Where did the ecosystem services value go? Adaptive supply, demand and valuation of new urban green spaces

https://doi.org/10.1016/j.resconrec.2022.106616Get rights and content

Highlights

  • Novel framework to estimate changes in ecosystem services (ES) value dynamically.

  • Framework integrates a choice experiment, the travel cost method and spatial simulation.

  • Dynamic evaluation of ES demand and people's adaptive behavior behind ES demand.

  • Considering adaptive behavior needed to optimize the value of new green spaces.

  • Adaptive behavior to crowdedness needed for urban planning for ES supply.

Abstract

The value of ecosystem services (ES) is typically presented as static ‘snapshot’ maps. Static maps have however limited value for urban planning since ES value is determined by the interplay of supply and demand of ES which vary over time and as a function of human behavior. To overcome these challenges, this paper presents a novel valuation framework that combines a choice experiment, the travel cost method and spatially explicit modeling of adaptive demand. The result of the choice experiment confirms that temperature reductions by green spaces are valued while travelling distance and crowdedness reduce the value of the green space, thus playing a role in the adaptive behavior of users. After factoring in green space visiting behavior and value losses due to crowdedness, the optimal new green space locations changed compared to those identified using static approaches. The results of this paper caution towards ES urban planning that does not incorporate the adaptive behavior of users.

Introduction

Over half of the world's population lives in urban areas and this proportion is expected to increase to 68% by 2050 due to rapid global urbanization (United Nations, 2018). As a result, urban green spaces—defined here as areas of managed or unmanaged vegetation that is allocated for recreational or aesthetic purposes in an urban setting—become increasingly important for the well-being of urban residents (Kondo et al., 2018; Rojas-Rueda et al., 2019). To achieve a balanced integration of green spaces and urban land use demands, green development—defined as urban development that considers the social and environmental impacts of new developments in urban settings—is important. Several recent studies have highlighted the importance of green development efficiency in discussing the development of industry and environmental sustainability (Dong et al., 2021; Zhang et al., 2021; Zhu et al., 2020). Urban green spaces contribute a variety of ecosystem services (ES) that are relevant to human well-being (Liu et al., 2022) and mental health (Bratman et al., 2019), such as air purification, noise reduction, run-off retention, cooling and recreation (Derkzen et al., 2015; Meng et al., 2020).

To understand the value of green spaces on city dwellers, it is important to characterize the supply and demand of ES spatially (Bertram and Rehdanz, 2015). Previous work has demonstrated how spatial analyses of ES supply and demand play a vital role for land-use planning and decision-making (Cortinovis and Geneletti, 2020; González-García et al., 2020; Kroll et al., 2012; Larondelle and Lauf, 2016; Peng et al., 2020; Wang et al., 2019; Wilkerson et al., 2018; Wu et al., 2019; Yu et al., 2021; Zhai et al., 2020). However, the supply and demand of ES typically change over time, making the value of ES also change (Larondelle and Lauf, 2016), with this dynamic, adaptive aspect of ES value being rarely considered. One way to capture the dynamic aspects of ES value is to study the effects of land use change on ES supply (Hu et al., 2019; Kusi et al., 2020; Lin et al., 2021; Sun et al., 2019; Talukdar et al., 2020).

Although incorporating how land-use change affects the supply of ES covers an important aspect of changes in ES value, the demand is also susceptible to change. To capture these demand changes, the behavior of ES beneficiaries needs to be incorporated. Incorporating adaptive changes in demand is very rarely done, with some exceptions only starting to appear in the literature. Studies have begun to incorporate behavior through considering the movement from people to nature and how they access ES (Dolan et al., 2021) and through considering ES beneficiaries’ behavior when modeling supply-demand mismatches of ES in agricultural landscapes (Shaaban et al., 2021). The dynamic evaluation of ES demand and especially people's adaptive behavior behind ES demand have however been very rarely studied, which is a clear knowledge gap in the dynamic valuation of ES.

Two key determinants of the behaviors of beneficiaries of ES are travelling distance and crowdedness of green spaces. Travelling distance has been typically covered by travel cost analyses (Hanauer and Reid, 2017; Jaung and Carrasco, 2020; Langemeyer et al., 2015; Menendez-Carbo et al., 2020; Parsons, 2017). Choice experiments have also typically incorporated a travelling distance component (Holmes et al., 2017). For instance, considering travelling distance to examine the public preference for ES in urban neighborhoods' green spaces (Jaung et al., 2020) and evaluating tourism demand (Baltas, 2007; Huybers, 2003). Crowdedness has been less studied but it has been shown that this factor can affect the quality of the recreation experience in urban green spaces (Arnberger, 2012; Juutinen et al., 2011; Sever and Verbič, 2018).

To further contribute to the dynamic valuation of ES, the adaptive behavior of users behind ES demand as a function of travelling distance and crowdedness to a new urban green space was studied. Incorporating the values generated by the new green space versus the values after correcting for changes in demand could help identify the advantages of using a dynamic valuation method to support urban planning versus a static method that does not incorporate green space users' behavior.

This paper attempts to answer the following research questions:

  • How do distance and crowdedness affect the value of new green spaces?

  • How different would the chosen locations for a new green space be after considering green space visiting behavior and value losses due to crowdedness?

Section snippets

Overview

A raster map of Singapore was created with cell size of 25-hectare for the spatial simulation of a hypothetical new green space to estimate the changes in green space values under different spatial contexts (Figs. 1 and 2a). A one-hectare green space addition in each raster cell was simulated and the value change to find the optimal green space location was estimated. Cells that could not accommodate a new green space were excluded, these included areas covered by existing nature reserves,

Choice experiment

The MWTP of the six attributes indicates that people prefer new green spaces with facilities, increased bird species richness, reduced temperature and 75% tree canopy cover, whereas people do not prefer crowded green spaces with six or more visitors within a 10-meter radius and their MWTP is reduced as travelling time increases (Fig. 3). The results of the mixed logit model and distributions of random parameters are included in Table 1.

Temperature change

Areas of high green space value due to temperature change

Optimal green space locations considering static values

Human behavior matters when attempting to capture the value of ES provided when planning new urban green spaces. This result was demonstrated by the low overlap of the scenario that captured demand as static population density versus the adaptive scenario that considered visitors’ behavior. In the first local-based static scenario that represented a typical identification of ES values based on the distribution of residents and supply of ES, the optimal location of the green space to maximize

Conclusions

This paper provides a novel modeling framework using choice experiment, the travel cost method and spatial simulation to estimate the change of ES value through dynamic valuation of ES which incorporates the adaptive behavior in ES demand. The findings suggest that: (1) considering both travelling behavior and ES lost due to crowdedness would help optimize the allocation of new green spaces; (2) incorporating human behavior in the valuation of green spaces changes the optimal locations from

CRediT authorship contribution statement

Yanyun Yan: Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Wanggi Jaung: Formal analysis, Data curation, Writing – review & editing. Daniel R. Richards: Data curation, Writing – review & editing. L. Roman Carrasco: Conceptualization, Methodology, Writing – review & editing, Supervision, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme (NRF2016-ITC001–013) and Ministry of Education Tier 1 grant (R-154–000-C09–114).

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