Next Article in Journal
Research Progress of Heat Damage Prevention and Control Technology in Deep Mine
Previous Article in Journal
Microwave-Assisted Pyrolysis of Polyethylene and Polypropylene from End-of-Life Vehicles: Hydrogen Production and Energy Valorization
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 13
10.3390/su17136197
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Exploring the Impact of New Urbanization on Ecological Resilience from a Spatial Heterogeneity Perspective

by
Xinyu Wang
1,
Yuan Tian
2,*,
Yong Yang
2,
Le Yin
1 and
Baolei Zhang
1,*
1
College of Geography and Environment, Shandong Normal University, Jinan 250014, China
2
Shandong Provincial Territorial Spatial Ecological Restoration Center, Jinan 250010, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(13), 6197; https://doi.org/10.3390/su17136197
Submission received: 23 May 2025 / Revised: 23 June 2025 / Accepted: 2 July 2025 / Published: 6 July 2025
Browse Figures
Versions Notes

Abstract

New urbanization (NU) is an urban development strategy proposed by China that takes into account both urban development and ecological protection. It aims to improve the resistance and resilience of ecosystems, that is, to improve ecological resilience (ER). Whether NU has a sustained positive effect on ER is the focus of scholars, but they mostly ignore the fact that different scales and geographical conditions may lead to non-linear or threshold effects on ER. This study used a variety of spatial analysis models to construct a multi-scale heterogeneity analysis framework to explore this impact. The results show that (1) The impact of NU on ER has a threshold effect, which is affected by population agglomeration and innovation diffusion. (2) At the whole basin scale, the impact of NU on ER changed from negative to positive, while at the urban scale, it showed coordinated development in the south and an antagonism in the north. (3) The urban population density, education and technology expenditure, and urban greening rate are the dominant factors affecting ER. Their spatial differentiation rules verify the synergy mechanism between human capital and green infrastructure. This research has important guiding value for the ecological protection of rapid urbanization areas.

1. Introduction

Since the 21st century, the rapid development of urbanization, on the one hand, has led to the rapid development of economy [1] and brought great prosperity to human society. On the other hand, in the process of urbanization, ecological problems such as air pollution [2], urban heat island and biological habitat destruction [3,4] have become increasingly prominent, having a huge impact on the ecological environment. The dual impact of urbanization has made people realize that the traditional urbanization development model at the expense of the ecological environment is not desirable [5]. In this context, China, as the world’s largest and fastest urbanizing country [6], proposed a new urbanization (NU) strategy of coordinated urban and ecological development in 2005. NU is a systematic and complex process involving many factors such as economy, society, production and life. It emphasizes that urban development takes into account both quality and scale, and covers multiple people-oriented attributes, such as intensive, inclusive and sustainable [7]. Compared with traditional urbanization, the new strategy is mainly reflected in transformation, from focusing only on scale expansion and spatial expansion to focusing on the promotion of humanistic and social spiritual connotation [8], covering population, economy, space, society and other aspects, and promoting the coordinated development of the urban and ecological environment as the development goal [9]. However, in recent years, China’s ecological environment has been seriously degraded, which poses a serious threat to the stability and future sustainability of NU development [10]. Improving the ability of cities and ecosystems to resist shocks, adapt to changes and perform self-repair is an urgent problem to be solved [11]. As a new concept proposed to solve the problem of urban ecological risk, the concept of ecological resilience (ER) clarifies the need to improve the resilience and adaptability of ecosystems to external risks and enhance the self-recovery ability of ecosystems [12]. However, in the context of the development of the NU strategy, no in-depth research on the specific impact of NU on ER has been performed [13]. In different regions and at different scales, the impact of NU on ER may have spatial heterogeneity. However, the existing research pays more attention to the single impact of urbanization on ER [14,15], or analyzes the evolution of ER from a single scale [16], ignoring the significant differences in urbanization and geographical conditions in different regions [17]. Specifically, most of the literature is limited to the analysis of the administrative unit or urban agglomeration scale [18], ignoring the particularity of watershed as a complete ecological unit, which makes it difficult to capture the spatial correlation of upstream and downstream ecosystem services. This difference may lead to the non-linear or threshold effect of NU on ER [19]. Therefore, from the perspective of spatial heterogeneity, exploring the relationship between NU and ER is of great significance for formulating differentiated regional sustainable development policies.
The evaluation of the development level of NU and ER is a prerequisite for exploring the impact of NU on ER [20], and the selection of appropriate evaluation indicators is the basis of evaluation. From the perspective of NU evaluation, scholars mostly evaluate the traditional population, economic, social and spatial urbanization indicators to evaluate the level of NU [21]. However, the traditional urbanization evaluation indicators cannot reflect the people-oriented connotation of NU, and cannot accurately evaluate its development level. The choice of NU evaluation indicators should be targeted. From the perspective of ER assessment, the pressure–state–response model [22], the resistance–response–transformation framework [23], and the vulnerability–sensitivity–self-organization framework [24] are commonly used assessment methods. Some scholars also use the parameter substitution method to measure ER by calculating the potential, connectivity and resilience index [25]. These assessment methods pay more attention to the restoration of the ecosystem in the face of damage, ignoring the changes in the landscape structure and function within the ecosystem. Therefore, this study optimizes and improves the traditional evaluation system based on the connotation of NU and constructs the ER evaluation system based on the landscape perspective, aiming to measure the development level of the two more accurately.
In addition, the existing research focuses on NU and ER from different disciplines and perspectives [26], and uses various technical means such as big data analysis and data integration to explore the impact of urban climate change [27], urban expansion [28] and urban extreme weather [29] on ER. However, these studies are mostly based on the overall situation, using traditional analysis techniques such as the panel data model [30] and grey correlation model [31], which find it difficult to effectively deal with spatial heterogeneity and scale dependence. At the same time, in the exploration of the driving mechanism, the existing research shows a clear polarization tendency: on the one hand, the economic determinism school overemphasizes the leading role of GDP growth [32,33], ignoring the moderating effect of society and population; on the other hand, although the ecological constraint theory focuses on the environmental threshold effect [34,35], it lacks an analysis of the multi-dimensional characteristics of urbanization. In particular, it is worth noting that education and science and technology expenditure, as key moderating variables, are insufficiently discussed in the existing literature. These limitations directly affect the reliability of the research conclusions. Therefore, on the basis of identifying the main factors of NU’s influence on ER, it is of great significance to find a scientific method to quantitatively analyze the spatial heterogeneity of driving factors and reveal the differentiation law and genetic mechanism of influencing factors. As a powerful tool for driving force and factor analysis, geographic detector [36] can objectively reflect the priority of each driving factor in geographical phenomena. The spatio-temporal geographically weighted regression model (GTWR) [37] can construct a local spatial regression model for the relationship between the explained variables and the explanatory variables, and further explore the spatial heterogeneity of the driving factors.
In summary, based on the perspective of spatial heterogeneity, this study analyzes the multi-scale impact of NU on ER through spatial analysis models. The main contributions of this study are as follows: (1) Through the mutual feedback mechanism of NU and ER, a multi-dimensional comprehensive evaluation system of NU and ER is established to break through the limitations of traditional single-dimensional evaluation and quantify the non-linear relationship between NU and ER. (2) A variety of spatial analysis models are integrated to reveal the heterogeneous impact and driving mechanism of NU on ER at three scales: whole basin, sub-basin and city. (3) We identify the dominant urbanization factors that affect ER, reveal the spatial differentiation of the dominant factors, and verify the mechanism of population agglomeration and ecological compensation on ecology. This study can provide a theoretical basis for the coordinated optimization of NU and ER, and help regional high-quality sustainable development.

2. Materials and Methods

2.1. Study Area

The Yellow River Basin (YRB) is roughly located between 97° E–122° E and 30° N–43° N, covering 9 provinces and regions. The terrain is high in the west and low in the east. The terrain is dominated by plains and plateaus. The climate is dominated by a temperate continental climate and temperate monsoon climate, and the four seasons are distinct. By the end of 2024, the total population of the YRB was about 420 million, accounting for about 1/3 of the national population, the regional GDP in the basin is about CNY 31.64 trillion, accounting for about 1/4 of the total national economy, and the overall urbanization rate is about 62.8%. The YRB also has ecological problems related to environmental pollution and the excessive development of water resources [38]. In 2023, among the 266 state-controlled sections monitored in the YRB, the inferior V water quality section accounted for 1.5%, which was much higher than the national average of 0.7% [39]. In 2024, according to the National Bureau of Statistics, the inferior V water section is still higher than the national average. In addition, the area affected by soil and water loss in the YRB reached 259,300 square kilometers, accounting for 32.63% of the total area of the basin [40]. The average annual soil erosion amounted to 750 million tons, and 6.8% of the cultivated soil in the basin exceeded the standard of heavy metals. In addition, the utilization rate of water resources in the basin is 82%, far exceeding the international ecological warning line of 40% [41].
It is worth noting that this study adopts a three-level spatial classification system of whole basin, sub-basin, city. This division method has a strict definition in watershed ecology, but it needs to be specially explained in urbanization research: the whole basin is based on the main stream of the Yellow River as the axis, covering the complete geographical unit of all tributary catchments, which can reflect the macro overall relationship between NU and ER. The sub-watershed is divided into the upper, middle and lower reaches of the Yellow River according to the natural environment and hydrological conditions of the river channel (Figure 1), which can reveal the gradient effect of NU on ER. The urban unit is 62 cities flowing through the YRB, which focuses on the analysis of the impact of NU on ER in provincial capitals, and can more accurately identify the spatial differences in the impact of NU on ER.

2.2. Methods

2.2.1. Research Mechanism and Framework

The development of NU is a complex process of population, economy, society and space interaction [42]. It is also an important factor affecting the size, shape and density of cities. It is dynamic and multi-dimensional [43]. ER is the feedback and adaptation of the ecosystem to the scale of urban development, population density and spatial organization [44], and the two form a two-way mutual feedback system coupling relationship. Among them, the influence mechanism of NU on ER includes the fact that economic urbanization affects morphological resilience through industrial structure adjustment, that spatial urbanization shows the crowding effect of construction land expansion on ecological space, and that the built-up area is significantly negatively correlated with ecological land. Social urbanization affects density resilience through resource consumption, which has a significant negative impact on the ecological footprint [45]. Population urbanization has a scale pressure effect, and a high population density will reduce the benefits of ecological protection [46]. ER also has a restrictive feedback effect on NU, with morphological resilience limiting the disorderly expansion of cities through the ecological red line, density resilience regulating the speed of social development with the carrying capacity of resources and environment, and scale resilience constraining the scale of population agglomeration through environmental capacity. Based on the two-way influence of NU and ER, this study breaks through the limitations of traditional one-way influence analysis and constructs the following mutual feedback theoretical framework (Figure 2). It not only quantifies the four-dimensional action path of economy–space–society–population, but also reveals the synergy mechanism of the form–density–scale resilience subsystem.
Based on the above mechanism, this study aims to explore the spatial heterogeneity of the impact of NU on ER, and the research results provide a scientific basis for implementing the SDG11.3 goal (inclusive and sustainable urbanization). By evaluating the development level of NU and ER, the temporal and spatial evolution of NU and ER is revealed. The coupling coordination model and spatial autocorrelation analysis are used to explore the influence of NU on ER at different scales, and the geographic detector is used to identify the dominant factors affecting ER. The spatial heterogeneity of the intensity and direction of the dominant factors is explored by spatio-temporal geographically weighted regression (Figure 3).

2.2.2. NU and ER Evaluation Index

The study divides NU into four criteria layers: population, economy, society and spatial urbanization (Table 1); these cover multiple effects such as people-oriented, economic growth, social life and ecological environment, so as to reflect the development associated with NU. The weight of each urbanization factor is determined by the entropy method. In addition, in order to accurately evaluate the ER level, scale, density and morphological toughness were selected as ER evaluation indicators (Table 1). Among them, scale resilience reflects the relationship between urban scale and ecological environment, which can constrain the scale of urban development [47]; density resilience can reflect the carrying capacity of the resource consumption brought by the ecosystem to NU by calculating the ecological footprint and ecological carrying capacity [48]; morphological resilience reflects whether the built-up area and landscape layout are reasonable.

2.2.3. Study on the Effect of NU on ER

(1)
Coupling coordination model and spatial autocorrelation analysis
The coupling coordination degree model [49] is used to analyze the coordinated development level of things. The coordination degree can reflect the degree of benign coupling in the coupling interaction relationship. The specific calculation process is as follows:
C C D = C T
C is the coupling degree, whose value is less than 1, T is the coordination degree, and CCD is the coordinated development degree. The value is in [0, 1].
Spatial autocorrelation is an important method used to measure the spatial correlation of geographical elements [50]. The global Moran’s I index calculated and LISA analysis were used to explore the spatial correlation characteristics of NU and ER in the YRB from 2005 to 2020, and the cities were divided into four clusters: H-H (Hight-Hight), L-L (Low-Low), H-L (Hight-Low) and L-H (Low-Hight). H-H and L-L show that the NU and ER of the city are at a high level and a low level, respectively, H-L shows that the NU is higher and ER level is lower, and L-H shows that the NU is lower and ER level is higher.
(2)
Geodetector and Geographically and Temporally Weighted Regression (GTWR)
Geodetector is a statistical method used to explore the driving factors of phenomena by detecting spatial heterogeneity [51]. The specific calculation formula is as follows:
q = 1 − 1 N k 2 ∑ h = 1 L N h k h 2
q is the significant degree of influence of driving factors, q ∈ [0, 1]. N is the number of samples, N h and k h 2 are the sample size and variance of the h-th layer driving factor, respectively, and k 2 is the variance of the Y value of the entire region.
The GTWR realizes the simultaneous interpretation of the spatial and temporal difference mechanism of geographical phenomena [52]. The calculation formulas are as follows:
y i = β 0 u i , v i , t i + ∑ k = 1 P β k u i , v i , t i x i k + ε i
y i is the observed value, while u i , v i , t i and ε i are the spatial and temporal coordinates and random errors of the i-th observation point. β 0 u i , v i , t i is the constant term of regression, β k u i , v i , t i and x i k are the regression coefficients and values of k variables at the i-th observation point, and P is the total number of variables.

2.3. Data and Processing

The research mainly uses geographic data, remote sensing data and statistical data. The administrative boundary vector data in geographic data comes from the resource and environmental science data platform (https://www.resdc.cn/ (accessed on 12 September 2024)). The resolution of all remote sensing data is 30 m. The DEM data is derived from the Geospatial Data Cloud (https://www.gscloud.cn/ (accessed on 16 September 2024)). The land use, roads and rivers, and nature reserves data are derived from the Resource and Environmental Science Data Platform, and the slope is extracted from the DEM data. The statistical data cover NU and ER evaluation indicators, including GDP data, population data, built-up area and road area, the number of health institutions, investment in education and science and technology, and the resource and energy utilization status of various cities, all of which are derived from statistical yearbooks of China and various provinces and cities, statistical bulletins of national economic and social development, and the China Energy Statistical Yearbook. The missing data in some years are replaced by similar years.

3. Results

3.1. Spatio-Temporal Evolution of NU and ER

According to the time series evolution of NU from 2005 to 2020 (Figure 4), the level of NU in the YRB continued to increase, and the rate of increase slowed down after 2010. ER continued to decrease, but the rate of decrease continued to slow down, and remained basically stable from 2015 to 2010. NU may have a positive effect on ER. From the perspective of different segments of the basin, the overall evolution trend of NU and ER in each segment is the same, but there are large differences in values, which is also one of the main reasons for the spatial heterogeneity of NU’s impact on ER. The ER level in the middle reaches is much higher than that in the upper and lower reaches, and the damage and degradation of the ecological environment is less than that in the upper and lower reaches. In addition, due to the weak economic level, backward technological development and natural geographical conditions, the NU level in the upper and middle reaches is much lower than that in the lower reaches.
In order to further analyze the impact of NU on ER, the spatial distribution pattern of NU from 2005 to 2020 was analyzed (Figure 5). In general, the spatial distribution pattern of NU is high in the east and low in the west. The high urbanization areas are concentrated in the eastern and northern parts of the YRB, and their scope is expanding. From the perspective of urban scale, the high urbanization areas are concentrated in the provincial capital cities and their surrounding areas. These provincial capital cities are affected by policies and the environment, and the level of economic development is higher than other cities in the basin, which promotes the leading development of NU in the basin and forms a center–periphery structure dominated by provincial capital cities in the basin. From this point of view, the spatial distribution pattern of NU is greatly affected by location conditions such as economy and geographical environment. Highly urbanized areas are concentrated in areas with relatively rapid economic development and a superior geographical location.

3.2. Analysis of the Effect of NU on ER

3.2.1. Time Series Variation of Coupling Coordination

This research explores the coupling and coordination changes in NU and ER through the coupling coordination model and the center of gravity model. On the whole, from 2005 to 2020, the coupling coordination level of NU and ER in the YRB showed a continuous decreasing trend (Figure 6), and the CCD decreased from 0.484 in 2005 to 0.446 in 2020, showing the development characteristics of decreasing from near imbalance to mild imbalance. It is worth noting that despite the continuous decrease in CCD, the CCD level remained almost unchanged from 2015 to 2020, reflecting the relatively stable coupling and coordinated development of NU and ER, and the negative impact of NU on ER was effectively controlled.
From the perspective of different segments of the basin, the evolution of the coupling coordination level is quite different. The evolution of CCD in the upper and lower reaches is roughly the same as that in the whole basin. CCD continues to decrease, but the rate of decrease slows down. It remains relatively stable in 2020. The level of economic development in the upper reaches is relatively low, but compared with the industrial-based areas, the ecological investment is larger, the impact on the ecological environment is greater, and the coordination level is relatively low. The CCD in the middle reaches experienced a change from falling to rising, and the level of coupling coordination increased and exceeded that in the upper reaches in 2020, reflecting the positive effect of NU on ER in the middle reaches. From the perspective of urban scale, there are also great differences in the coupling coordination level of provincial capital cities. The higher cities are Xi’an and Zhengzhou, which are located in the middle and eastern part of the basin. The ER level of most cities in this region is relatively stable. The CCD in Hohhot and Yinchuan, located in the western part of the basin, is lower, and the coordination level between NU and ER development is poor. The largest change in CCD is Lanzhou City. From 2005 to 2020, NU in Lanzhou City continued to rise, ecological damage was serious, which had a negative impact on ER, and the coordination level between the two continued to decrease.

3.2.2. Spatial Autocorrelation Analysis of NU and ER

In order to further analyze the heterogeneity of the impact of NU on ER in the basin and the spatial correlation characteristics of the two, a bivariate spatial autocorrelation analysis was performed on the development levels of NU and ER from 2005 to 2020 (Figure 7), and all passed the 90% confidence test.
According to the LISA clustering diagram of NU and ER in the YRB from 2005 to 2020 (Figure 7), the number of cities covered by H-H type clustering in the basin decreased, and the clustering types of most cities were H-L, L-H and L-L, especially the L-L-type clustering. The number of cities included increased year by year, which further shows that the development of NU and ER in the basin gradually showed a positive correlation, and also verifies the time series evolution of NU increasing and ER decreasing in the basin. In addition, from the perspective of scatter distribution, from 2005 to 2015, the distribution of cities in the cluster map was more dispersed. From 2015 to 2020, the distribution of cities was more concentrated, and the levels of NU and ER among cities were similar. There is a strong spatial agglomeration phenomenon in the spatial distribution of the basin. From the perspective of Moran’s I (Table 2), the spatial correlation between NU and ER changed from negative to positive, and the development of NU and ER tended to be coordinated.
In addition, from 2005 to 2015, Moran’s I in the YRB was less than 0 (Table 2), and there was a significant negative spatial correlation between NU and ER in the basin. By 2020, Moran’s I was greater than 0, and the spatial correlation between NU and ER showed a positive correlation. The development of NU promoted the development of ER, but the positive correlation was weak. From the perspective of change trend, global Moran’s I increased, reflecting the strong spatial agglomeration characteristics of NU and ER distribution in the YRB. From the perspective of different sections of the basin, the upstream NU and ER are negatively correlated in space, and the gap between the two is large. The upstream still needs to overcome the limitations of geographical space on the development of NU. In 2020, Moran’s I in the middle and lower reaches was significantly higher than that in 2005, and NU and ER gradually showed a trend of benign interaction and coordinated development.

3.3. The Spatial Heterogeneity of the Effect of NU on ER

The ecological resilience of the YRB is affected by a variety of NU factors. In order to ensure the accuracy and comprehensiveness of the identification of the main driving factors, this study starts with the four aspects of population, economy, society and space, and selects 15 secondary evaluation indicators in the NU evaluation system as the explained variables. The ER of the corresponding year is used as the explained variable, and the differences in the role of the main urbanization factors in different spaces are explored through the geographical detector and the GTWR model.

3.3.1. Identification of Main Driving Factors

The independent variables all passed the significance test at the 0.05 level, reflecting that the development of ER in the YRB was affected by the combined effects of population, economy, society and space. From the perspective of explanatory power, social and spatial factors play a leading role in the development of ER, and population and economic factors play an important role in ER. Among them, the dominant factors affecting ER (q > 0.5) mainly include urban population density, education expenditure, science and technology expenditure and the built-up area greening rate (Appendix A, Table A1). From the perspective of time series, the explanatory power of each influencing factor fluctuates greatly from 2005 to 2020, but the factors that play a leading role in different periods include education and science and technology expenditure, urban population density and the greening rate of built-up areas.

3.3.2. Spatial Heterogeneity of Main Factors

The spatio-temporal geographically weighted regression of the dominant factors showed that the model passed the multicollinearity test and that the goodness of fit was high. In addition, the development level, coupling coordination degree and spatial autocorrelation of NU and ER all changed significantly in 2015. Therefore, the research mainly analyzes the spatial heterogeneity of the dominant factor in 2015.
From the perspective of spatial distribution (Figure 8), the influence of dominant factors on the ER of the basin is in a banded distribution. The regression coefficient of the urban population density is less than 0, which suggests that the urban population density has a significant negative effect on the development of ER, and shows a significant spatial gradient. The negative effect on the eastern part of the basin is the most prominent, and it decreases vertically to the west. The low-value areas of the regression coefficient are concentrated in the western regions of Lanzhou, Wuhai and Yinchuan. This is consistent with the theory of ecological overload. When the population density exceeds a certain value, the rate of ER decline will be significantly accelerated. This finding provides a scientific basis for formulating differentiated population control policies. In addition, the trend of the impact of science and technology expenditure on the basin is similar to that of urban population density, which decreases from east to west in an oblique band. The main reason for this spatial difference is that the low-value area is limited by the geographical environment, NU is relatively backward, and the urban population density and scientific and technological level are far behind the central and eastern parts of the basin, so ER is less affected by population density.
The regression coefficients of education expenditure, science and technology expenditure and the greening rate of built-up areas are all greater than 0, and their development has a significant positive effect on ER, but the impact strength is different. The impact of science and technology expenditure on basin ER shows obvious advantages in the east, suggesting that only when a certain threshold is reached can science and technology expenditure have a positive impact on the environment. The areas with the strongest impact on education expenditure and the built-up area greening rate are located in the western part of the basin, and the trend of action is similar, both decreasing from west to east, indicating that the development of ER in the western part of the basin is more affected by the dominant factors, and the low-value areas are concentrated in the eastern part of the basin. The main reason is that the level of education development in low-value areas is much higher than the overall level of the basin. Although the government invests more educational resources to produce more high-quality results, the relative ER improvement efficiency is not significant, revealing the law of diminishing marginal benefits of human capital accumulation, indicating that the precise delivery of educational resources should give priority to backward areas.
At the same time, the greening rate of the built-up area is directly related to the ecosystem function and has a greater impact on ER. Therefore, the western region with relatively lagging NU is a high-value area for regression. In general, the spatial distribution of dominant factors is similar to that of NU, which is affected by location conditions.

4. Discussion

4.1. The Effect of NU on ER at Different Scales

4.1.1. Multi-Scale Spatial Pattern Characteristics of NU Development

From 2005 to 2020, the NU process in the YRB showed significant scale-dependent characteristics (Figure 4). The analysis of the whole basin scale shows that the development level of NU has an obvious spatial autocorrelation (Moran’s I = 0.63, p < 0.01), forming a hot spot agglomeration area with the eastern region as the core, and showing a gradient pattern of decreasing from east to west. The development of NU shows a typical stepwise distribution of downstream > midstream > upstream, which is consistent with the conclusions of other studies [53]. The coefficient of variation within the river section reaches 0.42, reflecting a strong internal heterogeneity. In particular, the contradiction between urban development and ecological protection in the upstream and downstream is prominent. Similar to the findings of this study, the Mekong River Basin in Southeast Asia also faces this contradiction between upstream hydropower development and downstream ecological protection [54]. It is particularly worth noting that although the midstream region has a relatively fast urbanization growth rate, due to the resource-dependent development model [55], its NU quality index is significantly lower than that of the downstream region [56]. Urban-scale analysis further reveals the trend of polycentric development. NU spatial development is evolving towards a polycentric structure [57], and spatially, a center–periphery structure dominated by provincial capital cities is formed.

4.1.2. Scale Effects of NU-ER Interactions

This research reveals the complex scale effects of NU on ER through a multi-scale coupling coordination model, which is not a single promotion or hindrance. At the basin-wide scale, the influence of NU on ER shows a distinct phase transition. From 2005 to 2015, there was a significant negative correlation, but from 2015 to 2020, it turned into a weak positive correlation. This transformation may be attributed to two key mechanisms: one is the threshold effect of technological innovation, that is, when technological innovation reaches a certain threshold, the positive impact of NU on the environment can be achieved [58]. The other is the effectiveness of policy intervention [59], as green policies can effectively mitigate the adverse environmental impacts and overall negative effects brought about by urbanization [60]. At the sub-basin scale, the upstream resource curse effect is obvious [61], resulting in an average annual decrease of ER by 0.8%, and the ecological pressure in the middle reaches is greater [62]. However, due to the low level of NU, the CCD of NU and ER is relatively stable, there is an improvement in downstream green innovation efficiency [63], and the large capital investment in ecological restoration [64] makes CCD have an upward trend. At the urban scale, the impact of NU on ER is distributed in a north–south gradient. Zhengzhou and Xi’an are located in the south of the basin, and their CCD is much higher than that of other provincial capitals, while the CCD of Hohhot, Taiyuan and Yinchuan in the north of the basin is much lower than that of other provincial capitals. These findings provide a scientific basis for formulating differentiated regional sustainable development policies; when implementing ecological protection and high-quality development strategies in the YRB, it is necessary to fully consider the interaction mechanisms at different scales.

4.2. Spatial Heterogeneity of Dominant Factors and Its Formation Mechanism

NU can affect ER through numerous factors such as population, economy, society and space [65]. In the YRB, the factors that play a dominant role in ER are urban population density, expenditure on education and science and technology, and the green coverage rate of the built-up area. Due to the heterogeneity among different regions in terms of resource endowment, humanistic characteristics, basic conditions [66], etc., the intensity and direction of the effect of each dominant factor on ER are heterogeneous [67]. This heterogeneity is mainly reflected in three aspects. Firstly, from the perspective of the composition of driving factors, although urban population density, expenditure on education and science and technology, and the green coverage rate of built-up areas continued to be the dominant factors throughout the research period (q > 0.5), there were significant differences in their directions of action. Specifically, urban population density affects the green coverage rate [68] and shows a stable negative correlation with ER, which is consistent with the conclusion in existing studies that excessive agglomeration leads to ecological overload [69]. The expenditure on education and technology [70] and the green coverage rate of built-up areas [71] showed a significant positive promoting effect, verifying the key role of human capital investment [72] and green infrastructure in enhancing ecological resilience [73]. Secondly, in terms of spatial pattern, the influence intensities of each factor showed a distinct meridian gradient differentiation (Figure 8). Notably, the negative effect of urban population density showed a attenuation feature of stronger in the east and weaker in the west, which is closely related to the higher population agglomeration pressure in the eastern region [74]. The positive impact of educational expenditure and the green coverage rate of built-up areas shows an opposite pattern of strong in the west and weak in the east, suggesting that the marginal benefit of ecological improvement in the western region is higher [75]. Although the promoting effect of science and technology expenditure generally shows a decreasing trend from east to west, significant high-value areas have formed around innovation center cities (such as Xi’an and Zhengzhou), and the role of human capital in urban innovation is obvious [76].
Further analysis indicates (Table 3) that this spatial heterogeneity is mainly constrained by location conditions [77] and differences in development stages [78], and regulated by policy responses [79]. The superior geographical location of the eastern region has accelerated the urbanization process, but it has also led to a more prominent contradiction between population and resources [80]; the western region is in a period of accelerated urbanization [81], and the marginal ecological benefits of investment in education and green spaces are more significant. The differentiated implementation of ecological compensation policies within the river basin [82] has strengthened the spatial differentiation characteristics of the dominant factors. These findings provide a new theoretical perspective for understanding the coordination of human–land relations in rapidly urbanizing areas, confirm that the influence of NU on ER has a significant spatial dependence, and also provide a scientific basis for establishing differentiated regulatory policies based on spatial heterogeneity.

4.3. A Comparison with International Cases

Through a comparative analysis with similar international studies, we can find the commonalities and characteristics of the interaction between urbanization and ER in different geographical backgrounds. In North America, the urban economic development of the Mississippi River Basin shows a clear point–axis model [83], and its multiple ecological restoration policies [84] provide an important reference for the differentiated management and control strategies proposed in this study. The practice of the Rhine River Basin in Europe shows that cross-border collaborative governance frameworks and ecological planning [85] can significantly improve the resilience of urban systems, which is in line with the promotion of ecological resilience via investment in education and science and technology. In addition, in developing countries, studies in the Mekong River Basin in Southeast Asia and the Ganges River Basin in South Asia have shown that the water resource pressures [86] and ecological degradation problems [87] faced in the process of rapid urbanization are similar to those in the YRB, but their solutions pay more attention to cross-border cooperation [88]. The urban expansion of the Nile River Basin in Africa highlights the limiting effects of insufficient infrastructure work on ecological resilience [89]. This finding further validates the theoretical judgment that infrastructure construction is an important driving factor in this study. Through comparative analysis, it can be found that although the natural conditions and development stages of different basins are different, the spatial heterogeneity of the impact of urbanization on ecosystems is universal, which provides cross-regional verification of the multi-scale analysis framework constructed in this study. In addition, the establishment of an ecological compensation mechanism between the upper and lower reaches of the basin emphasized by the General Administration of Environment of China [90] is highly consistent with the differentiated management and control recommendations of sub-basins in this study, indicating that the research results have potential value for international promotion. These international comparisons not only enrich the theoretical connotation of the research, but also provide a variety of reference samples for sustainable development practices in different types of regions around the world. Future research can further deepen our cross-regional and cross-cultural understanding of the complex relationship between urbanization and ecological environment by establishing a global urban ecological resilience observation network.

4.4. Limitations and Future Research Directions

Although this study revealed the spatial heterogeneity of NU’s influence on ER through various models and methods, there are still some limitations. Firstly, there are certain limitations in the acquisition of indicators in the study. The statistical data of some years are missing, which may lead to certain deviations in the research results. Future research can combine more field investigation data and high-resolution remote sensing data to improve the accuracy and comprehensiveness of the data. Furthermore, the time span of the study was from 2005 to 2020, which was relatively short and could not reflect the long-term effect of NU on ER. Future research can extend the research time span, combine historical data and future prediction models, and explore the long-term trend of NU’s impact on ER. In addition, in the future, we will further deepen the research on multi-dimensional driving mechanisms, introduce social network analysis, explore how social factors such as policy transmission and population mobility interact with ecological resilience, so as to understand the dynamic relationship between NU and ER more comprehensively, provide more accurate scientific support for global sustainable development goals, and continuously optimize the implementation path of SDG11.3.

5. Conclusions

Based on the two-way mechanism of NU and ER, this study constructs a multi-dimensional evaluation system of the two, and integrates multiple spatial analysis models to systematically explore the scale dependence and spatial heterogeneity of the impact of NU on ER in the YRB from 2005 to 2020. The study found that the spatial and temporal evolution characteristics of NU and ER in the YRB are opposite. There is significant spatial and temporal heterogeneity in the impact of NU on ER in the sub-basin. The impact of NU on ER at the whole basin scale shows a stage change from negative to positive, which verifies that there is a threshold effect on the impact of NU on ER. These thresholds are affected by population agglomeration and innovation diffusion. The urban scale forms a north–south gradient differentiation, which is characterized by coordinated development in the south and antagonism in the north. The urban population density, education and science and technology expenditure, and the greening rate of built-up areas in the NU index are the dominant factors affecting the ER. Among them, the urban population density is the main negative driving factor, and education and science and technology expenditure and the greening rate of built-up areas have a significant positive effect on the ER. The spatial differentiation of the dominant factors shows a north–south gradient, which verifies the synergistic mechanism of human capital and green infrastructure.

6. Inspiration and Advice

The heterogeneity of the impact of NU on ER revealed by empirical analysis has an impact on the implementation of urban planning. The difference in urban population density between the upper and lower reaches has led to different decreases in ER, confirming the heterogeneity of the impact of NU on ER, which has promoted the establishment of a differentiated regulatory system for urban and spatial planning, rather than adopting a one-size-fits-all management model. At the same time, the GTWR model also provides a quantitative tool for the adaptation strategy required by SDG11.3. In addition, the urban population density, urban greening rate, and education and technology expenditure have an obviously different gradient to ER, which will urge urban planners to consider spatial layouts, facility configuration standards and green space system planning in planning, optimize the urban ecosystem structure based on dominant factors, realize the synchronization of urban development and ER promotion, and promote urban planning to be more standardized and keep pace with the times. The threshold effect found in the study will also support SDG11.3.2 (inclusive urban planning) monitoring, and then establish a dynamic monitoring model of NU and ER. This study has also directly spawned a three-level linkage ecological management model of the whole basin–sub-basin–city, which will promote urban planning from single-point optimization to system coordination.
Therefore, based on the research results, the following suggestions are put forward for the coordinated development of NU and ER in the YRB: (1) The central government and inter-provincial coordination agencies implement differentiated regional regulation strategies. The impact of NU on ER is strong in the east and weak in the west. The negative effect of urban population density is stronger in the east, and the positive effect of education expenditure and the greening rate is more significant in the west. Therefore, we should strictly control the population density in high-urbanization areas, promote the compact city model, and strengthen green technology innovation. For low-urbanization areas, priority should be given to increasing investment in education and ecology, reducing social inequality in urbanization, and conducting inclusive urban governance. (2) The provincial and municipal governments refine the implementation and optimize the driving factors of urbanization. Urban population density has a negative impact on ER, while education and technology expenditure and the built-up area greening rate are positively driven. Therefore, we should curb the negative driving force, implement the policy of ‘ecological red line + population easing’ in high-density cities (such as Lanzhou and Yinchuan), and guide the industry to spread to the surrounding satellite cities. At the same time, we should strengthen the positive drive, include education and science and technology expenditure in local government performance appraisals, and set up special funds to support ecological technology research and development. (3) Governments at all levels actively cooperate to build a multi-scale collaborative governance framework. The CCD of NU and ER changed from negative to positive at the whole basin scale, but the difference between the sub-basin and urban scale was significant. Therefore, upstream and downstream resource allocation and pollution prevention and control should be coordinated at the basin level, and the resilient city pilot should be implemented at the city level. Referring to the monitoring framework of SDG11.3, the ER index should be included in the urban planning approval process.
This study not only provides a precise target for the formulation of sustainable policies in the YRB, but also has important reference value for studying the ecological effects of urbanization in similar regions around the world. The urban development of global basin-type urban agglomerations, such as the Mekong River Delta and the Mississippi River Basin, can refer to the experiences related to regional regulation of the YRB, that is, upstream protection, midstream coordination, and downstream restoration. In arid and semi-arid regions (such as sub-Saharan Africa), the correlation between urbanization and ER can also be applied to control urban expansion at the edge of the desert. This is of great significance for the sustainable development of regions undergoing rapid urbanization.

Author Contributions

Data curation, X.W.; methodology, X.W. and Y.T.; writing—original draft, X.W.; validation, X.W.; visualization, Y.T.; conceptualization, Y.T. and Y.Y.; investigation, Y.Y. and L.Y.; resources, Y.Y.; funding acquisition, L.Y. and B.Z.; writing—review and editing, B.Z.; Supervision, B.Z.; Project administration, B.Z.; Formal analysis, B.Z. and X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shandong Province Graduate Teaching Reform Project, grant number SDYJSJGC2024068, and Shandong Province Undergraduate Teaching Reform Project, grant number Z20220004, and Jinan City-School Integration Project, grant number JNSX2023036, and National Natural Science Foundation of China, grant number 42201308, and Natural Science Foundation of Shandong Province, China, grant number ZR2021ME203, ZR2021QD127, and Shandong Philosophy and Social Sciences Youth Talent Team, grant number 2024-QNRC-02, and Taishan Scholar Foundation of Shandong Province, grant number tsqnz20231207 to L.Y.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors do not have permission to share data.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
NUNew urbanization
EREcological resilience
YRBThe Yellow River Basin
GTWRGeodetector and Geographically and Temporally Weighted Regression

Appendix A

Table A1. Geodetector results of ER impact factors from 2005 to 2020.
Table A1. Geodetector results of ER impact factors from 2005 to 2020.
CodeIndependent Variable2005201020152020q-MeansExplanatory Power Ranking
X1Urban population density0.77400.59310.88600.80090.76351
X2Proportion of urban population0.20390.22810.17590.17990.197014
X3The proportion of employees in the secondary industry0.37170.41630.6450.59260.50647
X4The proportion of employees in the tertiary industry0.07220.05180.05300.05390.057715
X5Per capital gross regional product0.50720.35460.52440.62450.50278
X6Proportion of non-agricultural output value0.74610.56080.40470.3840.52396
X7Urban per capita disposable income0.16210.11490.36630.17500.204612
X8Total Social Retail Consumer Goods0.12290.13210.26510.42830.237111
X9Number of health institutions per 10,000 people0.86600.12270.70710.27390.49249
X10Education expenditure0.82080.75550.63590.75410.74162
X11Technology expenditure0.79180.43490.70000.85820.69623
X12road area per capita0.33880.26350.41480.27250.322410
X13Built-up area0.22160.19110.16250.24020.203913
X14Proportion of construction land0.74440.52420.57540.55220.59914
X15Greening rate of built-up areas0.4930.58970.64100.51410.53955

References

  1. Zhao, Y.; Song, Z.Y.; Chen, J.; Dai, W. The mediating effect of urbanisation on digital technology policy and economic development: Evidence from China. J. Innov. Knowl. 2023, 8, 100318. [Google Scholar] [CrossRef]
  2. Sun, B.; Fang, C.L.; Liao, X.; Guo, X.M.; Liu, Z.T. The relationship between urbanization and air pollution affected by intercity factor mobility: A case of the Yangtze River Delta region. Environ. Impact Assess. 2023, 100, 107092. [Google Scholar] [CrossRef]
  3. Li, L.; Zhan, W.F.; Hu, L.Q.; Chakraborty, T.C.; Wang, Z.H.; Fu, P.; Wang, D.Z.; Liao, W.L.; Huang, F.; Fu, H.Y.; et al. Divergent urbanization-induced impacts on global surface urban heat island trends since 1980s. Remote. Sens. Environ. 2023, 295, 113650. [Google Scholar] [CrossRef]
  4. Wan, M.X.; Han, Y.W.; Song, Y.K.; Hashimoto, S. Estimating and projecting the effects of urbanization on the forest habitat quality in a highly urbanized area. Urban For. Urban Green. 2024, 94, 128270. [Google Scholar] [CrossRef]
  5. Tong, Y.J.; Shi, T.G.; Zhang, S.B.; Cheng, Y.J.; Liang, J.Y.; Lei, J. Study on the Interaction Mechanism Between Urbanization and Ecological Resilience-The Case of Urban Agglomeration on the North Slope of Tianshan Mountain. Appl. Sci. 2024, 14, 12066. [Google Scholar] [CrossRef]
  6. Zhang, Y.; Wang, Y. How does urbanization evolve heterogeneously in urbanized, urbanizing, and rural areas of China? Insights from ecosystem service value. Geogr. Sustain. 2025, 6, 100254. [Google Scholar] [CrossRef]
  7. Lu, C.; Wang, Y.; Liu, X.; Yang, X. Coupling Relationship between New Urbanization and Ecosystem Services: A Case of Lower Yellow River. Ecol. Econ. 2023, 39, 85–92. [Google Scholar]
  8. Guo, H.H.; Liu, X.X. Coupling and Coordination Mechanism of New Urbanization and Ecological Resilience. East China Econ. Manag. 2023, 37, 101–109. [Google Scholar] [CrossRef]
  9. Liu, H.H.; Wang, G.Y.; Zhang, P.H.; Wang, Z.L.; Zhang, L.P. Spatio-temporal evolution and coordination influence of coupling coordination between new urbanization and ecological resilience in Fenhe River Basin. J. Nat. Resour. 2024, 39, 640–667. [Google Scholar] [CrossRef]
  10. Liu, Y.X.; Fu, B.J.; Wang, S.; Zhao, W.W.; Li, Y. Research progress of human-earth system dynamics supported by spatial resilience theory. Acta Geogr. Sin. 2020, 75, 891–903. [Google Scholar]
  11. Fan, Y.; Wei, G. Assessment of ecological resilience and its response mechanism to land spatial structure conflicts in China’s Southeast Coastal Areas. Ecol. Indic. 2025, 170, 112980. [Google Scholar] [CrossRef]
  12. Wang, A.; Zhang, M.; Chen, E.; Zhang, C.; Han, Y. Impact of seasonal global land surface temperature (LST) change on gross primary production (GPP) in the early 21st century. Sustain. Cities Soc. 2024, 110, 105572. [Google Scholar] [CrossRef]
  13. Yu, J.Z.; Hu, W.Z.; Hou, L.C. Towards more resilient cities-analyzing the impact of new-type urbanization on urban resilience: Considering spatial spillover boundaries. Sustain. Cities Soc. 2024, 114, 105735. [Google Scholar] [CrossRef]
  14. Li, T.N.; Liu, Y.B.; Ouyang, X.; Zhou, Y.J.; Bi, M.; Wei, G.E. Sustainable development of urban agglomerations around lakes in China: Achieving SDGs by regulating Ecosystem Service Supply and Demand through New-type Urbanization. Habitat Int. 2024, 153, 103206. [Google Scholar] [CrossRef]
  15. Yang, F.; Liu, Z.; Zhou, Y.; Wei, L. Urbanization weakens vegetation resilience in the Pearl River Delta, China. J. Environ. Manag. 2025, 373, 123756. [Google Scholar] [CrossRef]
  16. Hu, H.; Yan, K.G.; Shi, Y.; Lv, T.G.; Zhang, X.M.; Wang, X.Y. Decrypting resilience: The spatiotemporal evolution and driving factors of ecological resilience in the Yangtze River Delta Urban Agglomeration. Environ. Impact Assess. 2024, 106, 107540. [Google Scholar] [CrossRef]
  17. Wang, L.; Yuan, M.K.; Li, H.L.; Chen, X.D. Exploring the coupling coordination of urban ecological resilience and new-type urbanization: The case of China’s Chengdu-Chongqing Economic Circle. Environ. Technol. Innov. 2023, 32, 103372. [Google Scholar] [CrossRef]
  18. Liu, L.; Lei, Y.; Zhuang, M.; Ding, S. The impact of climate change on urban resilience in the Beijing-Tianjin-Hebei region. Sci. Total Environ. 2022, 827, 154157. [Google Scholar] [CrossRef]
  19. Zhu, R.M.; Xia, Z.Y.; Liang, F.C.; Yuan, C.C.; Liu, L.M. Non-linear responses and critical thresholds of human well-being to ecosystem services across land-use intensities in urbanizing areas. Environ. Impact Assess. 2025, 114, 107921. [Google Scholar] [CrossRef]
  20. Chen, M.K.; Xu, X.B.; Tan, Y.; Lin, Y.Y. Assessing ecological vulnerability and resilience-sensitivity under rapid urbanization in China’s Jiangsu province. Ecol. Indic. 2024, 167, 112607. [Google Scholar] [CrossRef]
  21. Tang, H.X.; Yang, M.; Li, M.D.; Zhang, L.; Shao, Z.G. Research on Coupling Coordination of Urban Resilience and New-type Urbanization in the Shandong Peninsula Urban Agglomeration. Areal Res. Dev. 2024, 43, 85–90+126. [Google Scholar]
  22. Zhang, C.G.; Zhou, Y.J.; Yin, S.G. Interaction mechanisms of urban ecosystem resilience based on pressure-state-response framework: A case study of the Yangtze River Delta. Ecol. Indic. 2024, 166, 112263. [Google Scholar] [CrossRef]
  23. Wang, S.M.; Niu, J.L. Spatio-temporal evolution and influence factors of urban ecological resilience in the Yellow Basin. Acta Ecol. Sin. 2023, 43, 8309–8320. [Google Scholar] [CrossRef]
  24. Feng, X.H.; Zeng, F.S.; Loo, B.P.Y.; Zhong, Y.X. The evolution of urban ecological resilience: An evaluation framework based on vulnerability, sensitivity and self-organization. Sustain. Cities Soc. 2024, 116, 105933. [Google Scholar] [CrossRef]
  25. Luo, F.H.; Liu, Y.X.; Peng, J.; Wu, J.S. Assessing urban landscape ecological risk through an adaptive cycle framework. Landsc. Urban Plan. 2018, 180, 125–134. [Google Scholar] [CrossRef]
  26. Xiao, S.; Duo, L.; Guo, X.F.; Zou, Z.L.; Li, Y.A.; Zhao, D.X. Research on the coupling coordination and driving role of urbanization and ecological resilience in the middle and lower reaches of the Yangtze River. PeerJ 2023, 11, 15869. [Google Scholar] [CrossRef]
  27. Chambers, J.C.; Brown, J.L.; Campbell, S.; Green, S.A.; Reeves, M.C.; Schlaepfer, D.R.; Thacker, V. Indicators of ecological resilience and invasion resistance—Accounting for precipitation seasonality and climate change in southwestern U.S. drylands. Ecol. Indic. 2024, 168, 112749. [Google Scholar] [CrossRef]
  28. Zhou, L.; Qin, Y.; Cheng, J.L.; Zhu, H.Y.; Li, M.H.; Zhang, J.B.; Lebleu, C.; Shen, G.Q.; Chen, T.; Liu, Y. Urban ecosystem services, ecological security patterns and ecological resilience in coastal cities: The impact of land reclamation in Macao SAR. J. Environ. Manag. 2025, 373, 123750. [Google Scholar] [CrossRef]
  29. Wang, W.; Wang, Y.; Shen, C. Quantifying the nonlinear and interactive effects of urban form on resilience to extreme precipitation: Evidence from 192 cities of Southern China. Sustain. Cities Soc. 2025, 125, 106366. [Google Scholar] [CrossRef]
  30. Li, Q.; Zhao, Y.; Li, S.L.; Li, X.L. Spatiotemporal characteristics and driving forces of the coupling of social security and economic development in China. Geogr. Res. 2020, 39, 1401–1417. [Google Scholar]
  31. Wang, J.F.; Xu, C.D. Geodetector: Principle and prospective. Acta Geogr. Sin. 2017, 72, 116–134. [Google Scholar]
  32. Anwar, A.M.; Alhosain, N.; Bhatt, Y. Analyzing the interplay of urbanization, economic development, and seaborne trade: Revealing their connection to urban resilience in Saudi Arabia. J. Urban Manag. 2025, 14, 512–529. [Google Scholar] [CrossRef]
  33. Kyule, B.M.; Wang, X. Quantifying the link between industrialization, urbanization, and economic growth over Kenya. Front. Archit. Res. 2024, 13, 799–808. [Google Scholar] [CrossRef]
  34. Qiao, B.; Cao, X.Y.; Yang, H.; Wang, N.A.; Liu, X.J.; Zhou, B.R.; Zhao, H.; Liu, X.; Wang, Y.P.; Wang, Z.; et al. Nonlinear threshold effects of environmental drivers on vegetation cover in mountain ecosystems: From constraint mechanisms to adaptive management. Ecol. Indic. 2025, 173, 113328. [Google Scholar] [CrossRef]
  35. Wang, G.M.; Salman, M.; Zhang, K.W. Exploring the dynamic spatial spillover and nonlinear threshold effect of urbanization on urban green development efficiency in China. Ecol. Indic. 2025, 175, 113570. [Google Scholar] [CrossRef]
  36. Li, S.S.; Li, L.Z.; Liu, Y.B.; Deng, W.F.; Liu, C.H.; Wei, G.E. Impact of urbanization on carbon balance in the Poyang Lake Region. Geogr. Res. 2023, 42, 2245–2263. [Google Scholar]
  37. Wang, S.J.; Cui, Z.T.; Lin, J.J.; Xie, J.Y.; Su, K. The coupling relationship between urbanization and ecological resilience in the Pearl River Delta. J. Geogr. Sci. 2022, 32, 44–64. [Google Scholar] [CrossRef]
  38. Li, W.J.; Zhou, L.F.; Zhao, X.L.; Wu, X.W.; Liu, L.L.; Liu, W.F.; Wu, F.C. Current Status and Challenges in Diagnostic Research on Water Environment Problems in the Yellow River Basin. Res. Environ. Sci. 2024, 37, 32–41. [Google Scholar] [CrossRef]
  39. Cao, L. The Current Situation, Problems and Countermeasures of Ecological Protection and high-quality Development in the Yellow River Basin. China Natl. Cond. Strength 2024, 8, 20–24. [Google Scholar] [CrossRef]
  40. Men, D.; Pan, J.H. Incorporating network topology and ecosystem services into the optimization of ecological network: A case study of the Yellow River Basin. Sci. Total Environ. 2024, 912, 169004. [Google Scholar] [CrossRef]
  41. Zhang, C.H.; Wang, G.Q. Thoughts on Ecological Protection and High-Quality Development in the Yelliw River Basin. Yellow River 2024, 46, 1–7. [Google Scholar]
  42. Yan, X.; Xin, B.Q.; Cheng, C.G.; Han, Z.Y. Unpacking energy consumption in China’s urbanization: Industry development, population growth, and spatial expansion. Res. Int. Bus. Financ. 2024, 70, 102342. [Google Scholar] [CrossRef]
  43. Ran, Z.; Gao, S.; Zhang, B.F.; Guo, C.Y.; Ouyang, X.; Gao, J.H. Non-linear effects of multi-dimensional urbanization on ecosystem services in mega-urban agglomerations and its threshold identification. Ecol. Indic. 2023, 154, 110846. [Google Scholar] [CrossRef]
  44. Zhang, T.; Sun, Y.X.; Zhang, X.B.; Yin, L.; Zhang, B.L. Potential heterogeneity of urban ecological resilience and urbanization in multiple urban agglomerations from a landscape perspective. J. Environ. Manag. 2023, 342, 118129. [Google Scholar] [CrossRef]
  45. Cui, L.; Weng, S.; Nadeem, A.M.; Rafique, M.Z.; Shahzad, U. Exploring the role of renewable energy, urbanization and structural change for environmental sustainability: Comparative analysis for practical implications. Renew. Energy 2022, 184, 215–224. [Google Scholar] [CrossRef]
  46. Krishnadas, M.; Agarwala, M.; Sridhara, S.; Eastwood, E. Parks protect forest cover in a tropical biodiversity hotspot, but high human population densities can limit success. Biol. Conserv. 2018, 223, 147–155. [Google Scholar] [CrossRef]
  47. Hu, Y.Y.; Li, Y.; Li, Y.J.; Wu, J.C.; Zheng, H.Z.; He, H.M. Balancing urban expansion with a focus on ecological security: A case study of Zhaotong City, China. Ecol. Indic. 2023, 156, 111105. [Google Scholar] [CrossRef]
  48. Peng, B.H.; Li, Y.; Elahi, E.; Wei, G. Dynamic evolution of ecological carrying capacity based on the ecological footprint theory: A case study of Jiangsu province. Ecol. Indic. 2019, 99, 19–26. [Google Scholar] [CrossRef]
  49. Huang, Y.; Zhang, S.D.; Zhang, J.; Fan, F.F.; Zheng, H. Exploration of ecosystem asset-economy coupling coordination and its endogenous and exogenous drivers in mountainous regions. J. Clean. Prod. 2025, 486, 144460. [Google Scholar] [CrossRef]
  50. Wei, J.M.; Miao, Y.D.; Zhang, J.B.; Wu, J.; Shen, Z.L.; Bai, J.W.; Zhu, D.F.; Ren, R.Z.; Li, X.R.; Zhen, M.Y.; et al. Spatial heterogeneity of blood pressure control and its influencing factors in elderly patients with essential hypertension: A small-scale spatial analysis. Health Place 2025, 92, 103428. [Google Scholar] [CrossRef]
  51. Zhu, S.Y.; Feng, H.B.; Arashpour, M.; Zhang, F. Enhancing urban flood resilience: A coupling coordinated evaluation and geographical factor analysis under SES-PSR framework. Int. J. Disaster Risk Reduct. 2024, 101, 104243. [Google Scholar] [CrossRef]
  52. Liu, H.L.; An, Z.X.; Wang, H.K.; Zhang, P.H.; Wang, G.Y.; Wang, Z. The coupling of urban resilience and innovation efficiency for coordinated spatio-temporal evolution and spatial heterogeneity in the Beijing-Tianjin-Hebei urban agglomeration. Geogr. Res. 2025, 44, 577–602. [Google Scholar]
  53. Chen, L.; Yu, W.; Zhang, X. Spatio-temporal patterns of High-Quality urbanization development under water resource constraints and their key Drivers: A case study in the Yellow River Basin, China. Ecol. Indic. 2024, 166, 112441. [Google Scholar] [CrossRef]
  54. Li, M.B.; Sun, J.R.; Baldan, D.; Olden, J.D.; Liu, Q.; Ding, C.Z.; Tao, J. Human barriers fragment three-quarters of all rivers in the Mekong basin. Renew. Sustain. Energy Rev. 2025, 210, 115158. [Google Scholar] [CrossRef]
  55. Sun, Y.F.; Wang, N.L. Sustainable urban development of the π-shaped Curve Area in the Yellow River basin under ecological constraints: A study based on the improved ecological footprint model. J. Clean. Prod. 2022, 337, 130452. [Google Scholar] [CrossRef]
  56. Wang, C.; Wang, L.Y.; Zhan, J.Y.; Liu, W.; Teng, Y.M.; Chu, X.; Wang, H.H. Spatial heterogeneity of urbanization impacts on ecosystem services in the urban agglomerations along the Yellow River, China. Ecol. Eng. 2022, 182, 106717. [Google Scholar] [CrossRef]
  57. Wang, S.B.; Li, Z.Y.; Long, Y.; Yang, L.; Ding, X.Y.; Sun, X.L.; Chen, T. Impacts of urbanization on the spatiotemporal evolution of ecological resilience in the Plateau Lake Area in Central Yunnan, China. Ecol. Indic. 2024, 160, 111836. [Google Scholar] [CrossRef]
  58. Alfalih, A.A.; Hadj, T.B. Ecological impact assessment of green technological innovation under different thresholds of human capital in G20 countries. Technol. Forecast. Soc. 2024, 201, 123276. [Google Scholar] [CrossRef]
  59. Yin, S.G.; Zhou, Y.J.; Zhang, C.G.; Wu, N.N. Impact of regional integration policy on urban ecological resilience: A case study of the Yangtze River Delta region, China. J. Clean. Prod. 2024, 485, 144375. [Google Scholar] [CrossRef]
  60. Xu, C.; Huo, X.X.; Hong, Y.X.X.; Yu, C.; de Jong, M.; Cheng, B.D. How urban greening policy affects urban ecological resilience: Quasi-natural experimental evidence from three megacity clusters in China. J. Clean. Prod. 2024, 452, 142233. [Google Scholar] [CrossRef]
  61. Yang, P.Q.; Bai, Y.Y. The History, Characteristics and Path of New-Type Urbanization in the Yellow River Basin. J. Xi’an Univ. Financ. Econ. 2023, 36, 71–84. [Google Scholar] [CrossRef]
  62. Hu, H.; Tian, G.L.; Wu, Z.; Xia, Q. A study of ecological compensation from the perspective of land use/cover change in the middle and lower Yellow River, China. Ecol. Indic. 2022, 143, 109382. [Google Scholar] [CrossRef]
  63. Zhao, Y.; Ke, Z.H.; Zhang, N. Green productivity divergence and factor endowments: Evidence from the Yellow River Basin, China. Sci. Total Environ. 2023, 901, 165895. [Google Scholar] [CrossRef]
  64. Wang, Y.P.; Yuan, L.H.; Lu, Y.J.; Liu, H.X.; Zeng, Y.H. Structural succession and functional changes in the river ecosystem of the Yellow River estuary under the influence of ecological restoration measures. Ecol. Indic. 2025, 173, 113428. [Google Scholar] [CrossRef]
  65. Feng, Y.X.; Li, G.D. Interaction between urbanization and eco-environment in the Tibetan Plateau. J. Geogr. Sci. 2021, 31, 298–324. [Google Scholar] [CrossRef]
  66. Feng, Z.W.; Yang, X.L.; Li, S.B. New insights of eco-environmental vulnerability in China’s Yellow River Basin: Spatio-temporal pattern and contributor identification. Ecol. Indic. 2024, 167, 112655. [Google Scholar] [CrossRef]
  67. Tu, D.H.; Cai, Y.Y.; Liu, M.B. Coupling coordination analysis and spatiotemporal heterogeneity between ecosystem services and new-type urbanization: A case study of the Yangtze River Economic Belt in China. Ecol. Indic. 2023, 154, 110535. [Google Scholar] [CrossRef]
  68. Bille, R.A.; Jensen, K.E.; Buitenwerf, R. Global patterns in urban green space are strongly linked to human development and population density. Urban For. Urban Green. 2023, 86, 127980. [Google Scholar] [CrossRef]
  69. Liu, C.; Ni, T.H. Further exploring the driving mechanism of ecological carrying capacity changes at the urban agglomeration level. Ecol. Indic. 2023, 150, 110231. [Google Scholar] [CrossRef]
  70. Han, C.; Lyu, J.; Zhong, D.J. The impact of financial support and innovation awareness on farmers’ adoption of green production technology. Financ. Res. Lett. 2025, 79, 107259. [Google Scholar] [CrossRef]
  71. Fu, X.X.; Li, Z.H.; Ma, J.H.; Zhou, M.L.; Chen, L.L.; Peng, J.B. Divergent trends in vegetation greenness and resilience across China’s forestry ecological engineering regions. Ecol. Eng. 2025, 215, 107614. [Google Scholar] [CrossRef]
  72. Guo, T.J.; Tong, Y.; Yu, Y.Z. The influence of government health investment on economic resilience: A perspective from health human capital. Int. Rev. Econ. Financ. 2025, 99, 104050. [Google Scholar] [CrossRef]
  73. Wu, X.; Zhang, J.J.; Geng, X.L.; Wang, T.; Wang, K.; Liu, S.D. Increasing green infrastructure-based ecological resilience in urban systems: A perspective from locating ecological and disturbance sources in a resource-based city. Sustain. Cities Soc. 2020, 61, 102354. [Google Scholar] [CrossRef]
  74. Wang, S.Y.; Yang, Y.M.; Tang, Z.Y.; Wang, C.S.; Wang, F. Evolution of urban network patterns in the Yellow River Basin based on human mobility over 1300 years. Appl. Geogr. 2025, 178, 103587. [Google Scholar] [CrossRef]
  75. Yu, Y.; Hua, T.; Chen, L.D.; Zhang, Z.Q.; Pereira, P. Divergent Changes in Vegetation Greenness, Productivity, and Rainfall Use Efficiency Are Characteristic of Ecological Restoration Towards High-Quality Development in the Yellow River Basin, China. Engineering 2024, 34, 109–119. [Google Scholar] [CrossRef]
  76. Zhang, R.; Ying, W.H.; Wu, K.R.; Sun, H.P. The impact of innovative human capital agglomeration on urban green development efficiency: Based on panel data of 278 Cities in China. Sustain. Cities Soc. 2024, 111, 105566. [Google Scholar] [CrossRef]
  77. Ju, S.J.; Ma, J.; Li, Q.F.; Hu, S.G. Pattern evolution and driving mechanism of urbanization quality at county level in the middle reaches of Yangtze River. Econ. Geogr. 2023, 43, 93–102. [Google Scholar] [CrossRef]
  78. Sha, A.M.; Zhang, J.J.; Pan, Y.J.; Zhang, S.G. How to recognize and measure the impact of phasing urbanization on eco-environment quality: An empirical case study of 19 urban agglomerations in China. Technol. Forecast. Soc. 2025, 210, 123845. [Google Scholar] [CrossRef]
  79. Zhu, H.; Jiang, S.Y. Navigating urban sustainable development: Exploring the impact of low carbon policies on the urban ecological carrying capacity. J. Clean. Prod. 2024, 469, 143162. [Google Scholar] [CrossRef]
  80. Zhang, Y.; Yang, Y. Can the resource and environmental dilemma due to water-energy-carbon constraints be solved in the process of new urbanization? Sustain. Cities Soc. 2024, 114, 105748. [Google Scholar] [CrossRef]
  81. Qiu, M.; Yang, Z.L.; Zuo, Q.T.; Wu, Q.S.; Jiang, L.; Zhang, Z.Z.; Zhang, J.W. Evaluation on the relevance of regional urbanization and ecological security in the nine provinces along the Yellow River, China. Ecol. Indic. 2021, 132, 108346. [Google Scholar] [CrossRef]
  82. Zhou, Z.X.; Sun, X.R.; Zhang, X.T.; Wang, Y. Inter-regional ecological compensation in the Yellow River Basin based on the value of ecosystem services. J. Environ. Manag. 2022, 322, 116073. [Google Scholar] [CrossRef] [PubMed]
  83. Ma, J.D.; Deng, H.B. The Enlightenment of the Yangtze River Belt’s Development Based on the Mode of Typical River Basins’Development in the World. Reg. Econ. Rev. 2016, 2, 145–151. [Google Scholar] [CrossRef]
  84. Zhang, W.; Xu, Y.J.; Guo, L.; Lam, N.S.-N.; Xu, K.; Yang, S.; Yao, Q.; Liu, K.-B. Comparing the Yangtze and Mississippi River Deltas in the light of coupled natural-human dynamics: Lessons learned and implications for management. Geomorphology 2022, 339, 108075. [Google Scholar] [CrossRef]
  85. Kok, S.; Le Clec’h, S.; Penning, W.E.; Buijse, A.D.; Hein, L. Trade-offs in ecosystem services under various river management strategies of the Rhine Branches. Ecosyst. Serv. 2025, 72, 101692. [Google Scholar] [CrossRef]
  86. Bhusal, S.; Shrestha, S.; Aryal, T. Climate change impacts on flood hazards and surface-subsurface water interactions in the Lancang Mekong River Basin. J. Hydrol. 2025, 658, 133082. [Google Scholar] [CrossRef]
  87. Liu, C.L.; Li, Y.W.; He, D.M.; Deng, B.W.; Zhang, E.W.; Wei, S.Z.; Duan, X.W. Dynamic monitoring of eco-environmental quality in the Greater Mekong Subregion: Evolutionary characteristics and country differences. Environ. Impact Assess. 2025, 110, 107700. [Google Scholar] [CrossRef]
  88. Liu, J.G.; Chen, D.L.; Mao, G.Q.; Irannezhad, M.; Pokhrel, Y. Past and Future Changes in Climate and Water Resources in the Lancang-Mekong River Basin: Current Understanding and Future Research Directions. Engineering 2022, 13, 144–152. [Google Scholar] [CrossRef]
  89. Gaber, M.; Özden, Ö. Urban Growth and Its Environmental Impacts: Case of the Nile River, Egypt. J. Urban Cult. Res. 2023, 26, 237–244. [Google Scholar]
  90. Peng, F.; Liang, J. The Predicament and Institutional Improvement of Horizontal Ecological Protection Compensation in River Basins under Contractual Governance. J. Nanjing Technol. Univ. 2024, 23, 54–67+113–114. [Google Scholar]
Sustainability 17 06197 g001
Figure 1. Study area.
Figure 1. Study area.
Sustainability 17 06197 g001
Sustainability 17 06197 g002
Figure 2. The mechanism of action of NU on ER.
Figure 2. The mechanism of action of NU on ER.
Sustainability 17 06197 g002
Sustainability 17 06197 g003
Figure 3. Technical route of studying the NU effect on ER.
Figure 3. Technical route of studying the NU effect on ER.
Sustainability 17 06197 g003
Sustainability 17 06197 g004
Figure 4. Temporal evolution of NU and ER from 2005 to 2020.
Figure 4. Temporal evolution of NU and ER from 2005 to 2020.
Sustainability 17 06197 g004
Sustainability 17 06197 g005
Figure 5. Evolution of NU spatial pattern from 2005 to 2020.
Figure 5. Evolution of NU spatial pattern from 2005 to 2020.
Sustainability 17 06197 g005
Sustainability 17 06197 g006
Figure 6. CCD temporal evolution at different scales in the YRB from 2005 to 2020.
Figure 6. CCD temporal evolution at different scales in the YRB from 2005 to 2020.
Sustainability 17 06197 g006
Sustainability 17 06197 g007
Figure 7. LISA cluster analysis of NU and ER from 2005 to 2020.
Figure 7. LISA cluster analysis of NU and ER from 2005 to 2020.
Sustainability 17 06197 g007
Sustainability 17 06197 g008
Figure 8. Spatial distribution of regression coefficients of dominant factors in 2015.
Figure 8. Spatial distribution of regression coefficients of dominant factors in 2015.
Sustainability 17 06197 g008
Table 1. Urbanization evaluation index and weight.
Table 1. Urbanization evaluation index and weight.
IndexFirst Grade IndexesWeight or FormulaExplanation
NUPopulation urbanization0.2385The secondary indicators include urban population density, urban population proportion, proportion of employees in the secondary industry, and proportion of employees in the tertiary industry.
Economic urbanization0.2082The secondary indicators are per capita GDP, non-agricultural output value ratio, and per capita disposable income of urban residents.
Social urbanization0.2863The secondary indicators include the total amount of social retail consumer goods, the number of health institutions per 10,000 people, education expenditure, and technology expenditure.
Space urbanization0.2670The secondary indicators include per capita road area, built-up area, proportion of construction land, and green coverage rate of built-up area.
ERScale resilience R x = L s L d Ls is the suitable construction land area, and Ld represents the constructed land area.
Density resilience 1 − 12 % ∑ j = 1 n A j r j y j ∑ i = 1 n r i C i P i Yj and Aj are the average productivity and per capita biological production area of the j-th type of land, respectively. rj and ri were yield factor and equilibrium factor, respectively. Ci and Pi represent the annual per capita consumption of the i-th consumer goods and the annual average productivity of the corresponding productive land, respectively.
Morphological resilience L m ∑ i = 1 n min d i j Lij is the average distance index of the source–sink landscape, dij is the distance between source patch i and sink patch j. The m and n are the number of grids of source and sink patches, respectively, and L is a constant. The value is the average distance index of the source–sink landscape in the study area in 2000, that is, 2500.86.
Total ecological resilience ( R x ∗ R d ∗ R m ) 6 Rx is scale resilience, Rd is urban density resilience, Rm is morphological resilience.
Table 2. Global Moran’s I at different scales in the YRB from 2005 to 2020.
Table 2. Global Moran’s I at different scales in the YRB from 2005 to 2020.
Year2005201020152020
YRB−0.084−0.083−0.0460.057
Upstream−0003−0.005−0.03−0.037
Midstream0.019−0.0180.0520.163
Downstream0.1150.180.2290.228
Table 3. Analysis of the formation mechanism of spatial heterogeneity of dominant factors.
Table 3. Analysis of the formation mechanism of spatial heterogeneity of dominant factors.
Driving FactorDirection of ActionSpatial PatternFormation MechanismPolicy Implications
Urban population densitynegative directionstrong east and weak west Population agglomeration pressureImplement differentiated population control
Educational expenditurepositive directionstrong west and weak eastLaw of diminishing marginal benefitOptimize education resource allocation
Technology expenditurepositive direction core–periphery structureInnovation diffusion effectsStrengthen regional innovation synergy
Urban greening ratepositive directionstrong west and weak eastDifferences in ecological compensation policiesPerfect transverse compensation machine
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, X.; Tian, Y.; Yang, Y.; Yin, L.; Zhang, B. Exploring the Impact of New Urbanization on Ecological Resilience from a Spatial Heterogeneity Perspective. Sustainability 2025, 17, 6197. https://doi.org/10.3390/su17136197

AMA Style

Wang X, Tian Y, Yang Y, Yin L, Zhang B. Exploring the Impact of New Urbanization on Ecological Resilience from a Spatial Heterogeneity Perspective. Sustainability. 2025; 17(13):6197. https://doi.org/10.3390/su17136197

Chicago/Turabian Style

Wang, Xinyu, Yuan Tian, Yong Yang, Le Yin, and Baolei Zhang. 2025. "Exploring the Impact of New Urbanization on Ecological Resilience from a Spatial Heterogeneity Perspective" Sustainability 17, no. 13: 6197. https://doi.org/10.3390/su17136197

APA Style

Wang, X., Tian, Y., Yang, Y., Yin, L., & Zhang, B. (2025). Exploring the Impact of New Urbanization on Ecological Resilience from a Spatial Heterogeneity Perspective. Sustainability, 17(13), 6197. https://doi.org/10.3390/su17136197

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop