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Journal of Geographical Sciences >

2022 , Vol. 32 >Issue 9: 1683 - 1704

DOI: https://doi.org/10.1007/s11442-022-2018-1

Regular Articles

Spatio-temporal variations in ecological spaces and their ecological carrying status in China’s mega-urban agglomerations

  • WANG Shihao , 1, 2 ,
  • HUANG Lin , 3, * ,
  • XU Xinliang 1 ,
  • LI Jiahui 2, 3
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  • 1.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2.University of Chinese Academy of Sciences, Beijing 100049, China
  • 3.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
*Huang Lin, PhD and Associate Professor, E-mail:

Wang Shihao (1995-), PhD Candidate, specialized in remote sensing of the ecology. E-mail:

Received date: 2022-03-27

  Accepted date: 2022-05-17

  Online published: 2022-11-25

Supported by

The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20010202)

The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20010302)

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Abstract

The rapid expansion of China’s urban agglomerations in recent decades has resulted in over-occupied ecological spaces and increased ecological pressure that are restricting healthy regional development. This paper examines the structure and characteristics of distribution of “production-living-ecological” spaces in five mega-urban agglomerations in China: Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Chengdu-Chongqing (CY), and the middle reaches of the Yangtze River (MYR). We analyze spatial and temporal variations in the ecological spaces and factors influencing them from 1990 to 2020, and examine the comprehensive ecological carrying capacity and status of ecological spaces in the past 30 years based on the available water resources, regulation of water and air quality, and leisure and recreation. The results show the following: (1) Urban agglomerations in different stages of formation and development represent varying area ratios of “ecological-production-living” spaces. The modes of expansion and evolution of the living spaces are dominated by multi-center combinations as well as the spatial structure of ecological spaces, including barrier, compact, discrete, and fully enveloping spaces. (2) From 1990 to 2020, the area occupied by living spaces in urban agglomerations continued to increase significantly while that of spaces for ecological production decreased. Except in the GBA, ecological spaces have exhibited a trend of increase in area, especially in the past 10 years. The area ratios and spatio-temporal variations in the “production-living-ecological” spaces indicate that the main functions of production and ecological spaces in mega-urban agglomerations have shifted from supply to regulation and culture, and reflect the transition from rapid urbanization to sustainable urbanization in China. (3) The comprehensive ecological carrying capacities of 78.6%, 73.1%, 54.5%, 56.3%, and 25.8% of cities in BTH, YRD, GBA, CY and MYR are severely overburdened. Water supply and the regulation of water quality are the main factors restricting the ecological carrying capacity of BTH and YRD while leisure and recreation services have hindered the capacities of GBA and CY. Policymakers thus need to pay attention to the conservation and rational layout of ecological spaces to reduce the ecological pressure in urban agglomerations. The work here can provide a scientific basis for the green and sustainable development of urban agglomerations as well as the optimized configuration of “production-living-ecological” spaces.

Key words： mega-urban agglomerations; “production-living-ecological” spaces; ecological spaces; ecological carrying capacity; spatial and temporal variations

Cite this article

WANG Shihao , HUANG Lin , XU Xinliang , LI Jiahui . Spatio-temporal variations in ecological spaces and their ecological carrying status in China’s mega-urban agglomerations[J]. Journal of Geographical Sciences, 2022 , 32(9) : 1683 -1704 . DOI: 10.1007/s11442-022-2018-1

1 Introduction

Ecological spaces refer to the physical space occupied by the ecosystem, the regional hinterland on which its metabolism depends, and the multi-dimensional relational space involved in its functions (Wang et al., 2014). They fulfill the human demand for supply, regulation, support, and culture (Li and Fang, 2016). Urban ecological spaces are green and blue spaces, e.g., ground vegetation and water bodies, that provide ecosystem functions within cities and surrounding areas (Li and Wang, 2004; Ngom et al., 2016; Wang et al., 2017). They perform the functions of reducing the urban heat island effect, improving the quality of the atmosphere and the water environment, and providing places for leisure and entertainment (Dong et al., 1999), and thus play a significant role in ensuring urban ecological security, improving residents’ quality of life, and ensuring the healthy development of cities (Zhao et al., 2020). Prevalent research in the area has focused on measuring the demand for urban ecological land, identifying the types of urban ecological spaces (Peng et al., 2015), temporal and spatial variations in them, and their human footprint (Yin et al., 2020; Zhao et al., 2020; Wang et al., 2021), and assessing their structure, services, and values (Zhao, 1990; Li and Wang, 2004; Li et al., 2011; Li and Yang, 2014), optimization and promotion, and planning and management (He, 2010; Wang et al., 2014). Spatio-temporal changes in ecological spaces have been analyzed at the scales of individual cities and urban agglomerations as well as at the national level, and the results show that ecological spaces exhibit regional differences and gradient effects in their evolution, where the time of inflection varies across regions (Yin et al., 2020).
Rapid population growth, fast economic and social development, and expanded urban and rural land (Liu et al., 2016) have significantly altered land cover and the ecosystem (Zhang et al., 2013; Zeng et al., 2014). Ecological spaces are heavily occupied by spaces for production and living (Rong et al., 2017), and their quality is constantly declining (Li et al., 2011). Aggravated pollution, ecological degradation, and resource shortages pose severe threats to sustainable development and the quality of urban life (Yu et al., 2010; Lu and Chen, 2015). Urban agglomerations are the promoters of integrated and interactive development between major regional centers and sectors in China. They contain 45% of China’s urban population and account for 50% of its total economic output and 60% of foreign investment (Fang et al., 2016); they are also responsible for more than 75% of China’s pollution (Fang, 2014). Changes in the spatial pattern of ecological spaces affect the ecological environment of urban agglomerations. Rapid urbanization causes a large number of problems, such as urban heat islands, water pollution, air pollution, and urban waterlogging (Gunawardhana et al., 2011; Liu et al., 2011; Wang et al., 2015; Wang et al., 2021). These problems have now become important factors restricting the healthy development of urban agglomerations (Kuang et al., 2011; Li et al., 2016; Ren and Fang, 2017).
The ecological carrying capacity of urban agglomerations is the ability of their ecological spaces to provide ecosystem services, prevent ecological problems, and ensure ecological security (Xu et al., 2017). The sizes of the population and the economy that can be supported by ecosystem services are determined by the structure, process, and spatial pattern of the ecosystem (Cao et al., 2015). It is also a basic tool to judge whether the regional economic and social activities are coordinated with the ecosystem (Zhao et al., 2019). The ecological carrying capacity is mainly determined based on the ecological footprint (Wei et al., 2018), state-space techniques (Shen et al., 2019), systematic models (Zhu et al., 2017), human appropriation of net primary production (Haberl et al., 2002), and the consumption of ecosystem services (Cao et al., 2015). In contexts ranging from qualitative description to quantitative analysis and mechanistic examination (Du et al., 2018), it has been applied to study the ecological carrying capacity of urban agglomerations in the Changsha-Zhuzhou-Xiangtan Region, Min Delta Area, and the middle reaches of the Yangtze River (Zhu et al., 2017; Wei et al., 2018; Shen et al., 2019). The basic principle is to use the ratio of supply to demand to reflect the balance between ecosystem supply and population demand in a certain space (Wang et al., 2020). At present, it is difficult to quantify the supply capacity of an ecosystem, which is the interaction between human activities and the ecosystem. The ecological needs of different regions are also challenging to identify. This introduces considerable uncertainty to the assessment of ecological carrying capacity (Zhao et al., 2019).
The quantity, layout, and carrying capacity of ecological spaces are important for improving the urban environment, providing spaces for living and leisure, and ensuring urban ecological security. However, the incompatibility among urban production, the ratio of living and ecological spaces in urban areas, spatial allocation, and functional integration seriously damage the ecological sustainability of urban development (Liu and Sun, 2020). Only when supported by certain resource carrying and environmental capacities can ecological spaces form the basis for the sustainable development of urban agglomerations (Wang et al., 2014). In particular with improvements in the environment of human settlements (Yang and Zhang, 2016), increasing attention has been paid to the perceived benefits of urban ecological spaces (Coon et al., 2011; Zhao et al., 2020). Therefore, optimizing the allocation of ecological spaces according to their state and ecological carrying capacity is an important way to resolve the contradiction between urbanization and ecological protection (Su et al., 2007; Li et al., 2011). However, most previous studies on the ecological carrying capacity of urban agglomerations have focused on supply, with little consideration for regulation and cultural services. This makes it difficult to reflect the functions of ecological spaces in different urban agglomerations, and explains the rarity of comparative analyses of models of ecological spaces and stages of their evolution.
Megacities are not only the core areas of new-type urbanization and economic development but are also regions that are highly sensitive to ecological problems (Fang et al., 2016). The development of urban agglomerations requires attending to the protection and restoration of the ecological environment to achieve the sustainable development goals of “intensive and efficient production spaces, livable and moderate living spaces, and beautiful ecological space” (CPC Central Committee and State Council, 2014; Li and Fang, 2016). This raises the question of what the major patterns of spatio-temporal variations in the ecological spaces of China’s major urban agglomerations have been since the reform and opening up in 1978. Moreover, how are ecological spaces affected by urban expansion and regional differences in it? How do we measure the carrying capacity of ecological spaces in urban agglomerations from the perspectives of supply and demand? What are the effects of temporal and spatial changes in ecological spaces on their ecological carrying capacity? These questions need to be clearly answered. This paper uses the Beijing-Tianjin-Hebei (BTH) region, Yangtze River Delta (YRD), Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Chengdu-Chongqing (CY) area, and the middle reaches of the Yangtze River (MYR) as study areas, the supply-demand balance model of ecological carrying capacity is improved by referring to the assessment methods of ecological footprint and ecological carrying capacity. The spatial and temporal variations in ecological spaces, their occupation due to urban expansion, and changes in their ecological carrying capacity are systematically analyzed. In addition, the factors limiting changes in the ecological carrying capacity from 1990 to 2020 are assessed. The work here provides scientific evidence for the structural optimization and configuration as well as the sustainable development of urban agglomerations under the backdrop of the Chinese goal of the construction of an ecological civilization.

2 Materials and methods

2.1 Study areas

The BTH generates 10.3% of the China’s GDP, houses 7.3% of the Chinese population, and occupies 2.4% of the country’s land (Political Bureau of the CPC Central Committee, 2015). Rapid urbanization has led to water shortages and increasingly severe ecosystem degradation (Bao and He, 2017; Li and Kuang, 2019). The YRD contributes 18.5% to the national GDP, with 11.0% of the population and 2.2% of the land of China (NDRC and MHURC, 2016a). However, resource shortages and environmental pollution have led to a continued decline in ecological quality. The GBA generates 12.5% of the national GDP, and contains 5.1% of the population and occupies 0.6% of China’s land (CPC Central Committee and State Council, 2019). It has undergone a reduction in the diversity of species and an increase in the scale of floods (Lin et al., 2019). With 6.7% of the country’s population and 1.9% of its area, CY generates 5.5% of the national GDP (NDRC and MHURC, 2016b). However, its resource utilization is inefficient, ecosystems are degraded, and natural disasters are frequent. The MYR generates up 8.8% of the GDP, with 8.8% of the population and 3.3% of the area of land (NDRC, 2015). It suffers from severe water shortages, soil pollution, and soil erosion (Shen et al., 2019).

2.2 Data

2.2.1 Land use data

Datasets for changes in the land use and land cover from 1990, 2000, 2010, and 2020 were obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (http://www.resdc.cn). These datasets used Landsat TM/ETM orLandsat 8 images as the main source of information, and contained vector data of land use types obtained through manual visual interpretation (Liu et al., 2014). This was used to generate data at a spatial resolution of 100 m × 100 m. The primary land use and land cover types consisted of cropland, forest land, grassland, wetland, built-up land, and unused land. The area occupied by arboreal forests and sparse forests of the secondary type was used as input data to calculate the carrying capacity.
Figure 1 Distribution of five mega-urban agglomerations in China

2.2.2 Regional statistical data

The data on the population, surface water resources, water consumption (living consumption, production consumption, ecological consumption), sewage discharge, and SO2 discharge were collected from such public databases as statistical yearbooks and water resources bulletins for cities and counties in the five urban agglomerations.

2.2.3 Meteorological observation data

Based on data on the daily average precipitation from the China Meteorological Data Service Center (CMDSC) (http: //data.cma.cn/), grid data at a spatial resolution of 1 km × 1 km were obtained by interpolation using ANUSPLIN.

2.2.4 Remote sensing inversion data

MODIS NDVI data (MOD13Q1) (https://ladsweb.modaps.eosdis.nasa.gov) were used to calculate vegetation coverage. The spatial resolution of the data was 250 m × 250 m and their temporal resolution was 16 days. Savitzky-Golay filtering was used to process long-term time series NDVI data to eliminate the influence of clouds and atmospheric noise.

2.2.5 Empirical parameters

The water resource yield factor (Huang et al., 2008) is shown in Table 1. At a single-point scale, the threshold of rainfall runoff yield was collected through the published literature, and daily rainfall as measured by neighboring meteorological stations was used to correct the TRMM daily 3-hour precipitation data obtained in the same period. The ratio of runoff yield-induced precipitation to the total precipitation was determined according to part of the cumulative single rainfall that exceeded the threshold of the rainfall runoff yield. A linear relationship was established between this and the multi-year average river runoff coefficient to obtain a spatial distribution.
Table 1 Yield factors of water resources for different provinces in mega-urban agglomerations of China
Region Beijing Tianjin Hebei Henan Shanghai Jiangsu Zhejiang Anhui Guangdong Chongqing Sichuan Hubei Jiangxi Hunan
Yield factors 0.77 0.41 0.4 0.78 1.39 1.02 2.81 1.54 3.21 2.04 1.76 1.68 2.71 2.45

2.3 Methods

2.3.1 Extraction of “production-living-ecological” spaces

By referring to the classification of ecological spaces, and the theories of “production-living-ecological” spaces and ecosystem services, this paper considers forests, grasslands, water bodies, and wetlands that can directly or indirectly provide ecosystem provision, regulation, and cultural services, and have the abilities of self-regulation, restoration, maintenance, and development as ecological spaces. We consider economic output as the core goal of cultivated land as space for production. The land for urban and rural construction that people use for leisure, entertainment, and consumption is considered to constitute living space (Fu et al., 2017; Liu et al., 2017). We extracted the spatial distribution of “production-life-ecological” spaces in mega-urban agglomerations based on land use data. This was used to statistically analyze the changes in ecological spaces and their spatial distribution in urban agglomerations from 1990 to 2020 at the county scale to obtain information on the transformation of ecological, production, and living spaces. The ecological space occupied by living spaces due to urban expansion in each county was then analyzed.

2.3.2 Quantification of supply and demand for ecosystem services

The services provided by ecological spaces in urban agglomerations mainly depend on the status of the ecosystem while the demand for ecosystem services depends on the level of economic and social development (Zhang et al., 2010). While research on the human demand for ecosystem services in urban agglomerations has tended to focus on subjective needs and well-being, such as regulation, culture and recreation, and the ecological spaces of urban agglomerations dominated by forests, grasslands, and wetlands, we mainly consider water supply, the regulation of air and water quality, and recreation and leisure in this study (Li et al., 2014; Yan et al., 2017; Yu et al., 2018; Wu and Zhou, 2019).
The supply of water resources in ecological spaces includes the water stored in water bodies and water conservation in ecosystems. The volume of stored water is measured by the surface water resources, and water conservation is normally calculated using the water balance method and the precipitation storage method (Jiang, 2003; Zhao et al., 2004). The former method focuses on watershed processes, such as through the InVEST model, but has such limitations as difficulties in measuring evapotranspiration and a limited study area. The latter method uses empirical values of forest evapotranspiration with a focus on precipitation retention by the vegetation. It is simple and useful. We thus use the precipitation storage method to estimate water conservation by forests, grasslands, and vegetated wetlands (Jiang et al., 2007; Liu and Gao, 2008; Wu et al., 2016). The formula is as follows:
$W=A\times Pr\times R$
$Pr=P\times K$
where W is the increase (m3) in water conservation by forests, grasslands, and vegetated wetlands compared with bare land, A is the area (ha) of forests, grasslands, and vegetated wetlands, Pr is the annual runoff precipitation (mm), R is the profit coefficient of decreasing runoff of an ecosystem compared with bare land, calculated according to vegetation coverage (Zhu et al., 2003), P is annual precipitation (mm), and K is the ratio of runoff precipitation to total precipitation.
Wetlands, such as swamp wetlands, can absorb wastewater (Ji et al., 2002) by slowing down the flow of wastewater effluent to cause toxic substances to precipitate and remove them. Plants in wetlands do so by absorbing toxic substances from wastewater to purify it. Forests can absorb air pollutants such as SO2, nitrogen oxides, and other harmful gases (Guo et al., 1999), and can block dust (Zhao et al., 2004). Green and blue spaces, such as forests, grasslands, water bodies, and wetlands, can provide leisure and recreation services, i.e., urban recreational spaces, including urban parks, ecological scenic spots, and public green spaces (Sun et al., 2016). A large volume of research has focused on assessing the function and layout of these spaces (Sun et al., 2016; Yu et al., 2018; Wang et al., 2020). The following indicators and methods are used to quantify the human demand for ecosystem services in urban agglomerations: The demand for water resources is derived from domestic use, use for production, and ecological use, which is calculated based on the regional water consumption. The demand for regulating water quality is based on the discharged sewage, that is, the sewage discharge during domestic and production-related uses. The demand for regulating air quality is derived from the emission of air pollutants, calculated using industrial SO2 emissions (Zhao et al., 2004). With regard to leisure and recreation, previous studies have focused on the types of functions and their spatial distribution. Prevailing methods to this end include surveys conducted in recreational spaces such as parks and scenic spots. The demand for leisure and recreation in this paper is quantified based on the population and the per capita green space (11 m2/person) according to the National Standards for Eco-Garden City.

2.3.3 Ecological carrying status

Among the common methods for analyzing the ecological carrying capacity, the ecological footprint is intuitive and simple but the parameters of its model are not sufficiently flexible, and it cannot perform multi-functional measurements of the ecosystem (Ma et al., 2017; Zhao et al., 2019). The comprehensive evaluation method and the system model consider more comprehensive and flexible factors but methods require more input data, which often becomes a restraint. The method based on a balance between supply and demand calculates the difference between the amount of resources provided by the ecosystem and the socio-economic demand under the given development model (Xiang and Meng, 2012). By referring to the equivalence factor and yield factor of the value of ecosystem services and the supply-demand balance model of ecological carrying capacity (Liu et al., 2012; Guo et al., 2020), this paper improves the method to calculate the ecological footprint and the ecological carrying capacity. We use the ratio of potential ecological functions to the demand for such services to assess the ecological carrying status:
$ECS{{I}_{i}}=\frac{E{{F}_{i}}}{EC{{C}_{i}}}$
where ECSIi is the index of the ecological carrying status of ecosystem service i, EFi is the ecological footprint (ha), and ECCi is the ecological carrying capacity (ha).
The index of ecological carrying status for water resources: We convert regional water consumption (W, t) into the area of land occupied by water resources to calculate the ecological footprint of water resources (EFwr), and convert water supply (WR, t) into the area of land for water resources to calculate the ecological carrying capacity of water resources (ECCwr). The index is calculated by the following formula:
$E{{F}_{wr}}=\frac{{{r}_{w}}\times W}{{{p}_{w}}}$
$EC{{C}_{wr}}=\frac{0.4\times \varphi \times {{r}_{w}}\times WR}{{{p}_{w}}}$
where rw is the equivalence factor of global water resources (5.19) (Huang et al., 2008), pw is the average production capacity of global water resources (3140 t/ha) (Min et al., 2011), 0.4 is the proportion of water resources left after 60% deduction for maintaining the ecological environment and biodiversity (Yu and Xu, 2014; Liu et al., 2018), and φ is the yield factor of water resources.
The index of ecological carrying status for water quality regulation: We calculated the ecological footprint of water quality regulation (EFwc) by using the area of wetland required to absorb regional sewage discharge (Cwc, t), and converted the wetland water conservation (WRw, t) into the area of land occupied by water resources to calculate the carrying capacity for water quality regulation. The index was calculated by the following formula:
$E{{F}_{wc}}=\frac{{{r}_{w}}\times {{C}_{wc}}}{{{p}_{wc}}}$
$EC{{C}_{wc}}=\frac{0.4\times \varphi \times {{r}_{w}}\times W{{R}_{w}}}{{{p}_{wc}}}$
where pwc is the average amount of sewage absorbed by water bodies per unit area (365 t/ha) (Duan et al., 2012).
The index of ecological carrying status for air quality regulation: The ecological footprint of air quality regulation (EFaq) was calculated using the area of forest needed to absorb SO2 emissions (${{E}_{S{{O}_{2}}}}$, kg), and the ecological carrying capacity for air quality regulation (EFaq) was quantified by using the area of regional forests:
$E{{F}_{aq}}=\frac{{{E}_{S{{O}_{2}}}}}{{{p}_{aq}}}$
where paq is the average capacity of forests to absorb SO2 (88.65 kg/ha) (National Environmental Protection Agency, 1998).
Calculation of ecological carrying status for leisure and recreation: The area of green spaces that demanded by the regional population (N) was considered the ecological footprint of leisure and recreation (EFr), and the ecological carrying capacity for leisure and recreation (ECCr) was measured by the area of regional ecological spaces.
Analysis of ecological carrying status: Equation (3) was used to calculate the index of ecological carrying status of regional water supply, regulation of water air quality, and leisure and recreation. The higher the index was, the greater was the carrying pressure. To qualitatively analyze and evaluate the differences between and changes in the ecological carrying status of mega-urban agglomerations, we classified the ecological carrying status into high surplus, surplus, balanced, overloaded, and severely overloaded by referring to the plan for classification used in studies on the resource carrying capacity of land (Feng et al., 2008) (Table 2). By using the “barrel effect,” we then chose the lowest of the four ecological carrying capacities as the threshold of regional ecological carrying capacity to assess the capacity of mega-urban agglomerations. The higher the index of ecological carrying status was, the greater was the carrying pressure, indicating an overloaded state. On the contrary, the lower the index of ecological carrying status was, the more moderate was the carrying pressure, indicating a state of surplus.
Table 2 Classification for ecological carrying capacity
Ecosystem service Ecological carrying status and thresholds
High surplus Surplus Balanced Overloaded Severely overloaded
Water resources supply 0-0.4 0.4-0.8 0.8-1.2 1.2-5 >5
Water quality regulation 0-0.4 0.4-0.8 0.8-1.2 1.2-5 >5
Air quality regulation 0-0.2 0.2-0.5 0.5-1.5 1.5-10 >10
Leisure and recreation 0-0.4 0.4-0.8 0.8-1.2 1.2-10 >10

3 Results and analysis

3.1 Structure of “production-living-ecological” spaces

The characteristics of distribution of ecological resources in relation to the spatial structure of mega-urban agglomerations were analyzed from three aspects: structure of the area of “ecological-production-living” spaces, the modes of expansion and evolution of the living spaces, and types of ecological spaces. In terms of the ratio of the area structure of “ecological-production-living” spaces in 2020 (Table 3), the ratio of BTH to YRD was 4: 5: 1 and that of CY was approximately 3: 6: 1, with the highest proportion of production spaces at 59.7%. Its ecological spaces accounted for about 36.4% of the total. The same ratio for GBA was roughly 6: 2: 2 and for MYR was about 6: 3: 1, the two regions with the highest proportions of ecological spaces at 63.3% and 60.0% of the land, respectively. GBA, YRD, and BTH had the highest proportions of living spaces at 14.8%, 14.0%, and 13.2%, respectively, while those of CY and MYR were lower, at approximately 3.9% and 4.5%, respectively.
Table 3 Statistics of ecological-production-living spaces in mega-urban agglomerations of China in 2020
Type Statistical indicator BTH YRD GBA CY MYR
Ecological spaces Area (106 ha) 9.04 8.47 3.49 7.02 20.96
Area percentage (%) 40.54 40.16 63.33 36.38 59.96
Production spaces Area (106 ha) 10.33 9.68 1.20 11.53 12.42
Area percentage (%) 46.31 45.87 21.83 59.74 35.53
Living spaces Area (106 ha) 2.93 2.95 0.82 0.75 1.58
Area percentage (%) 13.15 13.97 14.84 3.88 4.51
Apart from CY, the patterns of the formation and growth of the urban agglomerations (Fang et al., 2018) featured a multi-centered combination from the perspective of the structural distribution of living spaces. CY exhibited a dual-centered combination. From the perspective of the patterns of expansion and evolution of living spaces, the urban agglomerations had evolved from a three-dimensional boiled-egg growth model through a flat expansion-based fried-egg model into a merged and reorganized scrambled-egg model (Fang, 2019). This had led to the organic integration, inter-dependence, and mutual promotion of cities. MYR fitted the boiled-egg growth model (Figure 2e), CY fitted the fried-egg model (Figure 2d), and BTH, YRD, and GBA fitted the scrambled-egg model (Figures 2a-2c). The structural distribution of ecological spaces in mega-urban agglomerations also differed. Barrier-type ecological spaces were found in BTH (northern and western areas) and YRD (southern areas), and the other three urban agglomerations featured enveloping-type areas. The difference was that GBA had a compact enveloping-type area, CY had a discrete enveloping-type area, and MYR had a fully enveloped area.
Figure 2 Distribution of ecological-production-living spaces in mega-urban agglomerations of China in 2020

3.2 Spatio-temporal variations in ecological spaces

From 1990 to 2020, the area of living spaces in the five urban agglomerations considered here continued to increase significantly whereas that of production spaces continued to decrease substantially, resulting in differences in changes in the areas of ecological spaces (Figure 3). Except for GBA, ecological spaces in the other urban agglomerations showed a trend of increase that became more prominent from 2010 to 2020. In terms of ecological space, that of BTH decreased from 1990 to 2010 and increased significantly from 2010 to 2020, those of YRD and MYR continued to increase substantially, and that of GBA increased slightly from 1990 to 2000, and decreased from 2000 to 2020. Ecological spaces in CY decreased slightly from 1990 to 2000 and increased from 2000 to 2020. In the past 30 years, living spaces in mega-urban agglomerations have increased mainly due to an increase in spaces for production, and the occupation of ecological spaces by living spaces has gradually increased. From 1990 to 2000, the ratio of ecological spaces occupied by living spaces was relatively small. From 2000 to 2010, more than half of the reduced ecological space had been occupied by living spaces. From 2010 to 2020, nearly all the reduced ecological spaces had been occupied by living spaces (Table 4).
Figure 3 Changes in the area of ecological-production-living spaces in mega-urban agglomerations of China in 1990-2020
Table 4 Ecological spaces and changes in mega-urban agglomerations of China during 1990-2020
Time Statistical indicator BTH YRD GBA CY MYR
1990-2000 Changes in area of ecological spaces (ha) -27799 60925 8806 -2235 21633
Percentage of area change (%) -0.12 0.29 0.17 -0.01 0.06
Counties with increased ecological spaces (%) 31.75 62.80 42.86 39.58 47.60
Counties with unchanged ecological spaces (%) 25.12 7.25 2.04 6.25 0.00
Counties with reduced ecological spaces (%) 43.13 29.95 55.10 54.17 52.40
Area of ecological spaces occupied by living spaces (ha) 27775 15998 43675 2346 17425
Proportion of reduced ecological spaces occupied by living spaces (%) 32.96 24.06 72.22 13.11 23.66
Percentage of number of counties with different ranges of proportions of living spaces occupying ecological spaces (%) ≥90% 6.64 16.43 34.69 11.11 8.30
50%-90% 12.32 17.39 24.49 9.72 17.03
<50% 43.60 41.06 36.73 66.67 74.24
No occupation 37.44 25.12 4.08 12.50 0.44
2000-2010 Changes in area of ecological spaces (ha) -55291 10889 -81350 48152 34822
Percentage of area change (%) -0.28 0.02 -1.55 0.25 0.10
Counties with increased ecological spaces (%) 23.70 39.61 20.41 74.31 58.08
Counties with unchanged ecological spaces (%) 26.07 4.35 2.04 2.78 0.44
Counties with reduced ecological spaces (%) 50.24 56.04 77.55 22.92 41.48
Area of ecological spaces occupied by living spaces (ha) 43500 49811 116735 7754 50685
Proportion of reduced ecological spaces occupied by living spaces (%) 53.45 63.46 99.03 69.37 60.46
Percentage of number of counties with different ranges of proportions of living spaces occupying ecological spaces (%) ≥90% 38.86 52.17 97.96 70.83 50.66
50%-90% 10.90 21.74 0.00 5.56 22.27
<50% 12.80 19.32 0.00 6.25 24.45
No occupation 37.44 6.76 2.04 17.36 2.62
2010-2020 Changes in area of ecological spaces (ha) 115946 146102 -38333 16079 110911
Percentage of area change (%) 0.50 0.52 -0.76 0.09 0.32
Counties with increased ecological spaces (%) 52.61 68.12 48.98 77.78 66.81
Counties with unchanged ecological spaces (%) 2.84 0.97 0.00 0.00 0.00
Counties with reduced ecological spaces (%) 44.55 30.92 51.02 22.22 33.19
Area of ecological spaces occupied by living spaces (ha) 12756 39322 25037 13551 101289
Proportion of reduced ecological spaces occupied by living spaces (%) 83.38 79.08 96.65 93.26 95.67
Percentage of number of counties with different ranges of proportions of living spaces occupying ecological spaces (%) ≥90% 42.65 71.01 81.63 81.94 82.10
50%-90% 4.74 8.21 10.20 4.86 12.66
<50% 3.32 6.76 0.00 4.17 1.75
No occupation 49.29 14.01 8.16 9.03 3.49
From 1990 to 2000, the total area of ecological spaces decreased in BTH and CY, with approximately 43.1% and 54.2% of counties in these regions recording reductions in area, whereas the total ecological spaces of YRD, GBA, and MYR increased, with approximately 62.8%, 42.9%, and 47.6% of the counties recording increases in the area of ecological spaces, respectively (Figure 4a and Table 4). The occupation of ecological spaces by living spaces was particularly prominent in GBA, accounting for roughly 72.2% of reduced ecological spaces. A total of 34.7% of its counties recorded reductions in ecological spaces, of which more than 90% had been occupied by living spaces (Figure 5a). By contrast, less than 30% of reduced ecological spaces had been occupied by living spaces in BTH, YRD, MYR, and CY.
Figure 4 Proportional changes in ecological spaces at the county scale in mega-urban agglomerations of China in 1990-2010
Figure 5 The proportion of ecological spaces occupied by living spaces in mega-urban agglomerations of China
From 2000 to 2010, the total area of ecological spaces in BTH and GBA decreased, where approximately 50.2% and 77.6% of counties recorded reduced ecological spaces, while the ecological spaces of YRD, CY, and MYR increased, with approximately 39.6%, 74.3%, and 58.1% of counties recording expanded ecological spaces, respectively (Figure 4b and Table 4). The proportion of reduced ecological spaces due to occupation by living spaces in GBA reached 99%, followed by CY, YRD, and MYR at about 69.4%, 63.5%, and 60.5%, respectively, and BTH ranked last at about 53.5%. In GBA, living spaces in 98% of the counties occupied more than 90% of the reduced ecological spaces. The value was 70.8% in CY and only 39.0% in BTH (Figure 5b).
From 2010 to 2020, the total area of ecological spaces in GBA decreased, where approximately 51% of counties recorded reduced ecological spaces, while ecological spaces in BTH, YRD, CY, and MYR increased, with approximately 52.6%, 68.1%, 77.8%, and 66.8% of the counties recording increases in ecological spaces, respectively (Figure 4c and Table 4). The proportion of ecological spaces occupied by living spaces in urban agglomerations continued to rise. In GBA, MYR, and CY, this proportion exceeded 90%, and in BTH and YRD exceeded 80%. However, except for CY, the area of ecological spaces occupied by living spaces in each of the other urban agglomerations decreased significantly. The proportion of the number of counties in GBA, CY, and MYR with more than 90% of ecological spaces occupied by living spaces was over 80%. In addition, the major areas with a high level of urbanization had less ecological space, and therefore the proportion of ecological spaces occupied was close to zero (Figure 5c).

3.3 Spatio-temporal variations in ecological carrying status

The results of assessment of the comprehensive ecological carrying status (Figure 6e) show that about 78.1% of the 96 cities in the five mega-urban agglomerations were ecologically overloaded or severely overloaded. In the urban agglomerations of BTH, YRD, GBA, CY, and MYR, 78.6%, 73.1%, 54.5%, 56.3%, and 25.8% of the cities were severely overloaded, respectively. These cities were mainly located in the eastern and southern areas of BTH, eastern and northern areas of YRD, central and western areas of CY, and the Pearl River Estuary of GBA. Only 6.2% of the cities were in a state of surplus or high surplus, such as the Nanchang urban agglomeration in MYR and Zhaoqing in GBA. A total of 15.6% of the cities had a balanced ecological carrying status, and were mainly located in the southeast of YRD and MYR. With regard to ecological functions, the carrying status of water resources was overloaded or severely overloaded in approximately 70.8% of the cities, and was in a surplus or high surplus in 18.8% of cities (Figure 6a). The carrying status with regard to water quality regulation was overloaded or severely overloaded in 59.4% of cities, and was in a state of surplus or high surplus in 13.6% of cities (Figure 6b). The carrying status with regard to air quality regulation was overloaded or severely overloaded in 34.4% of cities, and 45.8% of the cities were in a state of surplus or high surplus (Figure 6c). The carrying status with regard to leisure and recreation was overloaded or severely overloaded in 57.3% of cities, and was in a state of surplus or high surplus in 30.2% of cities.
Figure 6 Distribution of ecological carrying status of ecological spaces in mega-urban agglomerations of China
From 1990 to 2020, the comprehensive ecological carrying status of urban agglomerations in 71.9% of the cities considered remained overloaded. In 7.3% of cities, the overloaded status had been moderated or turned into a surplus. The overload status had escalated further in 4.2% of the cities, especially in the southeast of GBA and the northern part of CY (Figure 7e). With regard to the ecological carrying status of ecological functions, the carrying capacity for the supply of water resources remained overloaded, and unchanged, in 62.5% of cities. In regions such as the southern area of CY, the carrying capacity for the supply of water resources in 5.2% had become further overloaded, whereas that in 6.3% of the cities located in the central part of BTH, and in GBA, had become less overloaded (Figure 7a). Concerning water quality regulation, the carrying status remained overload in 37.5% of the cities. In the southwest of MYR, 15.6% of cities had become less overloaded and 14.6% had switched from a state of surplus or balance to one of being overloaded or severely overloaded (Figure 7b). With regard to the carrying status of air quality regulation, 27.1% of the cities had assumed a moderate status in the southwest of BTH, the eastern and southern areas of CY, and the northern part of MYR. In the central and western regions of BTH, southeast of YRD, northern area of CY, and the middle of MYR, 35.4% of the once-overloaded or balanced cities had assumed a status of surplus (Figure 7c). In terms of the carrying status of leisure and recreation services, 47.9% of the cities remained overloaded and 26% maintained the state of surplus. A total of 8.3% of cities had become further overloaded, and were mainly distributed in Tianjin and Baoding in BTH, southeast of GBA, and Chongqing in CY. The status of only 1% of the cities had been moderated or turned into a surplus (Figure 7d).
Figure 7 Distribution of changes in ecological carrying status in mega-urban agglomerations of China in 1990-2020

4 Conclusions and discussion

4.1 Conclusions

This paper systematically analyzed spatio-temporal variations in “production-living- ecological” spaces in BTH, YRD, GBA, CY, and MYR in China from 1990 to 2020, and discussed the changes in the ecological carrying status, including the supply of water resources, regulation of water and air quality, and leisure and recreation. The results showed significant structural differences in the “production-living-ecological” spaces of the five mega-urban agglomerations, indicating four area ratios of “ecological-production-living” spaces, three patterns of the expansion and evolution of living spaces dominated by a multi-center combination, and four structures of ecological spaces. From 1990 to 2020, the area of living spaces in urban agglomerations continued to increase significantly while that of spaces for production decreased. However, different urban agglomerations differed significantly in terms of the area of ecological spaces. Except for GBA, ecological spaces in the other urban agglomerations have increased, especially in the past 10 years. During 1990-2000, 2000-2010, and 2010-2020, the proportion of ecological spaces occupied by living spaces gradually increased, indicating a shift in the functions of the production-related and ecological spaces of mega-urban agglomerations from supply to regulation and culture. In BTH, YRD, GBA, CY and MYR, about 78.6%, 73.1%, 54.5%, 56.3%, and 25.8% of the cities showed a severely overloaded ecological carrying status, respectively. The supply of water resources and water quality regulation were the main factors restricting the ecological carrying status of these urban agglomerations.

4.2 Discussion

A comparison with past work makes it clear that the ecological carrying status of water supply and pollution obtained from the evaluation of MYR roughly corresponds with the results of this paper (Shen et al., 2019). Past studies have used the water balance equation to calculate water conservation in the areas with key ecological functions in BTH (Xu et al., 2017), and their results differ from those of our study because different approaches of assessment were used. Both the per capita ecological footprint and the ecological pressure index of four typical cities in YRD in these studies had exhibited an increasing trend, indicating that human activities apply growing pressure on the ecosystem, and that the conditions for sustainable development are far from promising (Wen et al., 2020). Both these findings match the results of this paper. However, a few uncertainties persist in our results: (1) This study divided the “production-living-ecological” spaces based on a macroscopic perspective, with little consideration for the versatility of land. The estimation of area based on land use type considered only horizontal differences and ignored vertical structures. Moreover, the internal ecological spaces of urban agglomerations were not measured due to limitations of spatial resolution of the data. Hence, further work is required. (2) The ecological carrying status for the supply of water resources, air and water quality, and leisure and recreation services was evaluated by using the ecological carrying index to determine whether an urban agglomeration was overloaded or had a surplus. We did not consider their correlations with biophysical and biochemical processes, or differences between them in terms of a combination of the structure and function of the ecosystem. We also did not consider the inter-regional flow of ecological services and the demands for ecological spaces of different groups of people. (3) In the context of air quality regulation, we focused on SO2 absorption and excluded other pollutants. The function of water purification of wetlands varies in different regions due to the influence of factors like the terrain, landscape, depth of water and its flow, temperature, rainfall, and pollution concentration. Thus, a multi-dimensional, multi-combination, and multi-functional assessment of the ecological carrying capacity is needed in future studies. (4) Current methods for the measurement methods of functions and potential services of the ecosystem seem reasonable but are limited. For instance, the supply-demand relationships of different jurisdictions are used without considering the characteristics of flow at both ends (supply and demand) and the differences in their scopes of benefit.
Mega-urban agglomerations are core strategic areas for national economic development and the main regions of new urbanization (Fang et al., 2016). These highly urbanized areas have a large economic scale and tremendous overall strength. The focus of future development in mega-urban agglomerations will persist on economic growth, but in the process of urbanization, ecological and environmental protections must also be ensured. The management and control of urban spaces should be improved, and their spatial distribution should be optimized. Ecological spaces are the main provider of ecosystem services in urban agglomerations. As such, when promoting the integrated ecological construction and the joint construction of structures of landscape ecology, and when striving for the ecological goal of enjoying the benefits of the natural environment, we must respect the laws of nature as well as the principle of a balance between supply and demand (Fang, 2017). The category-based and overall assessments of the carrying status of ecological spaces may help us gain greater insight into factors restricting the ecological carrying capacity of urban agglomerations. For cities that are overloaded or severely overloaded, the impact of such restricting factors ought to be considered when trying to improve the quality and efficiency of urban areas. Moreover, cities in China should implement the targeted planning of ecological spaces, pursue green, low-carbon, and habitable goals, and commit to the path of ecological, green, and sustainable urbanization with Chinese characteristics (Fang, 2016).

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