Multi scale evaluation and analysis of international trade objectives of port cities

Abstract

The analysis of the multi-scale evaluation of port city’s international trade goal is conducive to the sustainable development of port city’s international trade. In order to make a more in-depth study on the realization ability of port city’s international trade sustainable development goal, this paper proposes a new multi-scale evaluation method of port city’s international trade goal. This method selects the evaluation indexes, uses the improved normalization method to process the indexes, uses the combination of AHP and factor analysis method to form the subjective and objective combination weighting method, brings the processed indexes into the least square optimization combination evaluation model, calculates the index weight, and uses the fuzzy evaluation method to carry out multi-scale index evaluation on the international trade of port cities to realize its multi-scale evaluation and analysis. The results show that the standardized index of Shanghai’s foreign trade dependence is 0.0056, indicating its independence in international trade. In the comprehensive evaluation, the evaluation values of Shanghai, Tianjin, and Guangzhou are 92.56, 87.89, and 88.45, respectively, which are very close to the actual results, which shows that the accuracy of the evaluation method is high, and provides a theoretical basis for the sustainable development of international trade in port cities.

Keywords

Port city international trade target AHP factor analysis method fuzzy evaluation method and multi-scale indicator evaluation

1. Introduction

International trade has become an important factor in economic growth, so it is crucial to accurately evaluate and analyze the international trade goals of its most important distribution center-port cities. Port cities play a crucial role in promoting global economic development and exchange, but there are still some problems in the development process that affect the sustainable development of their international trade.

In order to achieve sustainable development of international trade in port cities, scholars have conducted extensive research on port cities and achieved some results. For example, Xu et al. studied the impact of international trade on global sustainable development using a data set consisting of 17 Sustainable Development Goals and 169 specific goals. The results indicate that international trade has had a positive impact on achieving global sustainable development [1, 2]. Therefore, the conclusion that export and foreign trade should be regarded as one of the driving forces of national economic growth is drawn, which provides the direction for the foreign sector to pay attention to when building the economic growth forecast and economic policy model; Janssens C et al. used econometric models to analyze the evolution of net foreign trade inflows and the relationship between statistical indicators in order to explore the impact of foreign trade on the economic development of emerging EU countries. The results indicate that out of 9 economies, 7 have a positive impact on net foreign investment inflows from GDP [3]. In addition to the above scholars’ research on international trade in port cities, there are also many scholars who have made their own contributions to enrich the relevant theories of international trade [4].

Although there have been many studies on international trade in coastal cities, there are still shortcomings in the multi-scale evaluation and analysis of international trade goals in port cities, as well as in determining their sustainable development goals. In order to address these issues, this article starts from the sustainable development goals and constructs an indicator system for China’s foreign trade to facilitate sustainable development, including economic development, social population, resource environment, and technological progress. The weight distribution of evaluation indicators is carried out using a combination of subjective and objective weight methods, and a fuzzy comprehensive evaluation model is established to achieve multi-scale evaluation and analysis of international trade goals in port cities [5, 6].

2. Multi scale evaluation method of international trade target of port city

The sustainable development of foreign trade is the goal of international trade in port cities, but this is not the end point, but a process of continuous improvement. Therefore, when evaluating the international trade goals of port cities, it is necessary to measure their progress in sustainable trade development. Due to the lack of final standards, it is not possible to compare the evaluation object with it. This article only analyzes the gap between the evaluation of international trade goals in a certain year and other years from a time series perspective, or only compares a specific object with other objects in the same year. The evaluation content can be divided into five aspects: structural benefits of trade, economic benefits, social benefits, resource benefits, and environmental benefits. The evaluation methods are mainly divided into subjective evaluation method and objective evaluation method. This article selects the fuzzy evaluation method to evaluate the ability of port cities to achieve the sustainable development goals of international trade. Based on the evaluation content, establish a primary indicator system for evaluating the ability to achieve the sustainable development goals of international trade. Starting from the actual situation of the evaluation object, select appropriate secondary and tertiary indicators, and establish a reasonable evaluation indicator system [7]. Obtain statistical data through various channels, conduct quantitative and qualitative analysis based on fuzzy evaluation method, judge the development trend of international trade goals in port cities, and propose corresponding suggestions for achieving international trade goals in port cities.

2.1 Establish a multi-scale evaluation system for international trade objectives of port cities

The goal of international trade in port cities is to integrate the awareness of ecological environmental protection and the concept of sustainable development into every link of the production value chain, which needs to build a set of index evaluation system reflecting the overall status of national and regional international trade development. The system should include economic, social, ecological and other relevant indicators, reflecting the unity of systematicness and pertinence, independence and relevance, static and dynamic, theoretical and feasible, realistic and forward-looking [8].

(1)
Systematic and targeted. The index evaluation system is an organic whole, which should cover the scale and benefit of economic development, production and demand, quantity growth and quality improvement and other main aspects, and require representative indicators to be as comprehensive and systematic as possible. However, it is also necessary to avoid information redundancy caused by too many indicators, which is difficult to highlight the main contradiction.
(2)
Unity of independence and pertinence. Each index should independently reflect the facts of a certain aspect of economic life in order to understand the changes in economic life by analyzing the changes in the index. At the same time, indicators should be linked to each other and complement each other to reflect economic development from multiple angles.
(3)
The unity of static and dynamic. Horizontal comparability allows the same indicators in different regions to determine the level of regional development in a cross-sectional state. At the same time, the indicators should also have the characteristics of vertical dynamic comparison to truly reflect the development status and development path of regional economic and trade.
(4)
Unification of theory and feasibility. The international trade goal of port city is the theoretical basis for establishing the evaluation index system of foreign trade sustainable development, and the index system is the external quantitative reflection of the connotation of sustainable development.
(5)
The unity of realism and foresight. The index system should be able to objectively reflect the actual situation of trade development and guide the actual economic work towards sustainable development [9]. See Table 1 for details.

Table 1
Multi scale evaluation system of international trade objectives of port cities

System Target Index

Multi scale evaluation Trade scale C1 Export value C11

system of international Imports C12

trade target of port city C Dependence on foreign trade C13

Utilization of foreign capital C14

Trade competitiveness C2 Product competitiveness coefficient C21

Export technical efficiency C22

Import technical benefit rate C23

Structure of trade products C3 Export proportion of manufactured primary products C31

Export conversion rate of high-tech products C32

Export concentration C33

Proportion of service trade C34

Market structure C4 Regional trade difference C41

Distribution of export market C42

Distribution of import market C43

Industrial structure C5 Output proportion of secondary industry C51

Output proportion of tertiary industry C52

Contribution rate of industrial structure C53

Economic performance C6 Contribution rate of foreign trade to GDP C61

The pull degree of FDI to economic growth C62

Ecological benefit C7 Export trade wastewater discharge C71

Export trade emissions C72

Export trade waste discharge C73

Resource benefit C8 Efficiency of primary products C81

Energy intensity of import and export C82

Import proportion of resources and resource products C83

Social results C9 Employment in foreign trade C91

Average wage of foreign trade industry C92

2.2 Data processing of international trade target index system of port city

It is assumed that the international trade target of port city has $n$ evaluation indexes $f_{j}\left({1\leqslant j\leqslant n}\right)$ and $m$ evaluation objects $a_{i}\left({1\leqslant i\leqslant m}\right)$ . the matrix $X=\left({x_{ij}}\right)_{m\times n}$ is called decision matrix. Because the normalization method can reflect the relationship between the original index values more truly, and consider the differences between the index values, this paper uses this method to deal with the evaluation indexes [10].

In the decision matrix $X=\left({x_{ij}}\right)_{m\times n}$ of international trade objectives of port cities, the

$\displaystyle y_{ij}=\frac{x_{ij}}{\sum\limits_{i=1}^{m}{x_{ij}}}\left({1% \leqslant i\leqslant m,1\leqslant j\leqslant n}\right)$ (1)

The matrix $Y=\left({y_{ij}}\right)_{m\times n}$ is called the vector normalization standardized matrix. Obviously, the sum of column vectors of matrix $Y$ is equal to 1, i.e

$\displaystyle\sum\limits_{i=1}^{m}{y_{ij}}=1$ (2)

However, this method also has disadvantages, that is, it does not distinguish the positive and negative indicators. If there is $x_{ij}<$ 0, it is not applicable, so it needs to be improved. Because of the above mistakes, this paper uses the improved normalization method to study the international trade objectives of port cities. For the decision matrix, the improved normalization method is processed as follows:

(1) If there is a reverse indicator, first convert the reverse indicator into a positive indicator through the following formula.

$\displaystyle x_{ij}^{\prime}=\mathop{\max x_{ij}}\limits_{1\leqslant i% \leqslant m}+\mathop{\min x_{ij}}\limits_{1\leqslant i\leqslant m}-x_{ij}$ (3)

(2) If the $j$ index value $x_{ij}<$ 0 exists, coordinate translation can be performed first, and the translation formula is as follows:

$\displaystyle x_{ij}^{\prime}=x_{ij}-\mathop{\min x_{ij}}\limits_{1\leqslant i% \leqslant m}$ (4)

After this treatment, all indicators are non negative, avoiding the limitation that some evaluation methods are not applicable in $x_{ij}<$ 0.

(3) Finally, Eq. (1) is used for normalization.

This method can truly reflect the relationship between the original indicators and the advantages of forward and reverse indicators, and overcome the inadaptability of $x_{ij}<$ 0, avoiding the problem that the original indicators cannot be objectively reflected due to nonlinear transformation.

2.3 Determine the weight of international trade target evaluation index of port city

In the evaluation index system, the importance of each index to the objective and function of international trade system of port city is different. The weight indicates the relative importance of each index or the ratio coefficient of replacing one benefit with another. There are three commonly used methods to determine the weight, namely, subjective method, objective method and combination method. Different methods also include a series of sub methods, as shown in Fig. 1.

Figure 1.

Weight calculation method.

Port competitiveness evaluation involves many indexes, each of which reflects the competitiveness of the port from different aspects. The subjective weight method embodies the value (practical significance) of the index, and the objective weight method embodies the information (variation and correlation) of the index. Therefore, in order to make the index weight reach the same subjective and objective, the paper first uses the AHP method for subjective weight, then uses the factor analysis method for objective weight, and finally uses the least square method for subjective weight The objective weight combination method generates the final weight. These three methods are briefly introduced below [11].

2.3.1 AHP

In order to make the weights of the evaluation indexes of port city’s international trade objectives reach the same objective and subjective, this paper first uses AHP to weigh the weights of the indexes, which is roughly divided into four steps:

Step 1: According to the multi-scale evaluation system of international trade target of port city, establish the hierarchical structure model. Generally, the number of elements in each layer should not exceed 9. Because psychological experiments show that when the number of elements compared exceeds 9, the accuracy of judgment will decrease [12].

Step 2: Construct the judgment matrix. According to the criterion of adjacent upper level elements, each level element constructs a judgment matrix by comparing the two levels of elements in 1-9 scale method. As shown in Table 2.

Table 2
1–9 Scale method

Scale	Definition	Meaning
1	Equally important	Two elements are equally important to an attribute
3	Slightly important	Two elements are slightly more important to one attribute than another
5	Obviously important	Two elements are obviously more important to one attribute than another
7	Strongly important	Two elements are more important to one attribute than another
9	Extremely important	Two elements are extremely important to one attribute and one element is more important than the other
2, 4, 6, 8	Median of adjacent scales	Scale indicating the compromise between two adjacent scales
Scale reciprocal of the above column	Anti comparison	The scale of element $i$ to element $j$ is $a_{ij}$ . otherwise, it is $\frac{1}{a_{ij}}$ .

Step 3: Hierarchical single sorting and consistency inspection. The maximum eigenvalue and the corresponding eigenvector of the judgment matrix are solved and normalized to obtain the hierarchical single ordering weight vector. Steps of one-time inspection of judgment matrix:

1) The consistency index is calculated as follows:

$\displaystyle Z=\frac{P\max-d}{d-1}$ (5)

Among them, $Z$ represents the consistency index, $d$ represents the dimension of judgment matrix, and $P_{\max}$ represents the maximum eigenvalue.

2) The average random consistency index is obtained by querying Table 3.

Table 3

RI value of average random consistency index

Order number	RI	Order number	RI
2	0	9	1.4616
3	0.5149	10	1.4874
4	0.8931	11	1.5156
5	1.1185	12	1.5405
6	1.2494	13	1.5583
7	1.3450	14	1.5779
8	1.4200	15	1.5894

3) Calculate the consistency ratio as follows:

$\displaystyle\textit{CR}=\frac{\textit{CI}}{\textit{RI}}$ (6)

When $\textit{CR}\leqslant 0.1$ , accept the judgment matrix, otherwise modify the judgment matrix.

4) Level total sequencing and one-time inspection. In practice, the one-time test of total sorting can often be omitted.

2.3.2 Factor analysis

After subjectively weighting the weights of international trade target evaluation indexes of port cities by AHP, in order to further make the weights of indexes reach the same objective and subjective, this paper also needs to use factor analysis method for objective weighting. The basic idea of factor analysis is to group the original variables according to the correlation, so that the correlation between the variables in the same group is higher, while the correlation between the variables in different groups is lower. Each group of variables represents a basic structure, which is represented by an unobservable comprehensive variable. This basic structure is the common factor. For a specific problem under study, the original variable can be divided into two parts, one is a few unmeasurable linear functions of the so-called common factor, the other is a special factor unrelated to the common factor [13].

The process of calculating the weight of the evaluation index of port city’s international trade target by factor analysis is as follows:

Step 1: Standardize the original data of port city’s international trade target evaluation index to eliminate the differences in magnitude and dimension between variables (see Chapter 1.2).

Step 2: Find the correlation matrix of standardized data;

Step 3: Find the eigenvalues and eigenvectors of the correlation matrix;

Step 4: Calculate variance contribution rate and cumulative variance contribution rate;

Step 5: Determination factor: set F1, F2 The first m factors can be used to reflect the original evaluation index when the total information of the evaluation index data of port city’s international trade target (i.e. its cumulative contribution rate) contained in the first m factors is no less than 80%;

Step 6: Factor rotation: if the m factors obtained cannot reflect the original evaluation index or their actual meaning is not obvious, then the factors need to be rotated to obtain more obvious actual meaning.

Step 7: Use the linear combination of the original indicators to calculate the factor scores: use regression estimation method, Thomson estimation method to calculate the factor scores.

Step 8: Comprehensive score: take the variance contribution rate of each factor as the weight, and get the comprehensive evaluation index function from the linear combination of each factor.

$\displaystyle F=\frac{w_{1}\textit{F1}+w_{2}\textit{F2}+\ldots+w_{m}\textit{Fm% }}{w_{1}+w_{2}+\ldots+w_{m}}$ (7)

Here, $w_{1}$ , $w_{2}$ , $w_{m}$ are variance contribution rates of factors before or after rotation.

Step 9: Ranking of scores: the scores of port city’s international trade target evaluation indicators can be obtained by using comprehensive scores [14]. The specific process of objective weighting in factor analysis is shown in Fig. 2.

Figure 2.

Factor analysis objective weighting process.

2.3.3 Subjective and objective weight recombination method based on the least square meaning

In order to further calculate, the above two methods are combined to form the subjective and objective weight reorganization, and the weight is analyzed. The weight of each index given by the subjective weighting method is $W_{\alpha}=\left[{w_{\alpha 1},w_{\alpha 2},\ldots,w_{\alpha n}}\right]^{T}$ , the weight of each index given by the objective weighting method is $W_{\beta}=\left[{w_{\beta 1},w_{\beta 2},\ldots,w_{\beta n}}\right]^{T}$ , and the optimal combination weight of each index is $W_{c}=\left[{w_{c1},w_{c2},\ldots,w_{cn}}\right]^{T}$ .

If the standardized decision matrix of port city international trade target evaluation with $n$ evaluation indexes and $m$ evaluation objects is $Y=\left({y_{ij}}\right)_{m\times n}$ , then the evaluation value of the first evaluation object is

$\displaystyle f_{i}=\sum\limits_{j=1}^{n}{w_{cj}}y_{ij}\left({i=1,2,\ldots,m}\right)$ (8)

For all indexes of all evaluation objects of port city international trade target evaluation, the smaller the deviation of parity value under subjective and objective weight, the better. For this reason, the following least square optimization combined evaluation model is established.

$\displaystyle G\left(w\right)=\sum\limits_{i=1}^{m}{\sum\limits_{j=1}^{n}{% \left[{\left({\left({w_{\alpha j}-w_{cj}}\right)y_{ij}}\right)^{2}+\left({% \left({w_{\beta j}-w_{cj}}\right)y_{ij}}\right)^{2}}\right]}}$ (9) $\displaystyle s.t.\sum\limits_{j=1}^{n}{w_{cj}}=1,w_{cj}\geqslant 0\left({i=1,% 2,\ldots,n}\right)$ (10)

Solve this model and establish Langrange function as follows:

$\displaystyle L=\sum\limits_{i=1}^{m}{\sum\limits_{j=1}^{n}{\left[{\left({% \left({w_{\alpha j}-w_{cj}}\right)y_{ij}}\right)^{2}+\left({\left({w_{\beta j}% -w_{cj}}\right)y_{ij}}\right)^{2}}\right]}}+\lambda\left({\sum\limits_{j=1}^{n% }{w_{cj}}-1}\right)$ (11)

Order

$\displaystyle\frac{\partial L}{\partial\lambda}=-\sum\limits_{i=1}^{m}{2\left(% {w_{\alpha j}-w_{\beta j}-2w_{cj}}\right)y_{ij}^{2}+\lambda=0}$ (12) $\displaystyle\frac{\partial L}{\partial\lambda}=\sum\limits_{j=1}^{n}{w_{cj}-1% =0}$ (13)

Expressed as a matrix

$\displaystyle\left[\begin{array}[]{cc}A&e\\ e^{T}&0\\ \end{array}\right]\left[\begin{array}[]{l}W\\ \frac{\lambda}{4}\\ \end{array}\right]=\left[{\begin{array}[]{l}B\\ 1\\ \end{array}}\right]$ (14)

Where $A$ is $n\times n$ diagonal matrix, $e$ , $W$ , $B$ are $n\times 1$ diagonal vectors.

Calculate the above matrix equation, and get

$\displaystyle W_{c}=A^{-1}\left({B+\frac{1-e^{T}A^{-1}B}{e^{T}A^{-1}e}e}\right)$ (15)

2.4 Multi scale evaluation of international trade objectives of port cities

In this paper, a fuzzy evaluation method is used to evaluate the ability of port cities to achieve international trade goals. The specific process is as follows:

Step 1: According to Table 1, determine the fuzzy set of the relevant items of the multi-scale evaluation system for the international trade objectives of port cities.

$\displaystyle C=\left\{{C1,C2,\ldots,C9}\right\}$ (16)

In Eq. (16), $\textit{C1}=\left\{{\textit{C11},\textit{C12},\textit{C13},\textit{C14}}\right\}$ , $\textit{C2}=\left\{{\textit{C21},\textit{C22},\textit{C23}}\right\}$ , $\textit{C3}=\left\{{\textit{C31},\textit{C32},\textit{C33},\textit{C34}}\right\}$ , $\textit{C4}=\left\{{\textit{C41},\textit{C42},\textit{C43}}\right\}$ , $\textit{C5}=\left\{{\textit{C51},\textit{C52},\textit{C53}}\right\}$ , $\textit{C6}=\left\{{\textit{C61},\textit{C62}}\right\}$ , $\textit{C7}=\left\{{\textit{C71},\textit{C72},\textit{C73}}\right\}$ , $\textit{C8}=\left\{{\textit{C81},\textit{C82},\textit{C83}}\right\}$ , $\textit{C9}=\left\{\textit{C91},\textit{C92}\right\}$ , specific meaning is given in Table 1.

Step 2: Set the evaluation set as

$\displaystyle V=\left\{{\textit{V1},\textit{V2},\textit{V3},\textit{V4}}\right\}$ (17)

Among them, $\textit{V1},\textit{V2},\textit{V3},\textit{V4}$ respectively represent the evaluation of indicators as “excellent”, “good”, “medium” and “poor”.

Step 3: Determine the weight of each index of the port city’s international trade target realization capacity multi-scale evaluation (see Chapter 1.3).

Step 4: Ddetermine the evaluation membership matrix. According to the multi-scale evaluation of the port city’s ability to achieve the international trade goal, the grade standard is established, and the grade is evaluated by comparing with the standard, and the membership vector of the index to the evaluation set is obtained.

Step 5: Multi factor and multi-level fuzzy comprehensive evaluation.

Step 6: Deal with the evaluation results. After quantifying the elements in the evaluation set $V$ , the final evaluation result is obtained. It is specified that the quantitative critical values of various elements of $V$ in the evaluation set are $\textit{V1}=100$ , $\textit{V2}=85$ , $\textit{V3}=70$ , $\textit{V4}=40$ , so the final value of $V$ is between 100 and 40. The closer to 100, the better the evaluation result of port city’s international trade target; the closer to 40, the worse the evaluation result of port city’s international trade target [15].

3. Empirical analysis

3.1 Port city distribution and index data analysis

China has a long coastline and numerous rivers, so there are many port cities. These port cities are located in rivers, lakes, oceans and other waters. They have ports and functions of land and water transport hub. They can be divided into different types according to their geographical location and functional characteristics. This paper takes nine important port cities of Shanghai, Tianjin, Guangzhou, Ningbo, Qingdao, Dalian, Tangshan, Qinhuangdao and Yingkou as examples to analyze the multi-scale evaluation of international trade objectives. As shown in Fig. 3.

Table 4
Index data of Shanghai in 2020

Index	Data
Export value C11	52462665
Imports C12	44656255
Dependence on foreign trade C13	5
Utilization of foreign capital C14	22236
Product competitiveness coefficient C21	6.8
Export technical efficiency C22	87.3
Import technical benefit rate C23	60.5
Export proportion of manufactured primary productsC31	50.4
Export conversion rate of high-tech products C32	32.2
Export concentration C33	4.7
Proportion of service trade C34	1.23
Regional trade difference C41	3.68
Distribution of export market C42	3.52
Distribution of import market C43	4.74
Output proportion of secondary industry C51	5.36
Output proportion of tertiary industry C52	3.62
Contribution rate of industrial structure C53	8.47
Contribution rate of foreign trade to GDP C61	6.87
The pull degree of FDI to economic growth C62	3.25
Export trade wastewater discharge C71	563258
Export trade emissions C72	126244
Export trade waste discharge C73	855240
Efficiency of primary products C81	6.3
Energy intensity of import and export C82	3.5
Import proportion of resources and resource products C83	2.66
Employment in foreign trade C91	562522
Average wage of foreign trade industry C92	8522

Figure 3.

Port city distribution.

In order to quantitatively study the ability to achieve the international trade goals of the nine major port cities in China, this paper collects the required index data in 2020 from the statistical data such as Almanac of China’s Economy, China Commerce Yearbook, China Foreign Economic Statistical Yearbook, The Yearbook of World Economy, China’s Economic and Trade Yearbook, China Labor Statistics Yearbook, and so on Step by step quantitative research on the ability of port cities to achieve international trade goals. First, take Shanghai as an example, and the index data is shown in Table 4.

Table 5

Standard data of each index

Index	Standard data
Export value C11	0.2550
Imports C12	0.1523
Dependence on foreign trade C13	0.0056
Utilization of foreign capital C14	0.2351
Product competitiveness coefficient C21	1.0000
Export technical efficiency C22	1.3551
Import technical benefit rate C23	0.0000
Export proportion of manufactured primary productsC31	1.5244
Export conversion rate of high-tech products C32	0.3634
Export concentration C33	0.5246
Proportion of service trade C34	0.3254
Regional trade difference C41	0.2745
Distribution of export market C42	0.5284
Distribution of import market C43	0.2015
Output proportion of secondary industry C51	0.3651
Output proportion of tertiary industry C52	0.0000
Contribution rate of industrial structure C53	0.0058
Contribution rate of foreign trade to GDP C61	0.0584
The pull degree of FDI to economic growth C62	0.1524
Export trade wastewater discharge C71	0.0000
Export trade emissions C72	0.2655
Export trade waste discharge C73	0.0414
Efficiency of primary products C81	0.4126
Energy intensity of import and export C82	1.0000
Import proportion of resources and resource products C83	0.5565
Employment in foreign trade C91	0.2359
Average wage of foreign trade industry C92	0.4155

Table 6

Index weights of Shanghai’s ability to achieve international trade goals

Index	Index weight
Export value C11	0.0026
Imports C12	0.0145
Dependence on foreign trade C13	0.0225
Utilization of foreign capital C14	0.0150
Product competitiveness coefficient C21	0.0052
Export technical efficiency C22	0.0014
Import technical benefit rate C23	0.0233
Export proportion of manufactured primary productsC31	0.0652
Export conversion rate of high-tech products C32	0.0514
Export concentration C33	0.0041
Proportion of service trade C34	0.0068
Regional trade difference C41	0.0081
Distribution of export market C42	0.0150
Distribution of import market C43	0.0415
Output proportion of secondary industry C51	0.0522
Output proportion of tertiary industry C52	0.0045
Contribution rate of industrial structure C53	0.0060
Contribution rate of foreign trade to GDP C61	0.0415
The pull degree of FDI to economic growth C62	0.0336
Export trade wastewater discharge C71	0.0510
Export trade emissions C72	0.0068
Export trade waste discharge C73	0.0152
Efficiency of primary products C81	0.0147
Energy intensity of import and export C82	0.0025
Import proportion of resources and resource products C83	0.0362
Employment in foreign trade C91	0.0350
Average wage of foreign trade industry C92	0.0025

It can be seen from Table 4 that both export and import values of Shanghai are large, indicating its important position in international trade. The degree of Shanghai’s dependence on foreign trade is relatively low, which is 5, indicating that Shanghai has a certain independence in international trade. In terms of the utilization of foreign capital, the data of Shanghai is 22,236, indicating that Shanghai has a certain competitiveness in attracting foreign capital. The product competitiveness coefficient is 6.8, which shows that the competitiveness of Shanghai’s export products is strong. The export ratio of primary manufactured goods was 50.4%, and the export conversion rate of high-tech products was 32.2%, indicating that Shanghai has certain advantages in manufacturing and high-tech industries. The contribution rate of industrial structure is 8.47, and the contribution rate of foreign trade to GDP is 6.87, indicating that Shanghai’s economic growth is closely related to foreign trade and industrial structure optimization. In terms of environment, Shanghai’s export trade wastewater discharge, discharge and waste discharge are high, which may have a certain impact on the environment, and environmental protection needs to be strengthened. The number of employment in foreign trade is 562,522, and the average wage of foreign trade industry is 8522, indicating that the number of employment and wage level of foreign trade industry in Shanghai are relatively high. Specifically, Shanghai performed well in international trade, but continued efforts were needed to optimize industrial structure, improve efficiency and strengthen measures in environmental protection.

3.2 Index standardization

According to Section 1.2, standardize the original data of Shanghai’s international trade target capacity, and obtain the standard data of each index as shown in Table 5.

From Table 5, it can be seen that in terms of export and import values, Shanghai’s data are 0.2550 and 0.1523, respectively, which are relatively high, indicating its dominant position in international trade. The product competitiveness coefficient, the output proportion of the secondary industry, and the contribution rate of industrial structure are relatively high. The export proportion of primary manufactured goods, waste emissions from export trade, and the import proportion of resources and resource products are also relatively high. The efficiency of primary products, energy intensity of imports and exports, and the proportion of service trade are relatively low. Comprehensive analysis shows that Shanghai’s international trade target capacity exhibits varying degrees of advantages and disadvantages in various aspects, requiring reasonable policy planning and industrial structure adjustment.

3.3 Index weight calculation

Through the weight calculation method in Chapter 1.3, the weight of each index of Shanghai’s ability to achieve international trade goals is calculated, as shown in Table 6.

From Table 6, it can be seen that there are differences in the weights of various indicators of Shanghai’s ability to achieve international trade goals. Among them, indicators such as the proportion of primary manufactured goods exports, regional trade differences, the proportion of secondary industry output, the proportion of service trade, and foreign trade employment have high weights, indicating that these indicators have a significant impact on Shanghai’s ability to achieve international trade goals. Analysis shows that these indicators with higher weights are mainly concentrated in industrial structure, trade policies, and human resources. Therefore, in order to enhance Shanghai’s international trade target capacity, it is necessary to focus on these aspects and take corresponding policy measures. At the same time, we should also strengthen support for foreign trade employment and increase the average wage level in the foreign trade industry. Specifically, adjusting indicators with higher weights and adopting corresponding policy measures can help improve Shanghai’s ability to achieve international trade goals.

3.4 Comprehensive evaluation results

According to Chapter 1.4, the evaluation results of Shanghai’s urban international trade objectives are obtained. In the same way, the weights of international trade objectives of other port cities are calculated by single factor analysis and single analytic hierarchy process, and then the fuzzy evaluation method is also used for analysis. The final results are shown in Table 7.

Table 7
Comprehensive evaluation results

Method	City	Rating/rating	Real results
Multi scale Evaluation method in this paper	Shanghai	92.56/excellent	92.48/excellent
	Tianjin	87.89/excellent	87.80/excellent
	Guangzhou	88.45/excellent	88.43/excellent
	Ningbo	91.36/excellent	91.30/excellent
	Qingdao	90.66/excellent	90.58/excellent
	Dalian	84.87/good	84.82/good
	Tangshan	80.36/good	80.36/good
	qinghuangdao	86.33/good	86.30/good
	Yingkou	78.75/medium	78.66/medium
Evaluation method based on factor analysis	Shanghai	90.36/excellent	92.48/excellent
	Tianjin	88.66/excellent	87.80/excellent
	Guangzhou	85.25/excellent	88.43/excellent
	Ningbo	87.63/excellent	91.30/excellent
	Qingdao	92.36/excellent	90.58/excellent
	Dalian	80.25/good	84.82/good
	Tangshan	78.36/good	80.36/good
	Qinghuangdao	88.63/good	86.30/good
	Yingkou	75.25/ medium	78.66/medium
Evaluation method based on AHP	Shanghai	95.62/excellent	92.48/excellent
	Tianjin	90.25/excellent	87.80/excellent
	Guangzhou	90.20/excellent	88.43/excellent
	Ningbo	86.33/excellent	91.30/excellent
	Qingdao	95.30/ excellent	90.58/excellent
	Dalian	80.23/good	84.82/good
	Tangshan	77.21/good	80.36/good
	Qinghuangdao	87.69/good	86.30/good
	Yingkou	73.63/medium	78.66/medium

Note: the real results are obtained from the comprehensive analysis of China Foreign Economic Statistics Yearbook and 100 experts, which are of high reference value.

It can be seen from Table 7 above that the score of the development capacity of international trade target of 9 port cities obtained by the evaluation method in this paper is closer to the actual result, while the calculated score is quite different from the actual result based on the weight obtained by the single factor analysis method and the single level analysis method. This shows that the accuracy of this evaluation method is high, which provides a theoretical basis for the sustainable development of international trade in port cities [16].

4. Conclusion

Although the concept and development strategy of sustainable development of foreign trade have been generally accepted by people, if a concept and development strategy is transformed into a government operational management mode, it is necessary to establish a set of evaluation index system and related evaluation methods to evaluate its development degree. At present, the evaluation index system of international trade objectives of port cities in China has developed, but the research on the evaluation methods of international trade objectives of port cities in China is still rare and some of the evaluation methods have too much error. Therefore, this paper attempts to use the subjective and objective combination of weighting method and fuzzy evaluation method to achieve a more accurate evaluation of the port city’s international trade objectives, which has been proved to be more accurate. However, due to the availability of data, only objective second-hand statistical data are used in the selection of evaluation indicators, and no qualitative indicators are used. The experimental results indicate that Shanghai’s foreign trade dependence index is 5, indicating its independence in international trade. The product competitiveness coefficient of Shanghai is 6.8, indicating its strong trade competitiveness. The standardized values of Shanghai’s international trade import and export data are 0.2550 and 0.1523, respectively, proving Shanghai’s dominant position in international trade. The above conclusion proves the effectiveness of the research design method. Although this method avoids the subjective problem, the evaluation index system constructed in this paper still needs to be improved. Therefore, the future researchers can further improve the evaluation index system of port city competitiveness.

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System	Target	Index
Multi scale evaluation	Trade scale C1	Export value C11
system of international		Imports C12
trade target of port city C		Dependence on foreign trade C13
		Utilization of foreign capital C14
	Trade competitiveness C2	Product competitiveness coefficient C21
		Export technical efficiency C22
		Import technical benefit rate C23
	Structure of trade products C3	Export proportion of manufactured primary products C31
		Export conversion rate of high-tech products C32
		Export concentration C33
		Proportion of service trade C34
	Market structure C4	Regional trade difference C41
		Distribution of export market C42
		Distribution of import market C43
	Industrial structure C5	Output proportion of secondary industry C51
		Output proportion of tertiary industry C52
		Contribution rate of industrial structure C53
	Economic performance C6	Contribution rate of foreign trade to GDP C61
		The pull degree of FDI to economic growth C62
	Ecological benefit C7	Export trade wastewater discharge C71
		Export trade emissions C72
		Export trade waste discharge C73
	Resource benefit C8	Efficiency of primary products C81
		Energy intensity of import and export C82
		Import proportion of resources and resource products C83
	Social results C9	Employment in foreign trade C91
		Average wage of foreign trade industry C92

Multi scale evaluation and analysis of international trade objectives of port cities

Abstract

Keywords

1. Introduction

2. Multi scale evaluation method of international trade target of port city

2.1 Establish a multi-scale evaluation system for international trade objectives of port cities

Table 2 1–9 Scale method

3.1 Port city distribution and index data analysis

Table 4 Index data of Shanghai in 2020

3.3 Index weight calculation

3.4 Comprehensive evaluation results

Table 7 Comprehensive evaluation results

References

Table 2
1–9 Scale method

Table 4
Index data of Shanghai in 2020

Table 7
Comprehensive evaluation results