Spatial and Temporal Variations in the Coupled Relationship between Ecosystem Services and Human Well-Being in Gansu Province Counties and the Factors Affecting Them

Abstract

Investigating the interplay between ecosystem services and human well-being is crucial for enhancing ecological conservation and achieving a superior quality of development. This research examined the spatial–temporal disparities in ecosystem services and human well-being across 87 counties in Gansu Province using the coupling coordination degree model and geographically weighted regression analysis. The key findings include the following: (1) Over two decades, from 2000 to 2020, ecosystem services in Gansu Province witnessed a financial appreciation of approximately CNY 29.32 billion. The distribution displayed a notable trend, with higher values in the southeastern counties, particularly in Gannan and Longnan, whereas lower values prevailed in the Hexi area. (2) The well-being of the populace experienced a substantial enhancement, registering a 75% increase during the same period, characterized by higher well-being indices in the north, especially in Hexi, and the lowest indices in Gannan and the Linxia sector of Longzhong. (3) The coupling coordination degree between ecosystem services and human well-being escalated from 0.412 to 0.587, transitioning from moderate dysfunction to moderate coordination over the two decades. Regions such as Hexi and Gannan recorded a moderate discordance in their coupling coordination, whereas regions including Longzhong, Longnan, and Longdong demonstrated a more stable, basic coordination. (4) Influences on the coupling coordination degree between county ecosystem services and human well-being displayed significant spatial variability, often aligned along distinct geographic bands. Economic and natural foundations were predominantly aligned southeast to northwest, while investment and urbanization levels were more prominent from west to east, and industrialization levels were distributed along an east–west axis.

1. Introduction

Since the onset of the 21st century, the world has experienced a significant surge in population and swift urbanization processes, leading to escalated demands on natural resources due to human activities. These pressures have inflicted considerable harm on natural ecosystems and progressively diminished the efficacy of global ecosystem services [1,2,3]. Addressing the adverse effects of human endeavors on ecosystems and enhancing the capabilities of ecosystem services to foster their symbiotic progress with human welfare has emerged as a critical global issue. Initiatives like the Global Land Program (GLP), the Millennium Ecosystem Assessment (MA), and the United Nations’ 2030 Agenda for Sustainable Development underscore the importance of nurturing the synergistic growth of ecosystem services and human well-being [4,5]. The tension between ecological conservation and economic advancement is particularly acute in the economically lesser-developed western areas of China, where enhancing human welfare while safeguarding the environment poses a pressing challenge.

The term “ecosystem service” encompasses the myriad benefits that humans accrue from the functions and processes of ecosystems, with alterations in these services having a direct or indirect impact on human well-being [6,7]. A plethora of research has investigated the nexus between ecosystem services and human well-being [8]. Regarding methodologies, the majority employ the ecosystem service value equivalence scale and the InVEST model, formulated by Costanza and colleagues [9], for assessing service values and volumes [10]. Well-being assessment predominantly utilizes tools such as the Human Development Index [11], the National Well-Being Index [12], life satisfaction measures [13], and the Global Well-Being Index [14]. Studies have transitioned from qualitative assessments to quantitative analyses utilizing statistical methods [15]. In terms of content, the research explores the effects on human well-being via the equilibrium of ecosystem service supply and demand [16] and public perceptions of landscape ecological functionalities [17]. The influence of well-being on ecosystem services is examined through subjective well-being metrics [18], agricultural community livelihood adaptations [19], and strategic regional management [20]. Furthermore, the impact of land-use alterations on the interplay between ecosystem services and human well-being is also scrutinized [21]. While research predominantly covers the global [22], national [23], watershed [24], and provincial scales [25], there is a scarcity of quantitative studies at the county level. Generally, the focus has been more on the contributions of ecosystem services to human well-being and the dependency of well-being on these services, with less emphasis on the interdependencies between them, particularly the lack of detailed analysis concerning the determinants of these relationships.

Situated in the ecologically critical western region of China, Gansu Province is pivotal for the nation’s ecological initiatives, playing an essential role in nationwide environmental strategies. The ecological landscape of this area is particularly vulnerable, characterized by fragile ecosystems that are increasingly destabilized by the rapid pace of economic growth, which, in turn, exacerbates regional developmental imbalances [26]. In response to these issues, identifying the complex links between ecosystem services and human welfare becomes vital for the protection of the biophysical environment and the development of sustainable models.

This paper aims to analyze the changes that occurred over the period between 2000 and 2020 in 87 counties of Gansu Province. It uses a set of complex analytical tools, such as the HDI, CCD model, and GWR model, to analyze and explain the mechanisms that regulate the interactions between ES and human welfare. It is pertinent to identify more than just the spatial and temporal variations in these relations but the factors that are responsible for these fluctuations as well. Hence, through analyzing the causative factors and their effects, this study aims to contribute theoretical frameworks and effective policy recommendations for the synergy between ecosystem functions and human well-being in economically deprived regions.

2. Materials and Methods

This study targets Gansu Province, which is considered an underdeveloped area, and uses land use data, DEM data, and socio-economic data. Using the Human Development Index together with the coupling coordination degree model and the geographically weighted regression model, the study analyses the temporal and spatial characteristics of the coordination between ecosystem services and human well-being from 2000 to 2020. It also examines the determinants influencing this relationship. Based on these insights, the paper proposes a tailored strategy to enhance the synergistic integration of ecosystem services and human welfare, adapted specifically to the local conditions of the region (Figure 1).

2.1. Study Area

Situated at the intersection of the Qinghai–Tibet Plateau, Loess Plateau, and Inner Mongolia Plateau, Gansu Province stretches in a unique “northwest–southeast” orientation and serves as a critical ecological safeguard for the upper Yellow River, as well as a vital component of the Silk Road Economic Belt (Figure 2). The province features a complex and varied topography, predominantly composed of mountains and plateaus; its climate is mostly arid, characterized by a continental temperate monsoon climate, with average temperatures ranging between 0 and 14 °C and annual precipitation averaging 280 mm, which is spatially uneven. Gansu’s ecological environment is notably delicate and sensitive, with regions prone to soil erosion primarily located in the Loess Plateau and Longnan Mountains, while areas susceptible to desertification are mostly situated along the northern fringe of the Hexi Corridor [27]. This study focuses on the 87 counties and cities within Gansu Province, categorizes them into five distinct zones, namely, Hexi, Longzhong, Longdong, Longnan, and Gannan, based on the results of prior studies on the subject [28].

2.2. Data Sources

This study utilizes a rich data set with fine scale land use information, DEM and socio economic information. The land use patterns are carefully delineated from high-spatial-resolution remote sensing data from Landsat TM and ETM of 30 m by 30 m spatial resolution that were available in the geospatial data cloud. DEM data, crucial for understanding the topographical variations across the region, were also retrieved from this platform (http://www.gscloud.cn/, accessed on 27 May 2024), which has the same spatial resolution as the original. To ensure that human well-being was measured appropriately, this study relied on multiple sources of data, such as the Gansu Development Yearbook and Gansu Rural Yearbook, in addition to the China County Statistical Yearbooks for 2000, 2010, and 2020. Moreover, data from statistical yearbooks were used to augment the data set and provide sound results, with data from 87 counties in 14 cities and autonomous prefectures in Gansu Province used.

2.3. Methods

2.3.1. Valuation of Ecosystem Services

A new equivalence table by Xie and co-authors extend and update the evaluation of the ecosystem services of different types of land in China [29], including the consideration of changes in agricultural yields and market prices. For instance, from the year 2000 to 2020, in Gansu Province, the average grain yield was 6914 kg/hm², while the market price for grains in the year 2020 was CNY 2.4 per kilogram. The new estimates indicate that the total value of natural ecosystems without any agricultural interventions by human beings is roughly equal to one-seventh of the value provided by cultivated land in terms of area [30,31]. By this recalibration, the ecosystem service equivalency factor for Gansu Province was found to be 2370 CNY/hm², as presented in

Table 1. Ecosystem service value coefficients per unit area in Gansu Province.

Ecosystem Services Function		Land Use Types
First Category	Second Category	Cropland	Forestland	Grassland	Water	Unutilized
Provisioning services	Food production	25.49	5.83	5.38	15.11	0.23
	Raw material	5.65	13.38	7.92	8.42	0.69
	Supply water supply	−30.11	6.92	4.38	125.50	0.46
Regulating services	Gas regulation	20.53	44.01	27.84	30.80	2.54
	Climate regulation	10.73	131.67	73.59	67.94	2.31
	Environment	3.11	38.58	24.30	105.55	7.15
	Hydrological regulation	34.49	86.17	53.91	1458.83	4.84
Supporting services	Soil conservation	12.00	53.58	33.91	37.37	3.00
	Maintenance of nutrient cycling	3.58	4.09	2.61	2.88	0.23
	Biodiversity	3.92	48.79	30.84	120.19	2.77
Cultural services	Aesthetic landscape	1.73	21.40	13.61	76.36	1.15
Add up the total		91.13	454.42	278.30	2048.96	25.38

. Following this, the study selects the counties within Gansu Province as the main units of analysis to comprehensively examine the spatial–temporal changes in the ES values. This approach makes it possible to take into account the complexity of the effects of human activities on these values [32]. The value of ecosystem services (ESVs) for each county is meticulously calculated using the following designated formula, ensuring precise and context-specific evaluations:

$$\begin{array}{l}ESV={\displaystyle {\sum }_{i=1}^m{\displaystyle {\sum }_{j=1}^n{A}_j{E}_{ij}}}\\ \left(i=1,2,3.....,m;j=1,2,3.....,n\right)\end{array}$$

(1)

In this formula, an ESV represents the ecosystem service value for a given county. The variable $A_j$ denotes the surface area covered by ecosystem type j within the county, while $E_{ij}$ refers to the per unit equivalent value of ecosystem function i pertaining to ecosystem type j.

2.3.2. Assessment of the Level of Human Well-Being

Quality of life is commonly conceived from a number of aspects, which are often defined as quality of health, happiness, wealth, and a life that is considered as positive. This concept encapsulates the dreams that people have for their lives, and it is a general desire to have better standards of living and overall human and social development [33]. This paper integrates insights from researchers, such as Tian and Zhou [34,35], using the comprehensive Human Development Index tailored specifically to assess well-being within the unique socio-economic and ecological contexts of Gansu Province. The methodology for evaluating well-being begins with the application of the extreme difference standardization technique, which normalizes the indicators to ensure comparability. Following this, the entropy weighting method is utilized to determine the objective weights of these indicators, ensuring that each indicator’s influence on the overall assessment is proportionately represented [36,37]. The next step involves calculating the well-being indices for each county and district in Gansu Province through a method of weighted summation, which integrates the standardized indicators according to their assigned weights [38]. Finally, to provide a comprehensive view of regional well-being, the mean values of these indices are computed, offering a summary measure of human well-being across Gansu Province, as illustrated in

Table 2. Human welfare evaluation index system and weight.

Structure of Human Functioning	Functional Indicators	Specific Indicators	Indicator Characteristic	Weight
Basic functions	Health	Number of hospital beds per 10,000 persons (beds/million)	+	0.108
		Doctors per 10,000 persons (persons/million)	+	0.086
	Education	Pupil–teacher ratio in primary and secondary schools (%)	+	0.084
		Proportion of illiterate and semi-illiterate persons among the population (%)	-	0.105
Harmony function	Urban and rural integration	Urban disposable income/Rural disposable income	-	0.150
Development function	Living standards	Per capita net income of rural residents (CNY)	+	0.068
		Rural Engel’s coefficient (%)	-	0.117
		GDP per capita (CNY)	-	0.143
Sustainable function	Responding to climate change	Carbon intensity (CNY/kg)	-	0.138

.

2.3.3. Coupling Coordination Degree (CCD) Model

The coupling coordination degree model utilizes the principle of “coupling” from physics to assess the interaction strength and synergistic relationships among various systems [39]. This metric is employed to evaluate the level of harmonious development between ecosystem services and human well-being. The equation to compute this degree is detailed subsequently:

$$C=n\times {\left[\frac{U_1\times U_2}{{\left(U_1+U_2\right)}^n}\right]}^{\frac{1}{2}}$$

(2)

$$D=\sqrt{C\times T}$$

(3)

$$T=\alpha U_1+\beta U_2$$

(4)

$$\mu =\frac{U_1}{U_2}$$

(5)

In the presented model, C is defined as the degree of coupling, while D stands for the degree of coordination in the coupling. The index T is identified as a comprehensive index of the system coordination. Furthermore, β serves as the evaluation coefficient, quantifying the integrated coordination between the services provided by ecosystems and the well-being of community residents. The equation α + β = 1 underpins the framework, with an assignment of α = β = 0.5 to reflect the equal significance attributed to both parameters in this analysis. The variable μ, representing the degree of relative development, captures the differences in development levels between the services ecosystems provide and the well-being of the population. Drawing on insights from earlier research and observing the region’s actual developmental trajectory, the study categorizes stages of coupling coordination and outlines the developmental relationships among the subsystems, as detailed in

Table 3. Divisions of the ES-HWB coupling coordination phases and internal levels.

Degree of Coupling Coordination	Coupling Coordination Stage	Notation	Relative Degree of Development	Coupling Characteristics
0 ≤ D ≤ 0.2	Severe dysfunction	I	0 < μ ≤ 0.6 0.6 < μ ≤ 1.2 1.2 < μ	Lagging development of ecosystem services Low level of balanced development of both Lagging development of human well-being
0.2 < D ≤ 0.4	Moderate dysfunction	II	0 < μ ≤ 0.6 0.6 < μ ≤ 1.2 1.2 < μ	Lagging development of ecosystem services Low level of balanced development of both Lagging development of human well-being
0.4 < D ≤ 0.5	Basic coordination	III	0 < μ ≤ 0.6 0.6 < μ ≤ 1.2 1.2 < μ	Lagging development of ecosystem services Low level of balanced development of both Lagging development of human well-being
0.6 < D ≤ 0.7	Moderate coordination	IV	0 < μ ≤ 0.6 0.6 < μ ≤ 1.2 1.2 < μ	Lagging development of ecosystem services Low level of balanced development of both Lagging development of human well-being
0.7 < D < 1	High coordination	V	0 < μ ≤ 0.6 0.6 < μ ≤ 1.2 1.2 < μ	Lagging development of ecosystem services Low level of balanced development of both Lagging development of human well-being

[40].

2.3.4. Geographically Weighted Regression Models

The model of geographically weighted regression (GWR) is primarily employed to describe the spatial variability and smoothness of regression coefficients, distinguishing itself from conventional regression models by integrating geographic location into the coefficients [41,42]. This method has been utilized to analyze the determinants affecting the coupling coordination degree between ecosystem services and human well-being at the county level. The computational formula is provided below:

$$y_i={\beta }_0\left({\mu }_i,{\upsilon }_i\right)+{\displaystyle \sum _k{\beta }_k\left({\mu }_i,{\upsilon }_i\right)x_{ik}+{\epsilon }_i}$$

(6)

In the specified model, $y_i$ signifies the level of coupled coordination between ecosystem services and human well-being observed within county i; $x_{ik}$ represents the magnitude of k, the explanatory variable specific to county i; $\left({\mu }_i,v_i\right)$ refers to the geographical coordinates of the first county; ${\beta }_0\left({\mu }_i,v_i\right)$ denotes the baseline constant of the statistical regression for the initial county; ${\beta }_k\left({\mu }_i,v_i\right)$ displays the regression coefficient associated with the first explanatory variable in the first county; k is the total count of explanatory variables involved; and encapsulates the error term linked to variations within the county.

3. Results

3.1. Spatial and Temporal Changes in the Values of Ecosystem Services

3.1.1. Temporal Changes in the Values of Ecosystem Services

Between 2000 and 2020, the overall value attributed to ecosystem services (ESVs) in Gansu Province demonstrated a positive trend, climbing from an initial value of CNY 791.933 billion at the turn of the century to CNY 821.253 billion two decades later, reflecting a total augmentation of CNY 29.320 billion. This increase represents a cumulative growth rate of 3.70%, as detailed in

Table 4. Changes in the values of quasi-ecosystems in Gansu Province from 2000 to 2020.

ESV Relevant Indicators		Ecosystem Type
		Cropland	Forestland	Grassland	Waters	Unutilized Land	Total
ESV/billions	2000	597.82	1709.89	3968.99	1210.91	431.73	7919.33
	2010	583.85	1751.00	4013.64	1263.99	427.56	8040.04
	2020	597.75	1792.87	4077.62	1317.55	426.73	8212.53
Volume of change/USD billion	2000–2010	−13.97	41.11	44.65	53.08	−4.17	120.71
	2010–2020	13.9	41.87	63.98	53.56	−0.83	172.49
	2000–2020	−0.07	82.98	108.63	106.64	−5	293.2
Rate of change/%	2000–2010	−2.34	2.40	1.12	4.38	−0.97	1.52
	2010–2020	2.38	2.39	1.59	4.24	−0.19	2.15
	2000–2020	−0.01	4.85	2.74	8.81	−1.16	3.70
Contribution rate/%	2000–2010	7.55	21.59	50.12	15.29	5.45	100.00
	2010–2020	7.26	21.78	49.92	15.72	5.32	100.00
	2000–2020	7.28	21.83	49.65	16.04	5.20	100.00

. Within this broad trend, the ESVs associated with grasslands, forestlands, and waters all showed upward movements. Conversely, the ESVs for agricultural lands and unutilized lands experienced downward adjustments. The most notable growth occurred in grasslands, which saw an increase of CNY10.863 billion, whereas watersheds exhibited the highest proportional rise at 8.81%. On the other hand, desert ecosystems witnessed a marginal reduction in their service value, decreasing by 1.16% over these years, primarily due to the expansion of areas categorized as unutilized lands. Among the various types of ecosystems analyzed, grasslands stood out by contributing the most significantly to the total ESV, especially in the year 2000, when they constituted 50.12% of the overall ESV. This was largely influenced by the fact that grasslands represented 33.64% of the total land area of the province. Meanwhile, forests and aquatic environments contributed 21.78% and 16.04% to the overall ESV, respectively, during the same timeframe, with the least contribution of just 5.20% coming from unutilized lands.

During the two decades from 2000 to 2020, the valuation of individual ecosystem services in Gansu Province consistently demonstrated an upward trajectory, with variations in the extent of the increase across different services, as detailed in

Table 5. Changes in the ESVs of ecosystem services from 2000 to 2020.

Ecosystem Type		ESV/(108 CNY)			Rate of Change (%)	Average Contribution Margin (%)
		2000	2010	2020	2000–2020
Supply services	Food production	278.78	276.62	282.67	1.39	3.47
	Raw material	217.14	218.86	222.97	2.69	2.73
	Supply water supply	−26.93	−17.81	−17.49	35.05	−0.26
	Add up the total	468.99	477.66	488.15	4.09	5.94
Regulator services	Gas regulation	758.67	764.35	778.66	2.63	9.52
	Climate regulation	1694.78	1718.24	1750.63	3.30	21.36
	Environment	696.22	704.69	716.84	2.96	8.76
	Hydrological regulation	2263.85	2312.01	2375.58	4.94	28.76
	Add up the total	5413.52	5499.29	5621.70	3.85	68.40
Support services	Soil conservation	837.07	845.99	861.44	2.91	10.53
	Maintenance of nutrient cycling	81.78	82.06	83.65	2.29	1.02
	Biodiversity	767.24	778.66	793.89	3.47	9.68
	Add up the total	1686.09	1706.72	1738.98	3.14	21.23
Cultural services	Aesthetic landscape	350.74	356.38	363.70	3.70	4.43
Add up the total		7919.33	8040.04	8212.53	3.70	100.00

. Dominating this period, regulatory services accounted for a substantial 68.4% of the total ESV, overshadowing supporting services, which contributed 21.23% to the ESV. Cultural services, on the other hand, offered the least economic contribution, making up only 4.43% of the total. Among the regulatory services, those focused on hydrological and climate regulations proved to be most crucial, representing 28.76% and 21.36% of the ESV, respectively. Supportive services primarily derived their value from efforts of soil conservation and the maintenance of biodiversity. Notably, provisioning services related to the production of food and raw materials held significant value, yet it was the water-provisioning services that experienced the most substantial rate of increase, improving from a deficit of CNY 2.693 billion in 2000 to CNY 1.794 billion in 2020, a remarkable improvement of 35.05%. This substantial enhancement indicates a progressive alleviation of water scarcity issues within agricultural ecosystems. Predominantly, cultural services were composed of aesthetic landscape services, which accounted for 4.43% of the aggregate service value.

3.1.2. Spatial Changes in the Values of Ecosystem Services

An examination of the spatial distribution trends, as depicted in Figure 3, illustrates that during the observed period, ESVs within Gansu Province displayed a distinct gradient, being more pronounced in the southeast and tapering off toward the northwest. These directional decreases in ESVs from east to west and from south to north correlate well with the vegetative zonal patterns prevalent throughout the province. Areas such as Maqu, Luqu, Diebu, and Zhuoni counties on the Gannan Plateau, as well as Wudu, Wen, Kang, and Liangdang counties in the mountainous Longnan region, along with the Maiji district in Tianshui and Tianzhu county in Wuwei, are characterized by particularly high ESVs, typically exceeding 350 × 10⁴ CNY/km². The elevated values in these locales are largely due to their rich alpine grassland ecosystems and dense scrublands, abundant in biomass, coupled with extensive forested areas in Longnan, known for the robust vegetation coverage. On another level, areas such as Sunan county in the Hexi corridor and several counties in Longzhong region, such as Linxia, Dingxi, and Tianshui, along with Zhengning and Huachi in the Longdong region, show moderately high ESVs, which range from 261 × 10⁴ to 349 × 10⁴ CNY/km². These regions boast significant agricultural activities but have experienced some degradation of their natural ecosystems functions due to intensive farming practices. In stark contrast, the lowest ESVs per unit area were mainly observed in areas extending beyond Sunan and Tianzhu counties within the Hexi region, as well as in urban locales like Baiyin and Lanzhou in the Longzhong region. These areas, dominated by expansive desert landscapes and arid to semi-arid climates, suffer from fragile ecological conditions and severe soil erosion, with ESVs generally falling below 100 × 10⁴ CNY/km².

From 2000 to 2020, the ecosystem service values (ESVs) across Gansu Province predominantly exhibited upward trends. In detail, 68% of the counties in the province experienced growths in ESVs, 23% remained stable, and a mere 9% saw a decrease. Notable increases in ESVs were observed in the Gannan Plateau, specifically in Luqu, Maqu, Xiahe, and Linxia counties. In the Longzhong region, Anning District, Zhang county, and Weiyuan county exhibited similar positive trends. Additionally, the Longnan region reported growth in Kang county, Wudu District, and Maiji District. Furthermore, Lingtai county, the City of Huating, Qingcheng county, and Zhengning county in the Longdong region also reflected significant ESV enhancements. Excluding Sunan county and Tianzhu Tibetan Autonomous County, which demonstrated growth, all other counties west of the river maintained a steady ESVs.

3.2. Temporal Changes in Residents’ Well-Being

3.2.1. Temporal Changes in Residents’ Well-Being

Between 2000 and 2020, Gansu Province witnessed a significant enhancement in human well-being, with indices escalating from 0.332 to 0.583, marking a robust growth of 75%, as depicted in Figure 4. This improvement unfolded at varying paces, as follows: a modest 16.3% increase from 2000 to 2010, followed by a more pronounced surge of 50% in the subsequent decade. Analyzing the structural functions of human well-being during this period reveals substantial advancements across several domains, including health, education, urban–rural integration, and adaptability to climate change. Specifically, the rate of improvement in living standards and urban–rural integration progressed from 16% and 22.6% in the first decade to 50% and 46% in the second. The 18th CPC National Congress, in 2012, highlighted the importance of vigorously advancing ecological civilization and establishing a comprehensively prosperous society. In line with these directives, Gansu Province experienced marked improvements in residents’ living standards and urban–rural integration, contributing to a narrowing of the urban–rural divide.

3.2.2. Spatial Evolution of Human Well-Being

Utilizing the natural breakpoint methodology, the classification of human well-being in Gansu Province over the period from 2000 to 2020 was stratified into the following five distinct tiers: high, high, medium, relatively low, and low levels (Figure 5). During this two-decade span, a marked shift was observed in the geographical distribution of the levels of well-being, which evolved from predominantly lower levels in the southeastern parts to distinctly higher levels in the northwestern regions. Notably, the proportion of counties and districts identified as having a high well-being level increased from 12.5% to 36.2%. This expansion was particularly evident extending from Jiayuguan city and Jinchang city in the western part of the province to adjacent areas, such as Akse county, Subei county, Sunan county, and Ganzhou District. In the Longzhong region, there was noticeable growth in well-being extending outward from Lanzhou city and Baiyin city to surrounding areas like Anding District, Lintao county, Min county, and Linxia city. In the Longdong region, enhanced well-being levels were centralized around Kongdong District, Qingcheng county, and Xifeng District. In contrast, the areas categorized at the lower levels of well-being saw a decrease from 44.6% to 20.8%, primarily within the Gannan and Longnan regions and some parts of the Longzhong region, specifically Hezheng, Dongxiang, and Jishishan counties in Linxia, as well as Lintan and Zhouqu counties in Gannan and Liangdang, Kang, Xihe, Zhang, and Weiyuan counties in Longnan and Dingxi.

3.3. Spatial and Temporal Changes in the Coupled Relationship between Ecosystem Services and Human Well-Being

3.3.1. Temporal Changes in the Coupling Relationships

From the viewpoint of temporal change, the coupling coordination degree of ecosystem services and human well-being in Gansu Province showed steady growth (

Table 6. Temporal changes in the coupling coordination in Gansu Province from 2000 to 2020.

Area	Time	Degree of Coupling Coordination	Coupling Coordination Stage	Coupling Characteristics
Hexi	2000	0.386	Moderate dysfunction	Development of ecosystem services lagging behind
	2010	0.462	Basic coordination	Development of ecosystem services lagging behind
	2020	0.621	Moderate coordination	Balanced development of both at higher levels
Longzhong	2000	0.416	Basic coordination	Development of ecosystem services lagging behind
	2010	0.482	Basic coordination	Development of ecosystem services lagging behind
	2020	0.682	Moderate coordination	Balanced development of both at higher levels
Longdong	2000	0.374	Moderate dysfunction	Development of ecosystem services lagging behind
	2010	0.436	Basic coordination	Balanced development of the lower level of the two
	2020	0.587	Moderate coordination	Balanced development of both at higher levels
Longnan	2000	0.324	Moderate dysfunction	Lagging development of human well-being
	2010	0.513	Moderate coordination	Lagging development of human well-being
	2020	0.597	Moderate coordination	Balanced development of both at higher levels
Gannan	2000	0.284	Moderate dysfunction	Lagging development of human well-being
	2010	0.507	Moderate coordination	Lagging development of human well-being
	2020	0.578	Moderate coordination	Balanced development of both at higher levels
Gansu Province	2000	0.412	Moderate dysfunction	Lagging development of human well-being
	2010	0.527	Basic coordination	Basic balanced development of both
	2020	0.587	Moderate coordination	Balanced development of both at higher levels

), increasing from 0.412 to 0.587 from 2000 to 2020, an increase of 40%, and the type of coupling coordination changed from moderate dysfunction to moderate coordination. The coupling coordination degree of the Hexi region increased from 0.389 in 2000 to 0.462 in 2010 and then to 0.621 in 2020, the degree of coordinated development as a whole showed a benign development trend, and the type of coupling coordination changed from moderate dysfunction to moderate coordination. The coupling coordination degree of Longzhong region grew from 0.416 in 2000 to 0.682 in 2020, and the type of coupling coordination changed from basic dysfunction to moderate coordination, with the development of ecosystem services and human well-being in the region lagging behind during the period 2000–2010. The coupling coordination degree of Longdong region increased from 0.374 to 0.584 in 2000–2020, the type of coupling coordination changed from moderate dysfunction to moderate coordination, the degree of coupling coordination of Longdong region increased from 0.374 to 0.584, and the type of coupling coordination changed from moderate dysfunction to moderate coordination, with a higher level of balanced development in 2020. The coupling coordination degree of Longnan region increased from 0.324 in 2000 to 0.597 in 2020, and the type of coupling coordination changed from moderate dysfunction to moderate coordination, with both ecosystem services for human well-being developing at a high level in 2020. The coupling coordination degree of the Gannan region increased from 0.284 to 0.578 in 2000–2020, the type of coupling coordination changed from moderate dysfunction to moderate coordination, the coupling coordination degree of the Gannan region during the study period increased from 0.284 to 0.578, the type of coupling coordination changed from moderate dysfunction to moderate coordination, and the level of human well-being lagged.

3.3.2. Spatial Changes in the Coupling Relationships

In terms of the spatial distribution (Figure 6), the type of coupled coordination between ecosystem services and human well-being in Gansu Province in 2000 was mainly dominated by moderate dysfunction and basic coordination, of which there were 53 counties with moderate dysfunction, accounting for 60.92%, and 30 counties with basic coordination, accounting for 34.48%. In 2010, there were no counties in a state of serious dysfunction, which confirms that ecological environmental protection and human well-being in the counties of the province developed in harmony during the study period. Fifty counties in the province were in states of basic harmony in 2010, accounting for 57.47%, 23 counties in states of moderate harmony, accounting for 26.44%, and 14 counties in states of moderate harmony, mainly located in the province of Gansu, with the majority of counties in a state of moderate harmony. medium coordination, mainly distributed in Sunan, Subei, and Shandan counties of Hexi, Lanzhou, and Baiyin cities in Longzhong; most counties in Gannan; and some counties in Longnan and Tianshui. In 2020, there were 15 counties in the province with a high degree of coordination, accounting for 17.24 percent of the total, mainly located in the urban areas of the 14 cities and municipalities of the province. The proportion of the province’s counties with medium dysfunction from 2000 to 2020 was lower than the proportion of counties in moderate dysfunction, which dropped to 2.64 percent, while the proportion in a state of medium coordination increased to 52.87%. This shows that Gansu, in the process of social and economic development, attaches importance to the synergy between ecological protection and high-quality economic development, adhering to the development concept of “green water and green mountains are golden silver mountains”.

3.4. Analysis of Factors Influencing the Degree of Coupled Coordination of Services and Human Well-Being

3.4.1. Selection of Influencing Factors

Based on the scholarly insights of Jia and Li [43,44], this study carefully selected explanatory variables aimed at enhancing the economic, social, and ecological advantages by evaluating the interrelated and coordinated dynamics between ecosystem services and human well-being across the counties of Gansu Province. To effectively capture these dynamics, we chose variables that represent various aspects of development and environmental health. For the characterization of industrialization and economic structural dynamics, the per capita industrial output value and the proportion of tertiary industry in each county were chosen. These variables provide an indication of the level of industrialization and the move away from an industrial-based economy within the province. In the domains of investment and urban development, the per capita investment in fixed assets was chosen to capture the amount of capital flowing into long-term tangible capital stock—a critical component of sustainable development. Likewise, the share of the urban population was selected to represent the degree of urbanization and provide a view of the changes in the population’s location and requirements for transportation amenities and utilities. Moreover, to evaluate the environment that contributed to these socioeconomic changes, the Normalized Difference Vegetation Index was used. The NDVI serves as a robust indicator of vegetative vigor and health, illustrating the ecological substrate and its capacity to support both biodiversity and human communities. To rigorously analyze the factors influencing the coupled and coordinated development within the province, the study employed a methodological approach whereby changes in these selected variables were treated as independent variables. Concurrently, the alteration in the degree of coupled coordination was treated as the dependent variable. This approach facilitates a deeper exploration into how shifts in industrial, urban, and ecological parameters impact the synchronized development of ecosystem services and human well-being, aiming to identify actionable levers for policy and planning in Gansu.

3.4.2. Analysis of Influencing Factors

The analysis via OLS revealed no evidence of redundancy or multicollinearity among the variables, as the VIF remained below 7.5 (referenced in

Table 7. Examination results of OLS and GWR model.

Variant	OLS				GWR
	Estimated Value	Standard Error	T-Statistic	VIF	Average Value	Maximum Value	Minimum Value
Industrialization level	0.1681	0.0632	2.6570	1.9611	0.1988	0.2381	0.1702
Economic structure	0.0397	0.0587	0.6855	1.6188	0.0405	0.0694	0.0236
Investment level	−0.0184	0.0613	1.2598	1.2885	−0.3142	−0.0656	0.4721
Urbanization level	0.0905	0.0569	2.6099	1.1507	0.1157	0.0045	0.1575
Natural base	−0.0186	0.0312	−1.2374	1.8698	−0.0327	0.0638	−0.0515
R2	0.2923				0.3784
Adjusted R2	0.2208				0.2812
AICc	−197.87				−216.68

). The OLS model presented an adjusted R² value of 0.2923. In contrast, the GWR model demonstrated a corrected R² of 0.3784, coupled with a lower AICc compared to the OLS model, suggesting a superior fit. Consequently, the GWR model was selected, as it more effectively elucidates the determinants impacting the synchronization of ecosystem services and the well-being of the residents.

Figure 7 clearly illustrates the spatial variability in the regression coefficients that influence the degree of coordination between ecosystem services and human well-being, which tended to display zonal patterns of development. Positive influences were evident from factors such as the level of industrialization, economic structure, and degree of urbanization. Conversely, the levels of investment and the quality of the natural substrate exerted a negative impact. When ranked by the absolute magnitude of their effects, the sequence was as follows: the level of investment surpassed the industrialization level, which in turn exceeded economic structure, followed by the urbanization level, with the natural substrate having the least impact.

The spatial influence of the level of industrialization on the degree of coupled coordination was characterized by a pattern that was “high in the central regions and lower at both extremities”. A positive regression coefficient indicates that an increase in industrialization tended to boost the degree of coupled coordination. Specifically, the lowest values of these coefficients were found in the Longdong region, while higher values were noted in locations such as Zhangye, Wuwei, Jinchang, and Gannan, which were more significantly influenced by industrialization. As for the economic structure, its regression coefficients with the degree of coupled coordination exhibited both positive and negative values, reflecting the variable impact of this factor on the coordination level. Positive influences were predominantly found in certain counties within the Hexi region, as well as in Lanzhou, Linxia, and Dingxi in the Longzhong region and some counties in the Longdong region of Qingyang. Conversely, negative coefficients in other areas suggest that the economic structure did not consistently enhance the degree of coordination. The regression coefficient for the investment level exhibited a negative correlation, peaking in Maqu county while registering the lowest in Dunhuang city. High values of this coefficient predominantly appeared in the regions of Gannan and Linxia, whereas lower values were observed in Jiayuguan, Zhangye, and certain counties in Jinchang. Overall, the distribution followed a distinctive northeast to southwest band. On the other hand, the regression coefficient for the level of urbanization demonstrated a positive relationship with the degree of coupling coordination, suggesting that higher levels of urbanization tend to enhance coordination. The coefficient reached its highest level for Jinchang city and its lowest for Subei county. Regions displaying higher coefficients included Jinchang and certain counties west of the river in Wuwei and east of Longdong, while lower coefficients were predominantly found in Jiuquan and Jiayuguan. Additionally, the regression coefficient for the natural base was negative, and its spatial distribution reveals a band extending from southeast to northwest. The highest coefficient in this category was recorded for Akse county, with the lowest for Maqu county. The areas with higher coefficients were mainly in Jiuquan, whereas those with lower coefficients were scattered across several counties in Gannan, Longnan, and Longdong regions and Dingxi.

4. Discussion

4.1. Values of Ecosystem Services and Causes of Change in Levels of Human Well-Being

Exploring the interplay between ecosystem services and human well-being in economically less developed counties offers critical insights for crafting sustainable development strategies. This study observed a progressive increase in ecosystem services in Gansu Province from 2000 to 2020, exhibiting a spatial distribution trend of higher values in the southeast and lower values in the northwest, aligning with research by Li and Yin [45,46]. Post-2010, a marked enhancement in ecosystem services was noted, which reflected the successful impact of initiatives such as the conversion of farmland back into forests and grasslands, and the “Three-North Protective Forest Project” within the region. Furthermore, human well-being in Gansu Province surged by 75%, with a noticeable contraction in regional disparities. This improvement is intricately linked to the deployment of a western development strategy, emphasis on ecological conservation, high-quality development initiatives of the Yellow River Basin, and targeted poverty alleviation efforts. Since 2014, the poverty alleviation strategy notably boosted local incomes and substantially elevated levels of human well-being.

4.2. Coupling Coordination Enhancement Strategies under the Sustainable Development Goals (SDGs)

The spatial and temporal variations in the coupling and coordination between ecosystem services and human well-being across counties are pronounced, with distinct regional disparities. In the Hexi, Gannan, and Longnan regions, the levels of coupling and coordination were relatively low, largely due to significant ecological challenges in Hexi, and the minimal well-being in Gannan resulting from an underdeveloped industrial base and lagging economic progress. Conversely, the Longzhong and Longdong regions exhibited higher levels of coupling coordination, attributable to a better regional GDP per capita, advanced educational and medical facilities, superior human well-being, and improved ecological environments that enhance the value of ecosystem services. This study identified considerable spatial variability in how the selected influencing factors impacted the degree of coupling coordination. Future strategies for Hexi should include bolstering the conservation mechanisms at Qilian Mountain National Park, escalating restoration efforts, and advancing urban infrastructure and agricultural modernization. The Gannan and Longnan regions should increase government financial investments, further enhance infrastructure and public services, and improve basic education levels. Specifically, the Gannan Plateau should integrate ecological foundations with eco-tourism to boost the added value of local agricultural and pastoral resources, thereby enhancing the ecological functions and fostering high-quality regional development. The mountainous areas of Longnan must prioritize ecological considerations and manage human-land relations harmoniously to sustain district development. Longzhong should combine the national “One Belt, One Road” initiative and the high-quality development strategy for the Yellow River Basin to improve rural infrastructure construction and the living conditions of local residents and, at the same time, continue to promote ecological remediation project efforts in areas of serious loess erosion. Meanwhile, Longdong should continue to refine their ecological substrates and embody the “two mountains” concept, creating a new paradigm for the harmonious coexistence of humans and nature. Central cities play critical roles in driving surrounding counties’ development, promoting economic growth, and elevating population well-being.

4.3. Deficiency and Prospect

This study established an evaluation index system for assessing human well-being, recognizing that human well-being encompasses a multifaceted and intricate system influenced by natural, economic, and social factors. Although research on ecosystem services is relatively advanced, there is a need for deeper investigation into the pathways and mechanisms that drive the coupling and synergistic development between these services and human well-being. Future studies should aim to refine this evaluation index system further, incorporating data from field studies and interviews. This approach will help to better understand the regional differences, development processes, and factors that affect the integration and coordination of these factors for the purpose of ecological conservation and high-quality development in Gansu Province.

5. Conclusions

It is crucial to determine the interactive patterns of ecosystem services and human well-being to enhance the ecological conservation and sustainable development in Gansu Province. This research employed the coupling coordination degree model and geographically weighted regression model to analyze the temporal and spatial variability and the factors affecting the interaction between ecosystem services and human well-being in the counties of Gansu Province. The findings of this analysis are summarized as follows:

(1)

The ESV of Gansu Province increased by 3. 7% from 2000 to 2020 and has a regional distribution where the southeast has a higher ESV than the northwest. The regions of Gannan and Longnan predominantly featured high ESVs, whereas the Hexi and Longzhong regions were characterized by lower ESVs. Regarding changes in ESVs across the counties of Gansu Province, the majority demonstrated growth, with 68% experiencing an increase, 23% remaining stable, and only 9% showing a decline in ESVs.

(2)

Between 2000 and 2020, Gansu Province experienced a substantial enhancement in human well-being, registering a 75% increase. The spatial distribution of well-being displayed a pattern that had higher levels in the north and lower levels in the south, with residents west of the river enjoying greater well-being compared to those in the Gannan and Linxia regions of Longzhong, where the lowest well-being levels were recorded. Over the two decades, the disparity in regional well-being levels showed signs of narrowing, with areas of high well-being becoming more concentrated and areas of low well-being diminishing.

(3)

From 2000 to 2020, the coupling coordination degree between ecosystem services and human well-being in Gansu Province improved from 0.412 to 0.587, transitioning from a state of moderate dysfunction to moderate coordination. In the Hexi and Gannan regions, the level of coupling coordination remained low, characterized by moderate dysfunction. Conversely, the Longzhong, Longnan, and Longdong regions exhibit higher levels of coupling coordination, classified within the realm of basic coordination.

(4)

Spatial variations markedly influenced the coupling coordination degree between ecosystem services and the well-being of residents in a county, typically manifesting as band-like distributions. The patterns of the economic structures and natural substrates extended from southeast to northwest, whereas the patterns of investment levels and urbanization levels stretched from west to east. In contrast, the banding patterns of industrialization levels aligned from east to west.