Coupling Coordination of New Urbanization and Ecological Security and Spatial-Temporal Non-Stationarity of Its Driving Factors in Gansu Province

XU Jing¹, YANG Bin²
¹Belt and Road Institute for Economic Research, Lanzhou University of Finance and Economics, Lanzhou 730101, Gansu, China
²School of Agricultural and Forestry Economics and Management, Lanzhou University of Finance and Economics, Lanzhou 730101, Gansu, China

Abstract

Gansu Province plays a pivotal strategic role in China's national ecological construction. Investigating the coupling synergy between new urbanization and ecological security status in Gansu Province, along with its driving factors, is of paramount significance for consolidating the ecological security barrier in northwest China and the entire nation. This study examines 14 municipalities and prefectures in Gansu Province as the research area. Based on the "population-economy-society-space" model and the "driving force-pressure-state-impact-response-management" (DPSIRM) model, we constructed evaluation index systems for new urbanization and ecological security, respectively. A coupling coordination model was employed to analyze the synergistic development trends between the two systems from 2012 to 2021. Geodetector and spatiotemporal geographically weighted regression (GTWR) models were then applied to reveal the primary driving factors and their spatiotemporal non-stationarity. The results indicate that: (1) From 2012 to 2021, Gansu's new urbanization index increased from 0.213 to 0.328, remaining at a relatively low level, while its ecological security index rose by 0.099, transitioning from low to moderate security. (2) The coupling coordination degree between new urbanization and ecological security in Gansu increased from 0.527 to 0.628 during the study period, shifting from a state of barely coordinated to primarily coordinated, with a spatial distribution pattern characterized as "low in the north and south, high in the middle." (3) Fixed asset investment, secondary industry employment, urban population density, per capita GDP, and the proportion of secondary industry in GDP represent the main driving forces behind this synergy. (4) These driving factors exhibited non-stationarity across both temporal and spatial scales, with variations in both direction and intensity of influence. These findings can provide decision-making references for advancing new urbanization while maintaining regional ecological security in Gansu Province.

Keywords: ecological security; new urbanization; coupling coordination; non-stationarity; driving factors

1 Introduction

The harmonious development of socio-economic systems and natural environments presents both a scientific challenge and a major policy imperative. The 18th National Congress of the Communist Party of China proposed the new urbanization strategy, while the 20th National Congress explicitly called for its in-depth implementation. New urbanization emphasizes connotation, quality, and efficiency, with coordinated urban-rural development, industry-city interaction, harmonious development, and ecological livability as its fundamental characteristics. The Third Plenary Session of the 20th CPC Central Committee further noted that China is currently in a critical period of accelerated new urbanization promotion. Unlike past approaches focused simply on urban expansion and population agglomeration, new urbanization places greater emphasis on citizenization of the population, rationality of urban planning, and sustainable development.

As a key component and important foundation of sustainable development, ecological security constitutes the basic prerequisite for implementing people-centered new urbanization and building a new pattern of harmonious development between humanity and nature. The coordination between new urbanization construction and ecological security has become an inevitable requirement for promoting high-quality regional development. Existing research primarily focuses on four aspects: (1) New urbanization level assessment, often employing entropy weight methods, principal component regression models, and other approaches to construct indicator systems from demographic, economic, social, and ecological dimensions for evaluating regional new urbanization development status. (2) Influencing factors and driving mechanisms, mainly using least squares estimation, spatial statistical analysis, and LASSO regression to explore primary determinants of urbanization levels. (3) Socio-economic and ecological effects of new urbanization, typically analyzed through spatial mixture models, dynamic spatial panel models, and difference-in-differences methods to examine spatial spillover effects and characteristics. (4) Coupling coordination research, which applies coupling coordination degree models and Geodetector to explore spatiotemporal evolution patterns between new urbanization and rural revitalization, agricultural modernization, rural ecological environments, and atmospheric environmental quality.

Ecological security reflects ecosystem integrity, health status, and risk resistance capacity. International research emphasizes risk assessment and ecosystem service regulation, while domestic studies focus more on ecological security level measurement and pattern construction. A research paradigm has emerged for ecological security pattern construction involving "ecological source identification-resistance surface construction-ecological corridor and key node extraction." Under this paradigm, minimum cumulative resistance (MCR) models, matter-element models, and ecological footprint methods are primarily employed. Research objects involve administrative units such as provinces, cities, and counties, as well as natural regions including rivers, grasslands, oases, and deserts. Recent scholarship has begun examining the relationship between new urbanization and ecological security through comprehensive measurement and simulation studies, yet three deficiencies remain: (1) When constructing ecological security indices, economic and social factors are rarely considered. (2) Few studies measure the coupling coordination level and its dynamic evolution between new urbanization and ecological security. (3) Research has not adequately considered local effects of spatial objects and temporal correlations, neglecting variations in driving factor intensity and direction across spatiotemporal scales.

Based on these gaps, this study builds upon the DPSIRM model by adding driving force, impact, and management subsystems to construct a comprehensive framework encompassing economic, social, environmental, and resource elements. This approach aims to more comprehensively and systematically describe the logical relationships among causes, changes, and consequences of ecological security. Additionally, Geodetector and GTWR models are employed to analyze driving factors and their spatiotemporal non-stationarity in the coupling coordination between new urbanization and ecological security, providing theoretical foundations for advancing new urbanization and high-quality economic and social development in Gansu.

1.1 Study Area

Gansu Province is located in northwest China, between 32°11′–42°57′N and 92°13′–108°46′E, spanning 1,132 km from east to west and 1,480 km from north to south, with a total area of 425,900 km². The region features a complex and unique climate, including subtropical monsoon, temperate monsoon, temperate continental arid, and plateau mountain climates from south to north, with significant spatial variations in temperature (0–14°C) and annual precipitation (40–760 mm). The province exhibits diverse natural ecosystems, severe soil erosion, and high degrees of land desertification. Gansu governs 14 prefecture-level cities and 86 counties (cities, districts). In 2021, its regional GDP reached 1,186.38 billion yuan, with a permanent population of 24.6548 million. In recent years, Gansu's urbanization rate has steadily increased to 55.49%, though it still lags behind the national average. As an important water source conservation and supply area in the upper reaches of the Yangtze and Yellow Rivers, and a key component of the national "Two Screens and Three Belts" ecological security strategic pattern, the evolution of Gansu's new urbanization process and ecological security status has profound implications for regional sustainable development.

1.2 Data Sources

All data used in this study were obtained from publicly available domestic and international databases, covering the period 2012–2021. Elevation data were sourced from the Geospatial Data Cloud website. Annual average temperature and precipitation data were obtained from the National Earth System Science Data Center. Socio-economic data were collected from the China City Statistical Yearbook (2012–2021), China Environmental Statistics Yearbook (2012–2021), Gansu Statistical Yearbook (2012–2021), and statistical yearbooks of Gansu's prefecture-level cities, as well as national economic and social development statistical bulletins published on government websites. Missing values in individual annual indicators were supplemented using linear interpolation.

1.3 Methodology

1.3.1 Index System Construction

As a typical western inland province, Gansu faces challenges including uneven population mobility, relatively lagging economic development, insufficient social service resources, and increasing spatial development pressure. Based on the DPSIRM model and referencing relevant studies, this paper constructs evaluation index systems for new urbanization (Table 1) and ecological security (Table 2) that reflect demographic characteristics, economic development levels, social harmony, and the scientific rationality of spatial layout in the urbanization process. The DPSIRM model provides a comprehensive analytical framework covering driving elements, system pressure, development state, impact effects, response characteristics, and management measures, making it suitable for ecological security evaluation. According to the actual development conditions of the study area and existing research, the ecological security evaluation DPSIRM index system (Table 2) incorporates economic, social, environmental, and resource factors.

1.3.2 Coupling Coordination Model

The coupling coordination model represents an organic integration of coupling degree and synergy degree models, capable of evaluating the correlation and coordination among two or more systems. The formulas are as follows:

$$
C = \sqrt{\frac{U_a \times U_b}{(U_a + U_b)^2}}
$$

$$
T = \alpha U_a + \beta U_b
$$

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

where $U_a$ and $U_b$ represent the new urbanization index and ecological security index, respectively; $C$ and $T$ denote coupling degree and synergy degree; $\alpha$ and $\beta$ are both set to 0.5; and $D$ is the coupling coordination degree, with $D \in [0, 1]$. Higher values indicate greater coupling coordination levels. Reference to relevant studies establishes the classification criteria for coupling coordination degree (Table 4).

1.3.3 Geodetector

This study employs Geodetector to quantitatively investigate driving factors of the coupling coordination between new urbanization and ecological security. The method is as follows:

$$
q = 1 - \frac{\sum_{h=1}^{L} N_h \sigma_h^2}{N \sigma^2}
$$

where $q$ represents the explanatory power of driving factors on spatial heterogeneity of elements; $L$ is the number of strata; $N_h$ and $\sigma_h^2$ are the within-layer variance and total variance of elements, respectively; $N$ is the total number of samples. Based on the actual development of new urbanization and ecological security in Gansu and existing research findings, this study selects urban population density ($X_1$), proportion of secondary industry in GDP ($X_2$), normalized difference vegetation index ($X_3$), annual precipitation ($X_4$), annual average temperature ($X_5$), number of employees in secondary industries ($X_6$), and fixed asset investment ($X_7$) as driving factors to detect the driving forces behind the coupling coordination.

1.3.4 Spatiotemporal Geographically Weighted Regression Model

The GTWR model builds upon the geographically weighted regression (GWR) model by introducing a temporal dimension. By calculating spatiotemporal variation trends of parameters, it indirectly reflects the spatiotemporal non-stationarity characteristics of research data:

$$
Y_i = \beta_0(u_i, v_i, t_i) + \sum_{k=1}^{p} \beta_k(u_i, v_i, t_i)g_{ik} + \varepsilon_i
$$

where $Y_i$ is the sample value; $\beta_0(u_i, v_i, t_i)$ is the constant term; $(u_i, v_i, t_i)$ represents the spatiotemporal coordinates of sample point $i$; $g_{ik}$ is the value of independent variable $k$ at point $i$; $\beta_k(u_i, v_i, t_i)$ is the regression parameter for independent variable $k$ at sample point $i$; and $\varepsilon_i$ is the model residual. The regression parameter is calculated as:

$$
\hat{\beta}(u_i, v_i, t_i) = [Z^T W(u_i, v_i, t_i) Z]^{-1} Z^T W(u_i, v_i, t_i) Y
$$

where $\hat{\beta}(u_i, v_i, t_i)$ is the estimated value of $\beta$; $Z$ is the matrix composed of independent variables; $Z^T$ is the transposed matrix; and $W(u_i, v_i, t_i)$ is the spatiotemporal weight matrix.

2 Results

2.1 New Urbanization Index and Ecological Security Index

From 2012 to 2021, Gansu Province's new urbanization index increased from 0.213 to 0.328, remaining at a relatively low level. The ecological security index rose by 0.099, transitioning from low to moderate security status (Table 5).

[TABLE:5]

2.2 Coupling Coordination Degree Between New Urbanization and Ecological Security

From 2012 to 2021, the coupling coordination degree between new urbanization and ecological security in Gansu Province showed an upward trend, increasing from 0.527 to 0.628, with the synergistic state shifting from barely coordinated to primarily coordinated. Specifically, in 2012, Lanzhou City and Gannan Tibetan Autonomous Prefecture (hereafter Gannan Prefecture) recorded the highest and lowest coupling coordination degrees, respectively (Table 6). By 2016, Lanzhou's coupling coordination degree had increased to 0.728, showing an intermediate coordinated state. Except for Jiayuguan, Zhangye, Gannan, Baiyin, Dingxi, and Pingliang, all other prefecture-level cities showed varying degrees of improvement in their coupling coordination levels. In 2021, Jiayuguan's coupling coordination degree reached 0.589, transitioning from barely to primarily coordinated, while Tianshui's decreased to 0.519. By 2021, Baiyin, Tianshui, Zhangye, Dingxi, and Longnan had all improved their coupling coordination levels from barely to primarily coordinated, while Gannan Prefecture increased by 0.067, shifting from near-uncoordinated to barely coordinated. Overall, regional differences in coupling coordination between new urbanization and ecological security in the study area gradually narrowed (Figure 1).

[TABLE:6]

The coupling coordination degree exhibited a spatial distribution pattern of "low in the north and south, high in the middle" (Figure 2). During the study period, the east-west trend line of coupling coordination gradually flattened, indicating that differences between eastern and western regions were diminishing. Meanwhile, the north-south trend line remained steeper, suggesting that spatial differentiation along the north-south axis was more pronounced.

[FIGURE:1]

[FIGURE:2]

2.3 Driving Factor Detection Results

2.3.1 Single-Factor Detection Results

Single-factor detection results indicate that fixed asset investment, secondary industry employment, urban population density, per capita GDP, and the proportion of secondary industry in GDP are the core driving forces behind the coupling coordination between new urbanization and ecological security in Gansu Province (Table 7). Socio-economic factors thus play a significant role in determining this coupling coordination.

[TABLE:7]

2.3.2 Interaction Detection Results

Interaction detection results reveal that the coupling coordination between new urbanization and ecological security in Gansu is influenced by interactions among multiple factors (Figure 3). In 2012, the interaction with the highest explanatory power was between the proportion of secondary industry in GDP and fixed asset investment. In 2016, it was between urban population density and secondary industry employment. In 2021, the dominant interactions were between the proportion of secondary industry in GDP and secondary industry employment, and between secondary industry employment and annual average temperature. The main driving factors thus vary across years.

[FIGURE:3]

2.4 Spatiotemporal Non-Stationarity of Driving Factors

2.4.1 Data Testing and Model Selection

To avoid multicollinearity, variance inflation factor (VIF) tests were conducted on all driving factors, confirming the absence of collinearity. To enhance model selection rigor, ordinary least squares (OLS) and geographically weighted regression (GWR) models were used as benchmarks. Results show that the GTWR model achieved an adjusted R² of 0.823 and an Akaike information criterion corrected (AICc) value of -342.15, demonstrating superior fit (Table 8). Therefore, the GTWR model was selected for local estimation of driving factors influencing the coupling coordination.

[TABLE:8]

2.4.2 Temporal Non-Stationarity of Driving Factors

Urban population density, the proportion of secondary industry in GDP, and annual precipitation consistently showed negative correlations with coupling coordination, though the magnitude of their effects fluctuated interannually. Annual average temperature exhibited a positive correlation with decreasing regression coefficients, indicating its promoting effect gradually weakened. The normalized difference vegetation index shifted from negative to positive correlation, with its effect magnitude gradually strengthening. Secondary industry employment showed a positive correlation with higher regression coefficients than other factors, indicating its greater influence magnitude. Per capita GDP's positive effect increased significantly after 2018, peaking in 2020 before slightly declining in 2021. Fixed asset investment maintained a positive correlation, though its regression coefficient decreased, indicating a weakening promoting effect (Table 9).

[TABLE:9]

2.4.3 Spatial Non-Stationarity of Driving Factors

Results show that urban population density positively affected the coupling coordination in Lanzhou, Longnan, Linxia Hui Autonomous Prefecture (hereafter Linxia Prefecture), and Gannan Prefecture, but negatively impacted other regions. The proportion of secondary industry in GDP negatively affected all prefectures, particularly strongly inhibiting synergy in Gannan and Linxia. Annual precipitation positively promoted coupling coordination in Jinchang, Wuwei, and Zhangye, while inhibiting other regions. Annual average temperature negatively affected Jiayuguan and Jiuquan but positively influenced other regions. The normalized difference vegetation index showed substantial spatial variation, with the strongest inhibitory effect in Gannan and the strongest promoting effect in Pingliang. Secondary industry employment inhibited coupling coordination in Jiayuguan, Jiuquan, and Zhangye while promoting it in other regions. Per capita GDP positively affected all regions. Fixed asset investment had a weak negative effect only in Qingyang, with positive effects elsewhere (Figure 4).

[FIGURE:4]

3 Discussion

3.1 Analysis of Coupling Coordination Between New Urbanization and Ecological Security in Gansu

During the study period, the coupling coordination state between new urbanization and ecological security in Gansu shifted from barely to primarily coordinated, though coordination levels remain uneven across municipalities. In 2021, the top three regions in coordination degree were Lanzhou, Qingyang, and Tianshui. As the provincial capital, Lanzhou's higher socio-economic development level and ecological governance capacity provide a solid foundation for coordinated development. Qingyang, rich in oil and natural gas resources (with total oil resources of 5.974×10⁹ tons and proven geological reserves of 1.797×10⁹ tons), balances energy development with ecological protection in its new urbanization process, achieving coordinated development. Tianshui has implemented an "industry-strengthening city" strategy, promoting coordinated industrial upgrading and ecological environmental protection.

Gannan Prefecture's coupling coordination transitioned from near-uncoordinated to barely coordinated, the lowest level in Gansu, reflecting its unique ecological function positioning. As a crucial water source conservation area in the upper reaches of the Yellow and Yangtze Rivers, Gannan bears significant ecological responsibility, with its new urbanization level lagging behind its ecological security status. The overall spatial distribution pattern of "low in the north and south, high in the middle" reflects regional differences. Southern Gansu, dominated by the Qilian Mountains and Hexi Corridor plateau and mountain regions, has fragile ecosystems and low environmental carrying capacity, relying primarily on traditional agriculture and resource-based industries with limited modern industrial support, resulting in lower urbanization levels and greater ecological protection challenges. Northern regions possess certain resource advantages but face ecological pressure due to single economic structures. Central Lanzhou, with its relatively flat terrain, complete infrastructure, diversified industrial structure, and more developed economy, facilitates coordinated new urbanization and ecological security development.

3.2 Analysis of Driving Factors and Their Spatiotemporal Non-Stationarity

Socio-economic factors constitute important drivers of spatial differentiation in the coupling coordination between new urbanization and ecological security in Gansu, consistent with findings from Bao Xiangping et al. Fixed asset investment promotes this coupling coordination, though its positive effect gradually weakens. In the short term, increased investment provides employment opportunities, accelerates industrial development, and promotes new urbanization. However, in the long term, continuous investment growth cannot concentrate market resources in greener, more efficient, and innovative enterprises, nor can it stimulate optimization of existing asset allocation and operational efficiency. Guiding urban construction from expansion to robust development remains essential.

Urban population density inhibits coordinated development. During urbanization, population agglomeration exceeding regional carrying capacity threatens ecological security and hinders synergy. However, Lanzhou, Longnan, Linxia, and Gannan showed positive coefficients, as Lanzhou's urban population density actually decreased from 2,342 to 2,183 persons/km² during the study period, while the other three regions have relatively low population densities that did not adversely affect ecological security.

Industry-related factors, particularly the proportion of secondary industry in GDP and secondary industry employment, significantly influence coupling coordination. Secondary industry expansion generally increases energy consumption and pollutant emissions, intensifying environmental pressure and limiting ecological security. However, moderate expansion combined with efficient resource utilization and environmental technologies can promote harmonious economic and ecological development, facilitating sustainable new urbanization. The negative impact of secondary industry proportion was particularly pronounced in Gannan and Linxia, indicating these regions' over-reliance on traditional industries and urgent need for industrial transformation. Conversely, increased secondary industry employment helps optimize industrial structure and enhance ecological investment, positively affecting all prefectures.

Among natural environmental factors, annual precipitation shows spatiotemporal heterogeneity in its effects. Increased precipitation positively promoted coupling coordination in Jinchang, Wuwei, and Zhangye—typical arid regions where precipitation increases improve water supply, alleviate desertification, and provide ecological security for sustainable urbanization. For instance, Zhangye's annual precipitation increased by approximately 47 mm from 2012 to 2021, providing adequate water for irrigation and ecological restoration. However, precipitation increases did not benefit other regions, possibly because summer precipitation increases trigger flooding that affects ecological security. Rising temperatures exacerbate water shortages, land degradation, and ecological deterioration, further constraining coupling coordination in arid regions like Jiayuguan and Jiuquan.

In summary, both natural environmental and socio-economic factors exhibit spatiotemporal non-stationarity in their effects. Therefore, promoting coupling coordination requires comprehensive consideration of local natural conditions, resource advantages, and economic development levels to formulate context-specific strategies. First, strengthen urban planning and management with scientific population control policies to guide orderly population flow and distribution. Regions like Longnan, Gannan, and Linxia can moderately guide population agglomeration within ecological carrying capacity. Second, formulate differentiated industrial transformation policies to shift traditional industries toward green, low-carbon, and efficient directions. Gannan and Linxia should accelerate development of eco-friendly industries, while Jiayuguan and Jiuquan need to optimize industrial structure to reduce resource consumption and environmental pollution. Third, optimize investment structure and improve efficiency by directing capital toward green industries, ecological projects, and key new urbanization areas, particularly strengthening investment project evaluation and supervision in Qingyang to promote positive interaction between economic development and ecological security.

This study analyzed the dynamic evolution of new urbanization and ecological security levels in Gansu and elucidated the non-stationarity of driving factors across temporal and spatial scales. Future research should further consider impacts of national macro-control policies, local regulations, and landscape types to deeply analyze the internal mechanisms through which industrial structure adjustment guides the coupling coordination between new urbanization and ecological environment. Additionally, important opportunities such as rural revitalization, western development, and the Belt and Road Initiative should be integrated for more comprehensive analysis.

4 Conclusions

(1) From 2012 to 2021, Gansu Province's new urbanization index increased from 0.213 to 0.328, remaining at a relatively low level. The ecological security index increased by 0.099, transitioning from low to moderate security. Their coupling coordination level rose from 0.527 to 0.628, shifting from barely coordinated to primarily coordinated. Gannan and Linxia prefectures showed relatively low levels of coupling coordination development, particularly with new urbanization lagging behind ecological security status.

(2) The synergistic development of new urbanization and ecological security is influenced by the combined effects of social and economic factors. Fixed asset investment, secondary industry employment, urban population density, per capita GDP, and the proportion of secondary industry in GDP constitute the main driving forces. The coupling coordination is affected by interactions among multiple factors, though the dominant interactions vary across years.

(3) Driving factors exhibit spatiotemporal non-stationarity characteristics in their effects on the coupling coordination between new urbanization and ecological security in Gansu, with variations in both direction and intensity. Rising annual average temperature inhibits coupling coordination in Jiayuguan and Jiuquan, while the proportion of secondary industry in GDP negatively affects synergy in Gannan and Linxia, and urban population density primarily impacts Tianshui, Dingxi, and Baiyin negatively. Therefore, development strategies must fully account for each prefecture's natural resource endowments and economic development levels, with policies tailored to local conditions.

References

[1] Liu Hailong, Wang Gaiyan, Zhang Penghang, et al. Spatiotemporal evolution and coordination influence of coupling coordination between new urbanization and ecological resilience in Fenhe River Basin[J]. Journal of Natural Resources, 2024, 39(3): 640-667.

[2] Zhao Yang, Wang Jing, Pan Weihua. Regional characteristics and impact mechanisms of new urbanization and green development level in China at the provincial level[J]. Economic Geography, 2023, 43(9): 1-9.

[3] Cao N, Luo N, Peng Y, et al. Evaluation of urbanization development dynamics based on quadrilateral diamond structure model: Evidence from Chongqing, China, in the new era[J]. Journal of Urban Planning and Development, 2023, 149(3): 04023030.

[4] Zhao Lei, Fang Cheng. Distribution dynamics and driving mechanism of China's provincial new urbanization development level[J]. Journal of Quantitative & Technological Economics, 2019, 36(5): 44-64.

[5] Hu Feipeng, Zhao Jun, Sun Ziyun, et al. Spatiotemporal changes and driving mechanism of ecosystem service interactions in the Shiyang River Basin[J]. Arid Land Geography, 2024, 47(10): 1755-1766.

[6] Ou Jinfeng, Chen Dong. High-quality development of new urbanization in Guangdong Province: Spatiotemporal evolution, influencing factors[J]. Economic Geography, 2023, 43(3): 78-87.

[7] Lai S, Sha J, Eladawy A, et al. Evaluation of ecological security and ecological maintenance based on pressure-state-response (PSR) model, case study: Fuzhou City, China[J]. Human and Ecological Risk Assessment: An International Journal, 2022, 28(7): 734-761.

[8] Zhang R, Li P, Xu L. Evaluation and analysis of ecological security based on the improved three dimensional ecological footprint model in Shaanxi Province, China[J]. Ecological Indicators, 2022, 144: 109483.

[9] Chao Yunshu, Yan Yutong, Yan Sijia, et al. County-scale ecological security pattern construction based on MCR model: A case study of Yongchun County, Fujian Province[J]. Bulletin of Soil and Water Conservation, 2023, 43(4): 203-210.

[10] Liu Zhiyou, Qu Xiuwu, Wei Ran, et al. Evaluation of land ecological security and diagnosis of obstacle factors for oasis land in Yili River valley from perspective of ecological civilization[J]. Bulletin of Soil and Water Conservation, 2022, 42(1): 137-143.

[11] Yu B. Ecological effects of new type urbanization in China[J]. Renewable and Sustainable Energy Reviews, 2021, 135: 110239.

[12] Luo Z, Wang F, Pan H, et al. Evaluation and improvement strategy of tourism industrial park competitiveness from the perspective of new urbanization: Case study of the Suining Tourism Industrial Park[J]. Journal of Urban Planning and Development, 2022, 148(4): 05022029.

[13] Li X, Zheng Z, Shi D, et al. New urbanization and carbon emissions intensity reduction: Mechanisms and spatial spillover effects[J]. Science of the Total Environment, 2023: 167172.

[14] Cai J, Li X, Liu L, et al. Coupling and coordinated development of new urbanization and agro-ecological environment in China[J]. Science of the Total Environment, 2021, 776: 145837.

[15] Jiang B, Ding L, Fang X. Sustainable development of new urbanization from the perspective of coordination: A new complex system of urbanization-technology innovation and the atmospheric environment[J]. Atmosphere, 2019, 10(11): 652.

[16] Chang Xinfeng, Guan Xin. Spatial and temporal evolution of eco-efficiency and its influencing factors in the Yangtze River Delta urban agglomeration in the process of new urbanization[J]. Economic Geography, 2020, 40(3): 185-195.

[17] Xiang Li, Zhou Wei, Ren Jun, et al. Ecological security evaluation of plateau urban wetland based on DPSIRM model: With Xining section of Huangshui Basin as an example[J]. Chinese Journal of Ecology, 2022, 41(10): 2064-2071.

[18] Lü Tiangui, Qiu Rong, Zhao Qiao, et al. Temporal-spatial evaluation of regional ecological security and obstacle diagnosis of the urban agglomerations in the middle reaches of the Yangtze River based on DPSIRM model[J]. Research of Soil and Water Conservation, 2024, 31(2): 379-388.

[19] Xie Xianjian. Coupling coordination relationship and spatiotemporal evolution characteristics of land use benefit in Sichuan Province based on entropy weight TOPSIS and coupling coordination model[J]. Journal of Soil and Water Conservation, 2024, 38(1): 267-277.

[20] Hong Lei, Sun Jie, Liu Dong, et al. Research on the development of coupling coordinated relationship and the affecting factors of eco-environment and socio-economy in central urban agglomeration of Yangtze River Delta region[J]. Journal of Ecology and Rural Environment, 2024, 40(9): 1155-1166.

[21] Liu Yingjie, Wang Shuang. Coupling and coordination between elderly care industry and tourism industry in Yangtze River Economic Zone and its relationship with economic growth[J]. Economic Geography, 2022, 42(8): 123-131.

[22] Ma Fei, Zhang Dongwei, Chen Long, et al. Measurement of urban resilience level and identification of obstacle factors in urban agglomeration[J]. Journal of Chang'an University (Social Science Edition), 2024, 26(2): 112-124.

[23] Han Yan, Zhang Yuting. Coupling and coordination development between urbanization and eco-environment in Gansu Province[J]. Research of Soil and Water Conservation, 2021, 28(3): 256-263.

[24] Bao Xiangping, Zhou Ruiping, Song Yongyong. The coupling coordination and influencing factors of ecological safety and new type urbanization in Ulanqab City[J]. Journal of Northwest Normal University (Natural Science Edition), 2021, 57(5): 64-70.

[25] Lu Jing. Research on the impact of public service supply on regional differences in new urbanization development[J]. Reform of Economic System, 2024(2): 67-73.

[26] Ma Mingde, Li Junjie, Xue Chenhao. Spatiotemporal evolution and driving mechanism of environmental stress of regional development in Ningxia Hui Autonomous Region[J]. Arid Land Geography, 2024, 47(6): 1061-1072.

[27] Cai Shaohong, Gu Cheng, Zhang Zaijie. Spatial and temporal characteristics and influencing mechanism of coordination between new type urbanization and rural revitalization in western China[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(12): 202-213.

[28] Luo Liangqing, Xie Haiyang. Research on the influencing factors and spatiotemporal nonstationarity of provincial green total factor productivity in China[J]. Journal of Applied Statistics and Management, 2024, 43(2): 315-330.

[29] Sun Ruiqian, Yao Feng. Research on space and time coupling of green development and digital economy in Beijing-Tianjin-Hebei region[J/OL]. Environmental Protection Science. [2024-10-28]. https://doi.org/10.16803/j.cnki.issn.1004-6216.202403031.

[30] Li Wenjia, Wang Ruiyan, Xu Jiahao, et al. Detecting farmland irrigation in arid areas of northwest China using remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(17): 120-128.

[31] Chai Jian, Tian Lingyue, Zhang Xiaokong. Research on the dynamic evolution and influence factors of the coupling coordination development of the economy-energy-environment system[J]. Management Review, 2024, 36(1): 29-41.

[32] Li Yuanzheng, Feng Zhizhi, Li Li, et al. Evaluation of ecological sensitivity of terrestrial ecosystem in China[J]. Ecological Science, 2024, 43(4): 129-139.