Exploring the interactive coercing relationship between urbanization and ecosystem service value in the Shanghai–Hangzhou Bay Metropolitan Region
Graphical abstract
Introduction
As the foundations for maintaining human survival (Liu et al., 2019), ecosystem services (ESs) refer to life support products (such as raw materials and foods) and services (such as habitats provided) that are directly or indirectly provided by the structures, processes, and functions of ecosystems (Costanza et al., 1998; J. Wu et al., 2018). Land use changes are the most direct manifestations of human activities’ impact on ES (Fang and Wang, 2013; Tang, 2015). By changing the type of land cover, human activities affect the structure, process and function of the ecosystem, which is reflected in ecosystem service value (ESV) (Zhao et al., 2004; Mao et al., 2019). Thus, ESV can be used to evaluate the potentiality of regional ecosystems for human services and to probe the transformation of the ecological environment caused by human activities. In recent years, researchers have regarded ESV as one of the main indicators for evaluating changes in the ecological environment (Wang et al., 2016). The quantitative assessment of ESV has become the focus of ecological economics (Ye et al., 2018; Zhao et al., 2018; Luo et al., 2018). According to the Millennium Ecosystem Assessment (2005), ES is divided into four main categories: provisioning services, regulating services, supporting services and cultural services. In order to measure ES through a unified standard, two approaches including unit value based approach and primary data based approach were employed (Jiang, 2017). Primary data based approach used ecological process model and ecological elements data to calculate ESV (Guo et al., 2001; Zhang et al., 2018), but it’s pertinence and particularity makes it difficult to evaluate ES comprehensively. Based on the relative potential of various ecosystems, unit value based approach is comprehensive in evaluating ESV (Costanza et al., 1998; Xie et al., 2003; Xing et al., 2018). However, the ascertainment of value coefficient is subjective to a certain extent.
In recent years, urbanization has become one of the most significant social and economic phenomena, and the world’s fastest-growing cities are now located in developing countries, especially in China (Liu et al., 2015; He et al., 2017). The development of cities in China has been phenomenal in the past 30 years(Wu et al., 2011), during which the national urbanization level has risen from 17.9% in 1978 to 56.10% in 2015, approximately 1.2% higher than the world average (Lu et al., 2011; Wang et al., 2014; National Bureau of Statistics, 2016). As the most active areas of human activities, cities, especially those in metropolitan regions, are the regions with the strongest forces for natural changes in the ecological environment (Zhang et al., 2015; Zhao et al., 2013). According to statistics (CSB, 2016), 68.54% of the fixed asset investment and almost all of the foreign capital (98.06%) in China were concentrated in urban agglomerations. Driven by the high-intensity investment of domestic and foreign capital, China’s urban agglomerations have released huge amounts of energy, which resulted in a total gain of 78.78% in GDP output. Meanwhile, it also concentrates more than 3/4 of the country’s pollution output. In general, the relationship between ecological environment and urbanization is interactive ( Zhao et al., 2017). On the one hand, urbanization brings pressures on the carrying capacity of ecosystems and resources through pollutant emissions, which result in ecological problems and even ecological disasters (Gendron, 2014; X. Wu et al., 2018). On the other hand, ecological environment changes also influence urbanization through environmental degradation, resource shortage, and so on (Liu et al., 2018; Xing et al., 2019). The contradiction between environment and human activities becomes significant in areas under rapid urbanization (Wang et al., 2016). China’s urban agglomerations are highly sensitive areas where environmental pollution and deterioration of resourses are serious, which will hinder sustainable development. Therefore, detecting and ameliorating the deficiency in the relationships between ecosystems and urbanization of cities (as well as metropolitan regions) have become the focus of scholars worldwide.
Current researches on this issue mainly focus on how to describe (e.g., measure the “Environmental Kuznets Curve”) and coordinate the relationship between urbanization and ecological environment (Button and Pearce, 1989; Grossman and Krueger, 1995; Diao et al., 2009; Al-Mulali et al., 2016). Moreover, a considerable amount of literature focuses on quantitative analysis of the coupling coordination relationship between urbanization and the ecological environment (Li et al., 2012; Shen et al., 2018). As the metropolitan regions becomes increasingly important economically and socially, research on ecological environment and urbanization of metropolitan regions will become one of the new hot spots. In addition, those studies mostly regarded urbanization and ESV as two systems, and explored their relationship from the perspective of the whole system, yet few scholars have studied the interaction between the subsystems of urbanization and ESV.
In view of the above considerations, the objectives of this study were to: 1) identify the spatio-temporal variations of ESV in SHB metropolitan region during 1995–2015; 2) assess the urbanization level of the SHB metropolitan region during the time period from the perspectives of demography, space, economy and society; and 3) explore the interactive coercing relationship between urbanization and ESV on the system level and subsystem level using CCDM. The results would provide a reference for promoting the coordinated and sustainable relationship between urbanization and ecological environment in urban agglomerations with similar development characteristics.
Section snippets
Study area
The SHB metropolitan region (118°21′E-122°16′E, 28°51′N-31°53′N) is located on the eastern coast of China, consisting of one core megacity-Shanghai, and other 5 cities (Hangzhou, Ningbo, Shaoxing, Jiaxing and Huzhou) of Zhejiang Province (Fig. 1), which are connected through large-scale infrastructure construction. It has a large group of ports and numerous rivers; therefore, its waterway transportation is developed. In 1930s, the prototype of the urban agglomeration had already taken shape. It
Estimation of ESV
Table 3 shows the total ecosystem service value (ESV) and per unit area value of ecosystem service (PESV) of SHB. PESV was used to explore the differences of ESV in the areas of the same size. The results showed that the ESV and PESV in Hangzhou were significantly higher than those in other cities in SHB and were almost 10 times of that in Jiaxing. The ranking of other cities is Shaoxing > Huzhou > Ningbo > Shanghai > Jiaxing, with no significant change from 1995 to 2015. In terms of the
Loss of ESV under rapid urbanization
The evaluation results of ESV and urbanization level in SHB from 1995 to 2015 showed that the region suffered huge losses of ESV, which was closely related to the process of urban development. Throughout the study period, the 6 cities of SHB had made great progress in the fields of demographic, spatial, economic and social urbanization. However, the speeds and overall levels of urbanization of the cities were quite different, and the subsystem development of urbanization in each city was not
Conclusions
Using SHB metropolitan region as a case study, this research estimated the level of ESV and urbanization, and explored the interactive coercing relationship between ESV and urbanization from the system level and the subsystem level. The results showed that:
- (1)
The forest-rich cities in the southwestern part of the SHB region had relatively high ESVs, while the cities in the central and north-eastern plains were dominated by cultivated land resources, and their ESVs were relatively low. With the
Author Contributions
All authors contributed equally to this work. Rui Xiao, Meng Lin and Qingxiang Meng conceived and designed the experiments. Rui Xiao, Meng Lin and Xufeng Fei analyzed the data. Rui Xiao, Meng Lin, Zhonghao Zhang and Qingxiang Meng wrote the manuscript. Yansheng Li and Xufeng Fei helped improve the manuscript. All authors have read and approved the final manuscript.
Declaration of competing InterestCOI
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.
Acknowledgement
This research was supported by the National Natural Science Foundation of China under Grant 41701484 and Grant 41601459 and the Key-Area Research and Development Program of Guangdong Province under Grant 2019B111104001.
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