The Influence of Innovative Resources on the Comprehensive Carrying Capacity of China’s Urban Agglomerations: A System Dynamics Perspective

Abstract

The sustainable development of urban agglomerations greatly relies on their comprehensive carrying capacity (CCC). As society evolves, innovative resources emerge as core assets and serve as crucial pillars of this capacity. Despite existing CCC studies, the influence of innovative resources remains underexplored. This study analyzes the influence of innovative resources on the CCC of 19 urban agglomerations in China using a system dynamics approach. We find that innovative resources are an important subsystem of CCC. Increasing innovative resources is an effective strategy for enhancing CCC, yet the effects of different types of innovative resources vary. Merely increasing the number of universities and research institutions does not significantly improve the CCC level. Increasing the expenditures of higher education institutions, internal R&D, and the number of patents are effective approaches to enhance CCC. Moreover, these factors can form a virtuous cycle, mutually promoting innovation and CCC development, thus injecting new momentum into the sustainable development of urban agglomerations.

1. Introduction

Owing to the pandemic-induced economic downturn, cities worldwide have been influenced by multiple unexpected domestic and international factors [1]. Future success in building a modern socialist powerhouse requires sustainable urban development [2]. According to statistical data, the world’s urbanization rate reached 60% in 2022, and by 2050, two-thirds of the world’s population is predicted to live in cities [3]. The most common spatial organization in urban areas is urban agglomeration [4], relatively complete urban complexes centered around one or two core cities within a specific region and connected through an integrated transportation network.

Urban agglomerations are the products of urban interactions and carriers for efficient economic system operations [5]. They facilitate the free flow of factors, the large-scale aggregation of resources, geographical division of labor, a reduction in transaction costs for enterprises, and optimization of industrial chains. Urban agglomerations are the lifeblood and new growth poles of urban economic development and the basic units for participating in global competition [6]. For instance, urban agglomerations—such as the West Midlands and Central Scotland in the UK, the Ruhr region in Germany, and the Beijing-Tianjin-Hebei region in China have achieved significant economies of scale [7,8].

Despite their vast potential, urban agglomerations face significant sustainability challenges. With the large-scale influx of people and the rapid expansion of urban scale, the contradiction between the demand for a quality living environment and the relative inadequacy of urban agglomeration development is becoming increasingly apparent. Issues such as the environment [9], transportation [10], ecological quality [11], employment [12], housing [13], and public security have severely hindered the sustainable development of urban agglomerations.

The goal of sustainable development in urban agglomerations is to address multifaceted challenges in the social, economic, and environmental domains [14]. Central to this endeavor is the maximization of the CCC of urban agglomerations, encompassing diverse subsystems. These subsystems include resource-carrying capacity, ecological and environmental resilience, urban infrastructural sustainability, and various social resource capacities. The interplay of various factors, the coordinated growth of cohesive subgroups within agglomerations, and the functional optimization and upgrading of individual cities within these clusters jointly influence these subsystems. Consequently, they form resilient and dynamically adaptive systems that can withstand and respond to changing challenges.

Innovative resources play an important role in CCC. A lack of innovative resources lowers productivity, results in outdated environmental protection technologies, and increases pollution and waste. Nokia, once a giant in mobile phone manufacturing, peaked in the late 20th and early 21st centuries. Its core competitiveness lay in hardware manufacturing; however, with the rise of smartphones and the mobile internet, the market’s demand for software and ecosystems has increased daily. Nokia’s insufficient investment and innovation in software and application development meant that it failed to adapt to market changes over time, ultimately causing its decline [15].

Urban agglomerations have extremely significant innovative characteristics, and innovative resources play a pivotal role in enhancing their CCC [16,17]. With distinct innovative attributes, China’s urban agglomerations have gathered numerous scientific research institutions, higher learning institutions, and high-end talents to form a unique innovative ecosystem. Therefore, studying the influence of innovative resources on the CCC of China’s urban agglomerations is crucial for gaining a deeper understanding of their carrying capacity status, optimizing innovative resources, and enhancing their overall competitiveness. Through scientific planning and innovation-driven development, countries can better leverage innovative advantages, promote industrial structure upgrading and comprehensive economic and social development, and achieve high-quality and sustainable development of urban agglomerations in the future.

The remaining content of this article is as follows: the first part is a literature review; the second part is the research basis; the third part is the research framework; the fourth part is an analysis of the results; the fifth part is the conclusions and policy implications; the sixth part comprises the appendices, including Appendix A, Appendix B, Appendix C, Appendix D and Appendix E on the values of CCC and its subsystems and Appendix F on the abbreviations in this paper and their corresponding full forms.

2. Literature Review

2.1. Research on Innovative Resources

Romer’s theory [18] on the role of knowledge in economic growth profoundly reveals the positive influence of knowledge and skills on the economy, establishing a theoretical foundation for studying innovative resources. Subsequently, researchers have established a close link between knowledge-intensive services, innovation, and the effective utilization of innovative resources, emphasizing mutual reinforcement among the three [19]. Moreover, the significant influence of knowledge spillovers on the formation of industrial structures has been widely discussed, with foreign direct investment and industrial clustering recognized as key factors in enhancing the effectiveness of urban innovative resources [20]. Additionally, some scholars have considered innovative resources the core element in analyzing the role of internal spatial integration and coordinated industrial development in the urban agglomerations of the Yangtze River Economic Belt [21]. Furthermore, research has revealed that the rapid convergence of innovation within urban agglomerations is significantly influenced by the spillover of human capital and market interactions, both of which jointly enhance the utilization efficiency of innovative resources [22]. Finally, studies have indicated that the positive spillover effects of regional innovative capital surpass those of innovative talents, highlighting the dominant role of capital resources in driving innovation [23]. In summary, these studies underscore the crucial role of innovative resources in promoting innovation and sustainable urban development and reveal their active roles through aggregation and spillover effects.

2.2. Research on CCC

In 1943, carrying capacity was defined as the maximum density of a population that could be sustained by a given amount of biomass [24]. With rapid economic development in the 1970s, the global population soared, precipitating a continuous decline in per capita arable land. Consequently, the conflict between human activities and the ecological environment has become increasingly prominent. This has prompted scholars to focus on single-element carrying capacities, such as population [25], water [26,27], land, traffic [28], and resources [29,30].

In the 21st century, economic development and industrial structure upgrades have shifted attention beyond mere resources and the environment to encompass a broader range of carrying objects. Concepts such as economic and social carrying capacities have also been proposed. Furthermore, the accelerated urbanization process has comprehensively impacted the carrying capacity of modern urban agglomerations, prompting the emergence of the concept of CCC [31]. Despite the lack of a unified definition among the academic community, studying urban carrying capacity from the perspective of multiple factors—including land and water resources, the environment, ecosystems, urban infrastructure, social resources, economy, population, and transportation—has become a new trend. For instance, some scholars argue that urban CCC should incorporate not only natural environmental and resource factors but also urban energy and ecological factors [32]. To evaluate the CCC of China’s Yangtze River Economic Belt, factors such as ecological environment carrying capacity, transportation carrying capacity, factor market carrying capacity, and industrial economy carrying capacity were included in the evaluation framework [33].

Based on the literature review, we condense the CCC framework into three interdependent subsystems that collectively define a region’s sustainability threshold: economic carrying capacity (ECC), which encapsulates the ability of an economy to sustainably grow while ensuring economic stability, job creation, and the equitable distribution of wealth; public service carrying capacity (PSCC), reflecting the capacity of infrastructure and services, such as healthcare, education, transportation, and governance, to meet the demands of the population without compromising quality or accessibility; and natural resource carrying capacity (NRCC), which assesses the sustainability of a region’s natural resources, including water, land, air, and biodiversity, to support human activities without depletion or irreversible degradation. These three subsystems interplay, reinforcing or constraining each other, ultimately determining the overall CCC of a given region.

2.3. Research on the Influence of Innovative Resources on CCC

The existing literature primarily explores the spatial spillover and regional convergence effects of innovation activities, such as the promotion of economic growth, environmental optimization, and efficiency enhancement [34,35,36]. This finding indirectly validates the beneficial influence of allocating innovative resources on CCC development in urban agglomerations. Additionally, innovative resources exhibit real-time mobility both internally and across urban agglomerations and are utilized in diverse fields, including pollution control [37], transportation [38], communication [39], infrastructure [40], and enterprise production lines [41]. Despite these advancements, scholars have not explored the specific influence of innovative resources on CCC.

Thus, inspired by this gap in the existing research and building upon the foundational frameworks of ECC, PSCC, and NRCC, we propose the inclusion of innovative resource carrying capacity (IRCC) as a fourth, equally essential subsystem within the CCC paradigm. By incorporating IRCC, we aim to explore the specific mechanisms through which innovative resources contribute to CCC development, examining their influence on fostering economic growth with reduced environmental footprints, enhancing public services through technological advancements, and safeguarding natural resources through smart and sustainable utilization practices. Ultimately, this research endeavor seeks to provide a holistic understanding of how the optimization of innovative resources can serve as a strategic tool for advancing the sustainability threshold of urban agglomerations worldwide.

This study is expected to make the following academic contributions: First, we optimize the existing evaluation methods for the CCC index system, and this optimization process particularly focuses on innovative resources and treats them—along with economic, social, and natural resources—as subsystems of CCC. Second, to analyze the influence of changes in innovative resources on the future CCC of urban agglomerations better, we construct a system dynamics model. Finally, this study’s conclusions have rich policy implications, guiding future urban planning and development decisions to achieve more sustainable and efficient urban agglomeration development.

3. Research Basis

3.1. Theoretical Basis of CCC

The intricate interplay and multifaceted influences of NRCC, PSCC, ECC, and IRCC form the cornerstone of CCC in urban agglomerations. Each of these subsystems exerts a profound and interdependent effect on the overall sustainability and resilience of a region, constituting a complex yet harmonious web of factors that drive development. The subsequent paragraphs will explore the specifics of the specific roles and contributions of these individual components, elucidating how natural resources, vital public services, economic dynamics, and innovative resources intertwine to shape and enhance the CCC of urban agglomerations.

Natural resources, such as land, water and environment, constitute the material foundation for urban and social development, and their quantity, quality, and distribution have a direct influence on the CCC of a region [42]. For instance, scarcity of water resources can constrain the development of agriculture and industry, subsequently affecting the overall economic operation.

Vital public services, including healthcare, transportation, parks, and public budgets, improve urban life, strengthen a city’s attractiveness, and ultimately boost its CCC [43].

ECC reflects the economic scale and growth rate. A a region can sustain within a specific period, directly impacting employment, income, and fiscal conditions, providing solid material support for CCC [44].

Innovative resources, encompassing technological talent, research and development funds, and an innovative environment, are core elements driving social and economic progress [45]. Increasing IRCC enhances a region’s innovation and competitiveness, continuously boosting CCC.

In conclusion, NRCC, PSCC, ECC, and IRCC work together to shape a region’s CCC, relying on and enhancing each other. This intricate interplay seeks sustainable development within a dynamic balance.

3.2. System Dynamics Methodology

This study employs a system dynamics model to simulate the development trends in innovative resource and CCC in China’s urban agglomerations. System dynamics, founded by Forrester [46], combines system science with computer simulation, focusing on studying the feedback structure and behavior of systems. When dealing with complex systems, it decomposes the system, quantifies the interactive relationships, and simulates system operations, providing guidance for the design of real-world systems.

The advantage of system dynamics modeling lies in its ability to handle complex system problems. First, it is able to accurately quantify the interactive relationships between the system elements. Second, it can comprehensively consider numerous indicators and the utilization of historical data, enhancing the accuracy of its predictions and decision-making. Third, it has powerful data analysis capabilities, supporting multi-dimensional analysis and providing strong support for optimizing resource allocation and enhancing comprehensive carrying capacity. And finally, its approach of treating system movement as fluid motion enables rapid identification and description of complex systems, thus ensuring the efficiency and accuracy of research [47].

4. Research Framework

4.1. Research Scope and Data Sources

The 14th Five-Year Plan [48] proposes 19 urban agglomerations for optimization, growth, and cultivation, and the relevant departments have approved the corresponding core cities for the urban agglomerations. This has played an important role in the development of China’s urban agglomerations. Based on this, 31 core cities from 5 national-level, 8 regional-level, and 6 regional-level urban agglomerations were selected as samples (Figure 1). However, because of a lack of significant data, Hong Kong, the core city of the Pearl River Delta urban agglomeration, was not included. In addition, this research covers a time span from 2011 to 2040, with 2011–2020 representing historical data and 2021–2040 being predicted data.

4.2. Selection of the Indicators and Calculation Method for Historical CCC Values

The determination of the indicator boundaries is the foundation for evaluating the CCC of urban agglomerations. Owing to the numerous elements and complex relationships in the urban agglomeration system, the selection of indicators should follow the following principles to accurately express multidimensional variables: completeness, comprehensive coverage of all the aspects of the urban agglomeration, and the avoidance of research limitations; relevance, selecting representative and accurate indicators to reflect the evaluation goals and weaknesses; universality, selecting indicators that share common characteristics across all urban agglomerations; and operability, selecting data that are easy to obtain, organize, quantify, and operate to ensure data authenticity and subsequent research feasibility [49].

This study explores the influence of innovative resources on the CCC of urban agglomerations. Innovative resources refer to resources that introduce unprecedented new combinations of production factors and conditions into the production system, which can improve production efficiency and the ecological environment, showcase innovative achievements, and increase marginal returns [50]. Based on the definition of innovative resources and the principles of the indicator selection, the innovative resource-carrying capacity (IRCC) of urban agglomerations primarily encompasses research institutions, talents, funds, and innovative accomplishments. In terms of talent resources, we adopted the following four indicators: the number of college teachers; the number of jobs in scientific research and technology services; the number of jobs in information transmission, software, and information technology services; and R&D employment. In terms of financial resources, we focused on two key indicators, namely higher education funding and internal R&D expenditure. However, the regression analysis revealed that the expenditure of higher education institutions is primarily influenced by the education public budget expenditure and serves as a key determinant of the number of college teachers. To avoid redundant variable calculations, we did not consider higher education funding as an independent indicator when assessing the IRCC. Finally, we adopted the innovation achievement indicator of patent application authorization to measure the innovation capability of the urban agglomerations. In summary, the level of IRCC in urban agglomerations in this study was obtained from the weighted sum of seven innovative resource indicators, including the number of ordinary higher education and research institutes mentioned above.

According to the selection principle for the indicators and the research of scholars [51,52,53], we referred to national and provincial policy norms, as well as industry standards, to construct a CCC evaluation system for urban agglomerations, which includes the four subsystems of IRCC, economic carrying capacity (ECC), public service carrying capacity (PSCC), and natural resource carrying capacity (NRCC). Based on the requirements of system dynamics modeling and data availability, 28 indicators were selected for these four subsystems. Historical data were obtained from the National Bureau of Statistics, the statistical yearbooks of various cities, the China Economic Database, and the EPS Database (Table 1).

The variables involved in the CCC have different definitions and orders of magnitude; therefore, dimensional consistency processing is required before starting to work with the model. The comparison indicates that most variables positively influence CCC, with only industrial sulfur dioxide emissions and industrial smoke and dust emissions exerting a negative influence. The higher the value of the positive influencing variable, the higher the CCC, while the opposite is true for negative influencing variables. To ensure consistency in the direction of influence of the processed data on the load-bearing structure, this study adopted different methods of dimensional consistency treatment for the positive and negative influencing variables. The positively influencing variables were processed using Equation (1), whereas the negatively influencing variables were processed using Equation (2).

(1)${\alpha }_{ij}={\displaystyle \frac{{\beta }_{ij}-{\beta }_{\min }}{{\beta }_{\max }-{\beta }_{\min }}}(i=1,2,\cdots n;j=1,2,\cdots m)$

(2)${\alpha }_{ij}={\displaystyle \frac{{\beta }_{\max }-{\beta }_{ij}}{{\beta }_{\max }-{\beta }_{\min }}}(i=1,2,\cdots n;j=1,2,\cdots m)$

where $i$ is the region serial number; $j$ is the annual serial number; ${\alpha }_{ij}$ stands for the variables after consistency processing; ${\beta }_{ij}$ stands for the raw data; and ${\beta }_{\mathrm{m}\mathrm{a}\mathrm{x}}$ and ${\beta }_{\mathrm{m}\mathrm{i}\mathrm{n}}$ are the maximum and minimum values in all the raw data corresponding to a single variable, respectively.

Weighting methods for CCC indicators can be divided into subjective and objective weighting methods. The CCC system is complex, and subjective calculations are prone to bias; therefore, selecting a suitable objective weighting method is necessary. Among them, the principal component analysis method highly depends on the main indicators, resulting in an excessive weight of the main indicators. Thus, some factors affecting CCC cannot be reflected. The entropy rule is sensitive to abnormal data, and statistical errors in individual data can interfere with the simulation results. The mean square error method can fully consider the differences and influences between various indicators, accurately compare the differences between regions and the same region at different time points, improve the accuracy and comprehensiveness of the CCC evaluation, and is a more suitable objective weighting method for this study. The calculation step for this method first involves measuring the internal differences of variables in different regions and years and then calculating the CCC by weighting the contribution rate of variances. The specific calculation process is elucidated in Equations (3)–(6).

Step 1:

(3)$A_k={\displaystyle \frac{1}{l}}{\displaystyle \sum {\alpha }_{ij}}(k=1,2,\cdots q;i=1,2,\cdots n;j=1,2,\cdots m)$

where $A_k$ stands for the relevant indicators of the CCC and $l$ is the total amount of data covered by each indicator.

Step 2: Calculate the mean square deviation of each indicator $D_k$; see Equation (4).

(4)$D_k=\sqrt{{\displaystyle \frac{1}{l}}{\displaystyle \sum ({\alpha }_{ij}}-A_k{)}^2}(k=1,2,\cdots q;i=1,2,\cdots n;j=1,2,\cdots m)$

Step 3: Calculate the weights of each indicator $W_k$; see Equation (5).

(5)$W_k={\displaystyle \frac{D_k}{{\displaystyle {\sum }_{k=1}^q{D}_k}}}$

Step 4: Calculate the CCC; see Equation (6).

(6)$\mathrm{CCC}={\displaystyle \sum W_k}{\alpha }_{ij}(k=1,2,\cdots q;i=1,2,\cdots n;j=1,2,\cdots m)$

Based on this study’s actual situation, the CCC includes 31 sample cities and 28 evaluation variables over 10 years. Therefore, in Equations (1)–(6), $n$ stands for 31, $m$ stands for 10, $q$ stands for 28, and $l$ stands for 310.

By combining the indicator system and Equations (1)–(6) selected herein, the weights of each criterion and the indicator layers were calculated (Table 1). The weight of each indicator layer for IRCC was approximately 0.03, with the highest weight being the number of ordinary higher education institutions and research institutes (0.0493) and the lowest being the number of patent applications and authorizations (0.0276), with minimal difference in the weight values. This indicates that although a certain degree of imbalance exists in the allocation of innovative resources among cities within Chinese urban agglomerations, different types of innovative resources can still maintain a certain degree of balance overall.

The calculation methods for IRCC, ECC, PSCC, and NRCC are similar to CCC, and they are all derived from the containing indicators. As can be seen in Table 1, four subsystems comprise seven, one, six, and five indicators, respectively, and their values are calculated based on the metrics within each subsystem. The values of IRCC, ECC, PSCC, NRCC, and CCC for the cities included in this study from 2011 to 2020 are detailed in Appendix A, Appendix B, Appendix C, Appendix D and Appendix E.

4.3. The Simulation Prediction Framework for IRCC and CCC Based on System Dynamics

Current approaches to carrying capacity modeling include synthetic control [62], neural networks [63], and system dynamics [64,65,66]. Synthetic control efficiently integrates linear regressions across cities for policy influence analysis. Neural networks, suitable for complex systems, operate as black boxes that lack transparency. System dynamics, widely recognized for studying regional development, was employed herein to illustrate feedback loops, and our understanding of the system dynamics was enhanced through causal and flow diagrams. Our study outlines the steps involved in constructing a system dynamics model, encompassing the definition of the research scope and data sources, the selection of indices, the calculation of CCC for urban areas, the construction of the system dynamics model, and settings for the scenario simulations. The system dynamics modeling in this study was built using Vensim PLEx32 7.0 software.

Based on Section 4.2, we sorted the causal relationships among the system elements and depicted the future dynamic process of the influence of innovative resources on the CCC of urban agglomerations. A causal relationship diagram was constructed, wherein the parameters are enclosed in sharp brackets < > and indicated in gray, representing shadow variables appearing multiple times (Figure 2).

Upon examining the causal loops associated with innovative resources, we discern that patent applications authorized, education public budget expenditure, and internal R&D expenditure are the originating factors. Firstly, the surge in patent authorization volume augments valid invention patents, thereby enhancing both IRCC and CCC. Similarly, education public budget expenditure accelerates university development, attracting educators and nurturing research talents, which subsequently reinforces IRCC and CCC. Moreover, it fosters digital talents and expands R&D employment, further elevating competitiveness. Meanwhile, internal R&D expenditure in particular drives R&D job growth, nurtures research talents, and encourages the emergence of digital talents, all of which have a positive impact on IRCC and CCC. Furthermore, these pathways collectively illustrate how investments in research, education, and technological advancements fortify a region’s overall competitiveness and capacity.

Table 2 lists eleven causal loops related to innovation resources, all of which are reinforcing loops. This indicates that patent applications authorized, education public budget expenditure, internal R&D expenditure, and public budget expenditure for science and technology will influence valid invention patents, talent cultivation, and university development, thereby exerting an influence on IRCC or PSCC and further enhancing CCC.

Based on the causality analysis, we explored practical issues related to system operation and drew a detailed flowchart (Figure 3). This diagram illustrates the interaction paths between various elements in the system, involving 54 variables. Specifically, there are 3 state variables representing different states or conditions during system operation, 48 auxiliary variables providing the necessary information and support for various components of the system, 2 flow variables measuring the speed and volume of the data flow within the system, and 1 constant-a fixed and unchanging numerical value used to maintain balance during the calculation process.

Based on the above flowchart, a cross-interaction relationship exists between these 54 variables, and parameter equations must be set up for these variables to accurately measure the relationships between the system elements. Owing to the similar approach to the parameter settings and equation selection for different regions, we have only listed the calculation equations and main basis for each parameter in Shanghai. When dealing with parameters related to the three types of control variables (ECC, PSCC, and NRCC), we used historical data regression analysis or references to national development plans. For parameters with significant numerical differences across different regions and years and unclear overall patterns, the function with the highest goodness of fit by region was selected for processing. Owing to the complexity and uncertainty of the elements involved in this study, we optimized the set parameter equations repeatedly through multiple runs and debugging and determined the final parameter settings through simulation (Table 3).

Additionally, the four IRCC parameters of the number of ordinary higher education institutions and research institutes (HEIR), the expenditure of higher education institutions (EHEI), internal R&D expenditure (RDE), and patent application authorization volume (PAAV) have not set equations. This is because the subsequent scenario simulation settings need to directly set equations for these four innovative resources and analyze their future influence on CCC trends.

4.4. Scenario Simulation Settings

To gain a deeper understanding of the long-term influence of innovative resources on CCC, we assume that certain types of innovative resources will be enhanced while the general indicators will continue following their historical trends. We designed five development scenarios to predict the future influence of different types of innovative resources on CCC. Through comparative analysis, we found that increasing expenditure and patent applications effectively enhances the CCC of urban agglomerations. Nevertheless, the excessive expansion of universities and research institutions may hinder CCC’s improvement. Taking Shanghai, Qingdao, and Guangxi as examples, the simulation results obtained through Vensim PLEx32 7.0 software indicate that when the number of ordinary higher education institutions and research institutes increases by 20% while the other indicators remain unchanged according to their current trends, the CCC will decrease by nearly 6%, 7%, and 11%, respectively, by 2040. Consequently, these five innovative resource development scenarios vary in the expenditure levels of higher education institutions (EHEI), internal R&D expenditure (IRDE), and patent application authorization volume (PAAV).

The five scenarios proposed are as follows: Scenario 1, also known as the current scenario, maintains the current status of higher education institutions (EHEI), internal R&D expenditure (IRDE), and patent application authorization volume (PAAV) until 2040. Scenario 2, the 5% scenario, assumes a 5% increase in the development levels of EHEI, IRDE, and PAAV. Scenario 3, the 10% scenario, envisages a 10% increase in these indicators. Similarly, Scenario 4 represents a 15% increase in these three indicators, whereas Scenario 5 assumes a 20% increase.

5. Analysis of the Results

5.1. Analysis of Model Testing Results

The first step was to verify historical data (2011–2020). The simulated and real data for the five criteria layers in Shanghai were compared (Figure 4). From the curve, it can be observed that the trends in the simulated and real values over time are relatively consistent, and the values are also consistent. Among the 50 simulated values, the average error was 3.7%, and only two years had a difference of more than 10% in their economic carrying capacity (11% and 12%, respectively). All the simulated values met the standard of having an average error within 10–15%. Based on historical data testing, evidently, the system dynamics model developed in this study demonstrates an appropriate fit, accurately reflecting the operational principles of the system in a scientifically sound manner.

Step 2 involved extreme condition testing. This testing method sets certain variables in the model as extreme values, observes the changes in the simulation system, and evaluates whether the constructed model conforms to the actual situation. When we set the birth rate and initial variables of Shanghai to 0, the per capita GDP tends toward infinity, as does the economic carrying capacity. This result is logically reasonable.

In summary, the simulation model of the CCC system passed the tests with historical data and extreme conditions. Establishing a system simulation model requires continuous adjustment and optimization based on the historical data of all variables to enable the determined model to be closer to the actual situation naturally.

5.2. Analysis of Historical Data Revealing Innovative Resources’ Influence

To visually demonstrate the relationship between innovative resources and CCC within China’s urban agglomerations, we combined historical data from the 2020 IRCC and CCC values of 31 core cities within 19 urban agglomerations to create a map. This map was created using ArcGis10.7 software and the means of the five-fifths method [70]. IRCC and CCC were ranked according to five levels in sequence from lowest to highest, and distinct colors were used to differentiate between the various levels (Figure 5).

The IRCC and CCC levels exhibited a highly consistent trend. From a statistical perspective, IRCC significantly positively influences CCC, indicating that CCC is also relatively high in regions with higher levels of innovative resources. This observation is consistent with the conclusions of the theoretical model and the problem hypothesis, further confirming this theory’s effectiveness. From a single urban agglomeration perspective, innovative resources are predominantly concentrated in cities within national-level urban agglomerations, such as Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Pearl River Delta. These urban agglomerations have relatively complete scientific and technological innovation systems and high-quality talent resources and a high level of economic development and a high-quality social environment. Some cities in regional urban agglomerations in the central and western regions, such as the Chengdu–Chongqing economic zone, Central China, and the city of Wuhan in the Middle reaches of the Changjiang River, also performed well in terms of IRCC and CCC; the regional-level urban agglomerations in the northeast and western regions lack innovative resources, possibly attributable to their relatively lagging economic development, talent loss, and a lack of educational and medical resources. Noteworthily, although the West Coast of Taiwan Strait has a relatively high economic development level, its IRCC and CCC are relatively low. The main reasons for this are the small built-up area, the small road area, and fewer people engaged in scientific and technological research and development, precipitating a limited carrying capacity.

5.3. Analysis of the Predicted Data Revealing the Influence of Innovative Resources on CCC

This study comprehensively considered the CCC levels of urban agglomerations and selected data from Shanghai, Qingdao, and Xining from 2011 to 2040 for the next simulation prediction. Shanghai—as the core of the Yangtze River Delta urban agglomeration—has the strongest comprehensive strength and the highest economic share among the national-level urban agglomerations. Qingdao is at the core of the Shandong Peninsula urban agglomeration, with an important geographical location that connects multiple regions. Xining is the core of the Lanxi urban agglomeration, with an optimized industrial structure adjustment but a low development level, a fragile environment, and prominent problems in small towns. On the premise of passing the requisite tests, this study combined the system flowchart (Figure 3) and parameter equation (Table 3), inputted the variables and equations into the Vensim PLEx32 7.0 software, simulated the dynamic evolution brought about by system operation, and combined it with the actual situation of innovative resources and CCC. After multiple repeated experiments, the simulation time range was set to 2011–2040, with a total duration of 30 years and a step size of 1 year (Figure 6).

Figure 6 depicts the CCC simulation results for Shanghai, Qingdao, and Xining from 2011 to 2040. The corresponding curve exhibits the trend in the CCC over time. From a time trend perspective, irrespective of whether innovative resource optimization is implemented, the numerical value of CCC exhibits an increasing trend year by year. This phenomenon proves that China has entered a new stage of high-quality development, and the coordinated development levels of society, resources, and the economy are continuously improving.

Compared to the current scenario, the optimization of innovative resources in these three cities can result in varying degrees of CCC improvement, which aligns with existing research indicating that innovation activities can improve economic growth, pollution control, transportation, communication, infrastructure, and enterprise operations [34,35,36,37,38,39,40,41]. According to Figure 6a, the simulation results for Shanghai’s CCC in 2040 are as follows: about 2 under the current scenario, about 3 under the scenario of 10% optimization, about 7 under the scenario of 15% optimization, and about 18 under the scenario of 20% optimization. This indicates that optimizing innovative resources is an effective means for Shanghai to improve its CCC; moreover, when the optimization of innovative resources reaches a certain level, it will achieve a qualitative leap in its comprehensive carrying structure. Per Figure 6b, the simulation results for Qingdao’s CCC in 2040 are as follows: about 0.8 under the scenario of no optimization of innovative resources, about 1 under the scenario of 10% optimization, about 1.8 under the scenario of 15% optimization, and about 4.5 under the scenario of 20% optimization. This indicates that although the effect of innovative resource optimization in Qingdao on promoting the CCC is relatively limited compared to that in Shanghai, optimizing innovative resources is still an effective way to improve CCC; especially when the optimization degree reaches 15% or more, the optimization effect is significant. Similarly, the effect of innovative resource optimization on promoting the CCC in Xining, as presented in Figure 6c, is similar to that in Qingdao. At this point, it is evident that optimizing the allocation of innovative resources is an effective means for China’s urban agglomerations to achieve positive changes in their CCC structure.

6. Conclusions and Policy Implications

The CCC of urban agglomerations is influenced by the synergistic effects of multiple factors that form a complex system coupled with multiple factors, which contains a large number of linear and nonlinear relationships. This comprehensive study employs a multifaceted strategy, integrating system dynamics modeling for dynamic simulations, a literature analysis for theoretical grounding, theoretical insights for conceptual clarity, and statistical analysis for empirical validation. Its goal is to thoroughly apply the CCC evaluation framework, incorporating data calculation and predictive simulations while emphasizing the optimization of innovative resources. By harmoniously combining these methodologies, this study endeavors to gain a deeper understanding of how augmenting innovative resources can propel sustainable urban development and bolster the resilience of urban agglomerations, fostering long-term prosperity and adaptability. Specifically, the research underscores a correlation between the CCC framework and the improvement of innovative resources within urban agglomerations (Table 4).

Based on the conclusions drawn from the research, we propose the following policy implications. The central government should strengthen the top-level design of innovative resource allocation between and within urban agglomerations and focus on regional urban agglomerations with scarce resources. National-level urban agglomerations should promote the sharing and exchange of innovative resources and contribute to their leading global position. Less developed areas should optimize their resource allocation structures and take advantage of strategic opportunities to overcome and solve urban problems.

In the context of the diminishing comparative advantage of traditional resources, the comparative advantage of innovative resources is becoming an important driving factor in reshaping the CCC structures and enhancing the carrying capacity of global urban agglomerations. National-level urban agglomerations should strengthen policy guidance and their technological drive; deepen the industrial chain; and enhance high-end, intelligent, and green development. Resource-based regional urban agglomerations must optimize industrial innovation ecology, reduce resource development pressure, and help achieve carbon goals. Manufacturing-led urban clusters must promote industrial transformation and shift toward knowledge-intensive industries. During the cultivation period, regional urban agglomerations should accurately match innovative human resources, introduce professional talents, enhance the attractiveness of high-level talents, and assist in the technological self-reliance and modernization of industrial system construction.

The government and society should increase expenditure on universities and research institutes and optimize innovative resources. The government should increase the budget for the education sector, establish special expenditures for higher education, and encourage social donations. Technology-oriented enterprises should set clear research expenditure goals; establish scientific budgeting, approval, and performance evaluation mechanisms; improve utilization efficiency; and seek external financial support.

Simply increasing the number of universities and research institutes cannot effectively improve innovative resources, which may precipitate resource dispersion and inefficient utilization. Innovative talents form the core element, and human capital with heterogeneous skills achieves market equilibrium through agglomeration effects. It is necessary to encourage risk-taking; tolerate failure; and solve the problems of examination-oriented education, a lack of innovation, and talent loss abroad.

Effective invention patents are the key to enhancing the CCC of urban agglomerations, and it is necessary to establish a patent application support mechanism and a patent reward system and improve intellectual property laws and regulations. Simultaneously, policy design should encourage high-quality innovation and guide enterprises to engage in high-risk but promising innovations.

A virtuous cycle exists between higher education funding, internal research funding within enterprises, and effective invention patents. Funding from investment promotes scientific research optimization and improves resource quality and internal investment to support innovation and R&D within an enterprise. Innovative achievements bring economic returns through patent protection, attract investment and cooperation, further increase funding investment, and form a virtuous cycle.

However, this study aims to contribute to the field by exploring the role of innovative resources in enhancing the CCC of urban agglomerations. By analyzing the influence of innovation on various aspects of urban development, it lays the groundwork for future research in this domain. While further research is needed to delve into the synergies between innovative resources and three subsystems, economic carrying capacity (ECC), public service carrying capacity (PSCC), and natural resource carrying capacity (NRCC), this study has not extensively explored the interconnections among these subsystems. The aim is to gain a holistic understanding of urban agglomerations’ carrying capacity, which is essential for realizing their modernized and sustainable development.

Author Contributions

Conceptualization, L.Y. and W.Y.; methodology, L.Y.; software, L.Y.; validation, L.Y., H.L. and Q.Z.; formal analysis, L.Y.; investigation, L.Y.; resources, W.Y.; data curation, L.Y.; writing—original draft preparation, L.Y. and H.L.; writing—review and editing, L.Y.; visualization, L.Y. and H.L.; supervision, L.Y. and Q.Z.; project administration, H.L.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Level, names, and core cities of 19 urban agglomerations. Note: The urban agglomerations corresponding to the subpictures (a–s) are as follows: (a) is Pear River Delta, (b) is Beijing-Tianjin-Hebei, (c) is Middle Reaches of Changjiang River, (d) is Chengdu Chongqing Economic, (e) is Yantze River Delta, (f) is West Coast of Taiwan Straits, (g) is Central-South of Liaoning, (h) is Central Shanxi, (i) is Shandong peninsula, (j) is Central China, (k) is Beibu Gulf in Guangxi, (l) is North slope of tianshan mountain, (m) is Harbor-Yangtze, (n) is Ningxia along the Yellow River, (o) is Central Guizhou, (p) is Central Yunnan, (q) is Package Hubei Elm, (r) is Lanzhou-Xining, (s) is Jinzhong.

Figure 2. Causal relationship diagram of CCC of urban agglomeration.

Figure 3. Flow chart of CCC system for urban agglomeration.

Figure 4. Comparison between simulated and real data (2011–2020).

Figure 5. IRCC and CCC levels of China’s urban agglomerations in 2020.

Figure 6. IRCC and CCC under different scenarios in past and future for (a) Shanghai, (b) Qingdao, and (c) Xining.

Table 1

Multi indicator evaluation system and its weights for the CCC of urban agglomerations.

Criterion Layer	Criteria Weights	Indicator Layer	Indicator Weight	Basis for Selection
Innovative resource carrying capacity(IRCC)	0.2464	Number of ordinary higher education institutions and research institutes	0.0493	Research on innovative resources from the perspective of enterprises [54] and city [55]
		College teachers	0.0376
		Employment in scientific research and technology services	0.0320
		Employment in information transmission, software, and information technology services	0.0312
		R&D employment numbers	0.0381
		Internal R&D expenditure	0.0306
		Patent application authorization volume	0.0276
Economic carrying capacity(ECC)	0.3482	Per capita GDP	0.0331	Indicators of ECC and the impact of economic development on CCC [56,57]
		Proportion of the added value of the secondary industry in GDP	0.0383
		Proportion of the added value of the tertiary industry in GDP	0.0365
		Proportion of employment in the tertiary industry	0.0314
		Disposable income of urban residents	0.0397
		Completed housing area of real estate development enterprises	0.036
		Total profit	0.0348
		Savings deposit balance	0.0381
		Total sales of wholesale and retail goods above quota	0.0277
		Average salary of urban on-the-job employees	0.0326
Public service carrying capacity(PSCC)	0.2398	Road area	0.0416	Evaluation method for urban PSCC [58,59]
		Park green space- area	0.049
		Total collection of books in public libraries	0.0448
		Number of healthcare personnel in hospitals and health centers	0.0304
		Education public budget expenditure	0.0402
		Public budget expenditures for science and technology	0.0338
Natural resource carrying capacity (NRCC)	0.1769	Built-up area	0.0456	Comprehensive evaluation of resource and environmental carrying capacity [60,61]
		Harmless treatment rate of household waste	0.0409
		Centralized treatment rate of sewage treatment plants	0.0267
		Industrial sulfur dioxide emissions	0.0294
		Industrial smoke and dust emissions	0.0343

Table 2

Feedback loops for innovative resources.

Category	Variables
Patent applications authorized Loop 1	Patent application authorization volume → number of valid invention patents → IRCC → CCC
Education public budget expenditure Loop 1	Education public budget expenditure → university construction → college teachers → scientific research talents → IRCC → CCC
Education public budget expenditure Loop 2	Education public budget expenditure → digital talents → IRCC → CCC
Education public budget expenditure Loop 3	Education public budget expenditure → R&D employment numbers → IRCC → CCC
Education public budget expenditure Loop 4	Education public budget expenditure → scientific research talents → IRCC → CCC
Education public budget expenditure Loop 5	Education public budget expenditure → PSCC → CCC
Public budget expenditure for science and technology Loop 1	Public budget expenditure for science and technology→ digital talents → IRCC → CCC
Public budget expenditure for science and technology Loop 2	Public budget expenditure for science and technology→ PSCC → CCC
Internal R&D expenditure Loop 1	Internal R&D expenditure → R&D employment numbers → IRCC → CCC
Internal R&D expenditure Loop 2	Internal R&D expenditure → scientific research talents → IRCC → CCC
Internal R&D expenditure Loop 3	Internal R&D expenditure → digital talents → IRCC → CCC

Table 3

Main parameters equation of the CCC system.

Variable	Abbreviations	Unit	Equation	Basis
GDP	GDP	108 yuan	Ramp function	14th Five-Year Plan
Total population	TP	104 person	$T{P}_{\mathrm{t}-1}\times (1+BR-MR)+NIP$	General equation
Birth rate	BR	‰	$0.07\times power(0.990,(Time-2018))$	Select the function with the highest goodness of fit by region
Mortality rate	MR	‰	Regional average	The level within the region is relatively stable.
The proportion of added value of the secondary industry in GDP	PSIG	%	$0.298\times Power(0.960,(Time-2018))$	Select the function with the highest goodness of fit by region
The proportion of added value of the tertiary industry in GDP	PTIG	%	$0.699\times Power(1.015,(Time-2018))$	Select the function with the highest goodness of fit by region
Disposable income of urban residents	DIUR	yuan	$0.096\times percapitaGDP+24984$	The results of linear regression based on historical data
Completed housing area of real estate development enterprises	CHRE	104 m2	After 2020, a 5% decrease annually	The positioning of “housing is for living in, not for speculation” in the 14th Five−Year Plan and the current situation for China’s population
Total profit	TP	108 yuan	$\begin{array}{l}36.491\times PSIG+38.443\times PTIG-36.680\times RDE\\ +1.173\times RDFE+0.011\times MBI-3459.031\end{array}$	Linear regression, with independent variables referencing existing literature (Jiang, 2012) [67]
Savings deposit balance	SDB	108 yuan	$2913.910\times Power(1.001,(Time-2016))$	Select the function with the highest goodness of fit by region
Total sales of wholesale and retail goods above quota	TSWR	108 yuan	The annual growth rate is 8%	14th Five-Year Plan and historical data
Average salary of urban on-the-job employees	ASIE	yuan	The annual growth rate is 8%	14th Five-Year Plan and historical data
Road area proportion	RAP		Cities with a population under 2 million: 0.115; cities with a population over 2 million: 0.175	Referencing existing literature (Wang, 2014) [68]
Increment of park green space area	IPGS	km2	Setting multipliers based on regions for population growth	Referencing existing literature (Du and Liu, 2022) [69]
Attrition of public library books	APLB	104 copies	$0.035\times NAP{L}_{t-1}$	Survey results of multiple libraries
Number of newly acquired public library books	NAPL	104 copies	Calculating the average value by region	Regional historical data
Number of healthcare personnel in hospitals and health centers	NHPH	person	$0.009\times HE+8723.594$	The results of linear regression based on historical data
Health expenditure	HE	104 yuan	$\begin{array}{l}IFTHENELSE(Time<2020,\\ 0.060\times FE,0.070\times FE)\end{array}$	Historical data and IF function by regions
Fiscal expenditure	FE	104 yuan	$\begin{array}{l}IFTHENELSE(Time<2020,\\ 0.280\times GDP,0.300\times GDP)\end{array}$	Historical data and IF function by regions
Education public budget expenditure	EPBE	104 yuan	$\begin{array}{l}IFTHENELSE(Time<2020,0.355\times FE\\ +120.350,0.350\times FE+399.950)\end{array}$	Historical data and IF function by regions
Public budget expenditures for science and technology	PBSD	104 yuan	$\begin{array}{l}0.038\times FE+9819.421\times PTIG\\ -930830\end{array}$	The results of linear regression based on historical data
Built-up area	BUA	km2	$1237.850+(Time-2016)\times 12.379$	The results of linear regression based on historical data
Harmless treatment rate of household waste	HTHW	%	$IFTHENELSE(Time>2016,100,99)$	Historical data and IF function by regions
Centralized treatment rate of sewage treatment plants	CTST	%	$92.800+(Time-2018)\times 0.100$	The results of linear regression based on historical data
Per-unit-value sulfur dioxide emissions in secondary industry	SDES	ton	0.98 times the value of the previous year	Historical data trend
Per-unit-value smoke and dust emissions in secondary industry	ESDS	ton	0.007	The fluctuation range of the data is small, so take the average value
College teachers	NFTH	person	$\begin{array}{l}0.040\times HEF-0.0001\times HEIR\\ +124.726\end{array}$	The results of linear regression based on historical data
Employment in scientific research and technology services	EST	104 people	$\begin{array}{l}(0.106\times RDFE+652.312\times CT+123.833\\ \times EPBE-28868.510)\times 0.010\end{array}$	The results of linear regression based on historical data
Employment in information transmission, software, and information technology services	EISI	104 people	$\begin{array}{l}(0.027\times RDFE+1.083\times ASIE-77132.730)\\ \times 0.010\end{array}$	The results of linear regression based on historical data
Number of R&D personnel	NRDP	person	$\begin{array}{l}(0.027\times RDFE+1.083\times ASIE\\ -77132.730)\times 0.010\end{array}$	The results of linear regression based on historical data
The number of valid invention patents	NVIP	term	$0.680\times PAAV-134$	The results of linear regression based on historical data
Revenue from the sales of new products	RSNP	104 yuan	$320.458+0.560\times NVIP$	The results of linear regression based on historical data
Main business income	MBI	104 yuan	$1.071+0.473\times RSNP$	The results of linear regression based on historical data

Table 4

Research contents indicate that innovative resources have a positive influence on CCC.

Research Contents	Time Frame of the Section	Research Methods	Conclusions
Section 2.3	References within the most recent years	literature analysis	Innovative resources positively influence economic growth, pollution control, transportation, communication, infrastructure, and enterprise operations
Table 3: Main parameters in equations for the CCC system	2011–2040	Refer to14th Five-Year Plan, historical data, linear regression, existing literature, the highest goodness function, etc.	There exists a correlation between the parameters of innovative resources and other parameters related to CCC
Section 5.2	2020	Statistical methods	IRCC significantly positively influences CCC
Section 5.3	System dynamics	System dynamics methods	Optimizing the allocation of innovative resources is an effective means for China’s urban agglomerations to achieve positive changes in their CCC structure

Appendix A

Table A1

The values of IRCC from 2011 to 2020.

Year	City	IRCC	City	IRCC	City	IRCC	City	IRCC
2011	Dalian	0.025	Hangzhou	0.043	Qingdao	0.016	Xi’an	0.059
2012	Dalian	0.025	Hangzhou	0.045	Qingdao	0.017	Xi’an	0.060
2013	Dalian	0.028	Hangzhou	0.046	Qingdao	0.018	Xi’an	0.065
2014	Dalian	0.029	Hangzhou	0.048	Qingdao	0.019	Xi’an	0.065
2015	Dalian	0.028	Hangzhou	0.050	Qingdao	0.020	Xi’an	0.067
2016	Dalian	0.029	Hangzhou	0.053	Qingdao	0.022	Xi’an	0.068
2017	Dalian	0.029	Hangzhou	0.055	Qingdao	0.024	Xi’an	0.071
2018	Dalian	0.031	Hangzhou	0.058	Qingdao	0.026	Xi’an	0.073
2019	Dalian	0.032	Hangzhou	0.062	Qingdao	0.028	Xi’an	0.075
2020	Dalian	0.035	Hangzhou	0.050	Qingdao	0.031	Xi’an	0.075
2011	Beijing	0.149	Harbin	0.040	Shanghai	0.073	Xining	0.004
2012	Beijing	0.158	Harbin	0.039	Shanghai	0.074	Xining	0.004
2013	Beijing	0.168	Harbin	0.040	Shanghai	0.094	Xining	0.004
2014	Beijing	0.172	Harbin	0.040	Shanghai	0.091	Xining	0.005
2015	Beijing	0.179	Harbin	0.041	Shanghai	0.094	Xining	0.005
2016	Beijing	0.187	Harbin	0.041	Shanghai	0.094	Xining	0.006
2017	Beijing	0.195	Harbin	0.041	Shanghai	0.098	Xining	0.006
2018	Beijing	0.204	Harbin	0.041	Shanghai	0.103	Xining	0.007
2019	Beijing	0.217	Harbin	0.041	Shanghai	0.111	Xining	0.007
2020	Beijing	0.226	Harbin	0.042	Shanghai	0.123	Xining	0.007
2011	Changchun	0.030	Hohhot	0.013	Shenyang	0.037	Yinchuan	0.009
2012	Changchun	0.032	Hohhot	0.013	Shenyang	0.041	Yinchuan	0.009
2013	Changchun	0.032	Hohhot	0.014	Shenyang	0.043	Yinchuan	0.010
2014	Changchun	0.033	Hohhot	0.014	Shenyang	0.043	Yinchuan	0.010
2015	Changchun	0.034	Hohhot	0.015	Shenyang	0.044	Yinchuan	0.010
2016	Changchun	0.036	Hohhot	0.015	Shenyang	0.044	Yinchuan	0.011
2017	Changchun	0.038	Hohhot	0.015	Shenyang	0.045	Yinchuan	0.012
2018	Changchun	0.039	Hohhot	0.015	Shenyang	0.048	Yinchuan	0.012
2019	Changchun	0.040	Hohhot	0.015	Shenyang	0.049	Yinchuan	0.013
2020	Changchun	0.042	Hohhot	0.016	Shenyang	0.051	Yinchuan	0.013
2011	Changsha	0.040	Jinan	0.035	Shenzhen	0.031	Zhengzhou	0.043
2012	Changsha	0.041	Jinan	0.036	Shenzhen	0.038	Zhengzhou	0.045
2013	Changsha	0.042	Jinan	0.040	Shenzhen	0.042	Zhengzhou	0.050
2014	Changsha	0.043	Jinan	0.039	Shenzhen	0.042	Zhengzhou	0.057
2015	Changsha	0.044	Jinan	0.040	Shenzhen	0.047	Zhengzhou	0.058
2016	Changsha	0.045	Jinan	0.041	Shenzhen	0.054	Zhengzhou	0.058
2017	Changsha	0.046	Jinan	0.041	Shenzhen	0.062	Zhengzhou	0.061
2018	Changsha	0.048	Jinan	0.042	Shenzhen	0.080	Zhengzhou	0.065
2019	Changsha	0.050	Jinan	0.045	Shenzhen	0.088	Zhengzhou	0.072
2020	Changsha	0.053	Jinan	0.046	Shenzhen	0.104	Zhengzhou	0.080
2011	Chengdu	0.050	Kunming	0.031	Taiyuan	0.030
2012	Chengdu	0.056	Kunming	0.032	Taiyuan	0.030
2013	Chengdu	0.065	Kunming	0.033	Taiyuan	0.031
2014	Chengdu	0.066	Kunming	0.033	Taiyuan	0.031
2015	Chengdu	0.063	Kunming	0.034	Taiyuan	0.032
2016	Chengdu	0.065	Kunming	0.035	Taiyuan	0.032
2017	Chengdu	0.067	Kunming	0.036	Taiyuan	0.032
2018	Chengdu	0.072	Kunming	0.037	Taiyuan	0.034
2019	Chengdu	0.074	Kunming	0.037	Taiyuan	0.035
2020	Chengdu	0.078	Kunming	0.037	Taiyuan	0.035
2011	Chongqing	0.050	Lanzhou	0.017	Tianjin	0.050
2012	Chongqing	0.053	Lanzhou	0.018	Tianjin	0.054
2013	Chongqing	0.060	Lanzhou	0.019	Tianjin	0.057
2014	Chongqing	0.061	Lanzhou	0.021	Tianjin	0.059
2015	Chongqing	0.061	Lanzhou	0.021	Tianjin	0.062
2016	Chongqing	0.066	Lanzhou	0.022	Tianjin	0.064
2017	Chongqing	0.069	Lanzhou	0.024	Tianjin	0.063
2018	Chongqing	0.072	Lanzhou	0.027	Tianjin	0.064
2019	Chongqing	0.075	Lanzhou	0.024	Tianjin	0.066
2020	Chongqing	0.086	Lanzhou	0.024	Tianjin	0.068
2011	Fuzhou	0.038	Nanchang	0.041	Urumqi	0.010
2012	Fuzhou	0.044	Nanchang	0.041	Urumqi	0.011
2013	Fuzhou	0.046	Nanchang	0.041	Urumqi	0.013
2014	Fuzhou	0.051	Nanchang	0.043	Urumqi	0.014
2015	Fuzhou	0.054	Nanchang	0.042	Urumqi	0.014
2016	Fuzhou	0.058	Nanchang	0.042	Urumqi	0.014
2017	Fuzhou	0.060	Nanchang	0.043	Urumqi	0.015
2018	Fuzhou	0.069	Nanchang	0.044	Urumqi	0.015
2019	Fuzhou	0.076	Nanchang	0.045	Urumqi	0.017
2020	Fuzhou	0.084	Nanchang	0.045	Urumqi	0.017
2011	Guangzhou	0.083	Nanjing	0.049	Wuhan	0.077
2012	Guangzhou	0.086	Nanjing	0.052	Wuhan	0.079
2013	Guangzhou	0.094	Nanjing	0.057	Wuhan	0.080
2014	Guangzhou	0.097	Nanjing	0.058	Wuhan	0.081
2015	Guangzhou	0.103	Nanjing	0.060	Wuhan	0.083
2016	Guangzhou	0.105	Nanjing	0.062	Wuhan	0.084
2017	Guangzhou	0.112	Nanjing	0.065	Wuhan	0.085
2018	Guangzhou	0.116	Nanjing	0.074	Wuhan	0.089
2019	Guangzhou	0.121	Nanjing	0.080	Wuhan	0.091
2020	Guangzhou	0.132	Nanjing	0.084	Wuhan	0.094
2011	Guiyang	0.031	Nanning	0.019	Xiamen	0.013
2012	Guiyang	0.031	Nanning	0.019	Xiamen	0.014
2013	Guiyang	0.032	Nanning	0.020	Xiamen	0.015
2014	Guiyang	0.033	Nanning	0.021	Xiamen	0.017
2015	Guiyang	0.034	Nanning	0.022	Xiamen	0.017
2016	Guiyang	0.034	Nanning	0.022	Xiamen	0.018
2017	Guiyang	0.035	Nanning	0.024	Xiamen	0.020
2018	Guiyang	0.036	Nanning	0.025	Xiamen	0.020
2019	Guiyang	0.037	Nanning	0.025	Xiamen	0.021
2020	Guiyang	0.038	Nanning	0.027	Xiamen	0.022

Appendix B

Table A2

The values of ECC from 2011 to 2020.

Year	City	ECC	City	ECC	City	ECC	City	ECC
2011	Dalian	0.059	Hangzhou	0.073	Qingdao	0.066	Xi’an	0.063
2012	Dalian	0.064	Hangzhou	0.078	Qingdao	0.074	Xi’an	0.071
2013	Dalian	0.074	Hangzhou	0.091	Qingdao	0.080	Xi’an	0.073
2014	Dalian	0.056	Hangzhou	0.099	Qingdao	0.096	Xi’an	0.084
2015	Dalian	0.059	Hangzhou	0.108	Qingdao	0.105	Xi’an	0.086
2016	Dalian	0.058	Hangzhou	0.121	Qingdao	0.113	Xi’an	0.097
2017	Dalian	0.088	Hangzhou	0.137	Qingdao	0.109	Xi’an	0.106
2018	Dalian	0.092	Hangzhou	0.143	Qingdao	0.117	Xi’an	0.105
2019	Dalian	0.095	Hangzhou	0.157	Qingdao	0.128	Xi’an	0.113
2020	Dalian	0.098	Hangzhou	0.171	Qingdao	0.135	Xi’an	0.117
2011	Beijing	0.157	Harbin	0.055	Shanghai	0.147	Xining	0.047
2012	Beijing	0.168	Harbin	0.067	Shanghai	0.151	Xining	0.053
2013	Beijing	0.176	Harbin	0.072	Shanghai	0.173	Xining	0.054
2014	Beijing	0.190	Harbin	0.079	Shanghai	0.194	Xining	0.061
2015	Beijing	0.198	Harbin	0.085	Shanghai	0.197	Xining	0.063
2016	Beijing	0.204	Harbin	0.093	Shanghai	0.216	Xining	0.065
2017	Beijing	0.212	Harbin	0.094	Shanghai	0.229	Xining	0.076
2018	Beijing	0.223	Harbin	0.100	Shanghai	0.240	Xining	0.096
2019	Beijing	0.241	Harbin	0.112	Shanghai	0.252	Xining	0.108
2020	Beijing	0.256	Harbin	0.114	Shanghai	0.263	Xining	0.103
2011	Changchun	0.033	Hohhot	0.099	Shenyang	0.066	Yinchuan	0.063
2012	Changchun	0.040	Hohhot	0.107	Shenyang	0.070	Yinchuan	0.067
2013	Changchun	0.040	Hohhot	0.111	Shenyang	0.063	Yinchuan	0.071
2014	Changchun	0.039	Hohhot	0.120	Shenyang	0.067	Yinchuan	0.086
2015	Changchun	0.046	Hohhot	0.121	Shenyang	0.069	Yinchuan	0.095
2016	Changchun	0.050	Hohhot	0.126	Shenyang	0.078	Yinchuan	0.096
2017	Changchun	0.057	Hohhot	0.124	Shenyang	0.085	Yinchuan	0.101
2018	Changchun	0.063	Hohhot	0.131	Shenyang	0.089	Yinchuan	0.109
2019	Changchun	0.078	Hohhot	0.132	Shenyang	0.101	Yinchuan	0.121
2020	Changchun	0.080	Hohhot	0.140	Shenyang	0.107	Yinchuan	0.129
2011	Changsha	0.069	Jinan	0.075	Shenzhen	0.106	Zhengzhou	0.076
2012	Changsha	0.075	Jinan	0.078	Shenzhen	0.110	Zhengzhou	0.071
2013	Changsha	0.082	Jinan	0.088	Shenzhen	0.116	Zhengzhou	0.071
2014	Changsha	0.088	Jinan	0.092	Shenzhen	0.119	Zhengzhou	0.084
2015	Changsha	0.095	Jinan	0.100	Shenzhen	0.129	Zhengzhou	0.083
2016	Changsha	0.110	Jinan	0.106	Shenzhen	0.138	Zhengzhou	0.093
2017	Changsha	0.117	Jinan	0.109	Shenzhen	0.141	Zhengzhou	0.104
2018	Changsha	0.128	Jinan	0.110	Shenzhen	0.148	Zhengzhou	0.106
2019	Changsha	0.138	Jinan	0.112	Shenzhen	0.164	Zhengzhou	0.119
2020	Changsha	0.144	Jinan	0.122	Shenzhen	0.183	Zhengzhou	0.119
2011	Chengdu	0.062	Kunming	0.062	Taiyuan	0.046
2012	Chengdu	0.075	Kunming	0.066	Taiyuan	0.048
2013	Chengdu	0.085	Kunming	0.074	Taiyuan	0.052
2014	Chengdu	0.088	Kunming	0.075	Taiyuan	0.066
2015	Chengdu	0.083	Kunming	0.083	Taiyuan	0.072
2016	Chengdu	0.101	Kunming	0.085	Taiyuan	0.078
2017	Chengdu	0.098	Kunming	0.092	Taiyuan	0.078
2018	Chengdu	0.100	Kunming	0.093	Taiyuan	0.085
2019	Chengdu	0.135	Kunming	0.118	Taiyuan	0.087
2020	Chengdu	0.137	Kunming	0.126	Taiyuan	0.092
2011	Chongqing	0.048	Lanzhou	0.040	Tianjin	0.074
2012	Chongqing	0.085	Lanzhou	0.039	Tianjin	0.092
2013	Chongqing	0.092	Lanzhou	0.047	Tianjin	0.102
2014	Chongqing	0.084	Lanzhou	0.062	Tianjin	0.113
2015	Chongqing	0.097	Lanzhou	0.071	Tianjin	0.118
2016	Chongqing	0.099	Lanzhou	0.079	Tianjin	0.131
2017	Chongqing	0.115	Lanzhou	0.080	Tianjin	0.125
2018	Chongqing	0.107	Lanzhou	0.092	Tianjin	0.132
2019	Chongqing	0.123	Lanzhou	0.093	Tianjin	0.136
2020	Chongqing	0.131	Lanzhou	0.099	Tianjin	0.143
2011	Fuzhou	0.066	Nanchang	0.027	Urumqi	0.069
2012	Fuzhou	0.071	Nanchang	0.041	Urumqi	0.080
2013	Fuzhou	0.078	Nanchang	0.044	Urumqi	0.066
2014	Fuzhou	0.081	Nanchang	0.049	Urumqi	0.078
2015	Fuzhou	0.090	Nanchang	0.051	Urumqi	0.092
2016	Fuzhou	0.096	Nanchang	0.057	Urumqi	0.098
2017	Fuzhou	0.094	Nanchang	0.063	Urumqi	0.102
2018	Fuzhou	0.101	Nanchang	0.072	Urumqi	0.108
2019	Fuzhou	0.102	Nanchang	0.084	Urumqi	0.119
2020	Fuzhou	0.117	Nanchang	0.091	Urumqi	0.116
2011	Guangzhou	0.101	Nanjing	0.071	Wuhan	0.059
2012	Guangzhou	0.111	Nanjing	0.083	Wuhan	0.063
2013	Guangzhou	0.122	Nanjing	0.081	Wuhan	0.064
2014	Guangzhou	0.129	Nanjing	0.092	Wuhan	0.071
2015	Guangzhou	0.132	Nanjing	0.100	Wuhan	0.078
2016	Guangzhou	0.141	Nanjing	0.110	Wuhan	0.088
2017	Guangzhou	0.151	Nanjing	0.115	Wuhan	0.084
2018	Guangzhou	0.162	Nanjing	0.125	Wuhan	0.091
2019	Guangzhou	0.182	Nanjing	0.139	Wuhan	0.107
2020	Guangzhou	0.184	Nanjing	0.152	Wuhan	0.112
2011	Guiyang	0.053	Nanning	0.061	Xiamen	0.044
2012	Guiyang	0.056	Nanning	0.065	Xiamen	0.052
2013	Guiyang	0.065	Nanning	0.061	Xiamen	0.058
2014	Guiyang	0.071	Nanning	0.061	Xiamen	0.067
2015	Guiyang	0.079	Nanning	0.067	Xiamen	0.072
2016	Guiyang	0.079	Nanning	0.064	Xiamen	0.081
2017	Guiyang	0.077	Nanning	0.069	Xiamen	0.085
2018	Guiyang	0.084	Nanning	0.103	Xiamen	0.093
2019	Guiyang	0.086	Nanning	0.107	Xiamen	0.103
2020	Guiyang	0.094	Nanning	0.110	Xiamen	0.119

Appendix C

Table A3

The values of PSCC from 2011 to 2020.

Year	City	PSCC	City	PSCC	City	PSCC	City	PSCC
2011	Dalian	0.022	Hangzhou	0.030	Qingdao	0.021	Xi’an	0.020
2012	Dalian	0.024	Hangzhou	0.034	Qingdao	0.026	Xi’an	0.024
2013	Dalian	0.025	Hangzhou	0.036	Qingdao	0.029	Xi’an	0.028
2014	Dalian	0.026	Hangzhou	0.040	Qingdao	0.030	Xi’an	0.028
2015	Dalian	0.025	Hangzhou	0.047	Qingdao	0.031	Xi’an	0.031
2016	Dalian	0.029	Hangzhou	0.051	Qingdao	0.037	Xi’an	0.030
2017	Dalian	0.032	Hangzhou	0.059	Qingdao	0.041	Xi’an	0.040
2018	Dalian	0.035	Hangzhou	0.065	Qingdao	0.046	Xi’an	0.043
2019	Dalian	0.027	Hangzhou	0.071	Qingdao	0.050	Xi’an	0.048
2020	Dalian	0.030	Hangzhou	0.075	Qingdao	0.053	Xi’an	0.090
2011	Beijing	0.115	Harbin	0.020	Shanghai	0.118	Xining	0.003
2012	Beijing	0.136	Harbin	0.022	Shanghai	0.126	Xining	0.004
2013	Beijing	0.142	Harbin	0.024	Shanghai	0.129	Xining	0.002
2014	Beijing	0.150	Harbin	0.024	Shanghai	0.132	Xining	0.002
2015	Beijing	0.170	Harbin	0.025	Shanghai	0.138	Xining	0.002
2016	Beijing	0.172	Harbin	0.027	Shanghai	0.152	Xining	0.002
2017	Beijing	0.162	Harbin	0.028	Shanghai	0.160	Xining	0.003
2018	Beijing	0.197	Harbin	0.031	Shanghai	0.167	Xining	0.004
2019	Beijing	0.208	Harbin	0.032	Shanghai	0.133	Xining	0.004
2020	Beijing	0.210	Harbin	0.029	Shanghai	0.174	Xining	0.006
2011	Changchun	0.023	Hohhot	0.006	Shenyang	0.032	Yinchuan	0.004
2012	Changchun	0.026	Hohhot	0.006	Shenyang	0.035	Yinchuan	0.004
2013	Changchun	0.026	Hohhot	0.007	Shenyang	0.038	Yinchuan	0.005
2014	Changchun	0.026	Hohhot	0.007	Shenyang	0.038	Yinchuan	0.005
2015	Changchun	0.028	Hohhot	0.010	Shenyang	0.036	Yinchuan	0.007
2016	Changchun	0.029	Hohhot	0.012	Shenyang	0.033	Yinchuan	0.008
2017	Changchun	0.026	Hohhot	0.013	Shenyang	0.037	Yinchuan	0.009
2018	Changchun	0.030	Hohhot	0.011	Shenyang	0.039	Yinchuan	0.009
2019	Changchun	0.029	Hohhot	0.012	Shenyang	0.043	Yinchuan	0.010
2020	Changchun	0.035	Hohhot	0.012	Shenyang	0.044	Yinchuan	0.010
2011	Changsha	0.018	Jinan	0.023	Shenzhen	0.068	Zhengzhou	0.015
2012	Changsha	0.020	Jinan	0.026	Shenzhen	0.075	Zhengzhou	0.017
2013	Changsha	0.021	Jinan	0.027	Shenzhen	0.083	Zhengzhou	0.019
2014	Changsha	0.022	Jinan	0.029	Shenzhen	0.084	Zhengzhou	0.022
2015	Changsha	0.029	Jinan	0.033	Shenzhen	0.094	Zhengzhou	0.023
2016	Changsha	0.027	Jinan	0.037	Shenzhen	0.113	Zhengzhou	0.025
2017	Changsha	0.027	Jinan	0.039	Shenzhen	0.117	Zhengzhou	0.035
2018	Changsha	0.028	Jinan	0.043	Shenzhen	0.116	Zhengzhou	0.039
2019	Changsha	0.030	Jinan	0.053	Shenzhen	0.140	Zhengzhou	0.043
2020	Changsha	0.040	Jinan	0.058	Shenzhen	0.136	Zhengzhou	0.048
2011	Chengdu	0.032	Kunming	0.014	Taiyuan	0.013
2012	Chengdu	0.034	Kunming	0.015	Taiyuan	0.015
2013	Chengdu	0.036	Kunming	0.014	Taiyuan	0.018
2014	Chengdu	0.038	Kunming	0.021	Taiyuan	0.019
2015	Chengdu	0.043	Kunming	0.018	Taiyuan	0.020
2016	Chengdu	0.052	Kunming	0.021	Taiyuan	0.020
2017	Chengdu	0.057	Kunming	0.018	Taiyuan	0.022
2018	Chengdu	0.062	Kunming	0.020	Taiyuan	0.024
2019	Chengdu	0.067	Kunming	0.022	Taiyuan	0.025
2020	Chengdu	0.077	Kunming	0.025	Taiyuan	0.026
2011	Chongqing	0.062	Lanzhou	0.008	Tianjin	0.052
2012	Chongqing	0.070	Lanzhou	0.009	Tianjin	0.059
2013	Chongqing	0.072	Lanzhou	0.008	Tianjin	0.057
2014	Chongqing	0.079	Lanzhou	0.010	Tianjin	0.062
2015	Chongqing	0.087	Lanzhou	0.012	Tianjin	0.078
2016	Chongqing	0.097	Lanzhou	0.014	Tianjin	0.081
2017	Chongqing	0.103	Lanzhou	0.014	Tianjin	0.082
2018	Chongqing	0.111	Lanzhou	0.016	Tianjin	0.085
2019	Chongqing	0.119	Lanzhou	0.017	Tianjin	0.091
2020	Chongqing	0.126	Lanzhou	0.017	Tianjin	0.093
2011	Fuzhou	0.009	Nanchang	0.007	Urumqi	0.010
2012	Fuzhou	0.010	Nanchang	0.010	Urumqi	0.011
2013	Fuzhou	0.010	Nanchang	0.011	Urumqi	0.013
2014	Fuzhou	0.011	Nanchang	0.012	Urumqi	0.014
2015	Fuzhou	0.012	Nanchang	0.014	Urumqi	0.014
2016	Fuzhou	0.014	Nanchang	0.012	Urumqi	0.015
2017	Fuzhou	0.019	Nanchang	0.015	Urumqi	0.015
2018	Fuzhou	0.021	Nanchang	0.016	Urumqi	0.016
2019	Fuzhou	0.023	Nanchang	0.016	Urumqi	0.023
2020	Fuzhou	0.025	Nanchang	0.018	Urumqi	0.024
2011	Guangzhou	0.064	Nanjing	0.042	Wuhan	0.033
2012	Guangzhou	0.071	Nanjing	0.047	Wuhan	0.038
2013	Guangzhou	0.075	Nanjing	0.052	Wuhan	0.038
2014	Guangzhou	0.078	Nanjing	0.055	Wuhan	0.042
2015	Guangzhou	0.094	Nanjing	0.061	Wuhan	0.041
2016	Guangzhou	0.103	Nanjing	0.064	Wuhan	0.047
2017	Guangzhou	0.110	Nanjing	0.067	Wuhan	0.057
2018	Guangzhou	0.129	Nanjing	0.072	Wuhan	0.064
2019	Guangzhou	0.145	Nanjing	0.079	Wuhan	0.069
2020	Guangzhou	0.148	Nanjing	0.083	Wuhan	0.079
2011	Guiyang	0.006	Nanning	0.013	Xiamen	0.013
2012	Guiyang	0.009	Nanning	0.014	Xiamen	0.015
2013	Guiyang	0.013	Nanning	0.016	Xiamen	0.014
2014	Guiyang	0.014	Nanning	0.018	Xiamen	0.015
2015	Guiyang	0.013	Nanning	0.018	Xiamen	0.017
2016	Guiyang	0.014	Nanning	0.020	Xiamen	0.016
2017	Guiyang	0.016	Nanning	0.022	Xiamen	0.022
2018	Guiyang	0.018	Nanning	0.025	Xiamen	0.031
2019	Guiyang	0.019	Nanning	0.027	Xiamen	0.033
2020	Guiyang	0.022	Nanning	0.029	Xiamen	0.029

Appendix D

Table A4

The values of NSCC from 2011 to 2020.

Year	City	NRCC	City	NRCC	City	NRCC	City	NRCC
2011	Dalian	0.092	Hangzhou	0.128	Qingdao	0.126	Xi’an	0.125
2012	Dalian	0.100	Hangzhou	0.130	Qingdao	0.129	Xi’an	0.128
2013	Dalian	0.102	Hangzhou	0.129	Qingdao	0.133	Xi’an	0.130
2014	Dalian	0.081	Hangzhou	0.130	Qingdao	0.133	Xi’an	0.132
2015	Dalian	0.124	Hangzhou	0.130	Qingdao	0.136	Xi’an	0.135
2016	Dalian	0.130	Hangzhou	0.137	Qingdao	0.140	Xi’an	0.138
2017	Dalian	0.131	Hangzhou	0.140	Qingdao	0.142	Xi’an	0.142
2018	Dalian	0.131	Hangzhou	0.142	Qingdao	0.145	Xi’an	0.144
2019	Dalian	0.137	Hangzhou	0.143	Qingdao	0.147	Xi’an	0.144
2020	Dalian	0.138	Hangzhou	0.144	Qingdao	0.147	Xi’an	0.145
2011	Beijing	0.136	Harbin	0.051	Shanghai	0.130	Xining	0.107
2012	Beijing	0.139	Harbin	0.060	Shanghai	0.131	Xining	0.109
2013	Beijing	0.144	Harbin	0.065	Shanghai	0.134	Xining	0.111
2014	Beijing	0.148	Harbin	0.064	Shanghai	0.137	Xining	0.112
2015	Beijing	0.152	Harbin	0.081	Shanghai	0.139	Xining	0.113
2016	Beijing	0.158	Harbin	0.089	Shanghai	0.141	Xining	0.115
2017	Beijing	0.163	Harbin	0.098	Shanghai	0.142	Xining	0.115
2018	Beijing	0.166	Harbin	0.126	Shanghai	0.151	Xining	0.116
2019	Beijing	0.168	Harbin	0.133	Shanghai	0.152	Xining	0.117
2020	Beijing	0.168	Harbin	0.117	Shanghai	0.153	Xining	0.120
2011	Changchun	0.093	Hohhot	0.109	Shenyang	0.110	Yinchuan	0.118
2012	Changchun	0.092	Hohhot	0.112	Shenyang	0.115	Yinchuan	0.117
2013	Changchun	0.098	Hohhot	0.115	Shenyang	0.122	Yinchuan	0.119
2014	Changchun	0.103	Hohhot	0.116	Shenyang	0.123	Yinchuan	0.121
2015	Changchun	0.106	Hohhot	0.118	Shenyang	0.122	Yinchuan	0.122
2016	Changchun	0.113	Hohhot	0.118	Shenyang	0.123	Yinchuan	0.124
2017	Changchun	0.113	Hohhot	0.119	Shenyang	0.120	Yinchuan	0.126
2018	Changchun	0.120	Hohhot	0.119	Shenyang	0.116	Yinchuan	0.126
2019	Changchun	0.125	Hohhot	0.119	Shenyang	0.112	Yinchuan	0.127
2020	Changchun	0.130	Hohhot	0.123	Shenyang	0.108	Yinchuan	0.127
2011	Changsha	0.131	Jinan	0.122	Shenzhen	0.135	Zhengzhou	0.082
2012	Changsha	0.132	Jinan	0.124	Shenzhen	0.137	Zhengzhou	0.087
2013	Changsha	0.130	Jinan	0.126	Shenzhen	0.145	Zhengzhou	0.090
2014	Changsha	0.132	Jinan	0.128	Shenzhen	0.152	Zhengzhou	0.089
2015	Changsha	0.136	Jinan	0.130	Shenzhen	0.153	Zhengzhou	0.088
2016	Changsha	0.137	Jinan	0.133	Shenzhen	0.154	Zhengzhou	0.098
2017	Changsha	0.138	Jinan	0.135	Shenzhen	0.155	Zhengzhou	0.103
2018	Changsha	0.137	Jinan	0.138	Shenzhen	0.155	Zhengzhou	0.106
2019	Changsha	0.140	Jinan	0.146	Shenzhen	0.156	Zhengzhou	0.111
2020	Changsha	0.144	Jinan	0.150	Shenzhen	0.156	Zhengzhou	0.121
2011	Chengdu	0.130	Kunming	0.129	Taiyuan	0.125
2012	Chengdu	0.132	Kunming	0.130	Taiyuan	0.126
2013	Chengdu	0.131	Kunming	0.133	Taiyuan	0.127
2014	Chengdu	0.136	Kunming	0.136	Taiyuan	0.128
2015	Chengdu	0.139	Kunming	0.134	Taiyuan	0.129
2016	Chengdu	0.147	Kunming	0.136	Taiyuan	0.129
2017	Chengdu	0.149	Kunming	0.137	Taiyuan	0.130
2018	Chengdu	0.151	Kunming	0.137	Taiyuan	0.130
2019	Chengdu	0.153	Kunming	0.139	Taiyuan	0.131
2020	Chengdu	0.155	Kunming	0.141	Taiyuan	0.131
2011	Chongqing	0.102	Lanzhou	0.099	Tianjin	0.124
2012	Chongqing	0.102	Lanzhou	0.103	Tianjin	0.120
2013	Chongqing	0.105	Lanzhou	0.104	Tianjin	0.121
2014	Chongqing	0.103	Lanzhou	0.105	Tianjin	0.116
2015	Chongqing	0.111	Lanzhou	0.111	Tianjin	0.123
2016	Chongqing	0.134	Lanzhou	0.120	Tianjin	0.128
2017	Chongqing	0.138	Lanzhou	0.122	Tianjin	0.132
2018	Chongqing	0.129	Lanzhou	0.123	Tianjin	0.143
2019	Chongqing	0.123	Lanzhou	0.125	Tianjin	0.146
2020	Chongqing	0.151	Lanzhou	0.131	Tianjin	0.148
2011	Fuzhou	0.120	Nanchang	0.120	Urumqi	0.104
2012	Fuzhou	0.121	Nanchang	0.120	Urumqi	0.106
2013	Fuzhou	0.123	Nanchang	0.122	Urumqi	0.108
2014	Fuzhou	0.124	Nanchang	0.124	Urumqi	0.110
2015	Fuzhou	0.126	Nanchang	0.126	Urumqi	0.107
2016	Fuzhou	0.127	Nanchang	0.128	Urumqi	0.112
2017	Fuzhou	0.129	Nanchang	0.132	Urumqi	0.116
2018	Fuzhou	0.129	Nanchang	0.132	Urumqi	0.121
2019	Fuzhou	0.130	Nanchang	0.130	Urumqi	0.125
2020	Fuzhou	0.130	Nanchang	0.134	Urumqi	0.134
2011	Guangzhou	0.104	Nanjing	0.120	Wuhan	0.111
2012	Guangzhou	0.105	Nanjing	0.122	Wuhan	0.121
2013	Guangzhou	0.123	Nanjing	0.125	Wuhan	0.132
2014	Guangzhou	0.127	Nanjing	0.127	Wuhan	0.134
2015	Guangzhou	0.148	Nanjing	0.129	Wuhan	0.135
2016	Guangzhou	0.151	Nanjing	0.131	Wuhan	0.136
2017	Guangzhou	0.153	Nanjing	0.133	Wuhan	0.137
2018	Guangzhou	0.161	Nanjing	0.135	Wuhan	0.141
2019	Guangzhou	0.153	Nanjing	0.136	Wuhan	0.144
2020	Guangzhou	0.154	Nanjing	0.139	Wuhan	0.146
2011	Guiyang	0.101	Nanning	0.101	Xiamen	0.131
2012	Guiyang	0.105	Nanning	0.113	Xiamen	0.133
2013	Guiyang	0.109	Nanning	0.113	Xiamen	0.133
2014	Guiyang	0.113	Nanning	0.116	Xiamen	0.134
2015	Guiyang	0.112	Nanning	0.118	Xiamen	0.135
2016	Guiyang	0.116	Nanning	0.119	Xiamen	0.135
2017	Guiyang	0.122	Nanning	0.123	Xiamen	0.136
2018	Guiyang	0.125	Nanning	0.125	Xiamen	0.137
2019	Guiyang	0.127	Nanning	0.134	Xiamen	0.138
2020	Guiyang	0.132	Nanning	0.131	Xiamen	0.138

Appendix E

Table A5

The values of CCC from 2011 to 2020.

Year	City	CCC	City	CCC	City	CCC	City	CCC
2011	Dalian	0.198	Hangzhou	0.273	Qingdao	0.229	Xi’an	0.268
2012	Dalian	0.213	Hangzhou	0.287	Qingdao	0.247	Xi’an	0.283
2013	Dalian	0.229	Hangzhou	0.302	Qingdao	0.260	Xi’an	0.296
2014	Dalian	0.191	Hangzhou	0.317	Qingdao	0.278	Xi’an	0.308
2015	Dalian	0.236	Hangzhou	0.334	Qingdao	0.293	Xi’an	0.318
2016	Dalian	0.246	Hangzhou	0.362	Qingdao	0.312	Xi’an	0.334
2017	Dalian	0.280	Hangzhou	0.391	Qingdao	0.317	Xi’an	0.359
2018	Dalian	0.289	Hangzhou	0.408	Qingdao	0.335	Xi’an	0.366
2019	Dalian	0.292	Hangzhou	0.434	Qingdao	0.353	Xi’an	0.379
2020	Dalian	0.301	Hangzhou	0.441	Qingdao	0.366	Xi’an	0.427
2011	Beijing	0.557	Harbin	0.166	Shanghai	0.467	Xining	0.161
2012	Beijing	0.601	Harbin	0.187	Shanghai	0.483	Xining	0.170
2013	Beijing	0.630	Harbin	0.200	Shanghai	0.531	Xining	0.172
2014	Beijing	0.660	Harbin	0.207	Shanghai	0.554	Xining	0.180
2015	Beijing	0.699	Harbin	0.232	Shanghai	0.568	Xining	0.183
2016	Beijing	0.721	Harbin	0.249	Shanghai	0.603	Xining	0.188
2017	Beijing	0.732	Harbin	0.261	Shanghai	0.628	Xining	0.201
2018	Beijing	0.789	Harbin	0.299	Shanghai	0.661	Xining	0.222
2019	Beijing	0.834	Harbin	0.319	Shanghai	0.649	Xining	0.236
2020	Beijing	0.860	Harbin	0.301	Shanghai	0.713	Xining	0.236
2011	Changchun	0.178	Hohhot	0.228	Shenyang	0.245	Yinchuan	0.195
2012	Changchun	0.190	Hohhot	0.238	Shenyang	0.260	Yinchuan	0.198
2013	Changchun	0.196	Hohhot	0.247	Shenyang	0.265	Yinchuan	0.205
2014	Changchun	0.201	Hohhot	0.256	Shenyang	0.272	Yinchuan	0.222
2015	Changchun	0.214	Hohhot	0.264	Shenyang	0.271	Yinchuan	0.234
2016	Changchun	0.228	Hohhot	0.270	Shenyang	0.279	Yinchuan	0.239
2017	Changchun	0.233	Hohhot	0.270	Shenyang	0.287	Yinchuan	0.248
2018	Changchun	0.252	Hohhot	0.276	Shenyang	0.293	Yinchuan	0.257
2019	Changchun	0.272	Hohhot	0.278	Shenyang	0.305	Yinchuan	0.270
2020	Changchun	0.287	Hohhot	0.291	Shenyang	0.310	Yinchuan	0.279
2011	Changsha	0.259	Jinan	0.255	Shenzhen	0.339	Zhengzhou	0.215
2012	Changsha	0.268	Jinan	0.264	Shenzhen	0.360	Zhengzhou	0.220
2013	Changsha	0.275	Jinan	0.281	Shenzhen	0.386	Zhengzhou	0.230
2014	Changsha	0.286	Jinan	0.288	Shenzhen	0.398	Zhengzhou	0.252
2015	Changsha	0.304	Jinan	0.302	Shenzhen	0.423	Zhengzhou	0.252
2016	Changsha	0.318	Jinan	0.317	Shenzhen	0.459	Zhengzhou	0.274
2017	Changsha	0.328	Jinan	0.324	Shenzhen	0.475	Zhengzhou	0.303
2018	Changsha	0.341	Jinan	0.333	Shenzhen	0.500	Zhengzhou	0.316
2019	Changsha	0.358	Jinan	0.356	Shenzhen	0.548	Zhengzhou	0.346
2020	Changsha	0.382	Jinan	0.376	Shenzhen	0.579	Zhengzhou	0.367
2011	Chengdu	0.274	Kunming	0.237	Taiyuan	0.213
2012	Chengdu	0.297	Kunming	0.242	Taiyuan	0.219
2013	Chengdu	0.318	Kunming	0.254	Taiyuan	0.227
2014	Chengdu	0.329	Kunming	0.265	Taiyuan	0.243
2015	Chengdu	0.328	Kunming	0.270	Taiyuan	0.251
2016	Chengdu	0.365	Kunming	0.277	Taiyuan	0.259
2017	Chengdu	0.372	Kunming	0.283	Taiyuan	0.262
2018	Chengdu	0.385	Kunming	0.287	Taiyuan	0.273
2019	Chengdu	0.429	Kunming	0.316	Taiyuan	0.277
2020	Chengdu	0.447	Kunming	0.329	Taiyuan	0.284
2011	Chongqing	0.263	Lanzhou	0.164	Tianjin	0.300
2012	Chongqing	0.310	Lanzhou	0.169	Tianjin	0.324
2013	Chongqing	0.329	Lanzhou	0.178	Tianjin	0.337
2014	Chongqing	0.327	Lanzhou	0.197	Tianjin	0.351
2015	Chongqing	0.356	Lanzhou	0.215	Tianjin	0.381
2016	Chongqing	0.396	Lanzhou	0.235	Tianjin	0.404
2017	Chongqing	0.425	Lanzhou	0.241	Tianjin	0.402
2018	Chongqing	0.419	Lanzhou	0.259	Tianjin	0.423
2019	Chongqing	0.439	Lanzhou	0.260	Tianjin	0.438
2020	Chongqing	0.494	Lanzhou	0.272	Tianjin	0.452
2011	Fuzhou	0.233	Nanchang	0.195	Urumqi	0.193
2012	Fuzhou	0.245	Nanchang	0.212	Urumqi	0.208
2013	Fuzhou	0.258	Nanchang	0.219	Urumqi	0.199
2014	Fuzhou	0.267	Nanchang	0.227	Urumqi	0.216
2015	Fuzhou	0.282	Nanchang	0.233	Urumqi	0.228
2016	Fuzhou	0.295	Nanchang	0.240	Urumqi	0.240
2017	Fuzhou	0.302	Nanchang	0.253	Urumqi	0.249
2018	Fuzhou	0.321	Nanchang	0.265	Urumqi	0.261
2019	Fuzhou	0.331	Nanchang	0.275	Urumqi	0.284
2020	Fuzhou	0.356	Nanchang	0.289	Urumqi	0.291
2011	Guangzhou	0.351	Nanjing	0.282	Wuhan	0.280
2012	Guangzhou	0.373	Nanjing	0.303	Wuhan	0.300
2013	Guangzhou	0.414	Nanjing	0.314	Wuhan	0.314
2014	Guangzhou	0.431	Nanjing	0.333	Wuhan	0.328
2015	Guangzhou	0.477	Nanjing	0.350	Wuhan	0.337
2016	Guangzhou	0.500	Nanjing	0.367	Wuhan	0.354
2017	Guangzhou	0.526	Nanjing	0.380	Wuhan	0.364
2018	Guangzhou	0.568	Nanjing	0.406	Wuhan	0.384
2019	Guangzhou	0.599	Nanjing	0.435	Wuhan	0.410
2020	Guangzhou	0.618	Nanjing	0.458	Wuhan	0.431
2011	Guiyang	0.192	Nanning	0.195	Xiamen	0.201
2012	Guiyang	0.201	Nanning	0.212	Xiamen	0.213
2013	Guiyang	0.219	Nanning	0.211	Xiamen	0.221
2014	Guiyang	0.231	Nanning	0.217	Xiamen	0.233
2015	Guiyang	0.238	Nanning	0.224	Xiamen	0.241
2016	Guiyang	0.243	Nanning	0.224	Xiamen	0.251
2017	Guiyang	0.250	Nanning	0.237	Xiamen	0.263
2018	Guiyang	0.262	Nanning	0.277	Xiamen	0.282
2019	Guiyang	0.269	Nanning	0.293	Xiamen	0.295
2020	Guiyang	0.286	Nanning	0.298	Xiamen	0.308

Appendix F

Table A6

Abbreviations in this paper and their corresponding full forms.

Abbreviations	Corresponding Full Forms	Abbreviations	Corresponding Full Forms
CCC	Comprehensive carrying capacity	APLB	Attrition of public library books
IRCC	Innovative resource carrying capacity	NAPL	Number of newly acquired public library books
ECC	Economic carrying capacity	NHPH	Number of healthcare personnel in hospitals and health centers
PSCC	Public service carrying capacity	HE	Health expenditure
NRCC	Natural resource carrying capacity	FE	Fiscal expenditure
GDP	GDP	EPBE	Education public budget expenditure
TP	Total population	PBSD	Public budget expenditures for science and technology
BR	Birth rate	BUA	Built-up area
MR	Mortality rate	HTHW	Harmless treatment rate of household waste
PSIG	The proportion of the added value of the secondary industry in GDP	CTST	Centralized treatment rate of sewage treatment plants
PTIG	The proportion of the added value of the tertiary industry in GDP	SDES	Per-unit-value sulfur dioxide emissions in secondary industry
DIUR	Disposable income of urban residents	ESDS	Per-unit-value smoke and dust emissions in secondary industry
CHRE	Completed housing area of real estate development enterprises	NFTH	College teachers
TP	Total profit	EST	Employment in scientific research and technology services
SDB	Savings deposit balance	EISI	Employment in information transmission, software, and information technology services
TSWR	Total sales of wholesale and retail goods above quota	NRDP	Number of R&D personnel
ASIE	Average salary of urban on-the-job employees	NVIP	The number of valid invention patents
RAP	Road area proportion	RSNP	Revenue from the sales of new products
IPGS	Increment of park green space area	MBI	Main business income

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