Impact of ecological security on urban sustainability in Western China

Abstract

China has experienced rapid urbanization for over 40 years, posing a significant challenge to the ecological environment and urban sustainability, which is especially critical in cities in Western China. To critically measure the impact of ecological security on urban sustainability, we developed a quantitative approach to obtain evaluation results for decision-making. Taking Xi’an, one of the central cities in Western China, as an example, we used the methodology to conduct a quantitative analysis of the impact of regional ecological security on urban sustainability. The driving force–pressure–state–response framework has been used to construct a comprehensive assessment system and the distribution of ecological security index was analyzed using Geographic Information System (GIS) software. To understand the level of urban ecological security, the natural breaks classification method was adopted to divide the results into five categories: highly safe, satisfactorily safe, safe, low value safe, unsafe. The results have shown that the ecological security in Xi’an is basically stable, and the overall status is close to the safe status, but there is a significant difference within the research area. According to the assessment results, the impact of ecological security on sustainability of Xi’an is mainly concerning high-density population, high-density water consumption, high per capita energy consumption, low vegetation coverage, and low-density river corridors. In addition, through the quantitative analysis of the relationship between ecological security and terrain, it is observed that the ecological security level distribution of Xi’an decreases from mountains to tablelands to hills and plains. The paper shows that the comprehensive assessment system of urban ecological security established here is effective to identify natural and artificial ecological security factors that threaten urban sustainability.

Keywords

Urban sustainability ecological security index urbanization evaluation framework Xi’an

Introduction and background

Since the industrial revolution, especially after the Second World War, most countries around the world have become increasingly urbanized (Boscrup, 1981). In the process of global urbanization, China’s rapid growth has attracted particular attention (Guan et al., 2018; Liu and Cao, 2017; Shi et al., 2019; Tan et al., 2016). In the past 40 years, China’s urban population has increased from 170 million (17.9%) in 1978 to 830 million (59.6%) in 2018, with an annual growth rate of 1.04%. Urbanization provides a great opportunity for social and economic development in China. However, such a record speed and the scale of China’s urbanization has posed a great challenge to urban sustainability (Cui, 2018; Li and Li, 2017; Wang et al., 2019), which is considered as an urgent issue with national emphasis. Especially in Western China, where the ecological environment is fragile and the ecological carrying capacity is low, the risk of ecological security caused by rapid urbanization can seriously affect the quality of human life and hinder social and economic development (Zhou et al., 2017), thus posing potential threats to urban sustainability (Phillis et al., 2017). Consequently, conducting formal and quantitative assessments to critically analyze the effects of the urban ecological system on urban sustainability is essential for effective urban environmental protection as well as the formulation of urban sustainable development policies, especially in Western China.

In general, sustainability refers to “maintaining the existence of the ecosystem and its services, while also providing for human needs” (Goonetilleke et al., 2014), or “a continuing process of ensuring the balance of economic, environmental, and human well-being both now and in the future” (McPhearson et al., 2016). Since the 1990s, the concepts of sustainability have been applied to urban planning and design (Wheeler and Beatley, 2014), and thereby the notions of urban sustainability have emerged. Urban sustainability is defined as an adaptive process of facilitating and maintaining a virtuous cycle between ecosystem services and human well-being through concerted ecological, economic, and social actions in response to changes within and beyond the urban landscape (Wu, 2014). Ecosystem security is the basic guarantee for achieving sustainable urban development, and how to measure urban sustainability has attracted widespread attention from scholars (Nourry, 2008; Singh et al., 2012). Assessing urban sustainability requires consideration of the complex interactions between economic, environmental, and social factors (Fung and Kennedy, 2005). In sum, urban sustainability is closely related to urban ecology.

There is no canonical or clear definition of ecological security. In general, the concept of ecological security also has a broad or narrow definition. The former was proposed by the International Institute for Applied Systems Analysis in 1989, which refers to a non-threatened state of human life, health, well-being, basic rights, livelihood resources, necessary resources, social order, and human ability to adapt to environmental changes (Xing and Chenghu, 2005). The latter refers to the security of natural and semi-natural ecosystems, i.e. the overall level of ecosystem integrity and health (Xiao and Chen, 2002). The ecological security referred to in this paper is a broad concept of ecological security, which refers to the state in which the natural-economic-society complex artificial urban ecosystem is endangered or threatened.

Ecological security research has different scales and levels, from macro-global and national ecological security, meso-regional and urban ecological security coverage to micro-town ecological security (Xiao and Chen, 2002). Mesoscale urban ecological security research plays a key role because it provides a linkage connecting the macro and micro scales (Ren et al., 2013). It is crucial to assess ecological security at the urban mesoscale, as evidenced in the literature (Du et al., 2013; Gao et al., 2017). The scope of this research covers Xi’an with an area of 10,108 square kilometers, which is classified as mesoscale in physical size. The statistical data used in the assessment were census tracts based on administrative districts of an average size of around 780 square kilometers, reflecting the key characteristics in terms of the scale and average level of the regional social and economic conditions. The resolution of remote sensing data used in this research is 30 meters. For smaller-scale towns, higher-resolution data would be needed. Considering the physical size and data accuracy, the research focuses on the mesoscale.

Current literature on ecological security has been briefly summarized in terms of research foci, evaluation methods, and frameworks. For the research topics focused on in the current literature, it is proposed that ecological security research has mainly involved ecosystem security, ecosystem service functions, and the analysis and evaluation of these matters (Xing and Chenghu, 2005). For the evaluation methods, because the field is integrated with the disciplines of ecology, environment, and mathematics, the methods have developed from qualitative description to quantitative evaluation (Liu et al., 2006), including methods evolved from mathematical models, landscape ecology methods, ecological carrying capacity methods, and ecological models (Li et al., 2014, 2010; Pan et al., 2015; Sun et al., 2018; Zhang and Xu, 2017; Zhang et al., 2016). In terms of frameworks, it is largely dominated by the pressure–state–response (PSR) evaluation framework and its variations (see Table 1). In summary, ecological security research has formed a relatively complete research field with well-developed knowledge frameworks and evaluation methods, which has deepened our understanding of ecological security. However, less attention has been paid to the impact of ecological security on urban sustainability.

Table 1.

Comparison of ecological security evaluation frameworks.

Framework	Advantages	Disadvantages	Case studies
PSR	Emphasis is on the causal relationship between human economic activities and their impact on the environment; the indicators are comprehensive and flexible	There is no dynamic trend research and lack of guidance on specific practices and decision-making for improving regional ecological security	Hangzhou, China (Cen et al., 2015);Wuhan, China (Gao et al., 2017);
DSR	Considers the causes of environmental problems, environmental quality, changes in status and response	It is difficult to identify the causes of environmental problems and to select appropriate indicators to quantify them	Huaihe River Basin, China (Tan and Yu, 2012)
DPSIR	Emphasis is on the changes in ecological security characterized by system dynamics, reflecting the relationship between the factors that interact with human activities and the environment	The framework design is complex, and it is difficult to accurately distinguish the types of indicators such as driving force, pressure, state, impact, and response	Apulia, Italy (Spanò et al., 2017);Dar es Salaam City, Tanzania (Kazuva et al., 2018)
DPSR	Emphasis is on driving force indicators that drive environmental pressure changes, making the framework more complete	Similar to the DPSIR framework, it is not easy to accurately distinguish the four types of indicators: driving force, pressure, status, impact, and response	Suzhou, China (Liang and Zhang, 2011); Xiamen, China (Shi et al., 2018)

DPSIR: drivers–pressure–state–impact–response; DPSR: driving force–pressure–state–response; DSR: drivers–state–response; PSR: pressure–state–response.

Sustainable development theory can provide a key nexus that links urban sustainability and ecological security (Gao et al., 2006). World Commission on Environment and Development (1987) defines sustainable development as development that meets the needs of contemporary people without compromising the needs of future generations. The signing of the “21st Century Agenda” signifies that a sustainable development strategy has been agreed by most countries and has become a common guideline for countries to formulate policies (Liu et al., 2005). The goal of sustainable development is to continuously meet human needs (World Commission on Environment and Development, 1987), and the goal of ecological security is to meet the most basic needs of human survival to ensure that sustainable urban development can be achieved (Zou and Shen, 2003). The connotation of sustainable development suggests “demand” and “restriction” (Gao et al., 2006). Sustainable development requires meeting the basic needs of the people, while also stressing that it is subject to the constraints of the ecological environment and resource conditions, which is precisely the key to ensuring urban ecological security. Therefore, ecological security can be seen as the basis for achieving urban sustainability, and they are consistent in this regard. In addition, it is known from current literature that human well-being is the primary focus for urban sustainability (Wu, 2014), whereas ecological security reflects the degree of ecosystem integrity and health (Chen, 2002). Ecosystem integrity and health may provide guarantees for human survival and well-being, which also means a close relationship between urban sustainability and ecological security.

To critically measure the impact of ecological security on urban sustainability, this paper develops a methodological approach by following the logic of “propose a problem – analyze the problem – solve the problem” and validates the feasibility of the methodology through a case study in Western China. Based on the driving force–pressure–state–response (DPSR) framework, this paper establishes an urban ecological security evaluation index system composed of 32 indicators. With the support of GIS technology, the statistical data and remotely sensed data are combined to quantify the impact of regional ecological security on the sustainability of Xi’an. Xi’an has a total of 11 districts and 2 counties, with diverse topography and a total area of 10,108 square kilometers. As the central city in Western China, it is one of China’s higher education and high-tech industrial bases, as well as regional transportation hubs. The research will also contribute to ecological and environmental protection and to the well-being of people in Xi’an by promoting sustainable urban development in Western China. In particular, we aim to answer the following questions through the research: (1) What is the overall level of ecological security in Xi’an? (2) Which ecological security factors have a major impact on urban sustainability? (3) Is there a relationship between ecological security and topography?

Methodological approach and case study

A methodological approach to comprehensive assessment of urban ecological security

An urban area is a natural, social, and economic composite ecosystem, and the quantitative evaluation of its ecological security status is complicated. To formally study the assessment of urban ecological security, we developed a methodological approach by which the evaluation object goes through a series of evaluation steps to obtain evaluation results and use them for decision-making, as shown in Figure 1. The evaluation object here refers to the urban ecosystem. A case study was conducted to illustrate how the methodological approach can be used to quantify the impact of ecological security on urban sustainability.

Figure 1.

The methodological approach to comprehensive assessment of urban ecological security. ESI: ecological security index.

The above methodological approach has been established by us following the logic and sequence of “propose a problem – analyze the problem – solve the problem.” The current paper focuses on urban ecosystems. Next, the key is to choose an appropriate evaluation framework. Finally, ecological security assessment is a process of unification of subjective and objective contradictions. The evaluation process itself is based on a solid objective basis (such as evaluation data collection and analysis), and the evaluation results are often influenced by the subjective feelings of the evaluation subjects and decision makers.

Evaluation frameworks

The key to ecological security research is the selection and determination of an evaluation framework (Liu et al., 2007). The most internationally recognized indicator framework is OECD’s PSR, which is based on pressure indicators that describe the human impact on the environment; state indicators that assess the condition of the environment and resources; and response indicators that indicate the actions taken by people in response to environmental problems (Segnestam et al., 2003).

Differing from the PSR framework, the United Nations Commission on Sustainable Development established the drivers–state–response framework in 1996, which uses drivers to reflect the directing role of human socio-economic activities in environmental change.

There is a recognizable flaw in the PSR framework, i.e. the impact of human activities on the environment can only be reflected indirectly through changes in environmental status indicators over time (Ren and Zhan, 2006). Therefore, it was further extended by the European Environment Agency as the drivers–pressure–state–impact–response framework, and many authors use it to conceptualize model structure (Haase and Tötzer, 2012). Drivers underlie the causes, which lead to environmental pressures, and impact indicators express the results of pressures on the current state of the environment (Gabrielsen and Bosch, 2003).

The DPSR framework evolved from the PSR framework, which extended the meaning of the pressure module (Zuo et al., 2003). This framework can reflect the close relationship between ecological environmental systems and human activities (Zhang et al., 2005).

The above evaluation frameworks have their own characteristics and have been widely used in the field of ecological security and urban sustainability. The analysis and comparison of these frameworks are shown in Table 1.

Differing from other types of ecosystems, urban ecosystems are always affected by human activities and explicitly incorporate humans as drivers of and respondents to urban system dynamics along with non-human species and the system components (Pickett et al., 2001). In other words, the urban ecological environment is directly and clearly influenced by the social and economic driving forces of human beings. Shi et al. (2018) used the DPSR framework and ecological risk analysis to establish a comprehensive assessment model, and assessed the ecological risk of nitrogen deposition in Xiamen city. The results showed that the proportion of high-risk areas and low-risk areas in Xiamen city were 37.1 and 16.0%, respectively. Liang and Zhang (2011) used the DPSR framework and material flow analysis to propose a framework for sustainable urban management, analyzed the urban metabolism, and formulated sustainable urban development policies of Suzhou, another Chinese city. The results showed that after the implementation of the proposed policy, the resource consumption and carbon intensity of Suzhou in 2015 would be reduced by 14 and 44.9%, respectively, compared to those in 2005. Li et al. (2013) analyzed and compared the intensive land use levels of Jinzhou and Dalian based on the DPSR framework from 2004 to 2008, and found that the former intensive land use index showed a trend of initial decrease followed by increase, while the latter has a continuous increase. The DPSR framework has shown sound effects in the construction of the indicator system and decision-making in the literature. Therefore, this research uses the DPSR framework to establish an ecological security index (ESI) system to assess the impact of ecological security on urban sustainability.

Evaluation index system and index normalization

Establishing a scientific and reasonable ecological security evaluation index system is the basis for quantitative evaluation. The first step in constructing an evaluation index system is to choose representative indicators. Based on the DPSR framework (as shown in the Supplemental Material, Figure S1), this paper adopts a formal set of indicators developed by Wang and Dang (2019), and they have been extended and contextualized for the current study. The indicators (Wang and Dang, 2019) were developed using the questionnaire and Delphi methods to select proper indicators. First, the questionnaire was developed through consultation with experts, and 52 initial indicators were screened and selected based on the principle of ensuring comprehensiveness and avoiding overlap. Second, due to the time limit, we recruited 50 doctoral students from disciplines that are closely related to the field including urban planning, architecture, environmental science, and urban geography from universities located in Xi’an regions to conduct the questionnaire survey. We counted the frequency of the index supplied by the participants, as shown in Table 2. In the end, the indicators whose frequency was less than 70% were eliminated, and as a result 32 out of 52 indicators with higher frequencies were selected to establish our urban ecological security evaluation system. Among them, there are 3 indicators reflecting the driving force, 10 pressure indicators, 14 state indicators, and 5 response indicators (see Table 3).

Table 2.

Statistics on the frequency of initial evaluation indicators.

Target level	Criteria level	Index level		Frequency of occurrence (%)
Driving force (A₁)	Social and economic driving force (B₁)	1	GDP growth ratio	74
		2	Urban construction land ratio	88
		3	Population density	82
Pressure (A₂)	Natural resource pressure (B₂)	4	Water resource consumption density	96
		5	Land resource carrying capacity	82
		6	Per capita energy consumption	78
		7	Per capita arable land area	66
		8	Per capita food production	58
		9	Coal consumption per capita	64
		10	Gasoline consumption per capita	52
	Ecological environment pressure (B₃)	11	Industrial wastewater discharge density	90
		12	Industrial exhaust emission density	88
		13	Industrial solid waste discharge density	82
		14	Fertilizer application density	90
		15	SO₂ emission density	62
	Climate disaster pressure (B₄)	16	High temperature disaster	84
		17	Heavy rainfall disaster	78
		18	Low temperature disaster	72
		19	Drought disaster	58
State (A₃)	Natural state (B₅)	20	Relief degree of land surface	86
		21	Slope	82
		22	Slope direction	46
		23	Annual average temperature	56
		24	Annual accumulated temperature	62
		25	Annual rainfall	68
		26	Soil erosion	64
		27	Soil erosion intensity	76
		28	River corridor density	84
		29	River meandering	48
		30	Vegetation cover index	96
		31	Landscape resources	62
	Social state (B₆)	32	Urban agglomeration—fragmentation index	56
		33	Urbanization ratio	90
		34	Urban population growth rate	78
		35	Per capita government revenue	82
		36	Land output rate	86
		37	Per capita GDP	72
		38	The proportion of the tertiary industry	78
		39	Road traffic index	72
		40	Land output rate	42
		41	GDP annual growth rate	64
Response (A₄)	Pollution control (B₇)	42	Industrial wastewater discharge compliance rate	80
		43	Industrial soot emission compliance rate	82
		44	Industrial solid waste utilization rate	76
		45	Energy consumption per unit of GDP	86
		46	Concentration of PM2.5	82
		47	Drinking water source compliance rate	62
		48	Power consumption per GDP	66
		49	Domestic waste treatment rate	58
	Environmental awareness (B₈)	50	Environmental investment index	86
		51	Proportion of education investment	66
		52	Proportion of population with higher education	42

GDP: gross domestic product.

Table 3.

Ecological security index (ESI) system in the urban area.

Target level	Criteria level	Index level	Index interpretation	Unit	Index attribute
Driving force (A₁)	Social and economic driving force (B₁)	GDP growth ratio (C₁)	Comparison of GDP for the year and GDP for the previous year	%	−
		Urban construction land ratio (C₂)	Proportion of urban construction land to constructible land	%	−
		Population density (C₃)	Population per unit area	person/km²	−
Pressure (A₂)	Natural resource pressure (B₂)	Water resource consumption density (C₄)	Water resource consumption (including domestic water, industrial water, and agricultural water) per unit area	m³/km²	−
		Land resource carrying capacity (C₅)	Refers to the limit of the population that the land can carry (Bai et al., 2010). The calculation equation is as followsL_cci=P_a/Twhere L_cci is the land resource carrying capacity index, Pa is the regional permanent population, and T is the population that the regional land can carry	N/A	−
		Per capita energy consumption (C₆)	Ratio of annual energy consumption converted to standard coal to permanent population	ton of coal/ person	−
	Ecological environment pressure (B₃)	Industrial waste water discharge density (C₇)	Volume of industrial waste water discharged per unit area	ton/km²	−
		Industrial exhaust emission density (C₈)	Volume of industrial exhaust emissions per unit area	m³/km²	−
		Industrial solid waste discharge density (C₉)	Industrial solid waste discharge per unit area	ton/km²	−
		Fertilizer application density (C₁₀)	Consumption of chemical fertilizer per unit area	ton/km²	−
	Climate disaster pressure (B₄)	High temperature disaster (C₁₁)	Days above 35°C per year (Chen and Shi, 2007)	day/year	−
		Heavy rainfall disaster (C₁₂)	In meteorology, heavy rainfall disasters are usually reflected in annual heavy rainfall days (Yang and Guo, 2011)Based on the meteorological data from 1961 to 2004 in Xi’an, Yang and Guo (2011) defined the local heavy rainfall standard as more than 25 mm of daily rainfall. This paper also adopts this standard.	day/year	−
		Low temperature disaster (C₁₃)	Days with minimum temperature below 0°C	day/year	−
State (A₃)	Natural state (B₅)	Relief degree of land surface (C₁₄)	Refers to the altitude difference within some area (Feng et al., 2007), the calculation equation of relief degree of land surface is as followsRDLS={[Max(h)−Min(h)]/[Max(H)−Min(H)]}×[1−P(A)/A] where RDLS is the relief degree of land surface, Max(h) is the highest altitude in some area, Min(h) is the lowest altitude in some area, Max(H) is the highest altitude in study area, Min(H) is the lowest altitude in study area, P(A) is the area of some area, and A is the area of study area	N/A	−
		Slope (C₁₅)	Ratio of vertical height to horizontal distance	%	−
		Soil erosion intensity (C₁₆)	Amount of soil erosion per unit area and time	ton/km²·year	−
		River corridor density (C₁₇)	River length per unit area	km/km²	+
		Vegetation cover index (C₁₈)	This paper uses the normalized vegetation index (NDVI) to reflect regional vegetation cover conditions. The calculation equation is as followsNDVI=(NIR−VIS)/(NIR+VIS) where NDVI is the normalized vegetation index, NIR is the near infrared reflectivity, and VIS is the visible light reflectivity	N/A	+
		Landscape resources (C₁₉)	Landscape resources include scenic spots, forest parks, and nature reserves within the study area	N/A	+
	Social state (B₆)	Urban agglomeration—fragmentation index (C₂₀)	Measuring the degree of regional agglomeration–dispersion	N/A	+
		Urbanization ratio (C₂₁)	Ratio of urban population to resident population	%	+
		Urban population growth rate (C₂₂)	Ratio of the number of urban population growth to the total urban population during a year	%	−
		Per capita government revenue (C₂₃)	Ratio of government revenue to permanent population	Yuan/person	+
		Land output rate (C₂₄)	Land output value per unit area	Yuan/km²	+
		Per capita GDP (C₂₅)	Ratio of urban GDP to permanent population	Yuan/person	+
		The proportion of the tertiary industry (C₂₆)	Ratio of tertiary industry output value to GDP	%	+
		Road traffic index (C₂₇)	Road traffic conditions are positively related to road density and road area per capita (Xia et al., 2010)Based on Xia’s research, we developed the calculation equation as followsI= $\sqrt{R S}$ where I is the road traffic index, R is the road density, and S is the road area per capita	N/A	+
Response (A₄)	Pollution control (B₇)	Industrial waste water discharge compliance rate (C₂₈)	Ratio of waste water up to the standard for discharge	%	+
		Industrial soot emission compliance rate (C₂₉)	Ratio of industrial soot emission up to the standard for discharge	%	+
		Industrial solid waste utilization rate (C₃₀)	Reuse ratio of industrial solid waste	%	+
		Energy consumption per unit of GDP (C₃₁)	Ratio of energy consumption converted to standard coal to GDP	ton of coal/ 10,000 yuan	−
	Environmental awareness (B₈)	Environmental investment index (C₃₂)	Ratio of environmental investment to GDP	%	+

GDP: gross domestic product.

Note: In addition to the above 32 indicators, the frequency of the concentration of the PM2.5 index was reported at 82% and should have been retained. However the PM2.5 monitoring data released by the government began in 2012 (The State Council of China, 2012), there were no quantifiable data available for the study period (2006–2010), and therefore, it was removed from the selection. In future research, this indicator should be included in the urban ecological security evaluation system for quantitative evaluation depending on data availability.

In terms of the participants of the questionnaire, some scholars suggested that researchers need to select suitable people as their participants to ensure the data quality, and believed that researchers with similar skills and knowledge for research questions should be selected to ensure the validity of the findings (Homburg et al., 2012; Sharma et al., 2012). In this case, although the sample size of 50 samples is relatively small, nevertheless they are all professionals from disciplines that are closely related to ecological security research. We used this strategy to ensure that the limited sample can best represent the general pool for studying urban ecological security. Furthermore, these indicators have proven to be effective in a pilot study (Wang and Dang, 2019) on a collection of urban towns surrounding the metropolitan region. In the future, we will try to increase the sample size to fine-tune the selected indicators.

As shown in Table 3, there are inconsistencies between the 32 indexes, which are mainly reflected in the following two aspects. First of all, the unit of measurement (dimension) of each index is inconsistent. Second, the attributes of the indexes are inconsistent, and are divided into positive indexes and negative indexes (we use a “+” symbol to represent the positive indexes and a “−” symbol to represent the negative indexes in Table 3). The larger the former, the better, such as vegetation cover index (C₁₈). The smaller the latter, the better, such as population density (C₃). Therefore, it was necessary to normalize the raw data of the indexes to eliminate inconsistencies so as to use them effectively for ecological security evaluation. We use equations (1) and (2) to normalize 32 evaluation index values so that these dimensional raw data are mapped to a range of 0–1 for quantitative evaluation

Y = \frac{C_{i} - C_{\min}}{C_{\max} - C_{\min}}

(1)

Y = \frac{C_{\max} - C_{i}}{C_{\max} - C_{\min}}

(2)

where Y is the normalized index value, C_i is the actual value of the index, C_max is the maximum value of the index, and C_min is the minimum value of the index. Equation (1) applies to the positive indexes and equation (2) applies to the negative indexes.

Evaluation index weight value

We applied the analytic hierarchy process (AHP) to determine the weights of the indexes in the ESI system. The AHP method (Saaty, 1980) is a combination of both qualitative and quantitative decision-making, which can be used to model local experts’ perceptions, as collected through questionnaire and interviews, and provides rigorous quantitative measures to describe the relationships between the evaluation indexes (Thapa and Murayama, 2010). It decomposes complex problems into multi-factor, multi-level problems, and determines the levels in a two-way comparison. The relative importance of the factors in the hierarchy is determined by means of pairwise comparison, and the weight of the indicators is then determined (Wang, 2003). These weighted values of the indicators are calculated as

W_{i} = \frac{{(Π_{j = 1}^{n} a_{i j})}^{\frac{1}{n}}}{\sum_{i = 1}^{n} {(Π_{j = 1}^{n} a_{i j})}^{\frac{1}{n}}}, i = 1, 2, \dots, n

(3)

where a_ij is the importance of indicator i compared to indicator j, and W_i is the weighting value of indicator i. According to the relative weight results of each level, the calculation was performed level by level, and finally the weight values of each index were obtained.

ESI and evaluation classification

In analyzing the evaluation results, the next step is to calculate the value of the ESI.

ESI = \sum_{i = 1}^{n} W_{i} \times C_{i}

(4)

where ESI is the ecological security index, W_i is the index weight, C_i is the normalized standard index value, and n is the number of indexes. When using equation (4) to calculate the ESI value, the W_i value can be obtained using equation (3), and the C_i value can be obtained using equations (1) and (2). ESI is the basis for quantitative assessment of urban ecological security, while evaluation classification is the key to understanding the state of ecological security, which reflects the degree of regional ecological security. Currently, the evaluation classification methods can be divided into three categories: equal interval method, manual classification method, and natural breaks classification method (Zhang et al., 2018). The first method classifies the evaluation values by equal value intervals, while the second method classifies the evaluation values according to artificially specified numerical intervals. Both classification methods are highly subjective and do not objectively reflect the difference in the evaluation values. Different from the former two methods, the natural breaks classification method is a relatively objective classification method, which can automatically identify the classification interval of the evaluation values by using GIS software, properly grouping similarity values, and maximizing the differences in the classification. Therefore, we use the natural breaks classification method to classify the evaluation values. The evaluated ecological security values are distributed in the range of 0–0.655. In order to understand the level of unban ecological security, ESI is divided into five categories by the natural breaks classification method: unsafe (0–0.450), low value safe (0.451–0.485), safe (0.486–0.530), satisfactorily safe (0.531–0.570), and highly safe (0.571–0.655).

Case study

Case study area selection

Xi’an, the capital of Shaanxi Province, the central city and the transportation hub of Western China, is a research, education, and industrial base of the country. Xi’an has a total area of 10,108 square kilometers, roughly 37% is flat and 63% is mountainous. The mountainous areas are mostly located in the south and east, while the plains are in the center and northeast. Different from European and American cities, Chinese cities are metropolitan regions that include urban and rural areas (Huang et al., 2016). For example, Xi’an includes nine districts and four counties (Figure 2).

Figure 2.

Location of the case study area and its topography. (1: Xincheng District, 2: Beilin District, 3: Lianhu District, 4: Baqiao District, 5: Weiyang District, 6: Yanta District, 7: Yanliang District, 8: Lintong District, 9: Changan District, 10: Lantian County, 11: Zhouzhi County, 12: Hu County, 13: Gaoling County).

Data management

It is worth noting that 2006–2010 is the fastest period of urbanization in Xi’an in the past 20 years (as shown in Figure S2 in the Supplemental Materials). During the period, the urbanization rate of Xi’an has increased from 64.52 to 69.67%, with an annual growth rate of 1.29%. Therefore, the evaluation of urban ecological security status during this period is of significance.

Both statistical data and spatial data are used in this study. The former are used to assess the state of the city’s economy, population, and society. The latter, involving remotely sensed data and a digital elevation model (DEM), are used to assess urban land use, topography, and ecological conditions. The statistical data were obtained from the Xi’an Statistical Yearbook in 2006 and 2010 (Li and Li, 2007, 2011). Remotely sensed images with a spatial resolution of 30 meters were downloaded from the USGS website (https://glovis.usgs.gov) and the DEM with a spatial resolution of 30 meters was obtained from the Shaanxi Provincial Geographic Information Surveying and Mapping Bureau. The data used in this research are shown in Table S1 in the Supplemental Material.

It is worth noting that the statistical data are collected based on administrative divisions and cannot precisely reflect the spatial distribution of the evaluation indicators. Therefore, it is necessary to combine statistical data with spatial data to improve the evaluation accuracy. This paper proposes to use slope and land use classification as thresholds to determine the spatial distribution of some statistical data within the unit. Land use classification can be extracted based on 30-meter resolution remote sensing image data, while slopes can be obtained from the DEM.

We chose these two factors for the following considerations. First, a land use classification (divided into five categories: forest, water, farmland, construction land, and unused land) can be used to identify the statistical data distribution within administrative units. Take water resource consumption density (C₄) as an example; it includes domestic water density, industrial water density, and agricultural water density. The total amount of the above three types of water resources consumption can be obtained from statistical data, and their respective spatial distribution ranges can be obtained by means of land use classification. Because agricultural water is used to irrigate farmland, domestic and industrial water is consumed on construction land. Second, construction land is closely related to the slope. The smaller the slope, the flatter the land, and the lower the construction cost (Ye et al., 2013). When the slope is greater than 25%, the building layout is greatly restricted and it is generally not suitable as construction land (Xiaoguang, 2004). In addition, in order to prevent soil erosion, China’s policy of returning farmland to forests clearly stipulates that the cultivation of steep slopes above 25% is prohibited. Therefore, a 25% slope can be used as a key threshold to distinguish between land suitable for construction and non-construction land.

Evaluation index weight value

According to equation (1), pairwise comparison, reciprocal computation, value normalization, and principal vector weight computation were performed for each evaluation index. The weight calculation results are shown in Table 4, and the calculation process is presented in Tables S2 to S6 in the Supplemental Material.

Table 4.

Evaluation index weight value.

Target level		Criteria level		Indicator level		Final weights
A₁	0.189	B₁	1	C₁	0.147	0.028
				C₂	0.657	0.124
				C₃	0.196	0.037
A₂	0.351	B₂	0.648	C₄	0.785	0.179
				C₅	0.149	0.034
				C₆	0.066	0.015
		B₃	0.230	C₇	0.351	0.028
				C₈	0.189	0.015
				C₉	0.109	0.009
				C₁₀	0.351	0.028
		B₄	0.122	C₁₁	0.592	0.025
				C₁₂	0.333	0.014
				C₁₃	0.075	0.003
A₃	0.351	B₅	0.333	C₁₄	0.272	0.032
				C₁₅	0.125	0.015
				C₁₆	0.036	0.004
				C₁₇	0.129	0.015
				C₁₈	0.373	0.044
				C₁₉	0.65	0.008
		B₆	0.667	C₂₀	0.234	0.055
				C₂₁	0.158	0.037
				C₂₂	0.046	0.011
				C₂₃	0.070	0.016
				C₂₄	0.336	0.079
				C₂₅	0.031	0.007
				C₂₆	0.104	0.024
				C₂₇	0.021	0.005
A₄	0.109	B₇	0.750	C₂₈	0.096	0.008
				C₂₉	0.558	0.046
				C₃₀	0.096	0.008
				C₃₁	0.250	0.020
		B₈	0.250	C₃₂	1.000	0.027

Results and findings

Comparative evaluation results

Figures 3 and 4 and Table 5 show the evaluation results of the ecological security of Xi’an in 2006 and 2010 by applying the proposed methodological approach. In 2006, the ESI value ranges from 0.3 to 0.648 with an average of 0.529. In 2010, the ESI value ranges from 0.302 to 0.655 with an average of 0.521. From this result, the ecological security level of Xi’an is generally safe from 2006 to 2010.

Figure 3.

Ecological security classification map of Xi’an in 2006.

Figure 4.

Ecological security classification map of Xi’an in 2010.

Table 5.

Comparative evaluation results of ecological security in 2006–2010.

Ecological security classification	ESI value	Evaluation results of 2006		Evaluation results of 2010
Ecological security classification	ESI value	Areas (km²)	Percentages (%)	Areas (km²)	Percentages (%)
Unsafe	0–0.450	576	5.7	708	7.0
Low value safe	0.451–0.485	849	8.4	890	8.8
Safe	0.486–0.530	879	8.7	2133	21.1
Satisfactorily safe	0.531–0.570	3882	38.4	2295	22.7
Highly safe	0.571–0.655	3922	38.8	4082	40.4

ESI: ecological security index.

Compared with 2006, there is a significant change in the spatial distribution of ESI values in 2010. The unsafe areas are mainly distributed in Beilin District, Lianhu District, Xincheng District, and Yanta District and have a tendency to spread to the surrounding areas; the low value safe areas have not changed much, but the distribution is more dispersed; the safe areas are obviously expanding to the east and west ends of the main urban area; the satisfactorily safe areas show a decreasing trend, especially in the northern areas of Huxian County and Lantian County; the highly safe area is relatively stable, with a slight increase in Hu County, Chang’an District, and Lantian County, and a decrease in the northern part of Zhouzhi County.

The classification and statistics of the ecological security levels of 13 administrative divisions in Xi’an are shown in Figure 5. In 2006, most of Beilin District, Xincheng District, Lianhu District, and Yanta District, Weiyang District and a small part of Gaoling County were at an unsafe level; the low value safe areas were mainly concentrated in the northeast of Xi’an, including most areas of Yanliang District and Gaoling County; the safe areas are mainly distributed in Yanta District, the middle of Chang’an District, and the northwest of Lantian County; the satisfactorily safe areas include most of Chang’an County, Lantian District, and Linyi District, and part of Weiyang District and Lantian County; the highly safe areas include most of Zhouzhi County, Huxian County, and Lantian County.

Figure 5.

Ecological security levels in 13 administrative divisions of Xi’an in 2006 and 2010. Note: The percentages in Figure 5 represent the proportion of area of the five ecological security levels in every administrative unit.

Compared with 2006, the unsafe areas in 2010 have expanded, including the majority of Beilin District, Xincheng District, Lianhu District, Yanta District, and Weiyang District, and parts of Baqiao District and Chang’an District; the low value safe areas are mainly distributed in Yanta District, Yanqiao District, Weiyang District, and Gaoling County; the safe area has expanded rapidly, mainly in the Yanliang District, Gaoling County, and Zhouzhi County of Hu County; the satisfactorily safe areas have been reduced, mainly in Linyi District, Lantian County, Chang’an District, and Zhouzhi County; the highly safe areas are mainly distributed in the south of Zhouzhi County, Huxian County and the south of Chang’an District, and southeast of Lantian County.

Impact on urban sustainability

Regions with low levels of ecological security are the most prominent regions experiencing urban ecological security problems. The main ecological security problems in these regions can be reflected by indicators with low values, which have seriously affected urban sustainability and require measures to respond. We use equations (1) and (2) to normalize 32 evaluation index values in regions with low ecological security levels and classify them according to values in order to find ecological security factors that have a great impact on urban sustainability (Figure 6).

Figure 6.

Evaluation index normalization values for unsafe regions of Xi’an in 2006 and 2010.

As shown in Figure 6, among the 32 indicators, there are five indicators below 0.2 in both 2006 and 2010, including population density (C₃), water resource consumption density (C₄), per capita energy consumption (C₆), river corridor density (C₁₇), and vegetation cover index (C₁₈). The above five indicators with low values reflect the main ecological security problems and pose a serious threat to urban sustainability. First, the high-density population (12,000 people/km²) poses tremendous pressure on the natural resources and ecological environment on which to survive. Second, high water consumption density (1.52 million m³/km²) means it is difficult to meet the population’s demand for fresh water, and even leads to ecological water transfer from outside the region to alleviate water shortage, such as the Hanjiang-to-Weihe river valley water diversion project (Liu et al., 2014), which seriously threatens urban sustainability. Third, the per capita energy consumption of unsafe regions (3.7 tons of coal/person) is high, which poses great pressure on the ecological environment and sustainable urban development. Fourth, the river corridor density is extremely low (0.2 km/km²), making it difficult for river systems to exert ecological effects and needs to be improved. Finally, the low value of the vegetation coverage index (0.28) reflects the poor coverage of surface vegetation and growth, which leads to the degradation of urban ecosystem function.

Ecological security and topography

Xi’an has a variety of topography, which can be divided into plain, hills, tableland, and mountains (the Xi’an topography classification map is shown in Figure S3 in the Supplemental Material). The driving force, pressure, state, and response evaluation index values of different topographies are calculated as

DFI = \sum_{i = 1}^{3} W_{i} \times C_{i}

(5)

P I = \sum_{i = 4}^{13} W_{i} \times C_{i}

(6)

S I = \sum_{i = 14}^{27} W_{i} \times C_{i}

(7)

R I = \sum_{i = 28}^{32} W_{i} \times C_{i}

(8)

where DFI is the driving force index value, PI is the pressure index value, SI is the state index value, RI is the response index value, W_i is the index weight, and C_i is the normalized index value. In addition, the C_i value can be obtained using equations (1) and (2), the sum of these four evaluation indexes becomes the ESI value.

Figure 7 shows that the regional ecological security is closely related to topography. In the case study area, the mountain has the highest DFI value and the lowest value in the plain. This is because in the plain area, both the population density and the construction land ratio are high, which plays a directing role in the deterioration of the urban ecological environment. The PI value from high to low is mountain, tableland, hill, and plain. In general, the natural environment of the mountain is excellent, the ecological pressure is small, while industry and agriculture are developed on the plains, which brings great pressures on the resources and the environment. The difference in the SI value of the four types of topography is not obvious. The RI is opposite to the DFI, with the highest for the plains and the lowest for the mountain. The plains have the most investment in pollution control and ecological restoration, so the value is the highest, while in the mountains, economic development is slow, the pollution input is the least, and the value is the lowest. There is a relationship between the ESI value and the topography, and the overall trend is from mountains, to tablelands to hills, and plains, gradually decreasing. These results are consistent for both 2006 and 2010 (Figure 7).

Figure 7.

Evaluation index of different topography in Xi’an in 2006–2010. DFI: driving force index; ESI: ecological security index; PI: pressure index; RI: response index; SI: state index.

Comparison with similar studies using the DPSR framework

We compared the background, data types, research methods, and results with similar studies using the DPSR framework and have summarized the research contributions in Table 6.

Table 6.

Comparison with similar studies using the DPSR framework.

	Suzhou, China (Liang and Zhang, 2011)	Xiamen, China (Shi et al., 2018)	Xi’an, China
Background	Material flows between the urban economic system and the natural environment have a major impact on urban sustainability.	Excessive nitrogen deposition will have a negative impact on urban ecosystems and threaten regional ecological security and sustainable urban development.	Ecological security is the foundation and guarantee for sustainable urban development. The health and integrity of urban compound ecosystems is critical to urban sustainability.
Data	Statistical data (Suzhou Statistical Yearbook from 1998 to 2006)	Statistical data (Environmental Bulletin of Xiamen City in 2011 and 2016)Land use attribute data (Overall Planning of Land Use in Xiamen City (2006–2020))	Statistical data (Xi’an Statistical Yearbook in 2007 and 2011)Spatial data (remotely sensed data between 2006 and 2010, digital elevation model data in 2006)
Methods	Based on the DPSR framework and urban material flow analysis (MFA), Liang and Zhang (2011) proposed a framework for sustainable urban management and policy assessment.	Based on the ecological risk analysis (ERA) and DPSR framework, Shi et al. (2018) established a comprehensive evaluation model for assessing the ecological risk of nitrogen deposition.	Following the logic of “propose a problem – analyze the problem – solve the problem,” we developed a methodological approach to quantify the impact of regional ecological security on urban sustainability.
Results	Using the framework to identify the environmental pressures and analyze the urban metabolism in Suzhou, it is predicted that after the implementation of the proposed policy, the urban resource consumption and carbon emission intensity in 2015 will decrease significantly compared to 2005.	The evaluation results show that the impact of nitrogen deposition on the ecological subsystem of Xiamen city is different, from high to low in order of forest ecosystem, marine ecosystem, farm ecosystem.	First, we evaluated the ecological security status of Xi’an using the proposed methodological approach and found the main ecological security factors affecting urban sustainability. Second, ecologically unsafe areas have increased by 172 km² between 2006 and 2010 in Xi’an. Third, it is found that there is some relationship between ecological security and topography.
Contributions	A framework for sustainable urban management was proposed, which can be used to analyze the urban metabolism.	A comprehensive assessment model for the ecological risk of nitrogen deposition was proposed, which can be used to formulate policy recommendations for the sustainable development of regional ecosystems.	A methodological approach to quantify the impact of regional ecological security on urban sustainability.

DPSR: driving force–pressure–state–response.

The Suzhou and Xiamen cases differ from Xi’an in research methods and data types. In terms of the research methods, they both combined the DPSR framework with specific analysis techniques to build an evaluation framework. In the Suzhou case, the DPSR framework was used to identify urban environmental pressure, while in the Xiamen case it was used to analyze the ecological risk assessment results and direct decision-making. These two studies mainly used statistical data. Due to the lack of spatial data support, this may have impacted on the accuracy of their evaluation (i.e. the inability to reflect the spatial distribution of the evaluation results). In our research, both spatial data and statistical data were used as data sources, which improved the accuracy of the evaluation to some extent (i.e. the ability to reflect the spatial distribution of the evaluation results). We also developed a methodological approach by which a quantitative approach was proposed through a series of formal steps to measure the impact of ecological security on urban sustainability. Based on the methodological approach and the DPSR framework, we established an ESI system to contribute to urban ecological security research. Future research will see the increased use of a combined evaluation framework and multi-source data (including statistical data and spatial data, as well as other types of data) to advance the research on urban ecological security evaluation.

Discussion

After 40 years of development, cities in Western China are faster in urbanization but more fragile in their ecological environment, leading to greater challenges to urban sustainability. The integrity and health of urban ecosystems are the key to ensuring urban sustainability. Although the dynamic trajectory of cities may not be accurately predicted or controlled, the relationship between urban ecological security and human well-being can be deeply understood through ecological security evaluation, and the key factors affecting urban sustainability can be found to provide a scientific basis for promoting sustainable urban development.

In this study, we established and demonstrated a methodological approach through an evaluation system consisting of 32 indicators based on the DPSR framework. In the selection of indicators, we used the questionnaire and Delphi methods to reflect different disciplines and knowledge to ensure that the assessment was formal and objective.

The evaluation results show that there were changes in the area of ecological security at various levels in Xi’an from 2006 to 2010. Possible causes of these changes are as follows. On the one hand, the rapid urbanization process poses potential threats or pressure to ecological security. The increase of Xi’an’s urbanization rate (from 64.52% in 2006 to 69.67% in 2010) promotes urban expansion, which changes the land use and surface form and interferes with the structure and function of the urban ecosystem. Consequently, it leads to ecological impact such as reduced farmland, increased construction land, and reduced vegetation coverage, causing the expansion of unsafe and low value safe areas. On the other hand, the implementation of ecological environmental protection policies is conducive to improving ecological security. For example, Shaanxi Province’s Qinling Mountains Ecological Environmental Protection Regulations (People’s Congress of Shaanxi Province, 2007) were implemented, which played an active role in protecting forests, conserving water sources, and maintaining soil and water in the mountains of southern Xi’an, and promoted the growth of regional satisfactorily safe and highly safe areas. Nevertheless, the factors that can cause changes in ecological security are complex and diverse, and further research is needed.

According to the assessment results, the impact of ecological security on the sustainability of Xi’an mainly concerns high density population, high density water consumption, high per capita energy consumption, low vegetation coverage, and low density river corridor. We suggest that these five low-value indicators should be effectively controlled and managed in urban planning to improve regional ecological security and to promote urban sustainability. Specifically, it is necessary to appropriately reduce urban population density to relieve ecological pressure; save water to reduce water consumption; restore river corridors to promote the spread of urban thermal energy and pollutants, and alleviate the urban heat island effect; promote the application of new energy technologies and reduce consumption of traditional energy; and expand urban green areas to increase vegetation coverage.

Due to the scope of the research, there are some limitations with the case study for demonstrating the proposed methodological approach, especially relating to the incompleteness in the selection of indicators. First, because more attention is paid to the impact of human factors on urban ecosystems, there are fewer indicators on natural factors and some are not included such as biodiversity. Second, for disaster indicators, only meteorological disasters are considered, and indicators such as earthquakes, landslides, and floods, which have equal impact on urban sustainability, are not considered due to difficulties in quantification and measurement. Future work will explore how to incorporate such uncertain and sudden indicators into the evaluation system to further fine-tune the approach.

In addition, due to current data source constraints, the accuracy of the assessment could be improved in future studies. Because the statistical data are collected by administrative divisions, it has the disadvantage of low spatial accuracy. Although the paper uses slope and land use classification as thresholds to determine the spatial distribution range of the statistical data, it would be more accurate to use the administrative division as the distribution range. However, evaluation values based on statistical data are still general and can lead to potential losses in accuracy. In the future, with the advancement of computer sciences, for example, through the use of some sensing devices with higher precision and broader coverage, it would be possible to gradually replace the statistical data with data having higher spatiotemporal resolution to improve the accuracy of evaluation. In this regard, early attempts, such as using mobile phone data to obtain dynamic population distribution and population density (Xu et al., 2019), and mobile monitoring equipment to obtain pollutant emission data (Zheng, 2015), have shown promising potential.

Conclusions

This study has established an ESI system that used a methodological approach to comprehensively assess urban ecological security. The effectiveness of the approach has been demonstrated through a case study quantitatively analyzing the impact of ecological security on urban sustainability in Xi’an between 2006 and 2010. The case study shows that the comprehensive assessment system of urban ecological security established is effective in identifying natural and artificial ecological security factors that threaten urban sustainability.

The detailed results show that the ecological security level of Xi’an is generally safe, but there are large differences in various districts and counties. The ESIs of Beilin District, Xincheng District, and Lianhu District are the lowest, the highest ESIs are in Huxian County and Lantian County, and the remaining districts and counties are somewhere in between. Five of the 32 indicators have very low normalization values (less than 0.2), which reflect the main ecological security problems and have significant impacts on urban sustainability, including population density (12,000 people/km²), water resource consumption density (1.52 million m³/km²), per capita energy consumption (3.7 tons of coal/person), river corridor density (0.2 km/km²), and vegetation cover index (0.28). Moreover, the ESI index value has a certain relationship with the topography, and the numerical value generally shows a trend of decreasing from mountains to tablelands to hills and plains.

Supplemental Material

sj-pdf-1-epb-10.1177_2399808320931869 - Supplemental material for Impact of ecological security on urban sustainability in Western China—A case study of Xi’an

Supplemental material, sj-pdf-1-epb-10.1177_2399808320931869 for Impact of ecological security on urban sustainability in Western China—A case study of Xi’an by Fei Wang and Ning Gu in Environment and Planning B: Urban Analytics and City Science

Footnotes

Acknowledgements

The authors would like to acknowledge the contributions of Xuyang Sun of Tianjin University, for recommending further references to refine the background and literature review of the research.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research work was supported by the State Scholarship Fund of China (201806285077) and the Fundamental Research Funds for the Central Universities (xjj2016041).

Supplemental material

Supplemental material for this article is available online.

Fei Wang is a Lecturer in the School of Human Settlements and Civil Engineering at the Xi’an Jiaotong University, China. With great interests in urban ecological security and sustainable urban planning, he has conducted a lot of research on urban habitat environment and GIS application in urban planning. Several relevant papers have been published in journals and international conferences.

Ning Gu is a Professor of Architecture in UniSA Creative at the University of South Australia. Professor Gu’s most significant contributions have been made toward research in design computing and cognition. The outcomes of his research have been documented in over 180 peer-reviewed publications. Professor Gu’s research has been supported by prestigious Australian research funding schemes from Australian Research Council, Office for Learning and Teaching, and Cooperative Research Centre for Construction Innovation. He is an Associate Editor of Architectural Science Review (Taylor & Francis) and has guest-edited/chaired major international journals/conferences in the field.

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