Analysis of Multi-Factor Dynamic Coupling and Government Intervention Level for Urbanization in China: Evidence from the Yangtze River Economic Belt

2.1 Urbanization and ED

The necessity of coordinating urbanization and ED is evident in two aspects. The first reason lies in the adverse impact rapid urbanization poses on economic growth. Issues stemming from population urbanization and land urbanization, such as surging urban population, degradation of water and soil resources, reduction in arable land, and uncontrolled land use, hinder further economic advancement. Specifically, urbanization triggers changes in land use types, leading to severe ecological and environmental problems and jeopardizing the harmonious relationship between humans and nature (Arneth et al., 2017; Yin et al., 2020). The second reason is the mutually beneficial relationship between the urbanization process and ED. Economic progress provides essential material foundations, technical support, and institutional safeguards for urbanization. Urbanization enhances population quality, labor productivity, and resource allocation efficiency, facilitating solutions to critical ED challenges, including industrial transformation and upgrading, economic structural optimization, and enhanced developmental vigor (Vurur, 2022).

The coordination between urbanization and ED has become an urgent issue requiring resolution. Previous research in this area can be categorized into three main streams. The first stream has explored the coordination between population urbanization and land urbanization from the perspective of urbanization (Lu, 2007; Zhang & Shunfeng, 2003), interactions (Liu et al., 2012), as well as the negative impacts and influencing mechanisms of imbalance (Wei & Ye, 2014). Furthermore, it has focused on rational land resource utilization to propel the urbanization process (Choi & Wang, 2017; Lin et al., 2015; Siciliano, 2012; Yu et al., 2019), mitigate adverse effects on ED (Yang et al., 2020; Zhou et al., 2019b), and achieve sustainable development (Jaeger et al., 2010). The second stream centers on scientifically and reasonably promoting ED. Researchers have sought to explore pathways for coordinating urbanization and ED from perspectives such as trade openness, financial development, healthcare expenditures (Ahmad et al., 2021), transportation infrastructure (Maparu & Mazumder, 2017), urban transportation (Apostolopoulos & Kasselouris, 2022), energy consumption (Wang et al., 2018), and globalization (Wu et al., 2017). The third stream examines the role and function of government in coordinating urbanization and ED through policy lenses. It focuses on policy decision-making and implementation phases. Jin et al. (2009) modeled the decision-making framework for UD using scenario analysis. Shi and Gill (2005) constructed a system dynamics model to assess the effectiveness of government measures. To address conflicts arising from rapid urbanization, China implemented an urban containment strategy. Zhao evaluated the reliability of China’s urban containment strategy (Zhao, 2011).

2.2 Urbanization and Environment

Coordinating urbanization and environmental development represents a significant task driven by the mutual constraints between urbanization and the environment. These constraints manifest in two main aspects. First, urbanization can bring benefits like economic growth, social progress, and cultural prosperity, yet it can also lead to resource depletion, environmental pollution, and ecological degradation. Second, the environment provides the foundation for urban development while imposing limitations, such as disaster risks, carrying capacity constraints, and ecological security concerns.

Regarding the research theme of coordinating urbanization and the environment, we categorized scholars into the following three groups. First, some study the adverse impacts of urban land use expansion and transformation on the environment. Abass and Xu examined the influence of urbanization on arable land loss (Abass et al., 2018; Xu et al., 2013). Malek and Verburg found that increasing urban land use affects crop yield, the environment, and water resources (Malek & Verburg, 2020). Asabere et al. (2020) and Yang et al. (2020) conducted theoretical, methodological, and empirical analyses on the environmental impact of regional land use transitions. Second, there are discussions on environmental constraints and their effects on urbanization, primarily focusing on the influence of geographical location on changes in land use types. Godschalk (1975) asserted that environmental factors influencing land planning and urban development include local physical conditions and environmental carrying capacity. Zhao and Chen (2018) observed that geographical location affects green space changes due to significant variations in thermal conditions. Third, some directly investigate the coordination between urbanization and the environment, often employing dynamic coupling coordination models to estimate the relationship. Zhao et al. (2017) investigated the global coupling relationship between urbanization and the environment by analyzing World Bank data from 209 countries and regions worldwide. Wang et al. (2014) conducted a relationship study using data on urbanization and the environment in the Beijing–Tianjin–Hebei region.

2.3 ED and Environment

The tension between ED and EP is a prominent contradiction. On the one hand, socio-economic progress drives urbanization, resulting in increased urban construction land and environmental degradation (Abidin et al., 2011). On the other hand, environmental carrying capacity constrains the extent of ED. Environmental carrying capacity is defined as the ability of an area’s environmental resources to sustain maximum human activities within a specific timeframe. It is a robust reference and tool for economic policy formulation and management during ED (Liu & Borthwick, 2011). Specht’s study on the environmental carrying capacity of forest resources (1993) indicates that resource scarcity and environmental pollution can impact regional ED. A comprehensive analysis of China’s water resource-carrying capacity led Yang et al. (2015) to conclude that water resource crises can restrict regional economic growth. Therefore, coordinating ED and EP is increasingly vital for sustainable development.

Summarize research on coordinating ED and EP from the following perspectives. First, the scope of research encompasses the coordination relationships between resources (Bass et al., 2010), the environment (Saveriades, 2000), ecosystems (Liu et al., 2021), ED (Li & Yi, 2020; Sun & Cui, 2018), land urbanization (Ariken et al., 2020), and population urbanization (Liu et al., 2012). Second, sample selection primarily involves cities (Fan et al., 2019; Li & Yi, 2020) and city clusters (Wang et al., 2014; Wu et al., 2020), followed by regions (Li et al., 2022), and ultimately the national scale (Liu et al., 2018) and global scale (Zhao et al., 2017). Third, the research focus underscores EP, mainly driven by regional resources and environmental conditions to promote regional economic sustainability (Santoso et al., 2014).

In summary, while existing research has provided valuable insights into coordinating urbanization, ED, and the environment, certain limitations remain. We observe that researchers have sought to bridge urbanization and ED by enhancing ecological and environmental quality, focusing on the impacts of natural resource integration (Ahmad et al., 2021), environmental pollution (Liang & Yang, 2019; Zhang and Chen, 2021), and ecological environment protection on urbanization and ED. However, previous studies still need to improve. First, research perspectives often lean towards one dimension, mostly analyzing the relationships from an economic, social, or environmental standpoint, needing more comprehensive and holistic considerations. Second, the scope of study tends to be confined to specific regions or cities for case analysis, with limited cross-national or cross-regional comparative studies. Third, researchers often emphasize exploring the disharmony between urbanization, ED, and the environment rather than directly addressing the issues. This approach offers practical methodologies for mitigating the real-world challenges of their discord. Fourth, fewer studies have centered on government public policies and decision mechanisms as the focal point for intertwining urbanization, ED, and the environment. The government’s role is predominantly explored solely in studies related to urbanization and ED. Therefore, our approach will encompass government environmental policies in our analysis, investigating the coordination among urbanization, ED, and EP. This approach aims to identify optimal levels of government environmental intervention aligned with regional developmental stages, offering novel perspectives for research in regionally coordinated development.

3.1 Study Area

Figure 1 illustrates the study area. The YREB is a burgeoning region along the Yangtze River in China. Encompassing 11 provinces and cities – Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Yunnan, and Guizhou – the YREB spans an estimated 2,052,300 square kilometers, constituting 21.4% of the entire nation. With its expansive waterways, extensive economic hinterland, abundant resources, and robust foundation in sectors like agriculture, transportation, information, and technology, the YREB displays considerable developmental promise. It is noteworthy that the YREB’s growth aligns with the Chinese government’s strategic initiatives. In September 2016, the YREB Development Plan Outline was promulgated, ushering in a fresh development paradigm for the region (Chen et al., 2017). This plan seeks to foster economic progress while concurrently safeguarding the ecological integrity of the Yangtze River. Spanning both the eastern and western reaches of China, the YREB unites the Chengdu-Chongqing and Wuhan areas, prominent inland centers, with the coastal economic belt anchored by Zhejiang, Jiangsu, and Shanghai. By 2019, the YREB’s regional GDP had surged to 4,578.5 billion yuan, marking a 6.9% year-on-year increase. Within this, the Yangtze River Delta’s regional GDP reached 2,725.3 billion yuan, signifying a 6.4% expansion. While one of China’s most economically vibrant zones, the YREB also grapples with the tension between ED and ecological preservation.

Figure 1

Location of study area.

3.2 Methods

The rationale for employing the CD model in this study is to investigate the intricate interactions among ED, environmental conservation, and government policies impacting urbanization. We select the CD model for its capacity to facilitate a comprehensive analysis of these multifaceted and interconnected variables.

To effectively utilize the CD model, it is imperative to establish an index system for evaluating the progress in urbanization, economic growth, environmental preservation, and government strategies. This step is pivotal in ensuring research is grounded in objective and readily accessible data. By harnessing the CD model and a well-defined index system, the study offers a systematic, data-driven comprehension of the relationships among these pivotal elements. Such insights can prove invaluable for shaping policies and making informed decisions within urbanization.

After reviewing established evaluation indicators (Chen et al., 2017; Li et al., 2021) and considering data availability and accuracy, we selected refined indicators to measure UD, ED, EP, and government policy (GP). The finalized indicator system includes four indicators, each for population and land urbanization dimensions to assess UD, four indicators for ED and fiscal growth dimensions, and five indicators across the greenery, water resources, and pollutant emissions dimensions to evaluate EP efforts. Additionally, we use the number and proportion of government interventions in EP policies to gauge the government’s role in balancing diverse development processes. We sourced data from 30 statistical yearbooks spanning 2005–2019 in the YREB region, and policy data were collected and processed from 11 provincial government websites. Table 1 provides detailed information on the specific indexes employed.

The nature of evaluation indicators dictates variations in dimensionality and magnitude. In cases of differing dimensions, direct comparison of indicators is inappropriate. When indicator data exhibit substantial numerical variance, more considerable numerical indicators gain prominence, potentially overshadowing the impact of more minor numerical indicators. Hence, the normalization of raw index data is paramount to ensure the precision and reliability of evaluation outcomes. To achieve this goal, the study employs the min-max scaling technique, which uniformly maps evaluation data onto the [0,1] interval. Outline the method as follows:

where a ijk ′ is the standardized value, a ijk represents the value of the index j of the kth province in the year i, min { a 1 jk , a 2 jk , … , a mjk } and max { a 1 jk , a 2 jk , … , a mjk } , respectively, stand for the minimum and maximum values of the index j for all provinces and all years.

In order to ascertain index weights in a scientifically rigorous manner and circumvent the pitfalls associated with relying solely on objective or subjective approaches, this article employs a hybrid methodology that combines the EM with the AHP. By integrating subjective and objective analyses, this approach aims to achieve a well-balanced and accurate determination of index weights.

The AHP was initially employed. The team comprises three authors and six experts. Author 1, along with Expert 1 and Expert 4, specializes in public policy. Author 2, in collaboration with Expert 2 and Expert 3, focuses on public administration. Author 3, together with Expert 5 and Expert 6, specializes in environmental economics. They constructed an analysis matrix and calculated the weights for each indicator w 1 . The entropy value method is then applied to ascertain the weights of each index. The specific principles are as follows:

First, since the minimum value after standardization will appear as a zero, the zero cannot participate in the entropy calculation. It is necessary to perform translation processing on the normalization results of various data and then standardize the indicators.

Second, the standardized indicators need to be normalized again.

Third, calculate the entropy value of each indicator for the normalized data.

Fourth, through the results of entropy calculations, the redundancy of various indicators is further calculated.

Finally, calculate the weights of various indicators.

For the weights derived by the AHP and EM, we combine the subjective weight w 1 i and the objective weight w 2 i of the comprehensive indicator to get the combined weight w i , i = 1 – m . w i should be the closest possible to w 1 i and w 2 i . Based on the principle of minimum relative information entropy, we adopted the Lagrange multiplier method to generate an optimal combination weight calculation formula. Table 1 displays the results of the weight calculation.

After obtaining the final weights, we use the weights to calculate the evaluation results of the UD, ED, EP, and GP in each statistical period.

Among them, UD, ED, EP, and GP are the measured UD level, ED level, EP level, and GP intervention level within the prescribed years. V j , X j , Y j and Z j are, respectively, normalized values based on various index data, and w UD , w ED , w Ep , and w GP stand for the corresponding weights. Based on the conceptual analysis of coupling, the smaller the difference among UD, ED, EP, and GP, the higher the degree of coupling. Therefore, we introduce the CD model to measure the level of coordination between factors:

Following the CD model, the value of ‘n’ is set to 2 when assessing the CD between UD level and ED level or between any other two factors. When evaluating the CD among UD level, ED level, and EP level, ‘n’ is set to 3. For a comprehensive evaluation of UD, ED, EP, and GP levels, ‘n’ is set to 4, and ‘INDEX’ represents the respective measured index value.

For a clearer understanding of the CD model’s calculation outcomes and a more intuitive grasp of the degree of coupling exhibited by different provinces across various indicators, we have categorized the CD into four levels. A CD value below 0.3 indicates “Severe Imbalance” (SI). When the CD value falls between 0.3 and 0.5, we label the outcome “Low Coordination” (LC). Similarly, CD values ranging from 0.5 to 0.8 result in “Medium Coordination” (MC), while values exceeding 0.8 signify “High Coordination” (HC [Liao et al., 2012]). In total, we have calculated five CDs across three categories. Please refer to Table 2 for specific classification criteria.

4.1 CD of UD & ED, UD & EP, ED & EP

By employing the CD model, this article presents three coupling evaluation outcomes: the connection between UD and ED, the link between UD and EP, and the relationship between ED and EP. We compute CD values for the 11 provinces within the YREB over three distinct periods: 2005–2009, 2010–2014, and 2015–2019. The detailed results are available in Table 3.

Table 3 clearly illustrates that, except for Shanghai, Yunnan, and Guizhou, all provinces have CD values exceeding 0.9, signifying a high level of coordination. Notably, Guizhou exhibited its lowest CD value (0.51) during the 2010–2014. Provinces with intermediate CD values are ranked as follows: Zhejiang, Hubei, Jiangxi, Jiangsu, Hunan, Chongqing, Anhui, Sichuan, Yunnan, Shanghai, and Guizhou.

Shanghai, Yunnan, and Guizhou exhibit lower CD values in certain stages, with Guizhou having the lowest CD value of 0.51 during 2010–2014. The average CD values follow a descending order: Zhejiang, Hubei, Jiangxi, Jiangsu, Hunan, Chongqing, Anhui, Sichuan, Yunnan, Shanghai, and Guizhou. Regarding CD trends, Shanghai, Jiangsu, and Zhejiang display upward trends, while Sichuan, Hubei, Hunan, and Anhui exhibit declining trends (Figure 2).

Figure 2

Changes in the CD of UD and ED.

To illustrate the spatial distribution and trends of UD and ED in the YREB, the spatial distribution of UD and ED in the YREB (Figure 5) reveals a growing number of provinces with CD values exceeding 0.8. Full coverage was achieved during 2010–2014, indicating a high coordination level. Guizhou, located in the western part of the YREB, initially exhibited a lagging trend with a CD value of 0.68 in 2005–2009, significantly below the average. However, it attained a high CD value of 0.91 in 2014–2019.

The data concerning the CD of UD and EP (Table 3) indicate notable trends. Zhejiang, Shanghai, and Jiangsu exhibit CD values exceeding 0.9, reflecting a high coordination level. Hubei, with a low CD value during 2005–2009, shows an upward trajectory, reaching CD values above 0.9 in 2010–2014 and 2014–2019. Conversely, Jiangxi, Yunnan, and Guizhou demonstrate low CD values, signifying a lower coordination degree. Among these, Jiangxi displays an ascending trend, escalating from a low CD value of 0.45 during 2005–2009 to a medium coordination level of 0.74 during 2014–2019. Guizhou, however, registered a low CD value of 0.15 in 2010–2014. Generally, the ranking of the average CD values, from high to low, is as follows: Zhejiang, Shanghai, Jiangsu, Hubei, Anhui, Hunan, Sichuan, Chongqing, Jiangxi, Yunnan, and Guizhou. All provinces, except Jiangsu, demonstrate increasing trends (Figure 3).

Figure 3

Changes in the CD of UD and EP.

The spatial distribution of the CD between UD and EP (Figure 5) reveals that, on the whole, the CD values of UD and EP during 2005–2009 generally fall within a medium coordination level, with significant disparities between the two ends of the spectrum. Specifically, Zhejiang, Shanghai, and Jiangsu in the eastern part of the YREB exhibit a high level of coordination, whereas Yunnan and Guizhou in the western region record CD values below 0.3, indicative of severe imbalance in coordination. In 2010–2014, there was an upward trend in provinces with CD values surpassing 0.8. By the interval 2014–2019, most provinces in the YREB attained CD values exceeding 0.8 for both UD and EP, except for Jiangxi, Yunnan, and Guizhou, which achieved a high coordination level.

The CD data between ED and EP (Table 3) indicate that Shanghai and Jiangsu exhibit CD values exceeding 0.9. At the same time, Zhejiang maintains values above 0.9 except for a CD value of 0.88 during 2005–2009, showcasing a consistently high coordination level. Hubei and Chongqing closely trail behind, with average CD values surpassing 0.8, signifying a solid level of coordination. Conversely, Guizhou, Jiangxi, and Yunnan possess lower CD values, with Yunnan recording a low coordination level of 0.4 from 2005 to 2009. Regarding CD value ranking, the order from high to low is as follows: Jiangsu, Shanghai, Zhejiang, Hubei, Chongqing, Hunan, Anhui, Sichuan, Guizhou, Jiangxi, and Yunnan. Regarding trend changes (Figure 4), Jiangxi, Shanghai, and Zhejiang experienced an upward trajectory. Sichuan, Guizhou, Yunnan, Chongqing, Hubei, Hunan, Anhui, and Jiangsu all reached their peak CD values during 2010–2014, followed by a subsequent decline.

Figure 4

Changes in the CD of ED and EP.

The spatial distribution of CD between ED and EP (Figure 5) illustrates that the average CD values of ED and EP in the eastern YREB region (encompassing Shanghai, Jiangsu, and Zhejiang) consistently exceed 0.9 from 2005 to 2019, indicating a consistent high level of coupling. Furthermore, provinces situated in the northern part of the YREB tend to exhibit higher coordination compared to their counterparts in the south. Overall, the southern provinces, including Yunnan, Guizhou, and Jiangxi, display notably lower CD values than the other provinces within the YREB.

Figure 5

Spatial pattern of the CD of UD & ED, UD & EP, ED & EP.

4.2 CD of UD & ED & EP

Considering the three elements of urban development, ED, and EP in combination, rather than just two of them, is to provide a more comprehensive and integrated perspective. Table 3 presents the comprehensive CD outcomes for UD&ED&EP across the 11 provinces within the YREB during the three timeframes: 2005–2009, 2010–2014, and 2015–2019. We obtain these results through the utilization of the CD model.

Table 3 presents the comprehensive CD data for the three relationship groups. Only Jiangsu consistently maintains a high level of coordination, with CD values exceeding 0.9 across all three periods. Similarly, Zhejiang exhibits CD values above 0.8 in each period. Shanghai and Hubei provinces demonstrate a medium coordination level, with CD values below 0.7 in 2005–2009, but these values increased to above 0.84 in 2010–2014 and 2015–2019. Notably, Shanghai’s CD value reached 0.99 in 2015–2019.

Conversely, Jiangxi, Yunnan, and Guizhou present much lower CD values. Among these, Jiangxi records the lowest mean CD value of 0.32, indicating a low coordination level. Yunnan and Guizhou have CD values below 0.3, with Guizhou’s mean CD value at 0.1, classifying it as a severe imbalance. Ranking by mean CD values, the order of the relationship groups is Jiangsu, Zhejiang, Shanghai, Hubei, Anhui, Sichuan, Chongqing, Jiangxi, Yunnan, and Guizhou. Apart from Jiangsu and Anhui, most YREB provinces demonstrate an increasing trend. Notably, Jiangsu experienced a decreasing trend, while Anhui reached its highest value in 2010–2014 before declining (Figure 6).

Figure 6

Changes of the CD of UD, ED & EP.

Figure 7 depicts the spatial distribution of the CD across the three relationship groups. In the eastern region of the YREB, Jiangsu and Zhejiang consistently exhibit CD values exceeding 0.8 across 2005–2009, 2010–2014, and 2015–2019, indicating a sustained high coordination level. Moving to the central YREB area, Wuhan experienced a transition from a medium coordination level in 2005–2009 to a high coordination level in 2010–2014. Notably, Jiangxi progressed from a severe imbalance, with CD values below 0.3 in 2005–2009, to a low coordination level starting from 2010–2014. Conversely, in the western YREB zone, Yunnan and Guizhou consistently maintain CD values below 0.3 across all three time periods, indicating a severe imbalance level.

Figure 7

Spatial pattern of the CD of UD, ED & EP.

4.3 CD of UD & ED & EP & GP

Table 4 provides the comprehensive results of the CD across UD&ED&EP&GP from 2005 to 2019. The findings from the data encompassing the four relationships (Table 4) indicate that Jiangsu, Zhejiang, Hubei, Hunan, Shanghai, and Anhui all possess CD values surpassing 0.3, representing provinces with synchronized development. Among them, Jiangsu stands out with notably high CD values exceeding 0.9. On the other hand, Jiangxi, Sichuan, Yunnan, Guizhou, and Chongqing exhibit CD values below 0.3, indicative of a severe imbalance level. Notably, Guizhou and Chongqing possess the lowest CD values, measuring 0.0037 and 0.0001, respectively. The ranking of YREB provinces, from highest to lowest CD values, is as follows: Jiangsu, Zhejiang, Hubei, Hunan, Shanghai, Anhui, Jiangxi, Sichuan, Yunnan, Guizhou, and Chongqing.

The regional distribution of CD across the four relationships (Figures 5 and 8) highlights coupling centers in the upper, middle, and lower reaches of the YREB, with varying levels of coupling strength. Notably, these three centers, situated in the downstream, midstream, and upstream, respectively, exhibit a gradient of coupling strength from high to low. For instance, despite its CD value of 0.1731, placing it within the severe imbalance range, Sichuan is the coupling center for the upper Yangtze River. Interestingly, there exists a significant 0.17 difference between the CD values of Sichuan and Guizhou, which records the lowest CD value in the region. In the middle Yangtze River, Hubei emerges as the regional coupling hub with a CD value of 0.6156, signifying a medium coordination level. The CD value disparity between Hubei and Jiangxi amounts to 0.37, while Jiangxi displays the lowest CD value in the region, thus showcasing a substantial gap. Finally, in the lower Yangtze River, Jiangsu is the coupling center, with a substantial 0.49 CD value difference from Anhui, implying significant disparities.

Figure 8

Spatial pattern of the CD of UD&ED& EP&GP.