Analysis of the Effect of Ecosystem Services and Urbanization on Human Well-Being in Inner Mongolia Province

Abstract

Ecosystem services and urbanization processes are closely related to human well-being. Understanding the interaction between the three is of great importance for sustainable development. As a case study of northern China, Inner Mongolia Province, we attempt to build an effective framework to quantify human well-being from 1995 to 2020, using the entropy method and evaluating the interaction between ecosystem services, urbanization, and human well-being through the structural equation model. This model tries to understand the interaction between the three, as well as to provide some effective policies for local conditions to improve human well-being. The results showed that: (1) Except for the value of habitat quality, carbon storage and population density fluctuated, other ecosystem services and urbanization indicators have significantly improved at the province scale from 1995 to 2020. The ecosystem services indicators revealed differences between the western and eastern regions, while the high value of urbanization indicators showed a dispersed distribution. (2) Compared with 1995, human well-being improved significantly during the last twenty-five years, during which ecological human well-being increased about 30%, economic human well-being increased about 250%, and social human well-being increased about 170%. (3) Although the path coefficients revealed that ecosystem services and urbanization can significantly promote human well-being (ecosystem service: 0.517, urbanization: 0.878), urbanization had a significantly negative effect on ecosystem services with a path coefficient of −0.608. Taking ecosystem services and urbanization into consideration when studying human well-being can not only help to quantify the effects of human activities and natural resources on human well-being, but also to understand the driving mechanisms behind them. The results indicated that identifying the effect of natural resources and human activities on human well-being is beneficial for guiding effective sustainable development policies.

1. Introduction

Human well-being refers to people’s level of satisfaction with their current lifestyle, state of life, and pursuit of life [1,2]. It is a multifaceted concept that can be dissected from several angles, and it can be measured and perceived in different contexts. For example, Obrenovic et al. (2020) proposes a model for investigating the effect of work-family conflict on psychological safety and psychological well-being [3]. Guberina et al. (2023) constructs a model to investigate the effect of entrepreneurial leadership on job insecurity and employee psychological well-being during COVID-19 [4]. Improving and maintaining human well-being is the ultimate goal of sustainable development [1,2]. In the context of world ecosystem degradation, coordinating environmental protection and poverty alleviation has become the central theme of the United Nations Sustainable Development Goals [5]. In recent decades, human well-being has moved beyond philosophy and academic fields, and the pursuit of well-being has been included in many countries’ political agendas [6]. Therefore, deeply understanding the driving mechanisms behind human well-being can help to formulate effective public policies to improve the level of human well-being [7,8].

In the field of ecology, human well-being can be classified into five dimensions, including basic material for a good life, health, security, good social relations, and freedom of choice [1]. Ecosystem services are the benefits that people can derive from ecosystems, including supply services (such as food, water, and fiber), regulation services (such as air and water purification, climate, floods, diseases, disasters, and noise regulation), cultural services (such as entertainment, spirituality, religion, and other non-material benefits), and support services (such as soil retention, net primary production, and nutrient cycling) [1]. As vital links between human society and nature [9], the diversity and provision of ecosystem services are the foundation for maintaining and improving human well-being. By consuming ecosystem services, people continuously meet their own needs to enhance their well-being [2]. For example, providing ecosystem services (such as food supply) is the foundation for meeting basic needs (such as food and income) in impoverished areas [10], while scenic spots can provide entertainment for people and promote good social relationships [11]. Therefore, the importance of ecosystem services on human well-being is evident and many studies also proved this [12,13,14].

Urbanization is an inevitable trend in the period of social and economic development. In recent decades, global urbanization has rapidly developed and has become one of the most important components of global change [15,16]. It is a complex process, which involves population migration, land cover change, urban agglomeration, economic scale, and other issues [17]. In this process, urbanization not only significantly promotes economic, cultural, and social development, but also improves human well-being. However, in the era of global urbanization, human activities have gradually affected the composition, structure, and function of the ecosystem by changing land use and the consumption process of energy and resources [18], which has further affected the health status of the ecosystem and human beings. Such action has resulted in serious ecosystem degradation, the reduction in many ecosystem services on which human beings depend [19,20] and has threatened the sustainable development of human well-being. Therefore, comprehensively conducting study of the interaction process and the important links between these three aspects is important, since it can not only provide some scientific advice to better improve level of human well-being, but also achieve sustainable development.

Up to now, previous research on human well-being in the field of ecology has mainly focused on the effect of ecosystem services on human well-being, while failing to explore the relationship and interaction between the three aspects. For example, Fu et al. (2022) explored the relationships within and among ecosystem services and human well-being in the Xin’an River Basin and found that the relationship between ecosystem services and human well-being changed significantly from upstream to downstream [21]. Qiu et al. (2022) quantified the relationship between ecosystem services supply-demand and human well-being in Central and Western Inner Mongolia, and concluded that provisioning services and the regulating services had a direct and positive influence on human well-being [22]. Although the aforementioned studies involving the effects of ecosystem services on the human well-being have provided scientific references for the future research, it is necessary take the effect of urbanization into consideration.

However, due to the relationship between urbanization, ecosystem services, and human well-being being complex, it is difficult to accurately quantify the interaction process between the three. Previous statistical analyses (such as correlation analysis and regression analysis) enabled the analysis of the influence of independent factors on dependent factors, but have failed to explore the relationship between the dependent factors and the causal relationship. Under these circumstances, the structural equation model (SEM) has an advantage in solving the problem as it can quantify the relationships between multiple variables, as well as the relationships between multiple variables and independent variables. In addition, it can not only minimize the problem of non-normal, discontinuous distribution of indicators, but also allow for the existence of measurement errors [23].

Benefiting from the environmental conditions, Inner Mongolia Province has rich natural resources, which makes it play an important role in ecological protection in northern China [24]. Although ecological protection or restoration projects have been initiated in Inner Mongolia Province since 2000, the rate of China’s urbanization reached 67.48% in 2020. Under these circumstances, the contradiction between ecosystem services and urbanization intensified significantly, which has led to a series of socio-economic and eco-environmental issues [25]. Such a phenomenon has inevitably affected the level of human well-being in the region. Therefore, it is necessary to quantify the relationships between urbanization, ecosystem services, and human well-being in Inner Mongolia Province, which can not only contribute to formulating effective policies for future ecological restoration, but also provide relevant insights for improving human well-being. Given these research shortcomings, this study had three specific research objectives: (1) Quantify the temporal and spatial patterns of seven key ecosystem services and three urbanization indicators in Inner Mongolia Province from 1995 to 2020. (2) Quantify the temporal and spatial pattern of human well-being in Inner Mongolia Province from 1995 to 2020. (3) Quantify the interaction process between the three through the structural equation model.

2. Materials and Methods

2.1. Research Area

Inner Mongolia Province, extending from the northeast to the southwest in a narrow shape, is located at 97°12′–126°04′ E, 37°24′–53°23′ N, with a total land area of about 118,000 km² and an elevation of 88–3365 m. It is the third largest province in China, bordering Mongolia and Russia in the north, Ningxia, Shaanxi, Shanxi, and Hebei Provinces in the south, Gansu Province in the west, and Heilongjiang and Jilin Provinces in the east. The border line is 4200 km long. The climate is mainly temperate continental monsoon climate. Annual precipitation is 30–450 mm, increasing from southwest to northeast, and the annual evaporation in most areas exceeds 1200 mm. The annual average temperature is 0–18 °C. As an important barrier for maintaining ecological security in northern China, Inner Mongolia Province has multiple ecological elements, such as mountains, rivers, forests, cropland, lakes, grassland, and unused land. Grassland, unused land, and cropland are the main types of land use in the region (Figure 1).

There are 12 cities and 101 counties in Inner Mongolia Province, with a total population of 24.05 million in 2020. Due to the rapid development of urbanization, the proportion of the urban population in Inner Mongolia Province is rapidly increasing. In 2022, the gross domestic product of Inner Mongolia Province reached 2320 billion yuan.

2.2. Data Source and Description

Considering the drastic change in urbanization and ecosystem services in Inner Mongolia Province over the past 25 years, the temporal scale of this study was from 1995 to 2020, and the spatial scale was city scale. The land use grid data (1 km) from 1995 to 2020 were sourced from the Global Geographic Information System (GlobeLand30, http://www.globallandcover.com. Data of first access on 17 September 2023). Temperature and precipitation grid data from 1995 to 2020 were obtained from the China Meteorological Data Center (http://data.cma.cn/). The socio-economic data, which are used to evaluate human well-being, grain production, limiting value for nutrients and urbanization, were sourced from the Inner Mongolia Province Statistical Yearbook. Digital elevation model (DEM) data were sourced from geospatial data clouds (https://www.giscloud.cn).

2.3. Selection of Ecosystem Services, Urbanization, and Human Well-Being

A quantitative evaluation framework was constructed for the interaction process between urbanization, ecosystem services, and human well-being from 1995 to 2020 (Figure 2). This framework consisted of three main components, including the urbanization system (population urbanization, economic urbanization, and land urbanization), ecosystem service systems (water yield, soil retention, carbon storage, grain production, habitat quality, limiting value for nitrogen (N), limiting value for phosphorus (P)), and the human well-being system (social well-being, economic well-being, ecological well-being). Each component was a complex system and composed of multiple subsystems. The three systems had complex linear and nonlinear interactions through subsystems in different dimensions.

Urbanization is a complex and dynamic process that combines with the development of economic, population, and urban space [26]. Based on previous studies, this study evaluated urbanization systems using the three indexes proposed by Li et al. (2021) and Zhang et al. (2021), which include population growth, economic development, and the expansion of urban land use [27,28]. The three indicators were, respectively, expressed by population density, GDP, and construction land area.

For ecosystem service systems, this study quantified seven key ecosystem services related to environmental issues in the study area. Specifically, the grain production selected in this study was closely related to local GDP and residents’ livelihoods. With the expansion of agricultural scale, the main ecological issues in the research area were high evaporation rate and nutrient pollution, so water yield and limiting value for nutrients were included in the study. Soil retention was closely related to soil erosion and sandstorms [29,30,31]. Due to the significant effect of habitat quality on the quality of grasslands and croplands (the main land use types in Inner Mongolia Province), habitat quality and carbon storage were included in the analysis [32].

Human well-being refers to people’s level of satisfaction with their current lifestyle, state of life, and pursuit of life, including the basic material conditions that are necessary to maintain a high-quality life, health, good social relationships, safety, and freedom of choice and action. In order to assess human well-being, we established an evaluation index system combining the existing theories and empirical findings to reflect the local level of well-being [33] (

Table 1. Human well-being indicator system.

Human Well-Being	Index
Social well-being	Number of health institutions
	Number of beds in health institutions
	Number of teachers in middle schools
	Number of middle schools
Economic well-being	Consumption of grain (kg)
	Consumption of meat (kg)
	Livestock numbers
	Per capita income
Ecological well-being	Residential living area (km2)
	Percentage of area covered with forest (%)
	Percentage of area covered with grass (%)

). More specifically, the number of health institutions, beds in health institutions, teachers in middle schools and middle schools were included in the social well-being subsystem, since such indexes contribute to guarantees of life safety for residents [34]. Consumption of grain and meat, livestock numbers, and per capita income were included in economic the well-being sub-system, since these the reflect the level of human physical health, the basis for resident income, and for ensuring normal human life [33]. Residential living area and the percentage of area covered with forest and grass were included in ecological well-being, since such indexes reflect the level of residents’ housing security and are conducive to the physical and mental health of residents.

2.4. Quantification of Ecosystem Services

Among the seven ecosystem services, grain provision was obtained from the statistical yearbook of Inner Mongolia Province, while the description for other ecosystem services of the InVEST model process and the input parameters are shown in

Table 2. Description of the model process and input parameters for ecosystem services.

ES	Model Process	Parameters Description
Carbon storage	$C_S=C_{above}+C_{soil}+C_{under}+C_{dead}$	$C_S$ = total carbon storage $C_{above}$ = aboveground carbon storage (ton) $C_{soil}$ = soil carbon storage (ton), $C_{under}$ = underground carbon storage (ton) $C_{dead}$ = cadaver organic matter carbon storage (ton)
Water yield	$W_{xi}=(1-\frac{{AET}_{xi}}{P_x})\times P_x$	$W_{xi}$ = the annual water yield (mm) of land use type ‘i’ in grid ‘x’ ${AET}_{xi}$ = the actual annual evapotranspiration (mm) of land use type ‘i’ in grid ‘x’ $P_x$ = the annual total precipitation (mm) of grid ‘x’.
Soil retention [35] (Wang et al., 2022)	$RKLS=R\times K\times L\times S$ $RUSLS=R\times K\times LS\times C\times P$ $SR=RKLS-RUSLE$	RKLS = the potential soil erosion RUSLE = the actual soil erosion and SR is the actual soil retention (t/km2) R = the rainfall erosivity index K = the soil erodibility factor LS = the topographic factor in which L is slope length S = gradient C = the land cover management factor P = the retention practice factor.
Habitat quality	$Q_{xj}=H_{xj}\times [1-(\frac{D_{xj}^2}{D_{xj}^2+k^2}\left)\right]$	$Q_{xj}$ = the habitat quality of grid x in land-use type j $D_{xj}^{}$ = the habitat degradation degree of grid x in land-use type j $H_{xj}$ = the habitat suitability of grid x in land-use type j k is the half-saturation constant
The limiting value for nutrient [31]	$S_{N\left(x\right)}=D_{i,w}\times C_{EQS}$	where $S_{N\left(x\right)}$ is the limiting value of nitrogen or phosphorus purification of grid x (ton), $D_{i,w}$ is is the amount of water consumption in county i, $C_{EQS}$ is the standard concentration allowed by environmental quality standards for surface water (N: 1 mg/L; P: 0.2 mg/L).

.

2.5. Statistical Analysis

2.5.1. Determination of Weights for Human Well-Being Subsystems

This study used the entropy method to determine the weights of the human well-being subsystem [36]. Such a method has been widely used to evaluate ecosystem services and urbanization [32,37]. The detailed steps are as follows:

Data standardization

$$Y_0=\frac{(X_0-X_{min})}{(X_{max}-X_{min})}$$

(1)

where $Y_0$ is the standardized value, $X_0$ is the indicator value in the subsystem from 1995 to 2020, $X_{max}$ and $X_{min}$ are the maximum and minimum values of the indicator values in the subsystem.

Index weight calculation

$$\left\{{\displaystyle \begin{array}{cc}{\mu }_{pj}=\frac{1+X_{pj}}{\sum _1^n(1+X_{pj})}\hfill & \left(2\right)\hfill \\ e_j=-\frac{1}{\mathrm{l}\mathrm{n}\left(n\right)}{\displaystyle \sum _1^n}{\mu }_{pj}\times \ln \left({\mu }_{pj}\right)\hfill & \left(3\right)\hfill \\ g_j=1-e_j\hfill & \left(4\right)\hfill \\ W_j=\frac{g_j}{\sum _1^m{g}_i}\hfill & \left(5\right)\hfill \end{array}}\right.$$

where $X_{pj}$ is the standardized value of indicator “j” in “p” city, ${\mu }_{pj}$ is the proportion of indicator “j” in “p” city. $e_j$ is the entropy of indicator “j”, $g_j$ is the redundancy degree of entropy, $W_j$ is the weight of each index.

2.5.2. Establishment of Structural Equations

A structural equation is a multivariate statistical method that can explore logical relationships and complex influence paths within a limited sample size [22,38]. Considering the limited sample size and complex interaction mechanisms, a structural equation was established to quantify the effect of ecosystem services and urbanization on human well-being (Figure 2).

In the theoretical structural equation, ecosystem services were divided into seven potential variables: water yield, soil retention, carbon storage, grain production, habitat quality, limiting value for N, and limiting value for P. Urbanization was divided into three potential variables: population urbanization, economic urbanization, and land urbanization. Human well-being was divided into three comprehensive subsystems: the social well-being subsystem, the economic well-being subsystem, and the eco-environmental subsystem. The research hypotheses proposed in this study are as follows:

Based on the existing theories and empirical findings, the research hypotheses proposed in this study are as follows:

H1. 

The improvement of selected potential variables of ecosystem service systems (water yield, soil retention, carbon storage, grain production, habitat quality, limiting value for N, and limiting value for P) is positive for ecosystem health. Therefore, we speculate that the relationship between the selected potential variables and the ecosystem services system is positive [22].

H2. 

The improvement of selected potential variables of urbanization systems (population urbanization, economic urbanization, and land urbanization) is positive for urbanization process. Therefore, we speculate that the relationship between the selected potential variables and the urbanization system is positive [33].

H3. 

The expansion of construction land area will inevitably transform cropland, grassland, and forest land, with their strong carbon sink capacities, into construction land, which damages the ecosystem, soil environment, and natural habitat. In addition, the growth of population will increase the emission of greenhouse gases, which will result in a reduction in carbon storage. Therefore, we speculate that the relationship between urbanization and the ecosystem services system is negative [39].

H4. 

Urbanization can promote economic, cultural, and social development and plays an important role in improving human well-being, while the natural environment can provide materials and services for human survival and development. Therefore, we speculate that both the urbanization subsystem and the ecosystem service subsystem have a positive effect on human well-being.

The above assumptions were verified through structural models:

$$\eta =\beta \times \eta +\Gamma \times \xi +\zeta$$

(6)

where η is an endogenous latent variable, such as human well-being; ξ It is an exogenous latent variable, such as ecosystem service subsystem; Γ is the direct and indirect effect of ξ on η, which can be measured using path coefficients and the significance [38,39]. β is the relationship with η, ζ is the residual.

The measurement model of the structural equation also explored the contribution of the ecosystem service subsystem or urbanization subsystem to various indicators within the subsystem:

$${\rm X}{=\Lambda }_X\times \xi +\delta$$

(7)

$$Y{=\Lambda }_Y\times \eta +\epsilon$$

(8)

where ξ and η are exogenous and endogenous latent variables, respectively; X is ξ observed variables, such as water yield in the ecosystem services subsystem; Y is η observed variables, such as GDP in the urbanization subsystem; ${\Lambda }_X$ and ${\Lambda }_Y$ are the factor loading matrix of the observed variable on its potential variable; δ and ε are random errors.

3. Results

3.1. The Spatiotemporal Pattern of Ecosystem Services

Based on InVEST and ArcGIS, this article quantified and mapped seven types of ecosystem services at the municipal level from 1995 to 2020, including soil retention, limiting value for N, limiting value for P, habitat quality, grain production, carbon storage, and water yield.

For the changes at the province scale, except for habitat quality and carbon storage, other ecosystem services have undergone significant changes between 1995 and 2020. Among them, the limiting value for N, the limiting value for P, and grain production showed an increasing trend during the last twenty-five years. Compared to 1995, by 2020 these three ecosystem services had increased by 25%, 25%, and 240%, respectively. Soil retention and water yield showed a fluctuating trend. Specifically, soil retention having decreased by 14% in 2000, increased by 20% in 2005, decreased by 39% in 2010, increased by 23% in 2015, and ultimately increased by 59% in 2020. As for water yield, it decreased by 15% in 2000, increased by 3.5% in 2005, decreased by 14% in 2010, decreased by 27% in 2015, and ultimately increased by 46% in 2020 (

Table 3. Changes in ecosystem services, urbanization, and human well-being in Inner Mongolia Province.

		1995	2000	2005	2010	2015	2020
Ecosystem services
	Soil	3.5× 1012	3.0× 1012	3.6× 1012	2.2× 1012	2.7× 1012	4.3× 1012
	P	3115.8	3445.6	3495.2	3638.2	3715.4	3888.4
	N	15,579	17,228	17,476	18,191	18,577	19,442
	HQ	0.57	0.56	0.56	0.57	0.57	0.57
	Grain	1079	1205	1857	2318	3019	3664
	CS	5.8 × 109	5.7 × 109	5.8 × 109	5.8 × 109	5.8 × 109	5.8 × 109
	WY	3.3 × 108	2.8 × 108	2.9 × 108	3.3 × 108	2.4 × 108	3.5 × 108
Urbanization
	GDP	769	1417	4115	12,886	20,559	17,291
	Con	9748	10,105	10,313	10,412	13,043	13,745
	Pop	2237	2375	2386	2472	2511	2402
Human well-being
	Ecological	0.207	0.139	0.208	0.101	0.223	0.267
	Economic	0.286	0.307	0.348	0.686	0.853	0.785
	Social	0.253	0.353	0.267	0.275	0.585	0.685

Note: Soil refers to soil retention (ton). N refers to the limiting value for N (ton). P refers to the limiting value for P (ton). HQ refers to habitat quality. Grain refers to the grain production (ten thousand tons). CS refers to carbon storage (ton). WY refers to water yield (ton). GDP refers to the gross domestic product (ten thousand yuan). Con refers to construction land area (km²). Pop refers to population density (ten thousand people). Ecological refers to ecological well-being. Economic refers to the economic well-being. Social refers to social well-being.

).

For the changes at the city level, there were significant differences in different cities. Except for Hulunbuir city, the amount of soil retention in other cities remained basically unchanged between 1995 and 2020. For the limiting value for N and P, except for Hulunbuir city and Alxa League, the other cities did not change significantly from 1995 to 2020. In terms of carbon storage and habitat quality, there was a little difference in changes among all cities between 1995 and 2020. Except for Hulunbuir city and Alxa League, the water yield of most cities did not change significantly from 1995 to 2020. The water yield of Hulunbuir city showed an upward trend, while in Alxa League it showed a downward trend. In terms of grain production, most cities showed an upward trend between 1995 and 2020 (Figure 3).

In terms of spatial scale, most of the high mean values of ecosystem services were concentrated in the east of Inner Mongolia Province, in areas such as Hulunbuir city, Hinggan League, Tongliao city, and Chifeng city. The lower values were mostly located in the western region, in areas such as Wuhai city and Alxa League (Figure 4).

3.2. The Spatiotemporal Pattern of Urbanization

For the change in urbanization at the province scale, all three indicators of urbanization have undergone significant changes between 1995 and 2020. Among them, GDP and construction land area increased with year. Compared with 1995, by 2020 these two indicators had increased by 2148% and 41%, respectively. Population density showed a fluctuating trend. It increased with year from 2000 to 2015, increasing by about 12%, and ultimately decreased by about 4.3% in 2020 (Table 3).

For changes at the city level, there were significant differences among different cities. The GDP and construction land area of almost all of the cities showed an upward trend between 1995 and 2020. The trend of population density varied among cities, with half of the cities showing an upward trend and the other half showing a downward trend (Figure 5).

In terms of spatial scale from 1995 to 2020, most of the high average value regions in terms of urbanization indicators were concentrated in Chifeng city, Tongliao city, Hohhot city, and Ordos city. The lower values were mostly located in Alxa League in the west, Xilingol League in the center, and Hinggan League in the east (Figure 6).

3.3. The Spatiotemporal Pattern of Human Well-Being

For the temporal variation pattern at the province scale, all three indicators of human well-being showed a fluctuating upward trend. Compared to 1995, by 2020 ecological well-being, economic well-being, and social well-being had increased by approximately 28.9%, 174%, and 170%, respectively (Table 3).

For the changes at the city level, there were significant differences among different cities. For all indicators, the values of most cities showed an upward trend from 1995 to 2020. This indicates that human well-being has improved significantly during the past 25 years (Figure 7).

In terms of spatial scale, the high values of social well-being from 1995 to 2020 were located in the eastern region and some regions in the west, such as Hulunbuir city, Tongliao city, Hinggan League, Chifeng city, Baotou city, and Hohhot city, while the lower values were located in the center and west of Inner Mongolia Province, in areas such as Xilingol League and Alxa League. The lowest value was 0.0149, while the highest value was 0.3723, with a difference of approximately 2396%. The high values of economic well-being were located in the eastern region and some regions in the west, such as Hulunbuir city, Tongliao city, Hinggan League, Chifeng city, Baotou city, and Hohhot city, while the lower values were located in the central and western regions of Inner Mongolia Province, such as Xilingol League, Wuhai city, Bayannur city, Ordos city, and Alxa League. The lowest value was 0.0255, while the highest value was 0.5260, with a difference of approximately 1962%. The higher values of ecological well-being were located in the eastern region and some regions in the west, such as Hulunbuir city, Tongliao city, Hinggan League, Chifeng city, Baotou city, and Hohhot city, while the lower values were located in the center and west of Inner Mongolia Province, in areas such as Xilingol League, Wuhai city, Bayannur city, Ordos city, and Alxa League. The lowest value was 0.0112 and the highest value was 0.6680, with a difference of approximately 5864% (Figure 8).

As for results of weights for the human well-being subsystem, the weight of the number of middle schools were the highest on the social well-being subsystem, while the number of beds in health institutions was the lowest. In terms of the economic well-being subsystem, the weight of per capita income was the highest, while consumption of meat was the lowest. As for the ecological well-being subsystem, the weight of residential living area was the highest, while the percentage of area covered with grass was the lowest (

Table 4. Weights for the human well-being subsystem.

Human Well-Being	Index	Weight (Entropy Method)
Social well-being	Number of health institutions	0.19
	Number of beds in health institutions	0.17
	Number of teachers in middle schools	0.29
	Number of middle schools	0.35
Economic well-being	Consumption of grain (kg)	0.21
	Consumption of meat (kg)	0.10
	Livestock number	0.26
	Per capita income	0.43
Ecological well-being	Residential living area (km2)	0.37
	Percentage of area covered with forest (%)	0.33
	Percentage of area covered with grass (%)	0.30

).

3.4. The Effect of Ecosystem Services and Urbanization on Human Well-Being

According to the results of variance inflation factor testing, the variance inflation rates of various indicators were all less than 10 (

Table 5. Variance inflation factors of variance collinearity diagnosis.

Latent Variables	Observes Variables	VIF
Ecosystem services
	Soil retention	6.478
	Limiting value for N	5.145
	Limiting value for P	2.345
	Habitat quality	5.555
	Grain production	7.654
	Carbon storage	2.453
	Water yield	1.225
Urbanization
	GDP	2.336
	Construction land area	3.458
	Population density	2.486
Human well-being
	Economic well-being	6.547
	Ecological well-being	5.478
	Social well-being	4.121

). The simulation results of the structural variance model were effective, because CMIN/DF < 2.0 (1.352), GFI > 0.9 (0.978), AGFI > 0.9 (0.922), and RMSE < 0.1 (0.0954).

As shown in Figure 9, except for the limiting value for N and the limiting value for P, other ecosystem service indicators had a promoting effect on the ecosystem service subsystem. This result failed to verify the effectiveness of H1. Among them, grain production and soil retention had the most significant effect on the ecosystem service subsystem, while the effect of carbon storage was least significant. The indicators of urbanization had a promoting effect on the urbanization subsystem, which verified the effectiveness of H2. Among them, GDP had the most significant effect on the urbanization subsystem, while the effect of population density was least significant. The urbanization subsystem had a negative effect on the ecosystem service subsystem, which verified H3. Both the ecosystem service subsystem and the urbanization subsystem had a positive effect on human well-being, which verified H4. Among them, economic well-being had the most significant effect on human well-being, while ecological well-being had the lowest coefficient values (Figure 9).

4. Discussion

4.1. Reasons for the Changes in Ecosystem Services and Urbanization

This study aims to quantify the spatial and temporal patterns of ecosystem services and urbanization, as well as human well-being. The results of the research can not only improve the level of human well-being, but also contribute to sustainable ecological development.

Through the results obtained, the ecosystem services in Inner Mongolia Province have gradually improved compared with 1995 (Table 3), which was mainly due to the strong support of national policies for ecological protection. Inner Mongolia Province implemented the Grain for Green and Grassland Project and the Agriculture, Rural Areas and Farmers Project from 1995 to 2010 [31], which significantly increased ecosystem services (grain production and soil retention) by improving vegetation coverage and farmland quality. This result was similar to that of Zhang et al. (2020), which suggested that implementing the Grain for Green and Grassland Project can increase the supply of ecosystem services [40]. However, due to the spatial differences of ecosystem services in various regions being closely related to the spatial differences of physical and geographical conditions, the high-value areas of ecosystem services were mainly distributed in the northeast of Inner Mongolia Province. This result was similar to that of Wang et al. (2023) [32]. In the northeast of the study area, there was a biological gene pool of a large number of rare species due to the high density of shrubs and forests [41], which can help to improve the productivity of ecosystem services. In contrast, due to desert being the main land use type and its perennial arid climate and fragile environment, the capacity of the northwest area to supply ecosystem services is weak [26].

Urbanization is a long-term and complex process, including socio-economic activities. Since the reform and opening-up in 1978, China has achieved remarkable achievements in industrialization and urbanization [42]. Inner Mongolia Province has made great progress in population density, construction area, and GDP from 1995 to 2020 (Table 3). With the expansion of cities, the economy has experienced explosive growth. This is mainly attributed to national development policies and China’s accession to the World Trade Organization in 2001, in the context of economic globalization. This has had a profound effect on China’s urbanization [43]. The characteristics of urbanization in the early stage were mainly based on the joint development of three indicators. However, with the development of Inner Mongolia Province in recent years, and the effect of large population movement from central and western area of China to relatively developed coastal areas, the characteristics of the development pattern of urbanization were transformed to the rapid development of construction area and GDP, which were different from the urbanization characteristics explored by some researchers [44,45].

Therefore, as an important ecological barrier belt in northern China, Inner Mongolia Province should focus on the quality of urbanization rather than pursuing the rapid growth of its construction area. At the same time, effective measures needed to be established to attract more permanent population, so as to adapt the rapid urbanization of construction area. In addition, in order to promote healthy ecological development, urban construction should strengthen land integration, rather than encroaching on agricultural land or natural ecosystems.

4.2. Reasons for the Changes in Human Well-Being

In general, except for ecological human well-being which showed a fluctuated increasing trend, the economic human well-being and social well-being of Inner Mongolia Province gradually increased from 1995 to 2020. Such a phenomenon is mainly attributed to the campaign of Western Expansion and integration into the world economy and rapid urbanization. Benefiting from the abundant natural resources of Inner Mongolia Province, rapid urbanization gradually improves the personal quality of life. However, with further urbanization expansion, the high-yield farmland, grassland, and forest land were deteriorated, which inevitably generated a severe threat to the natural ecosystem [40] and will restrain the development of social human well-being and economic human well-being. That is why the lowest value appeared in 2010. However, with a series of policies proposed in recent years, such as the Mountain-River-Forest-Farmland-Lake-Grass System and Ecological Protection and Restoration, the ecosystem was protected from deterioration and ecological human well-being will be improved in the future.

Compared with 1995, although the human well-being indicators were improved b 2020, the spatial heterogeneity is significant. The highest values were mainly concentrated in Hulunbuir city, Hinggan League, Tongliao city, Chifeng city, Hohhot city, and Baotou city, while the lowest values were always located in Alxa League. Such a phenomenon can be explained by the difference in natural geographical factors and socio-economic development from the western to eastern regions [46]. Although Inner Mongolia Province has experienced rapid economic development in the past 20 years, regional economic development is uneven. Due to the main land use in Alxa League being unused land, the barren land is unable to meet the demand of ecological human well-being. Under these circumstances, the poor natural environment further restrains the development of social human well-being and economic well-being. In contrast, the most fertile natural environment is located in the eastern region, which can provide the basis for development of social and economic well-being. Therefore, only under the premise of protecting the natural environment can the human well-being be further improved.

4.3. The Driving Mechanism behind the Structural Equation Model

4.3.1. The Driving Mechanism behind the Measurement Analysis

For the ecosystem service subsystem, soil retention, and habitat quality had a significant positive effect on the ecosystem service subsystem (Figure 9). The increase in soil retention can reduce soil desertification and promote the increase in forest and grassland areas, while the improvement of habitat quality helps to increase biodiversity. The increase in both will ultimately improve ecological well-being in the human well-being subsystem. In addition, grasslands also provided food for animal husbandry, thereby promoting the increase in livestock in the study area and promoting economic well-being [47]. The effect of water yield on ecosystem services was positive, while the effect of the limiting value for nutrients was negative. Water yield was one of the important ecosystem services in Inner Mongolia Province, and also provided the basic resources for agricultural and industrial production. Meanwhile, the increase in water yield can improve the arid soil environment in Inner Mongolia Province, which plays a positive role in soil and water conservation and sand fixation. The negative effect of the limiting value for nutrients was mainly attributed to the fact that the limiting value for nutrients was a function of nutrient output and water consumption. The increase in water consumption will exacerbate the arid climate in Inner Mongolia Province and reduce the supply capacity of the natural environment, while the increase in nutrient output will worsen the water quality, and the increase in both will ultimately affect the level of human well-being. Grain production was an important ecosystem service in Inner Mongolia Province, as grains, meat, and milk maintained the lives and health of residents. This conclusion was also supported by the results of Ciftcioglu (2017) [13]. At the same time, the economic well-being of residents also depends on the production of food.

For the urbanization subsystem, GDP and construction area had a significant positive effect on the urbanization subsystem. The increase in GDP can increase the per capita wage level, promote residents’ consumption of food and meat, which promotes the growth of economic well-being. The increase in construction land can provide a material basis for hospitals and schools, which promotes the growth of social well-being.

4.3.2. The Driving Mechanism behind Path Analysis

According to the research results, social and economic well-being had a significant effect on human well-being in Inner Mongolia Province, while ecological well-being had less effect on human well-being than social and economic well-being (Figure 9). According to the results of the effects of ecosystem services and urbanization on human well-being, it can be seen that urbanization played a significant role in improving human socio-economic well-being compared to ecosystem services. Land development and socio-economic development had greatly improved people’s clothing, food, housing, transportation, health, and education conditions, and improved the supply of materials and the vitality of economic production.

Although previous studies have shown that with the process of urbanization, ecosystem services exhibited a non-linear “U” shaped change, GDP growth can contribute to the implementation of ecological restoration [48]. In this study, however, the increase in urbanization had a negative effect on ecosystem services [23]. This was mainly due to the fact that the current urbanization of Inner Mongolia Province has not been coordinated with ecosystem services. Low quality urbanization has led to a rapid increase in construction land and the rapid loss of grassland and forests, which poses a serious threat to regional ecological protection [40]. With the increased scarcity of natural resources, outdated development models in the past were no longer sustainable. The rapid loss of natural resources will lead to a decrease in ecosystem service supply, which ultimately leads to a decline in ecological well-being. This will not only lead to a decrease in total human well-being and an imbalance in development, but can also directly threaten the growth potential and sustainability of future urban development and human well-being [46].

4.4. Policy Implementation

Based on the results of the structural equation model, the spatiotemporal pattern of ecosystem services, urbanization, and human well-being, and the entropy weight, we put forward some scientific policies to improve human well-being in Inner Mongolia Province.

In terms of spatial scale, some cities, such as Hulunbuir city, Hinggan League, Tongliao city, Chifeng city, Hohhot city, and Baotou city, had high levels of the three types of well-being, but the limited reasons behind the phenomenon were different. For Hulunbuir city, Hinggan League, Tongliao city, and Chifeng city, due to their abundant natural environmental resources, the value of ecological human well-being was at a high level. The main limiting factor for improving human well-being is the relatively low level of urbanization. Therefore, local governments should actively attract investment and gradually develop urban construction in a reasonable manner. For Hohhot and Baotou, due to the urbanization level being relatively high compared with other cities, the main limiting factor for improving human well-being is the poor ability to supply the ecosystem. Local governments can put forward policies to promote sustainable consumption patterns, propose green consumption, and reduce unnecessary ecosystem service waste in the process of rapid socio-economic development by improving resource utilization and recycling efficiency.

For the Xilingol League, Ulanqab, Hohhot, Baotou, Bayannur, Ordos, Wuhai, and Alxa League, both a relatively poor natural environment and a low urbanization level inhibited the improvement of human well-being. Policymakers should formulate relevant policies to improve human well-being: (1) Formulating rational land use planning to optimize the residential living area and area covered with forest and grassland, so as to improve ecological human well-being. (2) Formulating rational policies to attract investment to increase per capita income (weight: 0.43), so as to improve economic well-being. (3) Considering the number of middle schools in such areas has gradually decreased and the number of teachers has increased, local government should advance the building of middle schools (weight: 0.35). Only in this way can the social human well-being be improved.

5. Conclusions

Understanding the effect of ecosystem services and urbanization on human well-being can provide scientific references for improving regional ecological management and sustainable development. This study quantified the temporal and spatial patterns of seven ecosystem services, three urbanization indicators, and human well-being indicators at the city and province scales in Inner Mongolia Province from 1995 to 2020, and also applied the structural equation model to explore the effect of ecosystem services and urbanization on human well-being. The results indicated that:

(1) Although other ecosystem services underwent significant changes between 1995 and 2020 at the province scale, except for habitat quality and carbon storage, this index showed high spatial characteristics in the eastern region and was low in the western region. In contrast with ecosystem services, the area with high spatial characteristics for three urbanization indicators is in a state of dispersed distribution.

(2) The three human well-being indicators were significantly improved during the last twenty-five years, in which ecological human well-being increased about 30%, economic human well-being increased about 250%, and social human well-being increased about 170%.

(3) Although ecosystem services and urbanization can significantly promote human well-being, urbanization had a significantly negative effect on ecosystem services with the path coefficients being −0.608.

One of innovations in this study is incorporating ecosystem services and urbanization into structural equations to explore how ecosystem services and urbanization affected human well-being, thereby providing a comprehensive understanding of quantified effect pathways. In addition, the weights in the measurement model quantified the relative importance of each indicator to the subsystems. Although the research results can provide some scientific references for sustainable development, there were still some limitations. For example, although there are seven key ecosystem services, three urbanization indicators and three human well-being indicators were selected to represent the state people live in in Inner Mongolia Province, the indicators which representing ecosystem services, urbanization, and human well-being should be more abundant and diversified, so as to maximize the comprehensiveness of the study.