followed by jogging, boating, partying, cycling, and fishing in Beijing’s parks; (2) high-income and older residents had higher willingness to visit the parks than did low-income and younger park visitors; (3) the fringe areas between the urban and rural regions showed a relatively low demand index for RES. This study exhibits a feasible method to predict RES demand based on surveys and statistical data. Our research suggests that improving park planning necessitates developing a diverse recreational infrastructure, a tradeoff among different stakeholders, and spatial optimization for sustainable urban development. The results provide a potential tool that can be used to assess the balance of RES in a scenario of urbanization and population growth.
1. Introduction Ecosystems provide a range of services that benefit human wellbeing, health, and livelihoods (De Groot et al., 2002; Costanza et al., 2014). Ecosystem services consist of flows of materials, energy, and information from natural capital stocks that improve human welfare (Costanza et al., 1997). The supply of ecosystem services is defined as an ecosystem’s potential to deliver services; meanwhile, the demand for ecosystem services is the extent to which a service is required or desired €gner et al., by society (Crossman et al., 2013; Plieninger et al., 2013; Scha � et al., 2013; Villamagna et al., 2013; Serna-Chavez et al., 2014; Baro 2015, 2016; Darvill and Lindo, 2015; Geijzendorffer et al., 2015; Vigl et al., 2016). Some well-known studies that have made this determina tion include: The Millennium Ecosystem Assessment (MA, 2005), the Economics of Ecosystems and Biodiversity (TEEB, 2011), and the Intergovernmental Platform on Biodiversity and Ecosystem Services (Pascual et al., 2017). A better understanding of the role of ecosystem services emphasizes having a good match between the supply, flow, and
demand for ecosystem services (Kroll et al., 2012; Barό et al., 2016; Hamel and Bryant, 2017). Human society is increasingly urban while the urban residents are being increasingly disconnected from nature. Cities are dependent on variety of ecosystems beyond the city limits, but also benefit from in ternal urban ecosystems. Recreational ecosystem services (RES) repre sent the recreational pleasures derived from natural or cultivated ecosystems (TEEB, 2011; Bertram and Rehdanz, 2015). The RES asso ciated with urban parks have become especially beneficial in urban re gions (Chiesura, 2004; Barbosa et al., 2007; Shan, 2014a; Gong et al., 2015; Soga and Gaston, 2016). The RES can provide direct and indirect benefits including benefits to ecological protection, human health, social and cultural education, as well as market economic value (Sikorska et al., 2017). Understanding the degree of match between the distribu tion of an urban population and the RES demand has been regarded as one important way to improve park planning and land management as part of an effort to better integrate environmental issues (De Groot et al., 2010; Bagstad et al., 2013; von Haaren et al., 2014; Guerry et al., 2015).
* Corresponding author. ** Corresponding author. University of Chinese Academy of Sciences, Beijing, 100049, China. E-mail addresses: [email protected] (R. Sun), [email protected] (L. Chen). https://doi.org/10.1016/j.jenvman.2019.109612 Received 28 April 2019; Received in revised form 13 August 2019; Accepted 19 September 2019 Available online 25 September 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.
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Most past studies of RES have focused on their capacity and supply (Burkhard et al., 2012; Sch€ agner et al., 2013; Lawler et al., 2014) and economic value (De Groot et al., 2010; Bagstad et al., 2013; Costanza et al., 2014; Kremer et al., 2016). Two main difficulties arise when the quantification of RES is directed towards the demand for RES. First, the quantification of RES demand is affected by specific cultural and social contexts. The demand for RES is influenced by individual characteristics of park visitors (Shan, 2014b; He et al., 2016; Xiao et al., 2017), such as gender, age, marital status, education, income, and ethnicity (Neuvonen et al., 2010; Bertram and Rehdanz, 2015; Mensah et al., 2017). Recent comparative studies have investigated how RES activities differ under diverse cultural and social contexts (Wang et al., 2015; Swapan et al., 2017). Second, the spatial mapping of RES demand has rarely been done because of a lack of spatial data associated with these individual char � acteristics (Maes et al., 2012; Geijzendorffer and Roche, 2014; Baro ~ a et al., 2015; Mensah et al., 2017). Some of these in et al., 2015; Pen dividual characteristics are hard to map spatially and explicitly based on data availability (Bastian et al., 2012; Geijzendorffer et al., 2015; Fagerholm et al., 2016). Therefore, the quantification of RES demand is essential to assess the degree of match between the distributions of a
population and urban parks. Research on the RES demand has been conducted in large Chinese cities, such as Beijing (Liu et al., 2017a), Shanghai (Xiao et al., 2017), Guangzhou (Shan, 2014b), Wuhan (Liu et al., 2017b), and Chengdu (Swapan et al., 2017). We selected the city of Beijing as an example because it has the largest population of any city in China and features various types of urban parks. The Beijing government is trying to meet the growing demand for RES of the residents (Gong et al., 2015). Therefore, the present study determined that Beijing would be an ideal site to examine spatial heterogeneity and potential drivers of the de mand for RES. Most past studies focused on the factors influencing RES demand based on questionnaire data. The present study investigated the spatial distribution of RES demand by combing questionnaire and spatial data. The study aimed to: (1) quantify the factors that influencing RES demand by using questionnaire data; (2) investigate the spatial distribution of RES demand based on demographic data; and (3) assess the degree of match between the distribution of the urban population and demand for RES in Beijing.
Fig. 1. The selected 20 urban parks in Beijing analyzed in the present study. 2
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2. Materials and methods
2.3. Logistic regressions of RES demand
2.1. Study area
The willingness of visitors to use parks repeatedly to meet RES de mand is a function of different percentages of individual groups. The hypothesis of the logistic regression model was based on the relationship between the willingness of visitors to use park repeatedly to meet RES demand and individual characteristics. The dependent variable was designated as the binary probability for the willingness (0 and 1). The individual characteristics were classified as categorical explanatory variables, including gender, age, education, and income. The gender of each visitors was designated as 1 (male) and 0 (female). The visitor age was designated as a dummy variable (age <30), 1 (30–50), and 2 (>50). Each visitor’s education was designated as a dummy variable (undercollege), 1 (some college education), and 2 (college graduate). The average monthly income was designated as a dummy variable (<3,000 Yuan), 1 (3,000–10,000 Yuan), and 2 (>10,000 Yuan). The predictors were selected based on the probability of RES demand defined by a logistic function:
In northern China, Beijing covers an area of 16,808 km2 and ranges between 39� 280 N – 41� 250 N and 115� 250 E 117� 300 E. Beijing lies within a warm temperature zone and has a typical continental monsoon climate. As the capital of China, the city has experienced rapid urbani zation in the last three decades. The gross domestic product of Beijing grew from 10.8 billion Renminbi (RMB, Yuan; about 1.57 billion US dollars) to 2,490 billion RMB (about 362 billion US dollars). The total population exceeded 21.7 million by the end of 2016 (BSIN, 2017). The number of urban parks increased from 122 in 2000 to 259 in 2010 with the park area increasing from 77.4 km2 to 129.3 km2 during this period (Mao et al., 2010). Gaining a better understanding of the demand for RES is very important in the management of Beijing’s urban parks. Beijing is divided into 16 districts in which 317 sub-districts have been organized. We used sub-districts as analytical units to assess the demand for RES in this study.
p¼
2.2. Questionnaire survey
eβ0 þβ1 X1 þβ2 X2 þβ3 X3 þβ4 X4 1 þ eβ0 þβ1 X1 þβ2 X2 þβ3 X3 þβ4 X4 �
We selected 20 urban parks to conduct questionnaire survey asso ciated with the demand for RES for two reasons (Fig. 1). First, these parks have large land areas and many visitors. Second, they have high proportions of water-bodies or wetlands which provide various types of RES for visitors. A pilot survey was conducted in four parks in 2010, in which 322 visitors were randomly selected to practice face-to-face in terviews and to test the questionnaire. All questions were designed to be short, concise and easy to understand so that more residents would enjoy participating in the questionnaire. Then, a revised questionnaire was employed at each of the 20 urban parks to record the individual char acteristics of park visitors and the recreational activities they chose to engage in. The questionnaire survey was conducted by three Ph. D students and six assistants. The Ph. D students were responsible for all the arrangements of the interviews. All the interviewers were trained during the pilot survey in four parks. Finally, to gather an adequate number of samples, we conducted >100 interviews in each park. Therefore, the interview process lasted for one year in 2010 and covered different seasons. These interviews were also assigned to represent different events and time periods including weekdays, weekends, and holidays. This study was designed to investigate the demand for RES in rela tion to the residents of Beijing. Therefore, park visitors from other countries or other administrative regions of China were excluded from the survey. The visitors were selected randomly as they entered a park. We tried to keep the interview results correct by designing multiple questions for each specific objective. In addition, we abandoned any incomplete questionnaires acquired during the interviews. Ultimately, a total of 4096 questionnaires were collected from local park visitors. The population profiles of participants are shown in Supplementary Table 1. No significant differences were observed in the gender, age, education, and income of visitors when comparing between the different parks. The recreational activities that visitors engaged in were determined through face-to-face interviews. To explain the underlying drivers of park visits, the participants were asked about their individual characteristics, including gender, age, education, and income. The willingness of visi tors to use parks repeatedly to meet RES demand was inquired about and quantified during the interviews. The willingness was designated as 1 if the participant had visited multiple parks or visited one park more than one time in one year. Otherwise, the willingness was defined as 0 if the visit of participant was visiting the park for the first time and would never visit again.
logitðpÞ ¼ loge
1
(1)
�
p p
¼ β 0 þ β 1 X1 þ β 2 X2 þ β 3 X3 þ β 4 X4
(2)
where p is the probability of RES willingness, β0 is a constant to be estimated, and βi is the coefficient to be estimated for each explanatory variable Xi. Eq. (1) can be transformed into the linear function displayed in Eq. (2). The results are presented as an Odds Ratios (OR) with 95% confidence intervals. The chi-square model is the difference between the likelihood for the best fitting model and likelihood for the null hy pothesis being correct. Goodness-of-fit of the models was assessed by the Hosmer-Lemeshow test. Statistical analyses were performed using SPSS 19 (IBM SPSS Inc., USA). The significance (p < 0.05) of each explanatory variable was used to select the key predictors. The coefficients of the regression model were refitted by these key predictors. Then, the regression model was used to predict the spatial pattern of RES demand in different regions. The de mographic data were derived from China’s 6th national population census data (NBSC, 2010). The population density (Rk, capita per km2) of urban residents in different groups of gender, age, education, and income was calculated in each sub-district k for a new prediction. The population density of the RES demand (Ek) was calculated as Ek ¼ logit (p)k in each sub-district k. Thus, the demand index of RES (Ik) was developed based on the population density between the total residents and the population associated with RES demand. Ik ¼
Ek β 0 þ β 1 X1 þ β 2 X2 þ β 3 X3 þ β 4 X4 ¼ Rk Rk
(3)
3. Results 3.1. Different recreational activities in urban parks We analyzed the main types of recreational activities enjoyed by visitors in urban parks based on questionnaire survey that included 4096 participants. The activities included sightseeing, jogging, boating, partying, cycling, fishing, and other types. Based on the questionnaire data, we found that each visitor usually participated in different types of recreational activities in a park when he or she made a return visit. Multiple choices in the questionnaire allowed researchers to ask about different types of recreation enjoyed by visitors. Results showed that approximately 82% of all participants visited the parks for sightseeing, following by jogging (56%), boating (33%), partying (26%), cycling (8%), fishing (6%), and other types of recreation (17%) (Fig. 2). The participants who came to parks for sightseeing had similar proportions in different parks while the percentages of those interested in boating 3
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Fig. 2. Main types of recreational activities enjoyed by visitors in urban parks. The dash-line represents the average percentage of each recreational activity.
and fishing were different in different parks.
visitors (age > 50) had higher demand for RES than the middle-aged visitors. Comparatively, the middle-income visitors (monthly income from 3,000 to 10,000 RMB) had similar demand for the RES when compared with the high-income visitors (>10,000 RMB). Other indi vidual characteristics, including gender and education level, were not significant factors in the logistic regression.
3.2. Key factors of RES demand A total of 2162 participants showed a high willingness to use parks repeatedly to meet RES demand which means they had made multiple visits to parks or were willing to visit again. The factors influencing RES demand were identified by the logistic regression (Table 1). A high chisquare value indicated that high RES demand was less expected under the null hypothesis than in the full regression model. Four predictors were found to be significant (p < 0.05) for model prediction. The older
3.3. Spatial pattern of RES demand Based on the above key factors and the related parameters, we developed a model to predict the RES demand in different sub-districts
Table 1 Statistical analysis of the predictors obtained by logistical regression. Predictor Age** Income**
Al (Age < 30) Am (Age 30–50) * Ah (Age >50) ** Il (<3,000 Yuan) Im (3,000–10,000 Yuan) ** Ih (>10,000 Yuan) *
Constant Chi squared ¼ 126.319** Hosmer-Lemeshow ¼ 7.192 (sig ¼ 0.516)
β
SE
Wald
Sig.
OR
95%CI
0 0.182 1.053 0 0.329 0.350 0.401
– 0.076 0.113 – 0.110 0.153 0.122
– 5.780 86.992 – 8.933 5.230 10.744
– 0.016 0.000 – 0.003 0.022 0.001
1 1.200 2.866 1 1.390 1.419 0.669
– 1.034–0.392 2.297–3.575 – 1.120–1.725 1.051–1.915 –
Note: *p < 0.05, **p < 0.01, β is the regression coefficient, SE is the standard error, Wald is the value of a Wald test, Sig. (p value) is the significance of each explanatory variable, OR is Odds Ratios, and 95%CI represents 95% confidence interval. 4
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from 16 to 40,566 capita per km2, with an average of 8068 capita per km2 (Fig. 3b). The RES demand index represented the degree of imbalance between RES demand and population density. The demand index ranged from 0.36 to 0.7, with an average of 0.54 (Fig. 3c). The population density of urban residents and RES demand decreased gradually from the urban to the rural regions. However, the spatial pattern of RES demand index was different from the distributions of total residents and high-income residents (Fig. 3d). The fringe areas between the urban and rural regions showed a relatively low demand index for the RES.
as seen in Eq. (4): Ek ¼ logitðpÞk ¼
0:401 þ 0:182 � Am þ 1:053 � Ah þ 0:329 � Im þ 0:35 � Ih (4)
where Ek is the demand index of each sub-district k, Am and Ah are the respondents from middle- and high-aged populations, respectively, while Im and Ih are the respondents from middle- and high-income populations, respectively. The RES demand was predicted by using demographic data in each sub-district (Fig. 3a). The population density of RES demand ranged from 10 to 23,863 capita per km2, with an average of 4288 capita per km2. Comparatively, the population density of urban residents ranged
Fig. 3. Spatial distribution of (a) population density for recreational ecosystem services (RES) demand, (b) urban population density, (c) demand index of RES, (d) high-income population density in 317 sub-districts of Beijing, China. 5
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4. Discussion
visit the parks were classified into multiple levels. Third, the area of the sub-districts varied substantially from 0.9 to 382 km2; therefore, the spatial heterogeneity of the RES demand in the large sub-districts needs to be considered in a future study. Last, this study focused on the de mand for RES by Beijing’s residents without considering the RES de mand of non-local visitors. This question is particularly important when the prediction model is used in relation to certain holidays.
4.1. Implications for urban planning The quantification of RES demand is particularly important for landscape planning in urban regions. Studies focused only on RES supply can provide one-sided information and may lead to inappropriate de cisions by management. For example, the relocation of urban parks has been regarded as a mitigation method for restoring and preserving ecosystem services (Ruhl and Salzman, 2006; BenDor, 2009). Although the balance of park acreage can preserve the supply of RES, it will result in a “loss” of RES because of the redistribution of different stakeholders. Clearly a need exists for assessing the rational distribution of urban parks that target the social expectations of users (Salvo and Signorello, 2015). The present study provides a model that can be used to predict and map the demand for RES. The demand of RES of different population groups should be considered in the future to guarantee an appropriate arrangement of urban parks. The method has several advantages and implications for the urban planning. First, the mapping of the demand for RES is helpful for spatial tradeoffs related to RES. Previous studies are mainly focused on the spatial pattern of the RES supply (Burkhard et al., 2012; Sch€ agner et al., 2013; Lawler et al., 2014). After overlying the RES demand, we can obtain the hot-points where the RES supply and demand are mis matched. Current urban park planning is usually based on the distri bution of urban residents. This study shows that the core city and rural regions of Beijing need more parks than the urban fringes although the rural regions have a low population density. Second, the mapping of RES demand indicates that urban planning will require more specific mea sures based on the population characteristics. The results of this study indicate that park planning should be made based on the age and income level of urban residents. This result supports similar findings in previous studies (Neuvonen et al., 2010; Rossi et al., 2015). However, the result is not consistent with a study in South Africa which suggests that age, gender, employment status, and economic situations have no significant influence on the perceived importance of cultural ES (Mensah et al., 2017). Therefore, the demand for the RES is city-specific and should be quantified prior to the implementation of management measures, including the recreational activities and social contexts. The rapidly aging population of Beijing will increase the demand for RES in the future. Park construction should be based on future scenarios of the economy and of population increases. Third, although the demand for RES is important for park arrangement, the quantification of RES supply and flow are also essential for urban planning. This study quantified the proportions of recreational activities enjoyed in different parks. For example, the recreational activities of jogging, boating, and partying are more popular in Beijing’s parks while fishing and cycling are less pop ular. The total proportions of recreational activities in Qinglonghu Park are almost twice than those in Chaoyang Park. The diverse types of recreational activities can provide more attractions in a park which in creases the ability of the RES supply. However, we note that travel time is an important factor which affects the RES flow. Travel time is deter mined by the road conditions and traffic tools. In the future, the RES supply and flow should be quantified based on other supplementary data, such as the big data from the internet and remote sensing images.
5. Conclusions The present study investigated the types and drivers of RES demand associated with urban parks in Beijing. The questionnaire survey shows that high income and older residents have a relatively higher willingness to visit the parks than did low income and younger visitors. The analysis of RES demand based on logistic regression produced a flexible and easily applicable model. The spatial mapping of RES demand at subdistrict levels shows that the fringe areas between the urban and rural regions had a relatively low demand index for the RES. While this study could be improved with more detailed information and data, the results expand our understanding of the heterogeneity of RES and the under lying drivers of RES demand. Our research suggests that the park plan ners should provide more diverse infrastructure for RES and optimize parks spatially to accommodate more urban residents. These results have the potential to inform decision makers on the status of spatial equality and availability of RES so that they may be prepared for different urbanization and population growth scenarios. Acknowledgements The work was financed by the National Natural Science Foundation of China (41590841; 41922007). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jenvman.2019.109612. References Bagstad, K.J., Semmens, D.J., Waage, S., Winthrop, R., 2013. A comparative assessment of decision-support tools for ecosystem services quantification and valuation. Ecosyst. Serv. 5, 27–39. Barbosa, O., Tratalos, J., Armsworth, P., Davies, R., Fuller, R., Johnson, P., Gaston, K., 2007. Who benefits from access to green space? A case study from Sheffield, UK. Landsc. Urban Plan. 83 (2–3), 187–195. Bar� o, F., Haase, D., G� omez-Baggethun, E., Frantzeskaki, N., 2015. Mismatches between ecosystem services supply and demand in urban areas: a quantitative assessment in five European cities. Ecol. Indicat. 55, 146–158. Barό, F., Palomo, I., Zulian, G., Vizcaino, P., Haase, D., Gόmez-Baggethun, E., 2016. Mapping ecosystem service capacity, flow and demand for landscape and urban planning: a case study in the Barcelona metropolitan region. Land Use Policy 57, 405–417. Bastian, O., Haase, D., Grunewald, K., 2012. Ecosystem properties, potentials and services – the EPPS conceptual framework and an urban application example. Ecol. Indicat. 21, 7–16. Beijing Statistical Information Net (BSIN), 2017. Beijing statistical yearbook 2016. http://www.bjstats.gov.cn/nj/main/2016-tjnj/zk/indexee.htm. BenDor, T., 2009. A dynamic analysis of the wetland mitigation process and its effects on no net loss policy. Landsc. Urban Plan. 89, 17–27. Bertram, C., Rehdanz, K., 2015. Preferences for cultural urban ecosystem services: comparing attitudes, perception, and use. Ecosyst. Serv. 12, 187–199. Burkhard, B., Kroll, F., Nedlkov, S., Müller, F., 2012. Mapping ecosystem service supply, demand and budgets. Ecol. Indicat. 21, 17–29. Chiesura, A., 2004. The role of urban parks for the sustainable city. Landsc. Urban Plan. 68 (1), 129–138. Costanza, R., D’Arge, R., De Groot, R., Farber, S., Grasso, M., Hannon, B., Limberg, L., Naeem, S., O’neill, R., Paruelo, J., Raskin, R.G., Sutton, P., Belt, M., 1997. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260. Costanza, R., De Groot, R.S., Sutton, P., Ploeg, S., Anderson, S.J., Kubiszewski, I., Farber, S., Turner, R.K., 2014. Changes in the global value of ecosystem services. Glob. Environ. Chang. 26, 152–158. Crossman, N.D., Burkhard, B., Nedkov, S., Willemen, L., Petz, L., Palomo, I., et al., 2013. A blue print for mapping and modelling ecosystem services. Ecosyst. Serv. 4, 4–14.
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