Passengers are traversing the square in front of Beijing Railway Station. Photo: TUCHONG

The global population is currently amid a period of rapid urban concentration, made clear by statistical analyses showing an “S-curve” urbanization pattern and unprecedented levels of urbanization worldwide. In China, the population is also rapidly concentrating in central cities nationwide, which are often the nucleus of massive urban agglomerations. Policymakers have begun asking the crucial question, how do central cities efficiently balance spatial population concentrations to drive high-quality urban development and stimulate the growth of their urban agglomerations? As China endeavors to blaze a new urban development path with Chinese characteristics, this question has become increasingly relevant. 

Coordination is an elegant approach to structural issues within systems as they evolve from disorder to order. Populations are the fundamental building block for economic and social development within urban agglomerations. Existing research has not sufficiently focused on the structural issues, internal mechanisms, and evolutionary changes of population development within urban agglomerations, an essential prerequisite for a systemic understanding of the constraints on coordinated development. The urban agglomeration systems in China, along with their evolutionary processes, meet the conditions for applying coordination theory—they are open, without equilibrium, and without linearity. Therefore, coordination theory can provide a theoretical foundation for researchers to study the issue of central cities driving coordinated population development within urban agglomerations.

Coordination theory

This study, based on coordination theory, seeks to identify the internal driving forces behind coordinated population development in urban agglomerations through the analysis of coordination mechanisms, servo mechanisms, and self-organization mechanisms. It constructs an analytical framework for urban agglomeration population coordination, focusing on the dimensions of competition, leadership, and aggregation.

First, the coordination mechanism identifies the type of coordinated relationship between central cities’ population systems and those of other cities within the urban agglomeration. This outlines the pattern of population coordination relationships within a central city’s urban agglomeration.

Second, the servo mechanism refers to the dominant subsystem for structural changes within the entire urban agglomeration system, which governs other subsystems. This mechanism governs how these other subsystems comply with the dominant subsystem.

Third, the self-organization mechanism describes how, under the influence of competition and cooperation, subsystems transition from disorder to order through self-organization.

Data and methodology

Based on data from China’s seven population censuses, this article analyzes the relationships, dominance, hierarchical patterns, and evolutionary trends of China’s central cities, which have coordinated population development across urban agglomerations from 1953 to 2020. By using central cities as anchors we can understand the basic framework for the high-quality development of urban agglomerations from the perspective of population coordination efficiency.

In constructing the spatial population coordination network system for urban agglomerations, past research has focused on the relationship between the population and resources, economy, and other dimensions, leaving a gap in research on the interactions within internal population subsystems. Therefore, this article draws from the traditional “gravity model” to review interactions between two regions, adding “direction” as a supplementary dimension, to construct a “directed population gravity matrix network” for urban agglomerations. This network will be used to study coordinated population interactions among the subsystems of cities within urban agglomerations.

Upon constructing the spatial population coordination network for urban agglomerations surrounding central cities, the article analyzes the evolutionary mechanisms of interactions among the internal subsystems and the formation mechanisms of self-organizing structures. To accomplish this, this article primarily uses Social Network Analysis (SNA) methods and Ucinet 6.0 software to calculate the centrality degree of the directed population gravity matrix for China’s urban agglomerations, thereby determining the function and status of central cities within coordinated development.

Two types of relationships

Overall, the dominant role of national central cities is prominent in population concentration and this trend is growing stronger. Population coordination relationship patterns of central cities nested within urban agglomerations can be divided into two types.

The first type is the “strong competitive-cooperative relationship,” which refers to Beijing, Tianjin, Shanghai, and Guangzhou. As central cities’ gravitational effect on the population strengthens within urban agglomerations, other cities in each urban agglomeration have an intensified gravitational pull towards these central cities. This means that while the population dominance of central cities is rising within the urban agglomerations, there are other strong competitive or cooperative counterparts growing within the urban agglomeration, causing the central city to also experience increasing population coordination effects.

The second type is the “weak competitive-cooperative relationship.” This includes Chengdu, Chongqing, Xi’an, Wuhan, and Zhengzhou. As central cities’ gravitational effect on the population strengthens within urban agglomerations, other cities experience minor fluctuations in their gravitational pull towards central cities. This suggests that while the population density of central cities is rising within the urban agglomeration, the relationship between other cities and the central cities is weak in competition and cooperation. Therefore, changes to population concentrations in these other cities do not significantly impact the population density of central cities.

Based on the calculated centrality degree of central cities, within their respective urban agglomeration population systems, we can compare the out-degree and in-degree centrality values to see that some central cities were not initially in a dominant position. Instead, their out-degree centrality values grew over time, transforming them from weak to strong, thereby establishing a strong leading position in population coordination within the urban agglomeration. Xi’an and Zhengzhou are typical examples of such growth trajectory.

In contrast, other central cities, such as Beijing and Shanghai, started with out-degree centrality values significantly higher than their in-degree centrality values, maintaining and even expanding their dominant positions.

Through comparing the out-degree and in-degree centrality values of different central cities across various population censuses, we can identify the population dominance patterns within their urban agglomerations:

Strong-strong sustained dominance type: Cities like Beijing, Shanghai, and Guangzhou have strong competitive-cooperative relationships with other subsystems in their urban agglomeration. Their out-degree centrality values consistently exceed their in-degree centrality values in each census, indicating a consistently strong dominant role in population coordination.

Strong-strong growing dominance type: Tianjin, though in a strong competitive-cooperative relationship, shows a growing strong dominant role in population coordination, evolving from a weak to a strong dominant position.

Weak-strong sustained dominance type: Cities such as Chongqing, Chengdu, and Wuhan have weak competitive-cooperative relationships with other subsystems within the urban agglomeration, yet they maintain a sustained strong dominant role in population coordination.

Weak-strong growing dominance type: Xi’an and Zhengzhou also have weak competitive-cooperative relationships but show a growing strong dominant role in population coordination, evolving from weak to strong dominant positions.

The hierarchical relationship patterns between central cities and the strong competitive-cooperative dominant cities within their urban agglomerations are primarily characterized by non-symmetric relationships. This means that population coordination layers, formed by central cities, exert a dominant influence over the population coordination layers of relevant strong competitive-cooperative cities.

Balanced development

For central cities, the effectiveness of coordination depends on the dominant power of the central city, the competitive-cooperative power of non-central cities, and the aggregate power of the urban agglomeration network. These three forces reflect the economic strength, development vitality, and expansive capacity of a city. The relative balance between competitive-cooperative power and aggregate power is crucial for the overall development of urban agglomerations. Excessive dominance and insufficient competitive-cooperative power may hinder the evolution and sustainable development of urban agglomerations.

First, the development of the collaboration effect of central cities is not solely determined by their population development characteristics but depends on the interplay between central cities, non-central cities, and the aggregate power of the urban agglomeration network. When these three forces collectively exhibit a strengthening trend, favorable conditions for the overall efficiency of urban agglomeration population development will emerge.

Second, the interaction between excessively high dominance of the central city and excessively low competitive-cooperative power of non-central cities can inhibit the sustainable development and evolution of the urban agglomeration.

China’s central cities demonstrate different efficiency characteristics in urban agglomeration development as circumstances vary regionally. “Task-oriented” central cities have a clear dominant position, with non-central cities exhibiting strong dependency and support relationships. For example, Beijing, Shanghai, and Guangzhou play significant roles in regional development strategies and undertake major strategic tasks.

However, “task-oriented” central cities face unique challenges. Their strong attractiveness can lead to an excessive concentration of critical functions, causing resources to skew and widening regional disparities. Also, continuous population growth in central cities may result in “urban diseases” such as traffic congestion, environmental pollution, and increased housing pressure.

“Growth-oriented” central cities have room for development in their relationships with non-central cities, such as Xi’an, Zhengzhou, Chongqing, Chengdu, and Wuhan. These central cities have weak competitive-cooperative relationships with other cities in their urban agglomerations, and stable collaborative divisions of labor have yet to form. Under these circumstances, the population development of urban agglomerations displays functional decentralization, and the coordination and complementarity among these cities need to be improved.

“Growth-oriented” central cities require substantial investment to enhance the development capabilities of non-central cities, including infrastructure construction, talent cultivation, and innovation support, which imposes higher demands on central cities and relevant government departments.

“Relationship-oriented” central cities exist in scenarios where multiple central cities have relatively weaker dominance and may even have strong competitive-cooperative relationships with other central cities, such as Tianjin. In such cases, the population development of urban agglomerations presents a complex relational network, necessitating careful handling of competitive and cooperative relationships among cities. Collaboration among multiple central cities requires the establishment of effective cooperation mechanisms and communication platforms to promote information sharing and resource integration.

Yin Deting (professor) and Zhao Zheng are from the Beijing Administration Institute; Shi Yi is from the China Population and Development Research Center.