Spatiotemporal Dynamics and Drivers of Shipping Service Industry Agglomeration and Port–City Synergy: Evidence from Jiangsu Province, China

Abstract

The shipping service industry plays a pivotal role in enhancing port competitiveness and fostering urban economic growth, yet limited studies systematically integrate its spatial temporal dynamics with the processes driving port–city synergy. This study constructs a three-dimensional analytical framework encompassing port operations, urban economic development, and shipping service industry agglomeration. Using data from 13 port cities in Jiangsu Province (2015–2023), we apply the entropy weight method, coupling coordination degree model, relative development model, and panel Tobit regression to evaluate interaction intensity, coordination patterns, and influencing factors. Results reveal a clear spatial gradient in coupling coordination, higher in southern Jiangsu and lower in the north, driven by disparities in economic foundations, port capacities, and service industry structures. In most cities, port operations and urban economies lag behind shipping service industry agglomeration, reflecting the predominance of low- and mid-end services. Port construction level, cargo and container throughput, economic development, openness, fixed asset investment, and population density significantly promote coordination, whereas R&D capacity shows no significant effect. The findings advance understanding of port–city service interlinkages and provide targeted policy recommendations for differentiated regional development, infrastructure enhancement, and upgrading toward high-end shipping services, with implications for maritime regions worldwide.

1. Introduction

The shipping service sector stems from the core functions of ports and maritime transport and has continually expanded and evolved within the context of global production and trade cooperation. Its rapid development has driven a transformation in port functions from the traditional focus on cargo handling to a diversified portfolio encompassing commerce, logistics, finance, and other services, significantly enhancing the overall competitiveness of both ports and port cities. At the national level, President Xi Jinping has emphasized that “a strong economy must also be a strong maritime and shipping power,” emphasizing the strategic worth of the ocean economy and the shipping industry in national development. Recent policy directives on accelerating the high-quality development of the modern shipping service industry further emphasize that it is necessary to optimize infrastructure systems, cultivate high-end shipping services such as maritime finance and insurance, and strengthen institutional support to enhance the sector’s role in industrial upgrading, international trade, and urban competitiveness. Extensive research has been conducted both domestically and internationally on port–city coordinated development. Existing studies have demonstrated that ports play a substantial role in promoting urban economic growth, with the coupling coordination degree commonly used as a metric to assess the level of coordination between ports and cities [1,2,3,4,5]. As the core hubs for external trade, ports ensure material supply, facilitate international exchange, and stimulate the agglomeration of shipping service enterprises, thereby generating industrial clustering effects [6,7]. Consequently, the shipping service industry has gained increasing attention, particularly in studies examining its spatiotemporal distribution and influencing factors. Scholars such as Beaverstock [8], Taylor [9], and Wang Liehui [10,11] have focused on global hub cities, exploring the spatial distribution, network structures, and evolutionary trends of shipping services. Wang Liehui [12] further analyzed the spatial patterns of low-, medium-, and high-end shipping services in the Yangtze River Delta, while Jiang Shuhua [13] employed hotspot analysis to describe the agglomeration characteristics of Shanghai’s shipping service clusters. In relation to influencing factors, Fan Qi [14] investigated Guangzhou’s shipping service sector using co-integration and Granger causality tests; Wang Wei [15] examined the contribution of high-end shipping services to symbiotic industrial clusters in Tianjin; and Sun Dehong [16] applied Bayesian ridge regression to quantify the drivers of shipping enterprise agglomeration along the Yangtze River Basin. Research on the port–city relationship and the maritime service economy further demonstrates that ports and urban economies develop through mutually reinforcing mechanisms involving logistics, trade, and industrial linkages. Studies employing coupling coordination and spatial interaction models show that port prosperity can significantly reshape urban industrial structures, while strong urban economic capacity supports port modernization and trade expansion [17,18]. Meanwhile, research on maritime producer and shipping services highlights their agglomeration in global hubs such as Rotterdam, Singapore, and Shanghai, revealing that the upgrading of high-end service functions, including maritime finance, insurance, and digital logistics, enhances both port efficiency and urban innovation capacity [19,20]. Despite these insights, most existing studies adopt a single analytical perspective, focusing either on the spatiotemporal evolution of the shipping service industry or on the determinants of its agglomeration, while lacking a systematic analysis of the synergistic relationship among the agglomeration of shipping services, port operations, and urban economic development. From a systems perspective, the agglomeration of shipping services enhances port efficiency through specialized, value-added functions; in turn, ports drive urban economic growth through their hub role; and dynamic urban economies through talent, capital, and market demand feed back into the further development of shipping services. Together, these elements form a closed-loop mechanism of “service empowerment hub-driven growth—resource feedback.” Yet, most studies have not quantitatively examined this three-way interaction across both temporal and spatial dimensions, or explicitly identified the driving forces behind such coordination.

To bridge this gap, the present study develops an integrated analytical framework that combines the coupling coordination model, the relative development model, and the panel Tobit model to quantitatively assess the interaction intensity (coupling degree) and coordination level (coordination degree) among the three systems. Taking 13 port cities in Jiangsu Province as the research objects, the study conducts an empirical analysis using data on shipping services, ports, and socio-economic indicators from 2015 to 2023. Jiangsu Province is selected as a representative case because it encompasses highly developed southern cities and relatively underdeveloped northern areas, providing a rich context to examine disparities in shipping service agglomeration and port–city coordination.

According to the Jiangsu Statistical Yearbook 2023, the province maintains over 168,000 km of highways, including more than 5000 km of expressways, placing it among the top three provinces nationwide. Its coastal and inland ports handled more than 1.1 billion tons of cargo in 2022, accounting for over 10% of China’s total coastal port throughput, while container volumes exceeded 25 million TEU, illustrating a highly developed port infrastructure system. Jiangsu also features a well-balanced urban hierarchy composed of 13 prefecture-level cities, including major metropolitan centers such as Nanjing and Suzhou, alongside medium-sized cities such as Yangzhou, Taizhou, Yancheng, and Lianyungang. This combination of infrastructure strength and balanced city scale provides an ideal setting for analyzing port–city-service coordination [21].

The objectives of this study are threefold: (1) to reveal the spatiotemporal evolution characteristics of the coupling coordination degree between port operations, urban economy, and shipping service industry agglomeration; (2) to identify the relative development status of each subsystem across different cities; and (3) to explore the key drivers influencing coordination levels, thereby providing theoretical and policy insights for promoting balanced and high-quality development in maritime regions.

2. Study Area and Methods

2.1. Study Area

Jiangsu Province is located on China’s eastern coast and comprises 13 prefecture-level cities, forming a well-balanced and functionally complementary urban system. The southern region including Nanjing, Suzhou, Wuxi, and Changzhou is characterized by strong manufacturing and service sectors, high population density, and advanced infrastructure. The central and northern cities such as Yangzhou, Taizhou, Nantong, Lianyungang, Yancheng, Huai’an, Suqian, and Xuzhou exhibit greater heterogeneity, with a combination of coastal industrial hubs, inland transport nodes, and emerging urban economies. This north–south gradient in development provides a natural context for examining differences in port–city-service coordination.

Jiangsu possesses an extensive and diversified port system that includes major coastal ports such as Lianyungang Port, Yancheng Port, and Nantong Port, along with major inland river ports along the Yangtze River, including Nanjing, Zhenjiang, Suzhou, and Taizhou. According to the Jiangsu Statistical Yearbook 2023, the province’s total port cargo throughput exceeded 1.1 billion tons in 2022, more than 10% of China’s coastal port traffic—while container throughput surpassed 25 million TEU, reflecting its position as one of the nation’s leading maritime regions. Jiangsu also maintains over 168,000 km of highways, including more than 5000 km of expressways, placing it among the top provinces nationwide in transport infrastructure density. These conditions underpin the region’s high capacity for port–city interaction and the clustering of shipping service activities.

In terms of territorial governance, Jiangsu implements integrated port and regional planning under the Jiangsu Coastal Development Strategy, the Yangtze River Economic Belt Plan, and the Integrated Development of the Yangtze River Delta Initiative. These policies promote functional differentiation among ports, encourage shipping service clustering, and support coordinated port–city development through unified industrial layout planning, multimodal transport integration, and service-industry upgrading. This institutional environment provides a suitable foundation for analyzing the interaction among port operations, urban economic development, and the shipping service industry.

2.2. Data Sources

This study employs annual panel data for 13 port cities in Jiangsu Province over the period 2015–2023. The selection of this time range is based on two considerations. First, 2015 marks the beginning of unified and standardized provincial statistical reporting for shipping service indicators, port operational data, and key socio-economic metrics in Jiangsu, ensuring consistency and comparability across cities. Second, this period coincides with several major national and provincial strategies, such as the Yangtze River Economic Belt, the Integrated Development of the Yangtze River Delta, and the Modern Shipping Service Industry Development Plan, making it a meaningful interval for capturing the spatiotemporal evolution of port–city–service coordination.

Data related to the shipping service industry were obtained from multiple authoritative sources, including the membership directories of the China Shipowners’ Association, the China Shipping Bluebook, and the International Federation of Freight Forwarders Associations. These datasets were supplemented with enterprise-level information from widely used commercial and regulatory databases, such as Qichacha, AiQicha, the National Enterprise Credit Information Publicity System, and the Trademark Office of the National Intellectual Property Administration, providing detailed records on enterprise registration, business type, and operational status.

Data on port operations, including cargo throughput, container throughput, berth capacity, and multimodal transport indicators, were sourced from the Jiangsu Provincial Department of Transport, the China Port Statistical Yearbook, and the Compendium of National Transportation Statistics, ensuring consistency in measurement across cities and years. Indicators related to urban economic development, such as GDP, industrial output, foreign trade volume, and R&D personnel, were collected from the Jiangsu Statistical Yearbook, the China City Statistical Yearbook, and the Statistical Bulletin of National Economic and Social Development of each city. Additional science and technology indicators were sourced from the Jiangsu Statistical Yearbook, which provides annual data on personnel engaged in scientific research and related activities. All datasets were systematically cross-validated to ensure completeness and accuracy. For variables with missing values for specific years, linear interpolation based on adjacent-year observations was applied to maintain continuity in the time series. The overall data acquisition, integration, and cleaning procedures are illustrated schematically in Figure 1, which outlines the multiple data sources for each subsystem and the subsequent processing steps.

2.3. Construction of the Evaluation Index System

To quantitatively evaluate the interaction among port operations, urban economic development, and the agglomeration level of the shipping service industry, this study constructs a multi-dimensional evaluation index system. The design follows the principles of systematic integrity, scientific validity, representativeness, and data availability, drawing on established domestic and international literature [16,22,23,24,25] while adapting to the developmental characteristics of Jiangsu Province. The evaluation system is divided into two main subsystems: (1) the port operations and urban economy subsystem, and (2) the shipping service industry agglomeration subsystem.

2.3.1. Evaluation Index System for Port Operations and Urban Economic Development

To quantitatively capture the multidimensional characteristics of port operations and urban economic development, this study constructs a hierarchical evaluation index system consisting of three levels: system, category, and indicator. The port operations subsystem reflects the physical foundation, service capability, and operational performance of ports, while the urban economic development subsystem represents the scale, structure, openness, and innovation capacity of the regional economy. Each subsystem is divided into functional categories, for example, port infrastructure and logistics efficiency for port operations, and economic scale, industrial structure, openness, and innovation capability for urban development.

Within each category, specific indicators are selected to provide measurable representations of subsystem performance. These indicators are classified as positive (+) or negative (−) depending on whether higher values enhance or hinder subsystem outcomes. Positive indicators include cargo throughput, berth capacity, GDP per capita, and foreign trade volume, while negative indicators reflect characteristics such as energy consumption per unit of output. The designation of indicator attributes follows established standards in port–city coordination studies and was verified through literature comparison and expert consultation.

This hierarchical structure ensures that the index system offers a comprehensive reflection of both the physical and economic dimensions of port–city development. Indicators such as port cargo throughput, container handling efficiency, GDP, industrial output, foreign trade volume, and R&D personnel together provide a holistic basis for evaluating the interaction between port operations and urban economic performance.

Table 1. Evaluation index system for port operation and urban development.

System	Category Level	Indicator Level	Indicator Attribute
Port Operations	Infrastructure	Length of operational berths (m)	+
		Number of berths	+
		Number of 10,000-ton-class berths	+
	Transport Services	Cargo throughput (10,000 tons)	+
		Foreign trade cargo throughput (10,000 tons)	+
		Container throughput (10,000 TEUs)	+
		Foreign trade container throughput (10,000 TEUs)	+
Urban Economy	Economic Scale	GDP (100 million yuan)	+
		GDP per capita (yuan)	+
		Total fixed asset investment (100 million yuan)	+
	Industrial Structure	Industrial added value (100 million yuan)	+
		Balance of deposits in financial institutions (100 million yuan)	+
	Domestic and Foreign Trade	Total retail sales of consumer goods (100 million yuan)	+
		Total value of foreign trade (100 million USD)	+

summarizes the complete system–category–indicator structure and the corresponding data sources.

2.3.2. Evaluation Index System for the Agglomeration Level of the Shipping Service Industry

Following existing classifications of the shipping service industry [16], enterprises are categorized into three tiers: low-end—transportation and warehousing; mid-end— agency and technical services; high-end—financial, insurance, and information services. To measure agglomeration, the Location Quotient (LQ) method [26,27] is employed. The LQ reflects the degree to which a city’s shipping service industry is concentrated relative to the provincial average, capturing both regional specialization and relative competitiveness. A value of LQ > 1 indicates that the industry is more concentrated than the provincial average, suggesting a comparative advantage in agglomeration. Conversely, LQ < 1 denotes a relative disadvantage. The formula for calculating the Location Quotient is shown in Equation (1):

$$s_i={\displaystyle \frac{q_i/Q_i}{{\displaystyle {\sum }_i{q}_i}/{\displaystyle {\sum }_i{Q}_i}}}$$

(1)

In the formula, $s_i$ represents the Location Quotient (LQ) index of the shipping service industry in city $i$; $q_i$ denotes the number of shipping service enterprises in city $i$; $Q_i$ stands for the number of enterprises in the transportation, warehousing, and postal sectors in city $i$; ${\displaystyle {\sum }_i{q}_i}$ is the total number of shipping service enterprises in the entire province; and ${\displaystyle {\sum }_i{Q}_i}$ indicates the total number of enterprises in the transportation, warehousing, and postal sectors across the province. In addition to the overall LQ analysis, this study separately computed the LQ for high-end shipping services to examine their relative scarcity and concentration patterns across cities.

2.4. Research Methods

This study combines multiple quantitative approaches to comprehensively examine the interaction among port operations, urban economic development, and shipping service industry agglomeration in Jiangsu Province. The methodological framework integrates four components: (1) the entropy method for indicator weighting, (2) the coupling coordination degree model to assess system synergy, (3) the relative development model to identify subsystem disparities, and (4) the panel Tobit model to analyze influencing factors.

2.4.1. Entropy Method

Given the dimensional heterogeneity of the evaluation indicators, the range standardization method is first applied to eliminate the effects of different measurement units. The entropy method, widely used in multi-criteria evaluation [4,28], is then employed to determine objective weights for each indicator based on the information content of the data. This ensures that indicators with greater variability across cities and years exert a proportionally larger influence on the composite index.

2.4.2. Coupling Coordination Degree Model

This study treats port operations, urban economy, and shipping service industry agglomeration as three subsystems, and utilizes the coupling coordination degree model to analyze the coupling and coordination relationship between shipping service industry agglomeration and port–city development. The CCD model was selected because it effectively quantifies the degree of interaction and balance among interrelated subsystems, which aligns with the triadic nature of the port–city services framework [22].

The formula for calculating the coupling degree is as follows:

$$C={\left[{\displaystyle \frac{{\prod }_{i=1}^n{U}_i}{{\left(\frac{1}{n}{\sum }_{i=1}^n{U}_i\right)}^n}}\right]}^{1/n}$$

(2)

In the formula, $C$ represents the coupling degree among the port operations urban economy shipping service industry agglomeration systems. A larger value of $C$ indicates a stronger interaction between the subsystems, while a smaller value indicates weaker interaction. The classification criteria for the coupling degree are detailed in

Table 2. Types of coupling classification.

Coupling Degree	Coupling Stage
$C=0$	Disordered
$0<C\le 0.3$	Low-level Coupling
$0.3<C\le 0.5$	Antagonistic
$0.5<C\le 0.8$	Transitional (Adjustment)
$0.8<C<1.0$	High-level Coupling
$C=1.0$	Ordered

.

Where U_i denotes the normalized development index of subsystem i. Normalization (min–max scaling) was applied to ensure comparability across indicators. This generalized form reduces to the three-subsystem expression used previously when n = 3 and facilitates replication and extension of our approach to other multi-subsystem studies [27].

The comprehensive development level is calculated by the following formula:

$$T=\alpha U_1+\beta U_2+\gamma U_3$$

(3)

In the formula, $T$ represents the comprehensive evaluation index of the “port operations, urban economy, shipping service industry agglomeration” system; $\alpha$ and $\beta$ are undetermined coefficients, which are assigned equal weights in this study, $\alpha =\beta =\gamma =1/3$.

The coupling coordination degree is calculated as follows:

$$D=\sqrt{\left(C\times T\right)}$$

(4)

In the formula, $D$ denotes the coupling coordination degree of the “port operations, urban economy, shipping service industry agglomeration” system. A larger $D$ value indicates a more orderly coordinated development among the subsystems, whereas a smaller value indicates a more disordered state. The classification criteria for the coupling coordination degree intervals are detailed in

Table 3. Intervals and levels of coupling coordination.

Coupling Coordination Degree	Coordination Type
$0\le D<0.2$	Severe Disorder
$0.2\le D<0.4$	Approaching Disorder
$0.4\le D<0.5$	Barely Coordinated
$0.5\le D<0.6$	Moderate Coordination
$0.6\le D<0.8$	Good Coordination
$0.8\le D<1.0$	Excellent Coordination

.

2.4.3. Relative Development Model

To better evaluate the relative development levels of port operations, urban economy, and shipping service industry agglomeration across different cities, this study introduces the Relative Development Model [26] for measurement. The formula is as follows:

$$E_1=U_1/U_3$$

(5)

$$E_2=U_2/U_3$$

(6)

In the formula, $E_1$ and $E_2$ represent the relative development degrees of port operations and urban economy with respect to the shipping service industry agglomeration, respectively. $0<E_1\le 0.8$ and $0<E_2\le 0.8$ indicate that port operations and urban economic development lag behind the shipping service industry agglomeration. $0.8<E_1\le 1.2$ and $0.8<E_2\le 1.2$ denote that port operations and urban economic development are basically coordinated with the shipping service industry agglomeration. $E_1>1.2$ and $E_2>1.2$ signify that the shipping service industry agglomeration lags behind port operations and urban economic development, respectively.

2.4.4. Panel Tobit Model

The coupling coordination relationship between shipping service industry agglomeration and port–city development is influenced by multiple factors. To better investigate the driving forces behind this coupling coordination, this study draws on existing research [11,16,23], and the actual development context of Jiangsu Province is shown in

Table 4. Selection of driving factors for the coupled coordination degree.

Category	Selected Driving Factor Indicator	Rationale for Selection
Port Construction Level	Number of Berths ($v_1$)	Reflects the level of port infrastructure construction
Port Operation Level	Cargo Throughput ($v_2$)	Reflects port scale and service capacity
	Container Throughput ($v_3$)
Economic Development Level	Per Capita GDP ($v_4$)	Reflects the degree of factor input
Degree of Openness	Foreign Trade Volume ($v_5$)	Reflects the level of economic openness
Government Regulation	Total Fixed Asset Investment ($v_6$)	Reflects social development guarantees
Service Industry Resource Agglomeration	Resident Population ($v_7$)	Reflects population concentration scale
	Proportion of Tertiary Industry Employment ($v_8$)	Reflects employment absorption capacity
Science and Technology	Proportion of R&D Personnel in Each City to Provincial Total ($v_9$)	Reflects the level of technological innovation

. The coupling coordination degree is taken as the dependent variable, while berth quantity, cargo throughput, container throughput, per capita GDP, foreign trade volume, total fixed asset investment, resident population, proportion of tertiary industry employees, and proportion of scientific research personnel are selected as independent variables [29,30]. A panel Tobit model is employed to conduct regression analysis on these driving factors.

$$Y=a_0+{\displaystyle \sum _{i=1}^9{a}_i{v}_i+\epsilon }$$

(7)

In the formula, $Y$ represents the coupling coordination degree of the “port operations, urban economy, shipping service industry agglomeration” system; $a_0$ is the constant term; $a_i$ denotes the regression coefficient of the variable $v_i$; and $\epsilon$ is the random error term. The coordination degree (D) was used as the dependent variable and is bounded within [0, 1]. Since D is a limited dependent variable, the Tobit model provides unbiased parameter estimation by accounting for this upper and lower censoring.

3. Empirical Analysis

3.1. Coupling Degree Analysis

Using the coupling model for the “port operations–urban economy–shipping service agglomeration” system, we observe pronounced inter-city heterogeneity alongside generally high interaction intensity. As summarized in

Table 5. Coupling degree among port operations, urban economy and shipping service industry agglomeration.

Year	Nanjing	Wuxi	Xuzhou	Changzhou	Suzhou	Nantong	Lianyungang	Huaian	Yancheng	Yangzhou	Zhenjiang	Taizhou	Suqian
2015	0.9774	0.9488	0.8463	0.7707	0.8377	0.9661	0.4442	0.8657	0.7991	0.8430	0.9829	0.6051	0.2528
2016	0.9718	0.9415	0.8345	0.7677	0.8315	0.9783	0.4886	0.9297	0.8454	0.8758	0.9770	0.6298	0.3947
2017	0.9539	0.9009	0.9128	0.7278	0.9704	0.8479	0.4054	0.8194	0.6573	0.7941	0.9390	0.6664	0.2816
2018	0.9647	0.8499	0.9005	0.7449	0.9870	0.8763	0.4594	0.8892	0.7173	0.8941	0.9215	0.6797	0.3134
2019	0.9676	0.8440	0.9040	0.7470	0.9894	0.8689	0.4692	0.8878	0.7158	0.8852	0.9012	0.6913	0.3551
2020	0.9617	0.9521	0.8721	0.7427	0.9855	0.8592	0.4645	0.9534	0.7152	0.8760	0.8950	0.6815	0.4065
2021	0.9673	0.9473	0.8533	0.7338	0.9619	0.8418	0.4584	0.9813	0.7715	0.8843	0.9143	0.7308	0.4487
2022	0.9532	0.9552	0.8412	0.7030	0.9599	0.8342	0.4234	0.9644	0.7600	0.8727	0.9187	0.7219	0.4607
2023	0.9534	0.9488	0.8246	0.6755	0.9559	0.8324	0.4337	0.9399	0.7557	0.8750	0.9137	0.6905	0.5060
Average	0.9634	0.9209	0.8655	0.7348	0.9421	0.8784	0.4497	0.9145	0.7486	0.8667	0.9293	0.6774	0.3799

, most cities maintain C > 0.80 across the study period, indicating a high-level coupling stage and a relatively stable three-system linkage. Spatially, a clear south–north gradient emerges: core southern cities such as Nanjing, Wuxi, Suzhou, and Zhenjiang sustain consistently high coupling, reflecting strong port capacity and mature service ecosystems. This pattern is broadly consistent with observations in global port systems such as Rotterdam and Hamburg, where advanced port operations co-evolve with diversified service clusters to sustain high coordination levels [17,19].

Nantong also performs well but exhibits slightly higher year-to-year variability, suggesting sensitivity to shifts in throughput and service-sector composition. In contrast, Changzhou, Yancheng, and Taizhou cluster in an adjustment band, meaning improvements in either port operations or urban economic support may be required to fully translate service-sector gains into systemic synergy. Lianyungang and Suqian remain persistently low, with antagonistic or weakly interactive dynamics in multiple years. This aligns with findings from global hub-port studies such as Shanghai, where weak service specialization has been shown to constrain the transition toward higher-order maritime functions [13,20].

Temporally, fluctuations are modest for high-performing cities (Nanjing, Wuxi, Suzhou), whereas mid-tier cities show periods of improvement followed by plateaus, implying heightened sensitivity to changes in fixed-asset investment and industrial structure. This developmental trajectory resembles the experience of Singapore and Hong Kong, where sustained coordination between ports and producer services required long-term institutional strengthening and continuous upgrading of advanced maritime services [19]. Notably, Huai’an shows marked improvement after 2020, consistent with enhanced port–city integration efforts, while Xuzhou peaks in 2017–2019 and then declines, hinting at structural constraints in its port-service interface.

Overall, the results confirm a robust but uneven interaction structure among the three subsystems. Cities with strong infrastructure, diversified service bases, and stable industrial systems show sustained high coupling, similar to international patterns in mature port–city regions. Meanwhile, structurally weaker cities demonstrate slower improvement and greater volatility, underscoring the need for differentiated and targeted policy interventions. This aligns with comparative international studies showing that coordinated port-service evolution depends heavily on institutional capacity, service specialization, and sustained investment [17,19,20].

The heat map in Figure 2 further illustrates the spatial and temporal distribution of the coupling degree from 2015 to 2023. Darker shades correspond to stronger subsystem interaction, with the southern cluster maintaining consistently deep tones similar to strong port–service synergy observed in global cases such as Rotterdam and Singapore. Mid-range cities including Nantong, Yangzhou, and Huai’an show moderate coupling, with Huai’an strengthening substantially post-2020. More variability is observed in Changzhou, Yancheng, and Taizhou, reflecting weaker structural momentum. The lightest shades remain concentrated in Lianyungang and Suqian, where long-term limitations in shipping service concentration and port–city coordination mirror patterns observed in lagging secondary ports internationally. Temporal patterns indicate that high-performing cities maintain stability while low-performing cities exhibit slow but gradual improvement, confirming a persistent yet uneven coupling structure across the province.

3.2. Analysis of Coupling Coordination Development

3.2.1. Temporal Evolution of Coupling Coordination Degree

The coupling coordination degree of the “port operations–urban economy–shipping service industry agglomeration” system for the 13 cities in Jiangsu Province is presented in

Table 6. Coupling and coordination among port operation, urban economy and shipping service industry agglomeration.

City	2015	2016	2017	2018	2019	2020	2021	2022	2023	Average
Nanjing	0.7286	0.7219	0.7722	0.7820	0.7939	0.7733	0.7247	0.7143	0.7092	0.7467
Wuxi	0.5619	0.5551	0.6352	0.6545	0.6456	0.5743	0.5508	0.5878	0.5956	0.5956
Xuzhou	0.3293	0.3405	0.3652	0.3575	0.3661	0.3422	0.3352	0.3333	0.3286	0.3442
Changzhou	0.3694	0.3511	0.4022	0.4214	0.4317	0.4270	0.4199	0.4108	0.4085	0.4002
Suzhou	0.8289	0.8300	0.9468	0.9658	0.9769	0.9721	0.9401	0.9332	0.9289	0.9248
Nantong	0.5904	0.5955	0.6540	0.6567	0.6745	0.6937	0.6767	0.6703	0.6624	0.6527
Lianyungang	0.4837	0.4553	0.4494	0.4519	0.4658	0.4586	0.4436	0.4383	0.4562	0.4559
Huaian	0.3222	0.3282	0.3701	0.3712	0.3550	0.3232	0.3014	0.3038	0.3020	0.3308
Yancheng	0.4024	0.4093	0.4496	0.4380	0.4537	0.4456	0.4492	0.4458	0.4452	0.4376
Yangzhou	0.4084	0.3984	0.4265	0.3962	0.4048	0.4107	0.3966	0.4028	0.4122	0.4063
Zhenjiang	0.3909	0.3903	0.4320	0.4160	0.4333	0.4329	0.4175	0.4099	0.4101	0.4148
Taizhou	0.4952	0.5107	0.5349	0.5447	0.5536	0.5459	0.5354	0.5395	0.5507	0.5345
Suqian	0.1764	0.1612	0.1614	0.1508	0.1415	0.1392	0.1379	0.1404	0.1437	0.1503
Average	0.4683	0.4652	0.5076	0.5082	0.5151	0.5030	0.4869	0.4869	0.4887

. Based on the thresholds in Table 3, clear inter-city differences emerge. Suqian remains in severe imbalance throughout the study period, while Xuzhou and Huai’an hover close to imbalance. In contrast, cities such as Suzhou, Nanjing, and Nantong fall within various categories of coordinated development. Suzhou reaches a high-quality coordination stage, reflecting its diversified economy and well-developed maritime service base. Nanjing and Nantong exhibit benign coordination, while Wuxi, Yangzhou, and Zhenjiang generally maintain moderate coordination levels. Most cities achieve values above 0.40, indicating that the three subsystems port operations, shipping service agglomeration, and urban economy interact positively and reinforce one another.

The strong performance of southern cities mirrors patterns observed in globally competitive port–city regions such as Rotterdam and Singapore, where robust port infrastructure, diversified service sectors, and continuous innovation support stable port–city integration [17,19]. Conversely, cities like Nantong and Yancheng, with more specialized industrial structures, show greater sensitivity to policy-driven investments, resembling the developmental path of emerging port regions where reliance on limited sectors constrains long-term coordination [20]. The persistent imbalance in Suqian and the fluctuating performance of Xuzhou parallel findings from international cases where insufficient service specialization or weak port–city institutional alignment hinder the transition toward higher-level coordination, as documented in studies of Shanghai’s maritime cluster [13,20].

Temporally, the evolution between 2015 and 2023 exhibits a pattern of initial growth followed by slight decline. Coordination peaked during 2017–2019, coinciding with a rapid expansion of shipping service enterprises and intensified infrastructure investment across Jiangsu Province. After 2019, coordination levels declined modestly yet remained within a moderate coordination stage, underscoring the resilience of the tri-system interaction despite external fluctuations. This temporal pattern aligns with international observations: for example, both Hamburg and Busan have experienced phases of accelerated port–service growth followed by structural adjustments as economic conditions shifted, highlighting the dynamic nature of port–city governance [17].

From a temporal perspective, the evolution of the coupling coordination degree between 2015 and 2023 is shown in Figure 3, which provides a visual representation of spatiotemporal variation in coordination. The pronounced south–north gradient is consistent with broader global evidence where regions with mature port–service ecosystems (e.g., Singapore, Rotterdam) maintain stable high coordination, while emerging or inland-linked port cities often struggle due to weaker service agglomeration or constrained infrastructure. Jiangsu displays a similar pattern: southern cities like Suzhou, Nanjing, and Nantong show deep shading, indicating strong synergy, whereas Xuzhou, Huai’an, and Suqian consistently show lighter tones, highlighting persistent structural limitations.

Figure 4 illustrates that the shipping service agglomeration level increased sharply from 0.3140 (2016) to 0.5550 (2017) and maintained strong momentum through 2019. This trajectory closely parallels the rising trend of the coupling coordination degree, demonstrating that improvements in service agglomeration act as a primary catalyst for enhancing port–city synergy—a relationship similarly noted in advanced maritime hubs such as Singapore and Shanghai [19,20]. Parallel fluctuations in port operations and urban economic performance further reinforce the conclusion that both port development and urban growth function as foundational drivers shaping coordinated interactions among the three subsystems.

3.2.2. Spatial Evolution of the Coupling Coordination Degree

Based on the classification criteria in Table 3, the years 2015, 2018, 2021, and 2023 were selected as representative periods to examine the spatial evolution of coupling coordination across Jiangsu Province. Figure 5, generated using ArcGIS Pro (version 3.1, Esri, Redlands, CA, USA), reveals a clear and persistent north–south gradient, indicating substantial spatial heterogeneity in port–city–service integration. The southern Jiangsu region including Nanjing, Suzhou, Wuxi, Changzhou, and Zhenjiang consistently exhibits the highest levels of coordination. Suzhou achieves high-quality coordination, while Nanjing demonstrates well-coordinated development. This strong performance reflects the advantages of southern Jiangsu’s diversified industrial base, dense transport networks, and advanced producer services. Similar patterns have been observed in leading global port–city regions such as Singapore and Hamburg, where strong port operations, robust economic structures, and highly specialized maritime services reinforce long-term synergy between ports and cities [17,19]. The sustained dominance of southern Jiangsu indicates that once a port–service–urban system reaches a threshold of functional maturity, it tends to reproduce stable spatial advantages, consistent with international findings on maritime cluster evolution [20].

In central Jiangsu (Nantong, Yangzhou, Taizhou), coordination levels are generally moderate. These cities benefit from relatively strong port functions and growing urban economies, but their shipping service agglomeration remains less advanced than that of the southern region. This “middle position” mirrors developmental trajectories observed in transitional port regions globally, where port infrastructure improves rapidly but high-end maritime services lag behind, thereby constraining the degree of system-wide synergy [13]. In contrast, northern Jiangsu (Yancheng, Huai’an, Suqian, Xuzhou, Lianyungang) continues to display low or marginal coordination, often falling into imbalance categories. The underperformance of these cities stems from weaker shipping service foundations, slower tertiary sector development, and fragmented port–city linkages. International studies have similarly shown that secondary port regions with limited service specialization such as inland-linked nodes or peripheral coastal ports struggle to achieve the integrated growth observed in core maritime hubs [17,20]. Although certain northern cities demonstrate improvement over time, the region as a whole remains structurally disadvantaged compared to the central and southern zones. This spatial configuration underscores the structural north–south divide in Jiangsu’s port–city–service interaction. Bridging this gap will require sustained investment in logistics corridors, targeted cultivation of mid- to high-end shipping services, and strengthened integration into regional freight and value chains. International experience, particularly from Singapore and Hamburg, indicates that upgrading maritime service functions and enhancing institutional coordination are critical to improving long-term port–city synergy [17,19]. For northern Jiangsu, adopting similar strategies such as improving multimodal transport connectivity and stimulating knowledge-intensive service clusters will be essential for achieving more balanced and sustainable coordinated development.

3.2.3. Comparative Analysis of Relative Development

The relative development levels of port operations, urban economic activity, and shipping service industry agglomeration, summarized in

Table 7. Relative development degree of port operation and shipping service industry agglomeration.

Year	Port Operations Lagging	Synchronized Development	Shipping Service Industry Lagging
2015	Wuxi, Changzhou, Nantong, Lianyungang, Huaian, Yancheng, Yangzhou, Taizhou, Suqian	Nanjing	Xuzhou, Suzhou, Zhenjiang
2018	Nanjing, Wuxi, Xuzhou, Changzhou, Nantong, Huaian, Yancheng, Yangzhou, Taizhou, Suqian	Lianyungang, Zhenjiang	Suzhou
2021	Nanjing, Wuxi, Xuzhou, Changzhou, Nantong, Lianyungang, Yancheng, Yangzhou, Taizhou, Suqian	Huaian, Zhenjiang	Suzhou
2023	Nanjing, Wuxi, Xuzhou, Changzhou, Nantong, Lianyungang, Huaian, Yancheng, Yangzhou, Zhenjiang, Taizhou, Suqian	None	Suzhou

and

Table 8. Relative development degree of urban economy and shipping service industry agglomeration.

Year	Urban Economy Lagging	Synchronized Development	Shipping Service Industry Lagging
2015	Nantong, Lianyungang, Huaian, Yancheng, Yangzhou, Taizhou, Suqian	Zhenjiang	Nanjing, Wuxi, Xuzhou, Changzhou, Suzhou
2018	Nantong, Lianyungang, Huaian, Yancheng, Yangzhou, Zhenjiang, Taizhou, Suqian	None	Nanjing, Wuxi, Xuzhou, Changzhou, Suzhou
2021	Nantong, Lianyungang, Huaian, Yancheng, Yangzhou, Zhenjiang, Taizhou, Suqian	Nanjing, Changzhou	Wuxi, Xuzhou, Suzhou
2023	Nantong, Lianyungang, Huaian, Yancheng, Yangzhou, Zhenjiang, Taizhou, Suqian	Nanjing, Changzhou	Wuxi, Xuzhou, Suzhou

, reveal a consistent structural pattern across Jiangsu Province. In most cities, port operations and urban economic development lag behind the growth of shipping service agglomeration, indicating that although port expansion has been effective in attracting clusters of shipping enterprises, the downstream spillover into broader port–city development remains limited. This mismatch is largely attributable to the dominance of low- and mid-end shipping services, which offer weaker value-added linkages compared with advanced maritime services such as finance, insurance, brokerage, and digital logistics. Similar challenges have been documented in other emerging maritime clusters, including in Shanghai, where insufficient upgrading of high-end functions initially constrained overall port–service synergy [13,20].

Several cities display more balanced subsystem dynamics. Nanjing and Wuxi demonstrate a relatively synchronized evolution between urban economic growth and shipping service development; however, port operations in both cities still lag, suggesting that their strong and diversified economies are not yet fully matched by corresponding port capacity or throughput efficiencies. This imbalance parallels international cases where advanced urban economies outpace port upgrading, weakening the long-term synergy of port–city systems [17].

In contrast, Suzhou exhibits the opposite structural configuration: despite strong port functions and a robust urban economy, its shipping service industry remains comparatively underdeveloped. This pattern of “emphasizing infrastructure while neglecting services” limits Suzhou’s ability to fully leverage its port and economic advantages. Even though Suzhou attains one of the highest CCD scores in the province, the relatively low proportion of high-end shipping services constrains its transition toward an innovation-driven maritime economy. Comparable findings have been observed in global maritime hubs where infrastructure-led growth generates initial advantages but fails to deliver sustainable competitiveness without parallel service specialization [19].

3.3. Analysis of Driving Factors

3.3.1. Regression Results Analysis

The Tobit regression model, estimated in Stata 17, provides important insights into the factors influencing the coupling coordination degree among port operations, urban economic development, and shipping service agglomeration in Jiangsu Province. As shown in

Table 9. Panel Tobit regression results.

Category	Variable	Coefficient	Std. Error	z	P > |z|
Port Construction Level	Number of berths ($v_1$)	2.19 × 10−4 ***	3.10 × 10−5	7.04	0.000
Port Operation Level	Cargo throughput ($v_2$)	7.42 × 10−6 ***	7.25 × 10−7	10.24	0.000
	Container throughput ($v_3$)	1.22 × 10−8 **	5.56 × 10−9	2.19	0.030
Economic Development Level	Per capita GDP ($v_4$)	7.13 × 10−7 ***	2.34 × 10−7	3.04	0.003
Degree of Openness	Total foreign trade volume ($v_5$)	4.62 × 10−5 ***	1.46 × 10−5	3.15	0.002
Government Regulation	Total fixed asset investment in the whole society ($v_6$)	2.66 × 10−5 ***	5.56 × 10−6	4.8	0.000
Agglomeration of Service Industry Resources	Permanent resident population ($v_7$)	2.49 × 10−4 ***	4.36 × 10−5	5.7	0.000
	Proportion of employment in the tertiary industry ($v_8$)	0.0019 *	0.0011	1.72	0.078
Science and Technology	Proportion of R&D personnel in each city relative to the province ($v_9$)	0.0466	0.1236	0.38	0.707
Other	_cons	0.1012 ***	0.0361	2.8	0.006

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

, several key variables including the number of berths, cargo throughput, per capita GDP, foreign trade volume, fixed asset investment, and permanent resident population are statistically significant at the 1% level. Container throughput is significant at the 5% level, and the share of tertiary-industry employment is significant at the 10% level, indicating meaningful but varied contributions across subsystems.

The significant positive effect of berth capacity underscores the foundational role of infrastructure in enabling port performance. As observed in global port studies, the expansion of berth and terminal facilities not only enhances operational efficiency but also stimulates the growth of advanced logistics and service functions, similar to patterns documented in Rotterdam, Hamburg, and other modernized port systems [17]. Cargo and container throughput further reinforce this dynamic: higher traffic volumes signal stronger service demand, attracting maritime service enterprises and generating agglomeration effects that strengthen the port–city linkage. Such feedback mechanisms parallel those found in international maritime hubs where throughput expansion accelerates producer service clustering [19,20].

Economic development indicators also play a critical role. Higher per capita GDP promotes structural upgrading toward knowledge-based and service-oriented activities, providing a conducive environment for the growth of modern shipping services. Likewise, increased foreign trade volume expands the diversity of service needs and deepens integration into global supply chains, reinforcing Jiangsu’s position in regional and international logistics networks. Fixed asset investment demonstrates a strong enabling effect by expanding and modernizing hard infrastructure including berths, logistics parks, transport corridors, and ICT facilities, thereby facilitating sustained coordination between port operations and urban development. These results are consistent with findings from the Yangtze River region, where infrastructure and openness have been identified as core drivers of port–city synergism [19,23]. Demographic factors also exert significant influence. A larger resident population and a higher proportion of tertiary-sector employment provide both a stable demand base and a pool of skilled labor, supporting the expansion of shipping services and associated urban industries. This aligns with international analyses showing that human capital density is an essential determinant in the upgrading of maritime service clusters [19].

In contrast, the proportion of R&D personnel is not statistically significant, revealing that innovation capacity has not yet emerged as a key determinant of port–city coordination in Jiangsu. This reflects the current structural reality in which low- and mid-end shipping services still dominate more than 80% of total activity, while high-end, innovation-intensive services such as maritime finance, insurance, and digital logistics remain underdeveloped. Similar limitations have been identified in early-stage maritime clusters in Asia, where insufficient service specialization restricts innovation spillovers [13,20]. Therefore, the insignificant coefficient suggests that the region’s development model remains driven primarily by extensive factors such as infrastructure expansion and trade volume rather than by technological upgrading. This highlights a structural challenge for Jiangsu’s future development: without strengthening innovation capabilities and fostering high-end, knowledge-intensive services, the region may struggle to transition toward a more resilient and innovation-driven port–city system [31,32,33].

3.3.2. Robustness Test

To test the stability of the regression results, this study conducts a robustness check following the approach used in existing literature [26]. An omitted variable, industrial added value, is incorporated into the model as an additional explanatory variable. Industrial added value is a key indicator of urban economic strength and captures the intensity of demand that regional industrial systems generate for port logistics, shipping services, and related infrastructure. The regression outputs (

Table 10. Robustness tests.

Category	Variable	Coefficient	Std. Error	z	P > |z|
Port Construction Level	Number of berths ($v_1$)	2.09 × 10−4 ***	3.08 × 10−5	6.78	0.000
Port Operation Level	Cargo throughput ($v_2$)	7.17 × 10−6 ***	7.21 × 10−7	9.94	0.000
	Container throughput ($v_3$)	1.47 × 10−8 ***	5.58 × 10−9	2.64	0.009
Economic Development Level	Per capita GDP ($v_4$)	9.302 × 10−7 ***	3.06 × 10−7	3.04	0.000
Degree of Openness	Total foreign trade volume ($v_5$)	4.349 × 10−6 ***	1.30 × 10−6	3.34	0.003
Government Regulation	Total fixed asset investment in the whole society ($v_6$)	3.00 × 10−5 ***	5.66 × 10−6	5.3	0.000
Agglomeration of Service Industry Resources	Permanent resident population ($v_7$)	2.66 × 10−4 ***	4.36 × 10−5	6.11	0.000
	Proportion of employment in the tertiary industry ($v_8$)	2.03 × 10−3 *	0.001124	1.81	0.082
Science and Technology	Proportion of R&D personnel in each city relative to the province ($v_9$)	0.081046	0.122598	0.66	0.51
Industrial Development	Industrial added value	2.01 × 10−5	8.76 × 10−6	2.3	0.113
Other	_cons	0.1023969 ***	0.03555	2.88	0.005

Note: * p < 0.1, *** p < 0.01.

) show that including industrial added value does not materially change the coefficients or significance levels of the main explanatory variables identified in Table 9. Core determinants such as berth capacity, cargo and container throughput, per capita GDP, foreign trade volume, fixed asset investment, and demographic indicators continue to exhibit statistically significant positive effects. This consistency confirms that the original findings are robust and that the model’s explanatory structure remains stable when accounting for additional economic variables.

Although industrial added value itself exerts a positive influence on the coupling coordination degree, its effect is comparatively weaker and statistically less stable than the impacts of infrastructure, trade openness, and demographic factors. This suggests that the traditional industrial sector, while still relevant, is no longer the dominant driver of port–city–service coordination. Instead, its influence is being partially replaced or supplemented by service-oriented drivers, including shipping service agglomeration, foreign trade integration, and infrastructure modernization. This pattern aligns with broader international evidence showing that modern port–city systems increasingly rely on advanced services and innovation capacity, rather than solely on manufacturing-driven demand, to sustain coordinated development.

4. Conclusions and Recommendations

This study developed a coupling coordination degree (CCD) model to assess the interaction among port operations, urban economic development, and shipping service industry agglomeration across 13 port cities in Jiangsu Province from 2015 to 2023. The findings reveal a clear south–north gradient in coordination, with cities such as Suzhou and Nanjing consistently performing strongly due to diversified economies, advanced infrastructure, and sustained innovation and policy support. In contrast, northern cities including Huai’an, Suqian, and Xuzhou show weaker coordination because of limited service agglomeration and underdeveloped port–city linkages.

A noticeable developmental lag exists among the three subsystems; in most cities, port operations and urban economic development have not kept pace with the rapid growth of the shipping service industry. The predominance of low- and mid-end service activities restricts spillover benefits to the broader economy and slows progress toward high-value maritime services. Coordination is jointly shaped by port construction and operational capacity, economic strength, openness, government support, and service-industry concentration, while the share of R&D personnel remains insignificant, indicating that innovation-driven forces have yet to become a major contributor to port–city synergy. Overall, Jiangsu’s port–city–service system is transitioning from infrastructure-led expansion toward service specialization and innovation-driven development, although disparities across regions persist.

To promote balanced and high-quality development, Jiangsu should adopt differentiated regional strategies. Northern cities should prioritize upgrading port infrastructure, improving multimodal connectivity, and attracting mid- to high-end shipping service enterprises to strengthen their industrial and service foundations. Southern cities, which are already more advanced, should shift from infrastructure expansion toward the development of high-end maritime services such as shipping finance, insurance, consulting, and digital logistics to consolidate innovation-led growth. The province should foster maritime innovation and service clusters near major ports, supported by fiscal incentives, talent cultivation, and partnerships with universities and technology-oriented firms. Establishing unified digital platforms linking port operations, shipping services, and municipal governance would enhance data sharing, streamline coordination, and support integrated decision-making. Strengthening intercity collaboration, particularly among Lianyungang, Xuzhou, and Huai’an, can further promote the formation of a regional hub network leveraging multimodal transport corridors and Belt and Road opportunities.

This study is limited by its relatively short time span (2015–2023) and its focus on a single province. Future research should expand this analytical framework to other coastal regions and incorporate environmental and social sustainability indicators to develop a more comprehensive assessment of coordinated port–city–service development.