Assessment of Urban Green Space Based on Bio-Energy Landscape Connectivity: A Case Study on Tongzhou District in Beijing, China

Abstract

Green infrastructure is one of the key components that provides critical ecosystems services in urban areas, such as regulating services (temperature regulation, noise reduction, air purification), and cultural services (recreation, aesthetic benefits), but due to rapid urbanization, many environmental impacts associated with the decline of green space have emerged and are rarely been evaluated integrally and promptly. The Chinese government is building a new city as the sub-center of the capital in Tongzhou District, Beijing, China. A series of policies have been implemented to increase the size of green urban areas. To support this land-use decision-making process and achieve a sustainable development strategy, accurate assessments of green space are required. In the current study, using land-use data and environmental parameters, we assessed the urban green space in the case study area. The bio-energy and its fluxes, landscape connectivity, as well as related ecosystem services were estimated using a novel approach, the PANDORA model. These results show that (1) in the highly urbanized area, green space is decreasing in reaction to urbanization, and landscape fragmentation is ubiquitous; (2) the river ecology network is a critical part for ecosystem services and landscape connectivity; and (3) the alternative non-green patches to be changed to urban, urban patches which can improve landscape quality the most by being changed to green, and conservation priority patches for biodiversity purposes of urban green were explicitly identified. Conclusively, our results depict the spatial distribution, fluxes, and evolution of bio-energy, as well as the conservation prioritization of green space. Our methods can be applied by urban planners and ecologists, which can help decision-makers achieve a sustainable development strategy in these rapidly urbanizing areas worldwide.

Complex Systems Thinking Approach to Urban Greenery to Provide Community-Tailored Solutions and Enhance the Provision of Cultural Ecosystem Services

“…With all these data, our method calculates the catchment areas of every green space as the population living a maximum of a 15-min walk away using only safe roads without barriers. Our results confirm previous findings [13,14,54] that indicated network analysis is an effective approach to investigate interactions between variables. To achieve satisfactory results, we outlined the necessity of verifying the effectiveness of the available dataset and, eventually, editing it.…”

“…For instance, Lahoti et al [11] studied the UGS's relationship to the distance from houses, Artman et al [12] from care facilities, Biernacka et al [10] from roads, Chen and Chang [13] from public transport stops, and Zhu et al [5] from points of interest in a city. Other studies examined the relationship between the UGS and other urban land covers [14], landscape patterns [15], urban zoning [16], and socio-demographic variables [17]. Scholars have also studied links between the UGS and other city infrastructures [18], including grey infrastructures [19] and other urban open spaces [20], such as urban squares [21].…”

Construction and Analysis of Governance Evaluation Index System of Beijing Urban Subcenter Based on System Theory and Information Theory

“…Data of Wuhan, Shenzhen, Nanjing, Xi'an, and Shenyang from 2012 to 2014 were selected to conduct comparative analysis and evaluation on the stability, legitimacy, and effectiveness of the urban government governance model [8]. Han et al collected data from 1990 to 2010 in Shanghai from three dimensions of social economy, geographical space, and community survey by combining subjective and objective methods, and analyzed and evaluated the spatial distribution and temporal evolution characteristics of life quality in Shanghai during the past 20 years [9]. Wang et al used the Pandora model to evaluate the spatial distribution of green space area in Tongzhou, the subcenter of Beijing, taking land use and environment as parameters [10].…”

Multiple Land-Use Fugacity Model to Assess the Environmental Fate of PAHs – A Mega-City Under COVID-19 Pandemic

“…The spatial distribution of PAHs caused ILCR is shown in Figure 7. The land use information of the study area is extracted based on the Beijing Land Use Planning (2006 -2020) and Wanghe et al 18 . Continue PAHs posed healthy risk were predicted by interpolation with the ILCR estimated by MLUF model and health risk model through multiple exposure routes in different land-use using the inverse distance weight method in ArcGIS 10.5.…”