The rising and heterogeneous demand for urban green space by Chinese urban residents: Evidence from Beijing

Zhang, Yingjie; Zhang, Tianzheng; Zeng, Yingxiang; Yu, Chang; Zheng, Siqi

doi:10.1016/j.jclepro.2021.127781

Cited by 34 publications

(17 citation statements)

References 71 publications

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“…We found little association between greenspace change and its demand, meaning all the neighborhoods were considered to have the same demand for accessible greenspace in spite of their differences in population, age structure, or economic status of residents. Our findings indicated that demands for greenspace may vary substantially across neighborhoods, which is consistent with previous studies [89]. For example, neighborhoods with a large number of residents would need more greenspace.…”

Section: Where To Put New Greenspace: Supply Considered Demand Not Consideredsupporting

confidence: 91%

Greenspace to Meet People’s Demand: A Case Study of Beijing in 2005 and 2015

Chen

Huang

2021

Remote Sensing

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Urban greenspace provides essential benefits and often depends on its distribution and spatial relationship with residents. Many cities set ambitious goals to increase the coverage of greenspace. In addition, to increase the total amount of greenspace, spatial patterns of greenspace supply and demand also need to be taken into account to make sure its ecosystem services can reach the residents. While previous research has examined greenspace distribution, its association with various ecosystem services, and its spatial relationship with residents’ socioeconomic characteristics, relatively few studies have considered the spatial pattern of greenspace demand to assess its supply change over time. To fill this gap, we evaluated the greenspace change of Beijing between 2005 and 2015 using 2.5 m and 0.5 m high resolution remote sensing images. We first identified all of the greenspace changes, then evaluated the improvement of greenspace that was accessible to residents, and finally, we examined whether such improvement met different levels of demand estimated by neighborhood population, age structure, and economic status. The results showed a net increase of 1472 ha (7.8%) from 2005 to 2015. On average, percent greenspace within 500 m of the neighborhood boundary increased from 21% to 24%. Areas with low greenspace supply had a significantly higher increase. The standard deviation reduced from 8% to 7%, which indicated a smaller disparity of accessible greenspace. However, results showed that greenspace increase had little variation among neighborhoods with different demand levels. Our findings indicated that the greening efforts improved spatial distribution and reduced inequality in accessibility but failed to address different demand levels among neighborhoods. Furthermore, we identified neighborhoods with low supply/high demand and that lost greenspace between 2005–2015. These neighborhoods need to be given attention in future greening projects.

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Section: Where To Put New Greenspace: Supply Considered Demand Not Consideredsupporting

confidence: 91%

Greenspace to Meet People’s Demand: A Case Study of Beijing in 2005 and 2015

Chen

Huang

2021

Remote Sensing

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“…Urban green space is the key to the coupling of urban spatial development and ecological environment, and it is also the key to effectively alleviate the problem of environmental degradation in urbanization areas. As an important living urban infrastructure form attached to urban land resources, urban green space is an important part of urban ecosystem with white and clean function, and it is also an important symbol to measure the level of urban sustainable development and civilization [9]. Due to the differences in the stages of urban development and the focus of ecological and environmental protection research at home and abroad, scholars at home and abroad have different definitions and understandings of green space.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial and Temporal Evolution of Urban Green Space Pattern Based on GIS Sensors and Remote Sensing Information: Taking Xi’an as an Example

Wang

Zhang

et al. 2022

Journal of Sensors

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In order to solve the practical application of the continuously developing remote sensing technology in urban planning, this paper proposes a method of temporal and spatial evolution of urban green spatial pattern based on GIS remote sensing information. Based on the Landsat Image data of the main urban area of Xi’an from 2000 to 2012, different classification methods are used to extract the urban green space information and compare the accuracy. The classification results with high accuracy are selected to analyze the temporal and spatial evolution law of urban green space and the change of landscape pattern in the study area. In this paper, the change of vegetation coverage can be divided into five levels: significant degradation: <-0.006; slight degradation: -0.006~-0.002; stable: -0.002~0.002; slight improvement: 0.002~0.006; and significant improvement: >0.006. The results of this paper prove that this method can be used to understand and evaluate the ecological consequences of urbanization and improve our quality of life. At the same time, it can provide basic information for decision-making.

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“…For every 1 percent increase in the area of nearby parks, housing prices will increase by 0.013 percent [29]. In addition, for every kilometer increase in the distance from the community to the nearest city park, housing prices increase by 3.1 percent [30], based on Beijing housing data in 2019 and 2012-2019, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because of this, it is possible to identify the preference households' intensity or willingness to pay for various characteristics by observing their actual location choice behaviors, or by observing the spatial differences and changes of housing prices or rents. According to previous studies, the semilogarithmic form of the hedonic price model performs better in improving the estimation accuracy and allowing variable heteroscedasticity [30]. We adopt this form of the hedonic price model, which is shown in Equation (1).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

“…From the perspective of the distribution of communities, Chaoyang District, Haidian District, and Fengtai District occupy most of the communities in the city, and the communities in Dongcheng District and Xicheng District have a much higher density than other areas. This spatial area is the most concentrated urbanized area in Beijing's built-up area, which has been widely recognized and used many times by the local statistics and research institutions [30]. In the 2015 edition of the new industrial restriction catalogue released by Beijing, the sixth district is considered as a whole, which is a combination of the capital functional core district and the urban functional expansion district.…”

Section: Datamentioning

confidence: 99%

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Will Good Service Quality Promote Real Estate Value? Evidence from Beijing, China

Liu

Kai-li

Zhang

et al. 2022

Land

Self Cite

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In the field of land use and urban management, real estate value-added issues have attracted much attention. Previous studies mainly focused on the premium of basic public services (such as infrastructure) in real estate value. As an important part of urban management, the contribution of property service is often underestimated. Few empirical studies mentioned the potential contribution of property service quality. Based on this, this paper aims to confirm and quantitatively evaluate the premium of good service quality in real estate value utilizing 155,845 samples of housing resale transactions in Beijing from 2012 to 2019. Furthermore, we also explore the dynamics and heterogeneity of the above premium. Our results show the following: Firstly, good service quality does show premium in real estate value, and the higher the service quality, the greater the premium in housing price. Secondly, this premium keeps increasing during our study period. With urbanization and rising incomes of residents, property service is increasingly important. Thirdly, the contribution of service quality to real estate value is greater when serviced houses have a higher price or larger area, are relatively newer, or further away from the city center. The findings of this current research not only deepen our understanding of service quality’s premium in real estate value, but also provide implications for urban management.

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The rising and heterogeneous demand for urban green space by Chinese urban residents: Evidence from Beijing

Cited by 34 publications

References 71 publications

Greenspace to Meet People’s Demand: A Case Study of Beijing in 2005 and 2015

Greenspace to Meet People’s Demand: A Case Study of Beijing in 2005 and 2015

Spatial and Temporal Evolution of Urban Green Space Pattern Based on GIS Sensors and Remote Sensing Information: Taking Xi’an as an Example

Will Good Service Quality Promote Real Estate Value? Evidence from Beijing, China

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