Efficiency Analysis of the Coastal Port Group in the Yangtze River Delta

Abstract

In recent years, the coastal ports of the Yangtze River Delta have rapidly developed with the progress of science and technology, which has caused some problems on account of the rapid development of ports. On the one hand, there is fierce competition within the same port group; on the other hand, many ports waste resources. This study selected the three-stage data envelopment analysis (DEA) and Malmquist index models to calculate and analyze the efficiency value of the coastal port group in the Yangtze River Delta; the study was conducted to make a reference for the formulation of the optimization strategy from the perspectives of static and dynamic efficiency. The results show that from the perspective of static efficiency, the comprehensive efficiency of the Yangtze River Delta coastal port cluster is at the upper-middle level. However, it has not yet reached the frontier surface, and the low scale efficiency is why the port group has not been called the frontier surface. From the perspective of dynamic efficiency, the total factor productivity of the Yangtze River Delta port group has increased by 3.6% in the past five years. Technological progress and comprehensive technical efficiency have improved. The optimization strategy was formulated according to the problems faced by the Yangtze River Delta port group and the reasons for not reaching the frontier.

1. Introduction

With the implementation of the open-door policy, the demand for port services in China has significantly increased [1,2,3]. The transportation of seaborne goods has rapidly grown over the past decade and plays an important role in the global supply chain [4,5,6]; it is the backbone of the global supply chain [7,8]. Determining atmospheric emissions from marine activities has become highly relevant on an international scale [9]. However, the rapid development of China’s economy has benefited from the extensive use of energy resources, which has ultimately increased carbon emissions. As a result, China has become the largest carbon emitter in the world [10,11]. Thus, controlling the transportation energy consumption and carbon emissions is an important issue for sustainable economic development [12].

Since 2011, the development of Chinese ports has entered a period of steady growth. With the rapid expansion of ports, the port facilities have been greatly improved. As a transportation node connecting the two sides of land and sea, ports play an essential role in international trade [13]. Now that the functions of single ports have been completed, the port groups and the port industry area have become the focus of development. The Chinese government has encouraged many ports in all coastal provinces to start constructing and expanding while decentralizing port governance [14,15,16]. Relying on the Shanghai international shipping hub, the Yangtze River Delta Group mainly includes Shanghai Port, Ningbo-Zhoushan Port, and Lianyungang Port in Figure 1. Because Shanghai Port is the most important port along the so-called 21st Century Maritime Silk Road, the development potential of Shanghai Port is enormous [17,18]. It is one of the busiest container ports in the world [19], and it is also an important port for supporting China’s “One Belt, One Road” strategy.

Due to the inaccurate functional positioning of the ports in the Yangtze River Delta, the major ports are in a state of a staggering economic hinterland. The services of each port can effectively cover the entire triangular area, and each port is competing for resources in the face of its interests, such that the competition among the ports is very fierce; as result, there is port overcapacity and an underutilization of port facilities [20]. Therefore, the resource integration of the port is insufficient, and the layout is still limited to various regions. Thus, cooperation between the different ports is needed [21,22]. The major ports have also implemented two modes of port cooperation: complementary cooperation and cooperative competition [23,24,25]. The competition and cooperation between Shanghai Port and Ningbo-Zhoushan Port is the subject of greatest concern in academic circles [26,27].

Although the operating income of port companies in 2021 will generally show an upward trend, due to the impact of crude oil production cuts, trade frictions, and other factors, the port operating costs have sharply risen, which has reduced ports’ profits to some extent. At present, Mexico continues to use fossil fuels in the industrial sector, especially for marine fuels such as marine diesel [28]. NO³⁻ and SO₄⁻² are released from the emissions that are related to the fuel combustion [29]. However, Nordic ports have led the shift from ship fuel to liquefied natural gas by installing petrol stations [30], as SOx, NOx, and PM emissions caused by the combustion of liquefied natural gas (LNG) are negligible. With new regulations designed for this purpose, the European Commission is now forcing all major EU ports to create LNG supply facilities [31]. Under such a severe situation, it has become particularly important to investigate how to improve the operation of the Yangtze River Delta port group and address the pollution problem.

The 14th Five-Year Plan for China’s transportation industry starts in 2020 and ends in 2025 [32]. The plan highlighted that the construction of smart ports should be accelerated to be in line with the world’s advanced level. The plan also pointed out that the comprehensive energy consumption and carbon emission rate, i.e., the port unit operating throughput, decreased by 3% and 4%, respectively; the clean energy and new energy truck retention rate increased by 50%.

Most of the existing literature has focused on measuring and analyzing the efficiency of a single or multiple ports, or the efficiency of a single port group. In this paper, the efficiency of the Yangtze River Delta port group is measured, and an optimization strategy is formulated. Systematic and comprehensive, the paper can fill the gap in the application.

2. Theoretical Model

2.1. Three-Stage DEA Model

Because the traditional DEA method has shortcomings in the research of port efficiency, Fried et al. analyzed the feasibility of the three-stage DEA model in their study for the first time [33]. Wang Ling et al. used the three-stage DEA model to measure and analyze the efficiency values of 21 coastal ports in China; the results showed that the development of Chinese ports has problems, such as a blind expansion of production scale, which has resulted in a low overall efficiency [34]. The three-stage DEA model uses the stochastic frontier analysis (SFA) principle, which can eliminate the environmental indicators and random influences found in the traditional DEA model. This can better reflect the actual situation of the port’s “multiple inputs and multiple outputs” to make a correct evaluation. Aihu and Wenling analyzed the dynamic efficiency of the top ten ports in the Pacific Ocean region from 1998 to 2008 using CCR, BCC, and three-stage DEA models; the authors concluded that the efficiency level estimated by the three-stage DEA model was the most objective of the three methods [35]. Tongzon sought to provide satisfactory answers to cross-port efficiency comparisons by applying the DEA analysis to samples of Australian as well as other international ports with relevant data [36].

2.1.1. First Stage: BCC Model

The first stage used the uncorrected DEA model to calculate the raw efficiency. The meanings expressed by the BCC model in

Table 1. The specific meaning of each indicator in the BCC model.

Index	Mean
n	The number of decision-making units
m	Number of inputs
t	Number of outputs
$S_i^{-}$	Input slack variable
$S_j^{+}$	Output slack variable value
xir	Input indicators
yjr	Output indicators
θ	The value of TE; if = 1, the decision-making unit is said to be valid, otherwise it is invalid.

and each indicator are as follows:

$$min\left[\theta -\epsilon \left({{\displaystyle \sum }}_{i=1}^m{S}_i^{-}+{{\displaystyle \sum }}_{j=1}^t{S}_j^{+}\right)\right]$$

(1)

$$s.t.:{{\displaystyle \sum }}_{r=1}^n{\lambda }_r{x}_{ir}+S_i^{-}-\theta x_{ir0}=0$$

(2)

$$D^t\left(X^t,Y^t\right)$$

(3)

$${\lambda }_r\ge 0,r=1,2,3\dots ,n$$

(4)

$$S_i^{-},S_j^{+}\ge 0$$

(5)

The technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) are obtained through the first stage, and the slack variables of the input variables are calculated on this basis. Because environmental influences and the influence of error factors were not removed, a second stage study was necessary.

2.1.2. Second Stage: SFA Model

The second stage adopted the SFA method. It is necessary to analyze the environment and random factors of the port. First, a slack variable model for input indicators is introduced, as follows:

$$S_{ni}=f\left(Z_i;{\beta }_n\right)+V_{ni}+{\mu }_{ni};i=1,2\dots ,n$$

(6)

The specific meaning of each indicator is shown in

Table 2. The specific meaning of each indicator in the second stage.

Index	Mean
Sni	The slack value of the nth input of the ith decision unit
Zi	Environment variable
βn	Coefficients of Environmental Variables
vni + μni	Mixed error term
vni	Random interference
μni	Management inefficiency, subject to a normal distribution truncated at zero, i.e., μ~N+ (0, σv2)
V~N (0, σv2)	Random error term
$X_{ni}^A$	Adjusted input
Xni	Investment before adjustment
ε= v − μ	Mixed error term

.

The second stage regression analysis aims to eliminate the influence of external environmental factors on the efficiency evaluation results so that all decision-making units are in the same environment. The adjustment formula for this stage is as follows:

$$\begin{gathered} X_{ni}^A=X_{ni}+\left[max\left(f\left(Z_i;{\widehat{\beta }}_n\right)\right)-f\left(Z_i;{\widehat{\beta }}_n\right)\right]+\left[max\left(v_{ni}\right)-v_{ni}\right] \\ i=1,2,\dots I;n=1,2,\dots N \end{gathered}$$

(7)

$max\left(f\left(Z_i;{\widehat{\beta }}_n\right)\right)-f\left(Z_i;{\widehat{\beta }}_n\right)$ is used to adjust the external environmental factors; $max\left(v_{ni}\right)-v_{ni}$ ensures that both the ports and port clusters are placed in the same external environment.

The regression takes the form of a cost function, and the separation formula is as follows:

$$\begin{gathered} E\left(\mu \mid \epsilon \right)={\sigma }_{*}\left[\frac{\varphi \left(\lambda \frac{\epsilon }{\sigma }\right)}{\varphi \left(\frac{\lambda \epsilon }{\sigma }\right)}+\frac{\lambda \sigma }{\sigma }\right] \\ {\sigma }_{*}=\frac{{\sigma }_{\mu }{\sigma }_v}{\sigma }, \sigma =\sqrt{{\sigma }_{\mu }{ }^2+{\sigma }_v{ }^2}, \lambda =\frac{{\sigma }_{\mu }}{{\sigma }_v} \end{gathered}$$

(8)

The formula for calculating the random error term μ is as follows:

$$E\left[v_{ni}\mid v_{ni}+{\mu }_{ni}\right]=s_{ni}-f\left(z_i,{\beta }_n\right)-E\left[{\mu }_{ni}\mid v_{ni}+{\mu }_{ni}\right]$$

(9)

2.1.3. The Third Stage: The Adjusted DEA Model

Using the regression results of the previous stage to adjust the value of the original input index, DEAP2.1 (DEAP Version 2.1, Tim Coelli, Queensland, Australia) software was then used to calculate the efficiency of each port group after adjusting the input index. This way, the efficiency of each port group could be more accurately measured.

Based on the above, the practical process of the three-stage DEA method used in this study is shown in Figure 2.

2.2. Malmquist Index Method

The Malmquist index model was first proposed in 1953. However, this method did not attract enough attention until Caves et al. applied it to measure productivity changes in the medical and financial fields, among others. As research into port efficiency increased, some researchers from home and abroad began to merge the Malmquist model with the DEA model. This measure, as opposed to the DEA model, can capture variations in port productivity. For example, Wu and Goh used the DEA model and Malmquist index to measure and compare port efficiencies in developed and developing countries [37]. Xiao Xianghong and Song Bingliang used the DEA model and Malmquist index method to study the well-developed ports in China. The results showed that the overall efficiency level of the ports was not high, and that corresponding measures should be taken to improve the port efficiency [38].

Assuming that $X^{t+1}$, $Y^{t+1}$, $X^t$, and $Y^t$ represent the input and output of the t + 1 and t periods, then $D^t\left(X^t,Y^t\right)$, $D^{t+1}\left(X^t,Y^t\right)$, $D^t\left(X^{t+1},Y^{t+1}\right)$, and $D^{t+1}\left(X^{t+1},Y^{t+1}\right)$ are the corresponding distance functions, respectively. At this time, the productivity indices in the t and t + 1 periods is expressed by $M^t$ and $M^{t+1}$:

$$M^t=D^t\left(X^t,Y^t\right)/D^t\left(X^{t+1},Y^{t+1}\right)$$

(10)

$$M^{t+1}=D^{t+1}\left(X^t,Y^t\right)/D^{t+1}\left(X^{t+1},Y^{t+1}\right)$$

(11)

Among them, $D^t\left(X^t,Y^t\right)$ refers to the distance function of the production efficiency in the t period relative to the optimal efficiency that can be achieved; $D^{t+1}\left(X^t,Y^t\right)$, $D^t\left(X^{t+1},Y^{t+1}\right)$, and $D^{t+1}\left(X^{t+1},Y^{t+1}\right)$ have the same explanation. $M^t$ and $M^{t+1}$ represent the change in the total factor productivity (TFP) under the condition of technical level in period t and period t + 1, respectively. The final Malmquist is as follows:

$$M\left(X^{t+1},Y^{t+1},X^t,Y^t\right)=\frac{D^t\left(X^t,Y^t\right)}{D^t\left(X^{t+1},Y^{t+1}\right)}\times {\left[\frac{D^{t+1}\left(X^t,Y^t\right)}{D^t\left(X^{t+1},Y^{t+1}\right)}\right]}^{\frac{1}{2}}$$

(12)

In order to separate the technical efficiency and technological progress from the above indicators, the formula of this index is extracted. Finally:

$$M\left(X^{t+1},Y^{t+1},X^t,Y^t\right)=\frac{D^t\left(X^t,Y^t\right)}{D^{t+1}\left(X^{t+1},Y^{t+1}\right)}\times {\left[\frac{D^{t+1}\left(X^t,Y^t\right)}{D^t\left(X^t,Y^t\right)}\times \frac{D^{t+1}\left(X^{t+1},Y^{t+1}\right)}{D^t\left(X^{t+1},Y^{t+1}\right)}\right]}^{\frac{1}{2}}=effch\times techch$$

(13)

$effch$ represents the change in the comprehensive technical efficiency. When $effch>1$, the ratio of the actual production distance to the optimal output distance is greater than 1, meaning that the comprehensive technical efficiency has been improved. $techch$ stands for the technological progress and changes; when $techch>1$, it means that the technological innovation has improved.

The productivity indicators mentioned above all assume constant returns to scale. However, considering changes in returns to scale, changes in the comprehensive technical efficiency can be segmented into the changes in pure technical efficiency (pech) and scale efficiency (sech).

3. Indicator Selection and Data Sources

3.1. Establishment of Evaluation Index System

3.1.1. Determination of Input and Output Indicators

The main distinction between ports and other industrial sectors is that ports can only provide services such as transportation as opposed to directly producing things; this makes choosing indicators very different. Based on summarizing relevant domestic and foreign literature, this study decided to use the production method to study port efficiency in combination with actual data availability. The production method takes the existing facilities of the port as the input index and the port output (namely the throughput) as the output index when calculating and evaluating the efficiency of the port.

The input indicators used in this paper are as follows:

• The number of berths for production (number). Suppose the number of berths is too low. In that case, the ship will not be able to start production immediately after arriving at the wharf and can only wait outside the wharf until there are free berths to berth, which delays much time and seriously affects the work efficiency of the port.
• Length of the wharf for production (in meters). The length of the production wharf serves as an important production indicator because it mostly influences the number of berths at each port. The longer the wharf is, the more berths can be created.

The output indicators used in this paper are as follows:

• Cargo throughput (10,000 tons): Cargo throughput refers to the amount of cargo that enters or leaves the port and is loaded and unloaded by water transport within a certain period, usually measured over the year. This indicator is the leading indicator to measure the production level.
• Container throughput (10,000 TEU): Container throughput refers to the number of containers entering and leaving the port within a certain period in units of 20-foot containers, which is usually measured over the year. Through this indicator, the development and operation of the port can be evaluated.

3.1.2. Determination of Environmental Factor Indicators

The operation of a port is related to its external environment, and the development of a port is affected by the external environment. Therefore, various environmental impact factors need to be considered. According to the actual situation of the port and the available data, this study selected the following two main environmental impact factors:

• The total import and export volume of port cities. The import and export trade volume within the region is the primary source of demand for ports. The transportation of coastal ports is the basis for developing foreign trade, which reflects the operation of the port to a certain extent. The volume of import and export trade will directly affect the operation of the port.
• The Gross Domestic Product (GDP) of port cities. The GDP of a city where a port is located can directly reflect the region’s economic development. The better the city’s economic development, the more demand it can provide for the port in the region, which can increase the use of the port and improve its throughput quantity, thus having a particular impact on the efficiency.

The specific port efficiency evaluation system is shown in Figure 3:

3.2. Selection of Samples and Data Sources

This paper studies the efficiency of the coastal port group in the Yangtze River Delta. The sample selects three representative ports in China’s coastal port group in the Yangtze River Delta: Shanghai Port, Ningbo-Zhoushan Port, and Lianyungang Port. Selecting these ports can comprehensively analyze the efficiency of China’s Yangtze River Delta coastal port group.

The source of the port data selected in this paper is the China Port Yearbook, China Statistical Yearbook, and the 2016–2020 statistical yearbook of each port city [39,40]. The output, input, and environmental variable data of each port from 2016 to 2020 are shown in the Appendix.

4. An Empirical Analysis of the Efficiency Evaluation of the Coastal Port Groups in the Yangtze River Delta

4.1. Static Efficiency Analysis of the Coastal Port Group in the Yangtze River Delta

4.1.1. First Stage of DEA Efficiency Value Analysis

In the first stage, the DEAP2.1 software used the BCC model to calculate the efficiency values of the port. According to the above, TE stands for comprehensive efficiency, PTE stands for pure technical efficiency, and SE stands for scale efficiency. According to the introduction of the above DEA method, the decision-making unit with a total efficiency value of 1 is effective, and vice versa. The results of the first stage are shown in

Table 3. The efficiency value of the coastal port group in the Yangtze River Delta from 2016 to 2020 in the first stage.

Year	Port	Technical Efficiency	Pure Technical Efficiency	Scale Efficiency	Returns to Scale
2016	Shanghai Port	0.671	1	0.671	drs
	Ningbo-Zhoushan Port	0.549	1	0.549	drs
	Lianyungang Port	0.663	1	0.663	irs
	Yangtze River Delta Port Group	0.628	1	0.628	--
2017	Shanghai Port	0.768	1	0.768	drs
	Ningbo-Zhoushan Port	0.636	1	0.636	drs
	Lianyungang Port	0.752	1	0.752	irs
	Yangtze River Delta Port Group	0.719	1	0.719	--
2018	Shanghai Port	0.758	1	0.758	drs
	Ningbo-Zhoushan Port	0.633	1	0.633	drs
	Lianyungang Port	0.709	1	0.709	irs
	Yangtze River Delta Port Group	0.7	1	0.7	--
2019	Shanghai Port	0.749	1	0.749	drs
	Ningbo-Zhoushan Port	0.595	1	0.595	drs
	Lianyungang Port	0.73	1	0.73	irs
	Yangtze River Delta Port Group	0.691	1	0.691	--
2020	Shanghai Port	0.726	1	0.726	drs
	Ningbo-Zhoushan Port	0.593	1	0.593	drs
	Lianyungang Port	0.686	0.849	0.808	irs
	Yangtze River Delta Port Group	0.668	0.95	0.709	--

Note: drs stands for diminishing returns to scale, irs stands for increasing returns to scale, and -- stands for constant returns to scale.

and

Table 4. Comprehensive Efficiency of the Yangtze River Delta port group from 2016 to 2020.

Yangtze River Delta Port Group	TE	PTE	SE
2016	0.628	1	0.628
2017	0.719	1	0.719
2018	0.7	1	0.7
2019	0.691	1	0.691
2020	0.668	0.95	0.709
Mean	0.681	0.99	0.689

:

This paper uses the mean value of port efficiency within the port group to represent the efficiency of the port group. The returns to scale of each port can be directly calculated through the DEAP software, and the returns to scale of each port is shown in Table 3. According to the results of the first stage, the comprehensive efficiency and comprehensive efficiency trend chart of the coastal port group in the Yangtze River Delta from 2016 to 2020 are presented in Table 4.

It can be seen from Table 4 that without considering the environment and random factors, the average comprehensive efficiency of the Yangtze River Delta coastal port group from 2016 to 2020 in the first stage was 0.681, which is at a medium level and has not reached the frontier in the past five years. A difference of 0.319 was observed from the leading edge. As can be seen from Figure 4, its comprehensive efficiency has been in a slow downward trend from 2017 to 2020, and the comprehensive efficiency has dropped from 0.719 to 0.668. From 2016 to 2019, the pure technical efficiency of the Yangtze River Delta port group was 1, and in 2020, it was 0.95, which was close to 1. The average scale efficiency of the Yangtze River Delta port group in the past five years was 0.689, and there was a downward trend from 2017 to 2019, and the relative pure technical efficiency is at a low level. The Yangtze River Delta port group’s low scale efficiency is the reason why it has not reached the frontier in the last five years. The two main ports in the port group, Shanghai Port and Ningbo-Zhoushan Port are in the process of resources, routes, and other aspects. Intense competition leads to disorderly competition without the rational allocation of resources, thus affecting the efficiency of the entire port group. The port group’s overall efficiency and scale efficiency have displayed a slow downward trend.

The first stage examined a single port in the Yangtze River Delta port group. In this port group, the comprehensive efficiency of Shanghai Port and Lianyungang Port is relatively high, and the comprehensive efficiency of Ningbo-Zhoushan Port is relatively low. The pure technical efficiency of Shanghai Port is 1, so the fundamental reason for its ineffectiveness is that the scale efficiency is low. The return to scale of Shanghai Port has been decreasing in the past five years, indicating that Shanghai Port is not operating at the optimal scale, resulting in a poor input and output match. Lianyungang Port has been increasing returns to scale in the past five years, and the low scale efficiency is also the main reason for its ineffectiveness. The comprehensive efficiency of Ningbo-Zhoushan port is lower than the average value of the comprehensive efficiency of the Yangtze River Delta port group, with decreasing returns to scale in the past five years, and its pure technical efficiency is 1. Therefore, the main reason for invalidation is the same as that of Shanghai Port.

4.1.2. Second Stage SFA Regression Analysis

In the second stage, Frontier4.1 (Frontier Version 4.1, Tim Coelli, Queensland, Australia) software takes the slack variables of each input index (number of production berths, length of production wharves) calculated in the first stage as the explained variables. The total import and export of the port cities and the GDP of the port cities were used as explanatory variables in the SFA regression analysis. The original input of each port sample was adjusted according to the regression results to exclude the influence of environmental factors.

Table 5. The second stage SFA regression results.

Dependent Variable	Production Berth Slack			Production Wharf Length Slack
-	Coefficient	Standard Error	T-Value	Coefficient	Standard Error	T-Value
Constant term	−71.7338 ***	0.9913	−72.3643	−4127.8979 ***	1	−4127.8629
Total import and export of port cities	−0.0078 **	0.0021	−3.7124	−0.4492	0.2991	−1.502
Port city GDP	0.0088 **	0.0014	6.1849	0.5100 ***	0.0636	8.0203
σ2	8773.8683 ***	1	8773.8804	119,935,070.0000 ***	1	1.2 × 108
γ	0.9999 ***	0.0002	4382.6898	0.9999 ***	0	34,448,179
Log likelihood	−81.5118 ***			−149.8594 ***
LR test value	7.2125 **			9.0469 ***

Note: ***, ** indicate significance at the 1%, 5% levels, respectively.

shows that most parameter estimates are statistically significant. It can be seen that the two environmental variables of total import and export of port cities and GDP of port cities have specific explanatory effects on the slack variables of the number of production berths and the length of production wharves. Table 5 shows that the γ-values of the two variables are close to 1, and both the 1% significance test and the LR test are passed, which indicates that the three-stage DEA is reasonable. The influence of environmental factors must be excluded.

According to the positive and negative results of the regression coefficient, the following results are as follows: when the coefficient is positive, it means that the input relaxation variable is positively correlated with the environment variable; that is, the increase in the environmental variable will lead to a rise in the input relaxation variable, which is an adverse effect and will reduce the efficiency; Therefore, when the coefficient is negative, it means that as the environment variable increases and the input relaxation variable decreases, which is a favorable effect and therefore can improve the efficiency.

The following conclusions can be drawn from the regression results.

• The environmental factor of the total import and export volume of port cities is beneficial to the slack variables of the two input indicators, as import and export and trade have a positive relationship. The increase in the total import will positively promote and impact the throughput and output of the port. In contrast, the expansion of the total import and export will stimulate the throughput of the port, which will directly lead to the throughput increasing, which expands the scale of the port and effectively solves the problem of waste of resources. As a result, the efficiency of the port has been improved.
• The port city GDP is unfavorable for the relaxation variable of the two input indicators. That is, the increase in the environmental variable of port city GDP will lead to a rise in the input relaxation variable. The results show that the effect of the port city GDP on the relaxation variable of the two inputs is negative. This means that with the growth of the port city’s GDP and economic prosperity, the government will continue to increase investment in ports. There is an excessive investment. Still, the profits of ports are not as high as expected, but this will lead to a decline in the efficiency of the ports.

4.1.3. Third Stage DEA Measure

According to the analysis of the second stage, the original input values of the port were adjusted according to the formula mentioned in the third part of this paper so that all decision-making units were in the same environment. The adjusted units were re-replaced into the DEA model and continued. The efficiency of the port was measured, and the final result is therefore a result that is not disturbed by environmental factors. The adjusted input index value of each port is shown in the Appendix. Finally, the improved efficiency value of the coastal port group in the Yangtze River Delta from 2016 to 2020 was calculated using DEAP2.1 and is shown in

Table 6. The efficiency values of the Yangtze River Delta coastal port group from 2016 to 2020 in the third stage.

Year	Port	Technical Efficiency	Pure Technical Efficiency	Scale Efficiency	Returns to Scale
2016	Shanghai Port	0.766	1	0.766	drs
	Ningbo-Zhoushan Port	0.592	1	0.592	drs
	Lianyungang Port	0.535	0.923	0.58	irs
	Yangtze River Delta Port Group	0.631	0.974	0.646	--
2017	Shanghai Port	0.856	1	0.856	drs
	Ningbo-Zhoushan Port	0.672	1	0.672	drs
	Lianyungang Port	0.614	0.98	0.626	irs
	Yangtze River Delta Port Group	0.714	0.993	0.718	--
2018	Shanghai Port	0.855	1	0.855	drs
	Ningbo-Zhoushan Port	0.667	1	0.667	drs
	Lianyungang Port	0.586	0.927	0.632	irs
	Yangtze River Delta Port Group	0.703	0.976	0.718	--
2019	Shanghai Port	0.855	1	0.855	drs
	Ningbo-Zhoushan Port	0.644	1	0.644	drs
	Lianyungang Port	0.604	0.944	0.64	irs
	Yangtze River Delta Port Group	0.701	0.981	0.713	--
2020	Shanghai Port	0.787	1	0.787	drs
	Ningbo-Zhoushan Port	0.62	1	0.62	drs
	Lianyungang Port	0.625	0.857	0.729	irs
	Yangtze River Delta Port Group	0.677	0.952	0.712	--
2016–2020	The average value of the Yangtze River Delta port group	0.685	0.975	0.701	--

Note: drs stands for diminishing returns to scale, irs stands for increasing returns to scale, and -- represents Returns to scale remains unchanged.

.

As can be seen from Table 3 and Table 6, the mean values of TE, PTE, and SE of the Yangtze River Delta coastal port group measured in the first stage from 2016 to 2020 are 0.681, 0.99, and 0.689, respectively, and 0.685, 0.975, 0.701 in the third stage, respectively. Through a comparison, it was found that the efficiency values of the two stages are indeed different, so it is necessary to exclude the influence of environmental factors. The comparative analysis of the efficiency values of the first and third stages of the Yangtze River Delta port group is shown in Figure 5, Figure 6 and Figure 7:

Comparing the results of the first and third stages, the efficiency values of the Yangtze River Delta port group have changed. The Yangtze River Delta port group’s comprehensive efficiency and scale efficiency have both improved, and they are at an upper-middle level but have not yet reached the frontier. The pure technical efficiency has significantly dropped, but from 2016 to 2020, the average pure technical efficiency of the port group is 0.975, which is close to 1, and the average scale efficiency is 0.701. Therefore, after excluding the influence of environmental factors, the port’s low scale efficiency is still the cause and main reason why the group has not reached the frontier.

In the Yangtze River Delta port group, the efficiency of each port is also quite different. In the third stage, the overall efficiency and scale efficiency of Shanghai Port and Ningbo-Zhoushan Port have improved, and Shanghai Port has the highest efficiency. Both ports are among the largest worldwide, but they have not been at the forefront for the past five years. The pure technical efficiency of these two ports is 1, indicating that the technical level of the ports is relatively high, but the scale efficiency is relatively low. The returns to scale have been decreasing in the past five years. This is because there is competition between Shanghai Port and Ningbo-Zhoushan Port regarding supply, routes, flights, and other aspects. Furthermore, the port positioning is not precise, which often causes a serious waste of resources, resulting in resource competition between ports and restricting efficiency improvement.

The return to scale of Lianyungang has been increasing in the past five years. The comprehensive efficiency measured in the third stage has significantly dropped. It has the lowest efficiency in the port group, and the pure technical efficiency is close to 1. Therefore, its ineffectiveness is mainly due to its low scale efficiency. It is impossible to realize the combined allocation of existing resources, so it is necessary to improve the management level, form a scientific management system, and optimize the allocation of port resources.

4.2. Dynamic Efficiency Analysis of the Yangtze River Delta Coastal Port Group

4.2.1. Analysis of Dynamic Efficiency in Each Year

In this study, DEAP2.1 software measured the Malmquist index and its decomposition terms for the Yangtze River Delta coastal port group from 2016 to 2020. The Malmquist Productivity Index (MPI) represents the total factor productivity, efficiency change (EC) represents the changes in the comprehensive technical efficiency, and technology change (TC) represents the changes in the technological progress. In this paper, the average port efficiency in each port group is the port group efficiency, as shown in

Table 7. Changes in the Malmquist Index and decomposition items in each year.

Year	Changes in Comprehensive Technical Efficiency	Technological Progress Changes	Pure Technical Efficiency Change	Scale Efficiency Changes	Malmquist Index
2016–2017	1.035	1.026	1.007	1.027	1.062
2017–2018	0.987	0.868	1	0.987	0.857
2018–2019	1.007	1.264	1	1.007	1.273
2019–2020	1.001	0.992	1	1.001	0.993
Mean	1.007	1.028	1.002	1.005	1.036

and Figure 7.

If the MPI, the total factor productivity index, is greater than 1, it means that the efficiency of the port has improved; if it is less than 1, it means that the efficiency of the port has declined. As shown in Table 7 and Figure 8, from 2016 to 2020, the Yangtze River Delta port group’s average total factor productivity index was 1.036, indicating that the overall total factor productivity increased by 3.6% in the past five years, and the port efficiency has improved. From 2016 to 2020, the comprehensive technical efficiency change index and the technological progress change index increased by 0.7% and 2.8%, respectively, indicating that the technical level, technological progress, and scale efficiency of the port have promoted the improvement of the total factor productivity of the port and the improvement of the port efficiency.

In terms of stages, the total factor productivity index from 2016 to 2017 was more significant than 1. The efficiency of the port improved, indicating that the port has accelerated the construction of wisdom and science while innovating in technology, management, and service and has achieved some results. From 2017 to 2018, the total factor productivity index was 0.857, the production efficiency of ports declined, and the change index of technological progress was 0.868, indicating that the technological progress of China’s coastal ports declined during this period, which is also the main reason for the decline in port efficiency. The total factor productivity index from 2018 to 2019 was 1.273, indicating that the international shipping market continued to pick up; the contradiction between supply and demand has eased, and the overall performance of the ports improved. During this period, all change indices exceeded 1, and the port efficiency improved. From 2019 to 2020, the total factor productivity change index was 0.993, the technological progress change index was 0.992, less than 1, and the comprehensive technical efficiency change index was more significant than 1, indicating that backward technological progress during this period was the reason for the decline in the total factor productivity.

4.2.2. Dynamic Efficiency Analysis of Port Groups and Ports

It can be seen from

Table 8. The port group, the MPI value of each port and its decomposition.

Port Group	Port	Changes in Comprehensive Technical Efficiency	Technological Progress Changes	Pure Technical Efficiency Change	Scale Efficiency Changes	Malmquist Index
Yangtze River Delta Port Group	Shanghai Port	1.024	0.945	1.005	1.019	0.968
	Ningbo-Zhoushan Port	1	1.008	1	1	1.008
	Lianyungang Port	0.997	1.141	1	0.997	1.138
	Yangtze River Delta Port Group	1.007	1.028	1.002	1.005	1.036

that the total factor productivity of the Yangtze River Delta port group has increased by 3.6% in the past five years, and both the technological progress and comprehensive technical efficiency have improved. It shows that the development trend of the port group is good.

In this port group, the total factor productivity of Ningbo-Zhoushan Port and Lianyungang Port from 2016 to 2020 was more significant than 1. In the past five years, the scale efficiency index and comprehensive technical efficiency index of Lianyungang Port decreased by 0.3%. The total factor productivity of Shanghai Port has dropped by 3.2%, and the technological progress has dropped by 5.5%. Therefore, the backward technological progress is the reason that the efficiency improvement of Shanghai Port has been hindered. It is necessary to increase investment in technology, adopt new equipment or improve old equipment, and improve port efficiency.

5. Conclusions and Recommendations

The findings of the aforementioned research led to the conclusion that the Yangtze River Delta port group’s overall efficiency is at an upper-middle level from the standpoint of static efficiency. However, it has not yet reached the frontier, and the low scale efficiency is why the port group has not reached the frontier. From the perspective of dynamic efficiency, the total factor productivity of the Yangtze River Delta port group has increased by 3.6% in the past five years. Both the technological progress and comprehensive technical efficiency have improved.

Shanghai Port and Ningbo-Zhoushan Port are important hubs for maritime trade, and both are among the largest ports in the world. In order to improve the competition in various aspects such as the source of goods, routes, and flights, the hardware construction of the port is being vigorously strengthened. The development of deep-water ports has led to unreasonable and excessive competition in the port, which has seriously hindered efficiency improvement. The comprehensive efficiency of Shanghai Port is the highest in the port group, and the comprehensive efficiency of Ningbo-Zhoushan Port is at a medium level. The pure technical efficiency of these two ports is 1, but the scale efficiency is relatively low. The return to scale of Lianyungang has been increasing in the past five years; in the third stage, its comprehensive efficiency was observed to have significantly dropped, and it is the lowest in the port group. The main reason for its ineffectiveness is its low scale efficiency. Therefore, all ports require proper planning with respect to the port scale to effectively improve the production efficiency of the ports and optimize the resources.

Furthermore, based on the former results, the recommended actions improve the ports’ competitiveness in the Yangtze River Delta region are as follows:

• Strengthen the cooperation of ports within the port group, effectively integrate internal resources, form comprehensive advantages, plan rationally, and break through the restrictions of administrative divisions and fight alone. Adopt a strategy of cooperation and win-win and formulate a cluster-type development plan. Optimize the allocation of port resources. Give full play to the advantages of the Ningbo-Zhoushan Port Deep-water Port and the advantages of the Shanghai Port Shipping Center. Narrow the differences in port development, take dislocation development and character development as the goal, establish a regional economic integration development pattern, and improve the international competitiveness of all Chinese ports.
• Increase the introduction of advanced technology and equipment in various ports and spend more in new technology research and development. Acquire advanced management experience and integrate with the world’s advanced level to form core competitiveness. Improve infrastructure construction and deep-water berth construction, improve China’s port development environment, breakthrough port development bottlenecks, rationally plan port scale, avoid excessive port expansion, effectively improve port production efficiency, and optimize resources. Use physical equipment and clean energy and improve energy efficiency. Improve the management level by optimizing the port operation management system through peak regulation.
• Accelerate the upgrading of the port industry, accelerate the construction of intelligent ports, strengthen the integration with other industries, drive the formation and development of new industries, and form a diversified development pattern of ports. Develop new industries such as tourism, boutique cruise ships, and modern logistics, and build tourism and modern comprehensive trade ports, which will gradually expand the functions of the port. It is necessary to make overall plans to further promote the integrated development of the port city so that the development of the port can promote the economic improvement of the hinterland city.
• The main conclusions we have drawn show that studying the Yangtze River Delta port group to dig out the potential of the port group improves the competitiveness of China’s port. Help the optimal allocation of port resources in the port group system, ensure the smooth flow and security of the international logistics supply chain, and promote the high-quality development of China’s ports.
• The efficiency of the Yangtze River Delta port cluster and the optimization strategy formulated by this calculation is systematic and comprehensive and fill the application gaps. However, some indicators such as the port accessibility are not considered as the three ports discussed are nearly the same in this regard. Therefore, we plan to add the discussion on the impact of port accessibility for the evaluation of small ports in the future.