Research on Coordinated Development of Shenzhen Port Logistics and Hinterland Economy

Abstract

As the main channel of global cargo transportation, the coordinated development of the logistics and hinterland economy of coastal ports is of great significance to promote the sustainable development of countries (regions). The improvement in port logistics’ performance lays the foundation for the smooth transportation of goods. The improvement in coastal port infrastructure facilities will also effectively reduce the resource loss and carbon emissions generated in the process of cargo handling and turnover. Aiming at researching the coordinated development of coastal port logistics and the hinterland economy, this paper constructs the index system of Shenzhen port logistics and Shenzhen’s economic development, selects the relevant data from 1996 to 2020, uses the grey correlation model to analyze the internal correlation between Shenzhen port logistics and Shenzhen’s economic development, constructs the system order degree model and the coupling coordination model, explores their coordinated development degree and collaborative trend, and further selects the Johansen cointegration test and Granger causality test to verify the development relationship between the two, and puts forward relevant suggestions to promote the sustainable development of port logistics and the hinterland economy. The empirical results show that there is a high correlation between Shenzhen port logistics and Shenzhen’s economic development, the degree of coordinated development is increasing year by year, and there is a long-term equilibrium relationship between the two, which has the characteristics of mutual promotion.

1. Introduction

With the continuous advancement of the process of ‘economic globalization’, it has effectively promoted the increase in foreign trade volume in coastal areas of countries (regions), promoted the development of the hinterland economy of coastal ports, and further promoted the improvement in the logistics level of coastal ports. The economic development of the hinterland is an important support for the improvement in the port logistics level [1], and the port is an important resource endowment for the economic development of the hinterland [2]. They are interdependent and mutually reinforcing. Therefore, the formation of a coordinated development model of port logistics and the hinterland economy has certain value for the sustainable development of the port city and the improvement in international competitiveness.

As key hubs for national import and export cargo transportation, coastal ports play a very important role in the logistics system. The improvement in port logistics’ performance lays the foundation for the smooth transportation of goods. The improvement in coastal port infrastructure facilities will also effectively reduce the resource loss and carbon emissions generated in the process of cargo handling and turnover. Secondly, the development of the port, logistics and manufacturing companies will also promote the employment of residents and the growth of the regional economy. Therefore, the improvement in the port logistics’ level has a certain driving effect on regional sustainable development. The coordinated development of port logistics and the hinterland economy can effectively promote the process of regional sustainable development. Based on this, this paper selects multiple index data of Shenzhen port logistics and regional economic development from 1996 to 2020, and adopts the method of combining the grey correlation model, order degree model and coupling coordination model. Firstly, the grey correlation model is used to explore the correlation between the internal indicators of Shenzhen port logistics and the hinterland economic development system, and then the order degree model and the coupling coordination model are used to explore the coordinated development degree of the two systems from 1996 to 2020. Referring to the research of Szaruga [3], in order to further confirm the relationship between port logistics and the hinterland economy, the cointegration test and Granger causality test are used to verify the interaction between coastal port logistics and hinterland economic development, which is innovative and provides some reference for the sustainable development of coastal port logistics and the hinterland economy.

After this chapter, the second chapter ranks and analyzes the literature related to this study. The third chapter elaborates on the selection basis and data source of the research indicators, and introduces the construction of the grey correlation model and the coupling coordination model in detail. Based on the empirical results, the fourth chapter analyzes the grey correlation and coordination degree between Shenzhen port logistics and hinterland economic development, and further tests the long-term equilibrium relationship and Granger causality. The fifth chapter gives targeted suggestions to promote the sustainable development of Shenzhen port logistics and the hinterland economy based on empirical analysis. The sixth chapter summarizes the empirical research conclusions.

2. Literature Review

In their study of port logistics, Wang et al. [4] used the coupling coordination model to explore the relationship between port integration and the coordinated development of port hinterland. The analysis shows that from a short-term perspective, port integration makes the coordination between the port and hinterland temporarily decline. However, from a long-term perspective, port integration can better promote coordinated development of the port hinterland. Cao [5] argued that strengthening the optimization of port governance models has a positive effect on trade growth. Dai [6] believed that logistics efficiency is related to port management, and poor port management reduces logistics efficiency. Liu et al. [7] used the method of grey correlation analysis to explore the construction of the port logistics ecosystem in Hainan Free Trade Port. They concluded that the correlation degree of Haikou Port is higher than that of Sanya Port. The two ports should pay attention to the interaction with the surrounding ports, and strengthen information sharing and the follow-up of infrastructure equipment. Wang et al. [8] used the analytic hierarchy process and entropy theory, constructed a comprehensive weighting evaluation model to explore the competitiveness of port logistics and compared the results of the two methods. It was found that the application of the comprehensive evaluation model in the evaluation of port logistics competitiveness was reasonable and effective. Liu et al. [9] analyzed the development factors of port logistics in countries along the ‘Belt and Road’ based on factor analysis and grey correlation analysis. They believed that port transportation, cost and infrastructure have a greater impact on the development of port logistics. Based on the data of Liaoning Province from 2005 to 2019, Ma et al. [10] used a VAR model to explore the relationship between port logistics and foreign trade. Through the Johansen cointegration test, they verified that there is a cointegration relationship between the total import and export volume and port cargo throughput. The Granger causality test verified that the total import and export volume is the Granger cause of port cargo throughput.

In their study of the regional economy, Luo et al. [11] studied the spatial evolution of the port hinterland in the three northeastern provinces of China. The conclusion shows that the economic development, location conditions and transportation accessibility of port cities have a great influence on the port’s potential energy and the spatial evolution of the hinterland. Yao et al. [12] used the index data of 284 cities in China from 2011 to 2018 as a sample, and used empirical analysis methods to explore the relationship between regional economic development and digital finance and technological progress. They believed that digital finance can promote regional economic development by promoting technological progress. Chen et al. [13], based on the relevant index data of Nanjing port logistics and hinterland economic development from 2010 to 2019, constructed a grey correlation model to study the coordinated development relationship between port logistics and the hinterland economy. They concluded that the coordination degree of Nanjing port logistics and the hinterland economy is at a high level, and the industrial output value and export trade volume have a great influence on the coordination degree. Li et al. [14] selected the data of 30 different provinces from 2013 to 2018, and used the coupling coordination model and fixed effect model to explore the coordinated development and influencing factors of the regional economy, digital logistics and carbon environment. The empirical analysis showed that there are differences in the degree of coupling coordination among different regions. The impact of ‘per capita disposable income’ on the degree of coupling coordination is greater than that of ‘freight volume’ and ‘Internet penetration rate’, and ‘ecological construction and protection investment’ has the least impact on it.

Research on the relationship between port logistics and the regional economy has mainly been from the aspect of their synergy. Zhang [2] studied the relationship between port prosperity and regional economic development, and verified that there is no two-way causal relationship between port prosperity and regional economic development through variance decomposition analysis and the Granger causality test. Chen [15] used the entropy weight method to construct the synergy degree model of Shanghai port and Shanghai economic order parameters, and explored the coordinated development relationship between Shanghai port logistics and the regional economy. It is believed that the synergy degree of Shanghai port logistics and the regional economy is on the rise. By 2018, the rate of increase has slowed down and reached the stage of extremely coordinated development. Jiang [16] used the entropy weight method to calculate the weight of each index, and constructed a coupling coordination model to study the development relationship between port logistics and the hinterland economy in Jiangsu Province. Through empirical analysis, it was concluded that port logistics and the hinterland economic development level have a positive role in promoting, and the coupling coordination degree in southern Jiangsu is higher than that in central and northern Jiangsu. Guo [17] used the Granger causality test and grey correlation analysis to study the relationship between port logistics and regional economic development in the Beijing–Tianjin–Hebei region. It was considered that there is a two-way causal relationship between Tangshan port logistics and regional economic development, while Tianjin port and Qinhuangdao port logistics and regional economic development are a one-way causal relationship. Wang et al. [18] used the coupling coordination model to explore the coordination between port logistics and the hinterland economy, and found that the coupling coordination degree of port logistics and regional economic development in the nine ports showed an overall upward trend in the time series from 2009 to 2018, and the hinterland industrial structure and port logistics operation mode were the main influencing factors of the coordination degree of port logistics and the regional economy. Cao et al. [19] studied the relationship between the coordinated development of port logistics and the hinterland economy in Taicang Port. Empirical analysis showed that the degree of synergy between port logistics and the regional economy in Taicang Port showed an overall upward trend from 2007 to 2016, and the focus of synergy shifted to Shanghai and Nantong. Zhou [20] used the method of grey correlation analysis to study the coordinated development of Nanjing port logistics and the hinterland economy, and put forward suggestions for strengthening port infrastructure construction and optimizing port functions.

In summary, the related research on the relationship between port logistics and regional economic development is mainly quantitative research. Most of them used the coupling coordination model, the Granger causality test, grey correlation analysis and other research methods. Therefore, the research methods in this paper are reasonable. However, the method application and evaluation index construction in the previous single research literature are relatively single. In this paper, the coupling coordination model, grey correlation model and Granger causality test are combined and used to fully explore the relationship between Shenzhen port logistics and hinterland economic development, which has certain research value.

3. Materials and Methods: Index Acquisition and Model Construction

3.1. Indicator Acquisition and Data Sources

Combined with the port logistics and economic development of Shenzhen port, and with reference to the availability of data, 1996–2020 was selected as the time interval, and the development level of port logistics was evaluated from the two dimensions of output capacity and input capacity. Port output capacity mainly refers to the ability of port operation, port cargo throughput and container throughput being important indicators of port output capacity. Port investment capacity mainly refers to the capital, labor and land investment required for port operation. Among them, capital investment accounts for the most important position, and the number of port berths is an important indicator of capital investment. The four dimensions of economic structure, hinterland trade, regional investment and economic income reflect the economic development of Shenzhen as shown in

Table 1. Port logistics and hinterland economic index system.

Study System	Evaluation Dimension	Index Structure
port logistics	output capacity	cargo throughput$x_{o1}$ (million tons) container throughput $x_{o2}$ (million tons)
	input capacity	port berths $x_{o3}$ (number) number of berths of ten thousand tons $x_{o4}$ (number)
hinterland economy	economic structure	first industrial output $x_{l1}$ (ten thousand yuan) second industrial output $x_{l2}$ (ten thousand yuan) output of the tertiary industry $x_{l3}$ (ten thousand yuan)
	hinterland trade	total export–import volume $x_{l4}$ (ten thousand yuan) total retail sales of consumer goods $x_{l5}$ (ten thousand yuan)
	regional investment	fixed investments $x_{l6}$ (ten thousand yuan) actual make use of foreign capital fund $x_{l7}$ (billion dollars)
	economic returns	regional GDP $x_{l8}$ (ten thousand yuan) general public budget revenue $x_{l9}$ (ten thousand yuan) total output value of agriculture, forestry, animal husbandry and fishery $x_{l10}$ (ten thousand yuan) value of industrial output $x_{l11}$ (ten thousand yuan) value of construction output $x_{l12}$ (ten thousand yuan)

. The grey correlation degree and coupling coordination model were constructed to study the coordinated development relationship between port logistics and hinterland economy. The reference data in this paper are from ‘Shenzhen Statistical Yearbook’.

3.2. Model Construction

3.2.1. Grey Correlation Model

Grey correlation degree is a research method used to measure the correlation between different things or internal factors of the system [21]. The trend of change between system variables tends to be consistent; that is, the higher the degree of synergy, the greater the correlation coefficient, which means the stronger the correlation between the two, and vice versa. In order to explore the internal correlation between port logistics and hinterland economic development, the grey correlation model of port logistics and hinterland economic development was constructed by referring to the research of Zhou et al. [22].

(1)

Determine the reference sequence and comparison sequence. Reference sequence$x_o=\left\{x_{o1},x_{o2}\cdots x_{on}\right\}$, comparison sequence$x_l=\left\{x_{l1},x_{l2}\cdots x_{lm}\right\}$.

(2)

The original data standardization. In order to eliminate the influence of objective factors such as dimensional differences between index data on the results, it is necessary to standardize the index data. The initial value processing method is as follows.

$${X^{\prime }}_i=\frac{x_i}{x_i\left(1\right)}$$

(1)

${x^{\prime }}_i$ represents the standardized results of the index data of the two subsystems of port logistics and hinterland economy, $x_i\left(1\right)$ represents the initial data of the first year of each system index, i represents each system index, $i=1,2\cdots ,k$.

(3)

Calculate the difference sequence and the maximum difference, minimum difference.

$$\Delta l_i=\left|{x^{\prime }}_{0i}-{x^{\prime }}_{li}\right|$$

(2)

$\Delta l_i$ represents the absolute difference, ${x^{\prime }}_{oi}$ represents the initial value of the logistics development index data, ${x^{\prime }}_{li}$ represents the initial value of the hinterland economic development index data.

$$\mathsf{M}=MaxMax\Delta l\left(i\right)$$

(3)

$$\mathsf{N}=MinMin\Delta l\left(i\right)$$

(4)

M is the maximum difference, N is the minimum difference and $\Delta l\left(i\right)$represents the absolute difference of the index i.

(4)

Correlation coefficient.

$$\mathsf{{\rm Y}}\left(x_{oi},x_{lj}\right)=\frac{\mathsf{{\rm N}}+\xi \times \mathsf{{\rm M}}}{\mathsf{\Delta }{l}_i+\xi \times \mathsf{{\rm M}}}$$

(5)

Among them, $\gamma \left(x_{oi},x_{li}\right)$ represents the correlation coefficient between the port logistics system i index and the hinterland economic system j index, $i=1,2,\cdots ,n$, $j=1,2,\cdots ,m$. ξ is the resolution coefficient, usually 0.5.

(5)

Calculate the grey correlation degree.

$$\delta \left(x_{oi},x_{lj}\right)=\frac{1}{m}{\displaystyle \sum }_{k=1}^m\gamma \left(x_{oi},x_{li}\right)$$

(6)

Among them, $\delta \left(x_o,x_l\right)$represents the grey correlation degree between the port logistics system i index and the hinterland economic system j index, k is the year, $k=1,2\cdots ,r.$

3.2.2. Coupling Coordination Model

The coupling coordination model is a research method for analyzing the level of coordinated development between different systems. The so-called coordinated development refers to the collection of ‘coordination’ and ‘development’, which symbolizes the positive interaction process of the change law of things themselves [23]. The degree of coupling reflects the degree of interdependence and mutual influence between different systems; that is, the coordinated development level of the two systems. The entropy weight method is an objective method to calculate the weight, which avoids the error caused by the subjective evaluation to a certain extent, and has a certain scientific nature. On the basis of obtaining the internal correlation between coastal port logistics and hinterland economy, the entropy weight method was used to determine the proportion of coastal port logistics and hinterland economic indicators, and the order degree of the two subsystems was obtained. Then, the coupling coordination model of coastal port logistics and hinterland economic system was constructed, and the coordinated development trend and current situation of the two subsystems were analyzed through the coupling coordination degree.

(1)

In order to avoid the impact of measurement unit differences between indicator data on empirical results, the normalization method is used to standardize the data of each indicator. The indicators in this paper are all positive indicators. The processing formula is as follows:

$${\widehat{X}}_{ij}=\left(\frac{x_{ij}-x_{min}}{x_{max}-x_{min}}\right)\times 0.999+0.001$$

(7)

Among them, ${\widehat{X}}_{ij}$represents the value after normalization, $x_{ij}$ represents the original value of each index, $x_{max}$ represents the maximum value of j index in the research time range, $x_{min}$ represents the minimum value of j index in the research time range, i represents the year, $i=1,2,\cdots ,n$, j represents the index, $j=1,2,\cdots ,m$.

(2)

Determine the contribution of each order parameter:

$$P_{ij}=\frac{{\widehat{X}}_{ij}}{{{\displaystyle \sum }}_{i=1}^n\widehat{X}ij}$$

(8)

$P_{ij}$is the proportion of each order parameter.

(3)

Calculating the entropy of the jth indicator:

$$e_j=-k{\displaystyle \sum }_{i=1}^n{P}_{ij}ln{P}_{ij}$$

(9)

where $k=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$ln\left(n\right)$}\right.$, and $k>0$, $e_j$denotes the entropy of the index j, and $e_j\ge 0$.

(4)

Difference coefficient:

$$d_j=1-e_j$$

(10)

(5)

Weight:

$$w_j=\frac{d_j}{{{\displaystyle \sum }}_{j=1}^m{d}_j},{\displaystyle \sum }_{j=1}^m{w}_j=1$$

(11)

$d_j$represents the difference coefficient of each index of the two subsystems of port logistics and hinterland economy, and $w_j$is the weight of each index.

(6)

Order degree model construction:

$$X_{ij}=w_j\times P_{ij}$$

(12)

$$X_i={\displaystyle \sum }_{j=1}^m{X}_{ij}$$

(13)

$X_{ij}$is order parameter order degree, $X_i$is subsystem order degree, $0\le X_{ij}\le 1,0\le X_i\le 1$.

(7)

Construction of coordination degree model:

$$C=\frac{X^1{}_i\times X^2{}_i}{{\left(a{X}_{i1}+b{X}_{i2}\right)}^{\tau }}$$

(14)

C is the coupling degree between the two subsystems of port logistics and hinterland economic development. $X^1{}_i$represents the order degree of port logistics system, $X^2{}_i$ represents the order degree of hinterland economic development system, a and b represent the specific weights of the two subsystems, respectively. Because of the mutual promotion relationship between port logistics and hinterland economic development, the values of a and b in this paper are 0.5, and τ represents the number of research subsystems. The value of this paper is 2, and$0\le C\le 1$.

$$T=\frac{X^1{}_i+X^2{}_i}{2}$$

(15)

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

(16)

$$D_i=\sqrt{\frac{X^1{}_i\times X^2{}_i}{0.5\times \left(X^1{}_i+X^2{}_i\right)}}$$

(17)

T is the comprehensive evaluation index, D is the coupling coordination degree of the two subsystems. According to Formulas 15 and 16, the coupling coordination degree $D_i$of port logistics and hinterland economic development is sorted out, and $0\le D_i\le 1$; the greater the $D_i$value, the higher the coordination degree of port logistics and hinterland economic development, and vice versa. Referring to the classification system and criterion of Liao [23] in the study of coordinated development of environment and economy, this paper divides the coordination interval of port logistics and hinterland economy into three levels: imbalance recession, transition and coordinated development. The coordinated development interval is divided into four stages: primary coordinated development, intermediate coordinated development, good coordinated development and high-quality coordinated development, as shown in

Table 2. Interval division standard of coordinated development degree of port logistics and hinterland economy.

D	0.000~0.399	0.400~0.599	0.600~0.699	0.700~0.799	0.800~0.899	0.900~1.000
stage of development	misadjustment recession stage	transition stage	Coordinated development stage
			primary coordinated development stage	Intermediate coordinated development stage	good coordinated development stage	High-quality coordinated development stage

.

4. Results: Empirical Analysis

Accelerating the formation of a convenient and efficient modern comprehensive transportation system is an important basis for building a global ocean center city. As an important hub of China’s transportation system, Shenzhen Port is a world-class container hub port with container throughput exceeding 30 million TEUs in 2022. As a world-class container hub port, it plays an important role in the construction of Shenzhen’s ocean center city. The improvement in its port logistics level also has a feedback effect on the economic development of Shenzhen.

4.1. Correlation Analysis

According to the initial data and grey correlation model of the two subsystems of Shenzhen port logistics and Shenzhen economic development, the reference sequence$x_o=\left\{x_{o1},x_{o2},x_{o3},x_{o4}\right\}$and the comparison sequence $x_l=\left\{x_{l1},x_{l2},x_{l3},x_{l4},x_{l5},x_{l6},x_{l7},x_{l8},x_{l9},x_{l10},x_{l11},x_{l12}\right\}$are determined. The initial value method is used for dimensionless processing of the original data, and the grey correlation degree of the two subsystems is obtained as shown in

Table 3. Grey Correlation between Shenzhen Port Logistics and Hinterland Economic Development from 1996 to 2020.

Indicators	xl1	xl2	xl3	xl4	xl5	xl6	Average
xo1	0.74365	0.82092	0.76593	0.88399	0.84794	0.85040
xo2	0.51254	0.58102	0.61100	0.55991	0.55950	0.56398
xo3	0.96817	0.71816	0.69150	0.76457	0.77559	0.77891
xo4	0.91821	0.73532	0.70672	0.78592	0.79925	0.80403
average	0.78564	0.71386	0.69379	0.74860	0.74557	0.74933
	xl7	xl8	xl9	xl10	xl11	xl12	average
xo1	0.79480	0.79482	0.77206	0.74887	0.77745	0.86822	0.80575
xo2	0.52058	0.59303	0.59382	0.51385	0.59313	0.53386	0.56135
xo3	0.94697	0.70546	0.72686	0.97270	0.69391	0.87837	0.80176
xo4	0.96722	0.72165	0.74562	0.92466	0.70853	0.90801	0.81043
average	0.80739	0.70374	0.70959	0.79002	0.69325	0.79711

. The grey correlation degree reflects the correlation between different indicators in the two systems. The horizontal and vertical intersection in Table 3 reflects the grey correlation degree between the corresponding indicators. The grey correlation degree value in the last row represents the correlation between the corresponding hinterland economic development indicators and the port logistics indicator set, reflecting the influence of the corresponding hinterland economic development indicators on the port logistics. The grey correlation degree value in the last column represents the correlation between the corresponding port logistics indicators and the hinterland economic development indicator set, reflecting the influence of the corresponding port logistics indicators on the hinterland economic development.

According to the analysis of the results of Table 3, from the overall perspective, the grey correlation degree between Shenzhen port logistics and Shenzhen economic development is distributed between 0.51 and 0.98. The average values of each row are 0.80575, 0.56135, 0.80176 and 0.81043, $x_{o4}>x_{o1}>x_{o3}>x_{o2}$, respectively. Except for container throughput, the horizontal average value of the correlation degree between the other three indicators of port logistics and the hinterland economy is higher than 0.80, which is at an upper level, indicating that the cargo throughput of Shenzhen port, the number of port berths and the number of 10,000 t berths have a strong correlation with the economic development of Shenzhen. Among the average correlation degree values of the items in the columns, $x_{l7}>x_{l12}>x_{l10}>x_{l1}>x_{l6}>x_{l4}>x_{l5}>x_{l2}>x_{l9}>x_{l8}>x_{l3}>x_{l11}$, the minimum value of the average value of each column is 0.69325, and the maximum value is 0.80739, all of which are distributed in the upper middle range, and the corresponding average value of each index is small, indicating that the gap between the economic development indicators of Shenzhen and the development of Shenzhen port logistics is small. Among them, the actual utilization of foreign capital has a greater impact on the development of Shenzhen port logistics.

4.2. Synergy Analysis

From the correlation analysis, it can be seen that the grey correlation degree between Shenzhen port logistics and hinterland economic development indicators is greater than 0.5, which is in the upper middle range. Shenzhen port logistics and Shenzhen economic development have a high correlation. In order to further explore the development status of the two subsystems and the coordinated development trend of the two subsystems, the order degree model and coupling coordination model of the two subsystems are constructed. The entropy weight method is used to calculate the weight of each index of the two subsystems of Shenzhen port logistics and the Shenzhen economy as shown in

Table 4. Shenzhen port logistics and hinterland economic development index weight.

Shenzhen Port Logistics		Economy of Shenzhen
cargo handling capacity xo1	0.29689	first industrial output xl1	0.08633	actual make use of foreign capital fund xl7	0.06688
container throughput xo2	0.27352	second industrial output xl2	0.07121	regional GDP xl8	0.08380
port berths xo3	0.21988	output of the tertiary industry xl3	0.09462	general public budget revenue xl9	0.11149
number of berths of ten thousand tons xo4	0.20971	total export–import volume xl4	0.06537	total output value of agriculture, forestry, animal husbandry and fishery xl10	0.06831
		total retail sales of consumer goods xl5	0.08601	value of industrial output xl11	0.06996
		fixed investments xl6	0.09688	value of construction output xl12	0.09913

, and the order degree of each order parameter and the order degree of the two subsystems are calculated according to the index weight as shown in

Table 5. Order parameter order degree of Shenzhen port logistics subsystem from 1996 to 2020.

Year	xo1	xo2	xo3	xo4	xi
1996	0.00030	0.00027	0.00022	0.00021	0.00100
1997	0.00454	0.00617	0.00767	0.01697	0.03534
1998	0.00564	0.01459	0.04490	0.02954	0.09466
1999	0.02103	0.02554	0.04490	0.04211	0.13358
2000	0.03386	0.03617	0.05234	0.05049	0.17286
2001	0.04603	0.04753	0.05607	0.05468	0.20431
2002	0.07286	0.07427	0.05607	0.05468	0.25788
2003	0.10384	0.10616	0.06351	0.07982	0.35334
2004	0.13310	0.13784	0.10447	0.11334	0.48875
2005	0.15601	0.16458	0.10819	0.12591	0.55469
2006	0.18439	0.18847	0.14542	0.14686	0.66514
2007	0.21465	0.21616	0.17148	0.15105	0.75334
2008	0.22893	0.21952	0.19382	0.15943	0.80171
2009	0.20671	0.18616	0.20499	0.17200	0.76985
2010	0.24122	0.23100	0.21988	0.18038	0.87248
2011	0.24409	0.23163	0.21988	0.18038	0.87598
2012	0.25017	0.23552	0.21988	0.18038	0.88596
2013	0.25764	0.23910	0.17148	0.17200	0.84022
2014	0.24407	0.24710	0.14914	0.17200	0.81232
2015	0.23627	0.24879	0.16031	0.17200	0.81737
2016	0.23253	0.24647	0.14542	0.19295	0.81737
2017	0.26696	0.25942	0.15659	0.20133	0.88429
2018	0.27947	0.26500	0.16031	0.20552	0.91030
2019	0.28778	0.26521	0.16404	0.20971	0.92673
2020	0.29689	0.27352	0.20499	0.20971	0.98511

and

Table 6. Order parameter order degree of Shenzhen economic development subsystem from 1996 to 2020.

Year	xl1	xl2	xl3	xl4	xl5	xl6
1996	0.03936	0.00007	0.00009	0.00007	0.00009	0.00010
1997	0.03887	0.00088	0.00088	0.00085	0.00070	0.00093
1998	0.04064	0.00181	0.00152	0.00088	0.00133	0.00204
1999	0.04007	0.00298	0.00217	0.00156	0.00215	0.00317
2000	0.04232	0.00437	0.00331	0.00333	0.00332	0.00453
2001	0.04436	0.00544	0.00418	0.00394	0.00451	0.00465
2002	0.04702	0.00715	0.00563	0.00638	0.00586	0.00594
2003	0.03646	0.00971	0.00714	0.01033	0.00780	0.00798
2004	0.02837	0.01263	0.00887	0.01425	0.00985	0.00980
2005	0.01723	0.01582	0.01024	0.01890	0.01231	0.01094
2006	0.00494	0.01893	0.01281	0.02605	0.01527	0.01210
2007	0.00861	0.02157	0.01641	0.03262	0.01820	0.01300
2008	0.00886	0.02484	0.01959	0.03425	0.02272	0.01456
2009	0.00265	0.02505	0.02267	0.03035	0.02636	0.01762
2010	0.00159	0.03026	0.02737	0.04038	0.03107	0.02061
2011	0.00209	0.03651	0.03293	0.04921	0.03714	0.02209
2012	0.00169	0.03968	0.03934	0.05612	0.04386	0.02530
2013	0.00264	0.04411	0.04568	0.06537	0.05005	0.02753
2014	0.00009	0.04817	0.05132	0.05886	0.05610	0.03041
2015	0.00633	0.05142	0.05804	0.05292	0.05887	0.03778
2016	0.01096	0.05598	0.06718	0.04716	0.06464	0.04767
2017	0.05957	0.06323	0.07609	0.04921	0.07249	0.06124
2018	0.07264	0.06794	0.08360	0.05440	0.07977	0.07448
2019	0.08542	0.07084	0.09107	0.05149	0.08601	0.08925
2020	0.08633	0.07121	0.09462	0.05271	0.08122	0.09688
Year	xl7	xl8	xl9	xl10	xl11	xl12	xj
1996	0.00378	0.00008	0.00011	0.02744	0.00007	0.00010	0.07135
1997	0.00007	0.00088	0.00042	0.02699	0.00086	0.00130	0.07361
1998	0.00010	0.00164	0.00109	0.03217	0.00174	0.00297	0.08791
1999	0.00118	0.00252	0.00168	0.03245	0.00293	0.00404	0.09691
2000	0.00292	0.00376	0.00281	0.03467	0.00439	0.00445	0.11417
2001	0.00892	0.00471	0.00402	0.03767	0.00552	0.00461	0.13253
2002	0.01463	0.00627	0.00412	0.04026	0.00733	0.00502	0.15562
2003	0.01874	0.00823	0.00487	0.03962	0.00995	0.00700	0.16783
2004	0.00662	0.01046	0.00578	0.03251	0.01303	0.00781	0.15997
2005	0.01251	0.01262	0.00850	0.01682	0.01645	0.00766	0.16000
2006	0.01537	0.01540	0.01115	0.00973	0.01969	0.00924	0.17066
2007	0.01911	0.01856	0.01584	0.00809	0.02242	0.01064	0.20508
2008	0.02261	0.02175	0.02010	0.01122	0.02581	0.01341	0.23970
2009	0.02385	0.02356	0.02250	0.00493	0.02577	0.01748	0.24279
2010	0.02515	0.02845	0.02926	0.00412	0.03091	0.02440	0.29356
2011	0.02802	0.03428	0.03622	0.00451	0.03715	0.03164	0.35178
2012	0.03402	0.03922	0.04048	0.00376	0.04027	0.03615	0.39988
2013	0.03629	0.04469	0.04793	0.00203	0.04446	0.04512	0.45590
2014	0.03950	0.04960	0.05843	0.00007	0.04865	0.04757	0.48876
2015	0.04608	0.05476	0.07769	0.00583	0.05199	0.04964	0.55137
2016	0.04832	0.06183	0.08993	0.01180	0.05644	0.05619	0.61812
2017	0.05468	0.06999	0.09578	0.04935	0.06358	0.06550	0.78072
2018	0.06231	0.07624	0.10195	0.05618	0.06748	0.08310	0.88009
2019	0.05857	0.08167	0.10897	0.06702	0.06996	0.09269	0.95295
2020	0.06688	0.08380	0.11149	0.06831	0.06992	0.09913	0.98251

. Among them, $x_i$represents the order degree of the Shenzhen port logistics subsystem, and $x_j$represents the order degree of the Shenzhen economic development subsystem. Furthermore, the formula is used to calculate the two subsystems’ coordination results and these are shown in

Table 7. Calculation Results of Coordination Degree between Shenzhen Port Logistics and Hinterland Economic Development from 1996 to 2020.

Year	Comprehensive Evaluation Index	Coupling Degree	Coupling Coordination Degree	Coordinated Development Type
1996	0.03618	0.05452	0.04441	misadjustment recession stage
1997	0.05448	0.87664	0.21853	misadjustment recession stage
1998	0.09129	0.99863	0.30193	misadjustment recession stage
1999	0.11524	0.97470	0.33515	misadjustment recession stage
2000	0.14352	0.95819	0.37083	misadjustment recession stage
2001	0.16842	0.95459	0.40096	transition stage
2002	0.20675	0.93884	0.44057	transition stage
2003	0.26058	0.87330	0.47704	transition stage
2004	0.32436	0.74315	0.49097	transition stage
2005	0.35735	0.69502	0.49836	transition stage
2006	0.41790	0.64998	0.52118	transition stage
2007	0.47921	0.67278	0.56780	transition stage
2008	0.52070	0.70877	0.60750	primary coordinated development stage
2009	0.50632	0.72911	0.60759	primary coordinated development stage
2010	0.58302	0.75350	0.66280	primary coordinated development stage
2011	0.61388	0.81771	0.70850	intermediate coordinated development stage
2012	0.64292	0.85710	0.74232	intermediate coordinated development stage
2013	0.64806	0.91208	0.76882	intermediate coordinated development stage
2014	0.65054	0.93815	0.78122	intermediate coordinated development stage
2015	0.68437	0.96223	0.81149	good coordinated development stage
2016	0.71774	0.98073	0.83900	good coordinated development stage
2017	0.83251	0.99613	0.91065	High-quality coordinated development stage
2018	0.89520	0.99972	0.94601	High-quality coordinated development stage
2019	0.93984	0.99981	0.96936	High-quality coordinated development stage
2020	0.98381	1.00000	0.99187	High-quality coordinated development stage

.

From Table 5 and Table 6 and Figure 1, it can be seen that the order degree of the Shenzhen’s economic development subsystem and the order degree of the Shenzhen port logistics subsystem showed a spiral upward trend from 1996 to 2020. Among them, due to the negative impact of the international financial crisis on China’s import and export of goods, the port cargo throughput and container throughput of Shenzhen Port were in a negative growth state in 2009. From 2008 to 2009, the order degree of the port logistics subsystem showed a sudden downward trend, from 0.80171 to 0.76985. In 2010, the Ministry of Transport of the People’s Republic of China emphasized the decision making to cope with the financial crisis and accelerate the development of the modern transportation industry, so as to further improve the number and professionalism of port berths.

The order degree of the port logistics subsystem decreased significantly from 2012 to 2014, from 0.88596 to 0.81232, which was related to the transformation of China’s foreign trade structure and growth rate. The development of China’s foreign trade changed from the previous high-speed growth stage to the medium–high-speed growth stage. Under the background of the slow recovery of the global economy, the low-cost advantage of the manufacturing industry in foreign trade gradually weakened, which inhibited the import and export of port goods. According to the statistical bulletin of Shenzhen’s national economic and social development, the total import and export of Shenzhen’s foreign trade in 2014 decreased by about 9% compared with the previous year. Shenzhen port’s cargo throughput decreased by 4.6% over the previous year, and the development of port logistics was suppressed to some extent. In addition, affected by the turbulence of international market demand and the rise in factor costs, the pressure of Shenzhen’s international market competition increased, and economic growth also had certain challenges. In this case, accelerating the transformation and upgrading of processing trade played a role in alleviating the negative impact to a certain extent. In 2016, the order degree of Shenzhen’s port logistics level and Shenzhen’s economic development subsystem accelerated, and then increased every year. By 2020, the order degrees of the two subsystems reached 0.98511 and 0.98251, respectively, and the development of port logistics and the hinterland economy reached a high level.

According to the analysis of Table 7 and Figure 2, from the overall perspective, from 1996 to 2020, the coupling coordination degree between Shenzhen port logistics and Shenzhen economic development increased year by year. In 1996, the coupling coordination degree of the two systems was only 0.04441, which was in the stage of disorder and recession. By 2001, it was transformed into a transitional stage. By 2008, it had reached 0.60750, which was a primary coordinated development stage. The period 2015–2016 was a good coordinated development stage. In 2017, it began to transform into a high-quality coordinated development stage. Shenzhen adhered to the innovation-driven development strategy and attached importance to the development concept of quality-led economic growth, which promoted the development of Shenzhen’s economic level. In 2020, Shenzhen’s GDP reached CNY 2767 billion, and the number of patent applications and authorizations ranked among the top in the country, achieving an effective improvement in Shenzhen’s international competitiveness. The rapid development of the regional economy and high tech has laid the foundation for the construction of port infrastructure in Shenzhen Port. In 2018, the Shenzhen Municipal People’s Government pointed out the importance of building a global maritime central city, emphasized the necessity of consolidating the location advantages of Shenzhen Port, promoting the coordinated development of port areas and forming a port–city linkage development model, and giving full play to the advantages of Shenzhen Port as a global container hub port. According to the calculation results of the order degree of the two subsystems, in 2020, the order degree of port logistics and hinterland economic development in Shenzhen port exceeded 0.98000, reaching a high level, and the coupling coordination degree between the two reached 0.99187, reaching the stage of high-quality coordinated development, with good development prospects.

4.3. Model Test

Based on the above research on the status of Shenzhen port logistics and Shenzhen’s economic development, the grey correlation and the level of coupling and coordination, in order to further verify the relationship between port logistics and the economic development of the hinterland, the cointegration test and Granger causality test were selected. Port cargo throughput (PCTHR) was selected as the proxy variable of Shenzhen port logistics, and the gross product of the primary industry (GPIP), the gross product of the secondary industry (GSIP) and the gross product of the tertiary industry (TIGDP) were selected as the proxy variables of Shenzhen’s economic development. LNPCTHR, LNGPIP, LNSIP and LNTIGDP represent the logarithmic values of the corresponding variables.

DLNPCTHR, DLNGPIP, DLLNSIP and DLNTIGDP represent the first-order difference sequences of the logarithmic variables. In order to alleviate the impact of heteroscedasticity on the results of the study, the original sequence was logarithmically processed. The main representative variables of the two subsystems are arranged as shown in

Table 8. Main representative variables of Shenzhen port logistics and hinterland economy.

Variable	Original Sequences	Log Posterior	First-Order Difference Sequence
Shenzhen port cargo throughput	PCTHR	LNPCTHR	DLNPCTHR
Port hinterland primary industry GDP	GPIP	LNGPIP	DLNGPIP
Port hinterland of the secondary industry GDP	GSIP	LNSIP	DLNSIP
Port hinterland tertiary industry GDP	TIGDP	LNTIGDP	DLNTIGDP

.

Descriptive statistics were first conducted and the results are shown in

Table 9. Descriptive statistical results.

Variable	Obs	Mean	Std. Dev.	Min	Max
LNPCTHR	25	2.249961	0.079353	2.081108	2.320929
LNGPIP	25	1.939489	0.178247	1.405493	2.064861
LNGSIP	25	2.451702	0.042769	2.394336	2.522542
LNTIGDP	25	2.848281	0.056028	2.737028	2.915881

. In order to avoid the pseudo-regression phenomenon of non-stationary data, the stata15 software was used to test the unit root of the logarithmic sequence, and the null hypothesis was rejected at less than 5%. The test results are shown in

Table 10. Unit root test results.

Sequence	Test Statistic	p-Value	Stationarity
LNPCTHR	−0.797	0.9660	unsteady
LNGPIP	−0.057	0.9936	unsteady
LNGSIP	−0.088	0.9932	unsteady
LNTIGDP	1.423	1.0000	unsteady
DLNPCTHR	−3.852	0.0141	stationary
DLNGPIP	−3.733	0.0202	stationary
DLNSIP	−4.255	0.0037	stationary
DLNTIGDP	−3.880	0.0129	stationary

, and the sequence is integrated in the first order.

On the basis of satisfying the same order single integration of variables, this paper selected the Johansen cointegration test method, which is more suitable for multivariate analysis, and used the software independent selection method to obtain the lag order determination countermeasure. The results are as shown in

Table 11. Lag order determination.

lag	LL	LR	df	p	FPE	AIC	HQIC	SBIC
0	10.0362				6.6e–06	−0.574881	−0.531702	−0.375924
1	146.161	272.25	16	0.000	7.4e–11	−12.0154	−11.7995	−11.0206
2	164.181	36.039	16	0.003	7.4e–11	−12.2077	−11.8191	−10.4171
3	181.795	35.229	16	0.004	1.2e–10	−12.3615	−11.8001	−9.77503
4	245.946	128.3 *	16	0.000	6.4e–12 *	−16.9472 *	−16.2132 *	−13.5649 *

* represents the optimal lag order option under different criteria.

. According to the analysis of the results in the table, when lag = 4, the five criteria (LR, FPE, AIC, HQIC, SBIC) are minimized. Therefore, the lag 4 order is selected, and there is a cointegration relationship. The cointegration rank is further determined as shown in

Table 12. Cointegration rank determination.

Maximum Rank	Parms	LL	Eigenvalue	Trace Statistic	5% Critical Value
0	48	154.77344		111.7764	39.89
1	55	199.09615	0.98532	23.1309 *	24.31
2	60	206.30023	0.49647	8.7228	12.53
3	63	210.60847	0.33655	0.1063	3.84
4	64	210.66162	0.00505

* represents the rejection of the corresponding null hypothesis when trace statistic is less than its 5 % critical value.

. The trace test results show that 23.1309 < 24.31. Therefore, the null hypothesis of cointegration rank 1 cannot be rejected at the level of 5%. * is automatically generated by stata software. There is a cointegration relationship among LNPCTHR, LNGPIP, LNGSIP and LNTIGDP. It can be considered that there is a long-term equilibrium relationship between Shenzhen port logistics and Shenzhen economic development.

The lag order is 4 and the cointegration rank is 1. The Johansen MLE method is used to estimate the vector error correction model (VECM). The calculation results using stata15 software (SE, Beijing Netnumber Times Technology Co., Ltd., Beijing, China) are shown in

Table 13. Estimation results of cointegration equation.

beta	Coef.	Std. Err.	z	P >|z|	95% Conf.	Interval
_ce1
LNPCTHR	1
LNGPIP	−0.3464919	0.0451333	−7.68	0.000	−0.4349516	−0.2580322
LNGSIP	−2.11292	0.08044	−26.27	0.000	−2.27058	−1.95526
LNTIGDP	1.02525	0.08132	12.61	0.000	0.865866	1.184633
_cons	14.03049

. The correction term of LNPCTHR is standardized to 1. The p-values corresponding to LNGPIP, LNGSIP and LNTIGDP are all 0.000, which are significant at the level of 1%. According to this result, the cointegration vector is (1, −0.35, −2.11, 1.02), so the estimated cointegration relationship is:

$$\mathrm{LNPCTHR}=-14.03+0.35\mathrm{LNGPIP}+2.11\mathrm{LNGSIP}-1.03\mathrm{LNTIGDP}$$

(18)

The Granger test is used to statistically estimate the dependence between different time series. The results of the Granger relationship test obtained by stata15 software are shown in

Table 14. Granger causality test results.

Equation	Excluded	chi2	df	Prob > chi2
LNPCTHR	LNGPIP	6.3483	4	0.175
LNPCTHR	LNGSIP	11.024	4	0.026
LNPCTHR	LNTIGDP	15.434	4	0.004
LNGPIP	LNPCTHR	13.271	4	0.010
LNGSIP	LNPCTHR	62.716	4	0.000
LNTIGDP	LNPCTHR	61.335	4	0.000

. The fifth column (prob > chi2) represents the p-value. The p-value of the first row is 0.175, greater than 5%, and the null hypothesis cannot be rejected. LNGPIP is not the Granger cause of LNPCTHR. The p-values in other rows are all less than the critical value of 0.05. LNPCTHR is the Granger cause of LNGPIP, and there is a two-way Ganger causality between LNPCTH and LNGSIP, LNTIGDP. That is, the development level of Shenzhen port logistics has a significant role in promoting the GDP of the primary industry, the GDP of the secondary industry and the GDP of the tertiary industry in Shenzhen. The GDP of the secondary industry and the GDP of the tertiary industry have a significant role in promoting the development of port logistics, while the GDP of the primary industry has no significant role in promoting the development of port logistics. This confirms the mutual driving relationship between port logistics and hinterland economic development.

5. Discussion

Port logistics and the hinterland economy depend on each other and promote each other. Port logistics is the support for hinterland economic development. Hinterland economic development also lays the foundation for the development of port logistics. The coordinated development of the two jointly promotes the process of regional sustainability. According to the empirical analysis of Shenzhen port and hinterland economic development, this paper puts forward some suggestions to promote the sustainable development of the port city:

(1)

Promote supply-side structural reform, promote regional economic development and consolidate port infrastructure construction. The grey correlation degree between the number of berths in Shenzhen port and the economic development of Shenzhen is more than 0.8, which has a strong correlation. Therefore, with the development of the hinterland economy as the driving force, we should pay attention to the improvement of coastal port hardware facilities, especially strengthening the number and specialization of port berths, realizing the interconnection and interaction between coastal port logistics and the hinterland economy, and achieving the purpose of better coordinated development between them.

(2)

Encourage technological and operational model innovation, emphasizing low-carbon development. From 1996 to 2020, the coupling coordination degree of Shenzhen port logistics and hinterland economic development showed an upward trend year by year. By 2020, the coupling coordination degree reached 0.99187, which realized the high-quality coordinated development of port logistics and the hinterland economy. Promoting the construction of green smart ports will promote the sustainable development of ports. Therefore, paying attention to the development of the secondary industry and the tertiary industry in the port hinterland, guiding innovation output, and supporting regional innovation and development can effectively promote the improvement in the intelligent level of coastal port equipment, and build more efficient port operations, thereby reducing carbon emissions and resource depletion, and promoting the sustainable development of coastal port logistics and the hinterland economy.

6. Conclusions

As a strong support for the sustainable development of the country (region), port logistics and the hinterland economy also have a certain promoting effect on the enhancement of the competitiveness of the country (region). According to the correlation analysis, synergy analysis and model test results of Shenzhen port logistics and hinterland economic development, the following conclusions can be drawn:

(1)

There is a high correlation between port logistics and hinterland economic development. From 1996 to 2020, the grey correlation degree between Shenzhen port logistics and hinterland economic development is greater than 0.5, and the whole is in the upper middle range, which proves that the internal correlation between the two systems is strong. The grey correlation degree between xo1, xo3, xo4 and the overall economic development system of the hinterland is more than 0.8. Therefore, the port cargo throughput, port berths and 10,000 t berths have a significant impact on the hinterland economy. The grey correlation degree between xl7 and the whole port logistics system is more than 0.8, so the actual utilization of foreign capital has a significant impact on the development of port logistics.

(2)

The coordinated development model of port logistics and the hinterland economy has been effectively improved. Within the scope of the research time, the order degree of Shenzhen port logistics and hinterland economic development is a spiral upward trend. In recent years, it has reached a higher level of development. The coupling coordination degree of the two has been increasing year by year. From 0.04441 in 1996 to 0.91065 in 2017, it has entered the stage of high-quality coordinated development. The Shenzhen Municipal People’s Government has further emphasized the importance of the coordinated development of the port city, and the coupling coordination degree has continued to rise.

(3)

There is a long-term equilibrium relationship between port logistics and hinterland economic development, and the two promote each other. The results of the Johansen cointegration test show that there is a cointegration relationship between LNPCTHR, LNGPIP, LNGSIP and LNTIGDP, and there is a two-way Ganger causality between LNPCTH and LNGSIP and LNTIGDP, which means that port logistics and hinterland economic development influence and restrict each other.