Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 20, No. 1, 2025 

 

7 

Evaluation of Beijing-Tianjin-Hebei Logistics 
Development Level and Suggestions for Improvement 

Fangyu Zhang * 

School of Economics and Management, Southwest Petroleum University, Chengdu, Sichuan, China 

* Corresponding author: Fangyu Zhang 

 

Abstract: In view of the accelerated pace of regional integration, the issue of city clusters has increasingly emphasized its 
importance in economic development. The Beijing-Tianjin-Hebei City Cluster, as the core of the Capital Economic Circle and 

the hub of logistics in the north, has seen a remarkable integration process, but the imbalance of logistics development amon g 

the cities within the cluster and its challenges should not be ignored. Therefore, based on this background, this paper constructs 

a set of logistics development level assessment system, and analyzes the logistics development status of each city in the Beijing-

Tianjin-Hebei city cluster by using principal component analysis. By calculating the composite score, we rank the logistics 

development level of each city, aiming to accurately identify the problems faced by each city in the field of logistics, and 

accordingly put forward targeted improvement strategies and enhancement suggestions. 

Keywords: Beijing-Tianjin-Hebei; Logistics Development Level; Principal Component Analysis. 

 

1. Introduction 

As China's economy moves towards a new stage of high-

quality development, the pace of regional economic 

integration has accelerated significantly, and the construction 

of urban agglomerations has become an important part of 

national strategic planning. With the in-depth implementation 

of the new development pattern of double-cycle, the 

development strategy of city clusters is not only a key path to 

strengthen the national economic strength, but also an 

important driver to promote inter-regional synergy, accelerate 

the pace of new urbanization and strengthen the construction 

of ecological civilization. According to the Opinions on 

Building a New Mechanism for Efficient Regional 

Coordinated Development issued by the Central Committee 

of the Communist Party of China (CPC) and the State Council, 

in the future, China will focus on the core city clusters, 

including Beijing-Tianjin-Hebei, Yangtze River Delta, 

Guangdong-Hong Kong-Macao Bay Area, Chengdu-

Chongqing, the middle reaches of the Yangtze River, the 

Central Plains, and the Central Guanzhong Plain, to promote 

them to become the engines of the integration of the country's 

major regional strategies. This strategy aims to strengthen the 

leading role of central cities, drive the synergistic 

development of the entire urban agglomeration, thereby 

realizing in-depth integration and interaction between regions 

and jointly drawing a new blueprint for synergistic regional 

development [1]. 

Among the many challenges to accelerate the "integration" 

process of Beijing-Tianjin-Hebei, logistics integration 

occupies a central position [2]. Logistics industry, as the 

cornerstone and strategic pillar of the national economy's 

solid development, has an inestimable value for industrial 

upgrading and transformation. In the grand blueprint of 

Beijing-Tianjin-Hebei coordinated development, the logistics 

industry plays a key role in connecting the past and the future, 

and its efficient operation not only injects a strong impetus to 

the regional economic growth, but also becomes an effective 

tool and prerequisite for promoting the coordinated 

development of the region. A sound and efficient 

transportation and logistics system is not only the "gas pedal" 

for regional economic take-off, but also an important means 

to lead regional harmony and achieve balanced development. 

Therefore, deepening the process of logistics integration is of 

far-reaching significance for promoting the comprehensive 

and coordinated development of the Beijing-Tianjin-Hebei 

region [3]. 

This paper focuses on the level of logistics development 

within the Beijing-Tianjin-Hebei city cluster. By constructing 

an evaluation index system containing three dimensions, 

namely, the scale of the logistics industry, the development 

environment and the potential for development, the level of 

logistics development of 14 cities in Beijing-Tianjin-Hebei 

was comprehensively evaluated by using the principal 

component analysis method. The study finds that there are 

significant differences in the level of logistics development 

within the Beijing-Tianjin-Hebei city cluster, with Beijing 

and Tianjin leading the way and other cities lagging behind. 

The article puts forward enhancement suggestions such as 

strengthening policy leadership, improving the core 

competitiveness of logistics enterprises and introducing high-

tech talents to promote the synergistic development of the 

logistics industry in Beijing-Tianjin-Hebei. 

2. Literature Review  

Yue Qi (2019) used gray correlation analysis, entropy 

weight method, and principal component analysis to 

comprehensively evaluate the logistics competitiveness in 

2011 and 2016, and verified the consistency of the three 

methods by Kendall synergy coefficient test. Further, 

systematic cluster analysis was used to divide the logistics 

competitiveness of each region into three tiers, and the current 

situation and reasons for changes in the level of logistics 

development in each region were analyzed [4]. Jiang naturally 

et al. (2020) studied the logistics development level of 14 port 

cities in the Yangtze River Delta region in 2017. The study 

constructed an evaluation index system from four dimensions: 

logistics operation, infrastructure, urban support, and 

information technology, and measured the logistics 



 

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development level score of each city. By analyzing and 

comparing the measurement results, it reveals the echelon 

differences, regional characteristics, and problems of logistics 

development in Yangtze River Delta port cities, and puts 

forward corresponding policy recommendations [5]. Lun 

Jiaxing (2022) studied the development level of logistics 

industry in 31 provinces and cities in China, and conducted a 

comprehensive evaluation and analysis by constructing an 

evaluation index system containing four dimensions: logistics 

resource input, logistics development demand, logistics 

development benefit and logistics informationization 

development, and adopting entropy value method and 

TOPSIS method [6]. Chen Yu (2022) studied the level of 

logistics development in North China, through the principal 

component analysis method of logistics development level 

evaluation indicators for dimensionality reduction processing, 

and measured the level of logistics development scores of 

each region, for the quantitative evaluation of the level of 

logistics development in North China to provide a scientific 

basis [7]. 

With the rapid development of China's economy and 

logistics industry, scholars in China have gradually invested 

in the study of logistics development level. With the 

accumulation of research literature on the level of logistics 

development in urban agglomerations, the imbalance of 

logistics development within the urban agglomerations has 

gradually surfaced, but most of the research still tends to 

focus on the province as a unit to conduct a macroscopic 

exploration, and rarely focus on the in-depth analysis of the 

specific city level. To address this research gap, this paper 

takes a unique approach to assess the logistics development 

level of each city and quantify it into a comprehensive score 

by using principal component analysis (PCA) with the help of 

SPSS software, taking 14 cities in the Beijing-Tianjin-Hebei 

region as the perspectives. On this basis, this paper analyzes 

the problems in the development of logistics in each city, and 

aims to put forward a series of practical countermeasures. 

3. Modeling and Data Description  

3.1. Modeling 

Assuming that there are p indicators in the actual problem 

we are discussing, we regard these p indicators as p random 

variables, denoted as X1, X2, ..., Xp, principal component 

analysis is to transform the problem of these p indicators into 

a problem of discussing linear combinations of p indicators, 

and these new indicators F1, F2, ..., Fk (k≤ p), fully reflect the 

information of the original indicators according to the 

principle of retaining the main amount of information and are 

independent of each other. μ: factor loading. 

F1= μ(11) X1+ μ(21) X(2) +...+μ(p1) Xp           

F2= μ(12) X1+ μ(22) X(2) +...+μ(p2) Xp           

F3= μ(13) X1+ μ(23) X(2) +...+μ(p3) Xp           

...... 

Fp= μ(1p) X1+ μ(2p) X(2) +...+μ(pp) Xp           

3.2. Data Description 

3.2.1. Data sources 
This paper analyzes the cities involved in Beijing-Tianjin-

Hebei based on the cross-section data of 2020, and the 

relevant data are taken from the 2021 China Statistical 

Yearbook, Hebei Provincial Statistical Yearbook and Henan 

Provincial Statistical Yearbook.  

3.2.2. Construction of the evaluation index system for the 
level of logistics development 

Reasonable indicators are conducive to the construction of 

the evaluation system for the level of logistics development, 

and the principles to be followed are as follows: 

Principle of comprehensiveness: the selected indicators 

should reflect well the cities involved in the regional 

integration of Beijing-Tianjin-Hebei region, and the 

dimensions of the indicators reflecting the level of logistics 

development should be considered from various aspects when 

they are selected. 

Principle of comparability: Although the regional 

integration of Beijing-Tianjin-Hebei is being gradually 

implemented, there are still great differences in the 

development of the cities involved in Beijing-Tianjin-Hebei, 

for example, Beijing and Tianjin, Beijing does not have water 

transportation, while Tianjin has very developed water 

transportation, if water transportation is selected as an 

evaluation indicator, it will lead to no comparability between 

Tianjin and other cities involved in Beijing-Tianjin-Hebei. 

Therefore, this aspect should be fully considered when 

selecting indicators. 

Principle of representativeness: The selected indicators 

should be representative enough to analyze and judge the 

problem more accurately. 

Principle of accessibility: Also known as the principle of 

operationalization, indicators are selected to ensure that the 

required data are available [8]. 

Principle of scientificity: subjectivity should be avoided to 

ensure that the data are true and reliable. 

This paper sets up three dimensions of logistics industry 

scale, logistics industry development environment, logistics 

industry development potential, and then according to the 

characteristics of these three dimensions themselves and 

unfolded to set up nine specific evaluation indicators, 

respectively, for the number of legal entities in transportation, 

highway freight volume, the number of employed people, the 

average wage, GDP per capita, Internet broadband access 

ports, the total amount of goods imported and exported, 

investment in fixed assets (excluding farmers) growth over 

the previous year, and R&D expenditure, and the final 

evaluation system is shown in Table 1. 

 

 

 

 

 

 

 

 

 



 

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Table 1. Indicators for evaluating the level of logistics development 

Level 1 indicators Secondary indicators Nature of the indicator variant 

Scale of the logistics 

industry 

Number of legal entities in transportation (thousands) forward X1 

Freight transported by road (one hundred million tons) forward X2 

Number of employed persons (10,000) forward X3 

Average wage (10,000RMB) forward X4 

Logistics Industry 

Development 

Environment 

GDP per capita (10,000RMB) forward X5 

Internet broadband access ports (10,000 ports) forward X6 

Total import and export of goods (one hundred million yuan) forward X7 

Logistics Industry 

Development Potential 

Growth in investment in fixed assets (excluding farm 

households) over the previous year (%) 
forward X8 

R&D expenditure (one hundred million yuan) forward X9 

3.2.3. Descriptive statistical analysis 

Table 2. Analysis of descriptive statistics 

 N minimum maximum average Standard deviation 

X1: Number of legal entities in transportation (thousands) 14 1.30 20.66 5.3936 5.86306 

X2: Freight transported by road (one hundred million tons) 14 .39 5.13 1.9907 1.43788 

X3: Number of employed persons (10,000) 14 .38 56.00 6.5206 14.70840 

X4: Average wage (10,000RMB) 14 4.70 12.29 7.5064 2.09661 

X5: GDP per capita (ten thousand yuan) 14 2.90 16.50 5.8643 3.73128 

X6: Internet broadband access ports (10,000 ports) 14 209.20 2084.10 591.6357 508.93079 

X7: Total import and export of goods (one hundred million yuan) 14 16.30 23216.00 2503.3000 6257.29577 

X8: Growth in investment in 14 -50.80 129.70 14.1857 50.02373 

X9: R&D expenditure (one hundred million yuan) 14 6.400 297.400 74.28571 88.240884 

Valid N (list status) 14     

 

4. Beijing-Tianjin-Hebei Logistics 
Development Level Empirical 
Analysis 

4.1. Empirical Analysis 

4.1.1. Data normalization and standardization 
Since all the indicators selected in this paper are 

normalized so there is no need to do the normalization process, 

and in the process of using SPSS software, SPSS can 

standardize the non-standard data by itself. Therefore, here no 

longer standardized processing. 

4.1.2. KMO test and Bartlett's test of sphericity 
In order to test the significance and suitability of the data 

for constructing the evaluation indicators of logistics 

development level, KMO test and Bartlett's ball test are 

needed, and the results are shown in Table 3: 

 

Table 3. KMO and Bartlett's test 

 KMO and Bartlett's test  

KMO  .720 

Bartlett’s Test of 

Sphericity 

Approximate chi-square 191.418 

 Degrees of freedom 36 

 significance .000 

The KMO value is between 0 and 1. The higher the KMO 

value, the stronger the commonality of the variables and the 

more appropriate the principal component analysis, the 

general criteria are as follows: KMO>0.9, very suitable for 

principal component analysis; 0.8<KMO<0.9, more suitable 

for principal component analysis; 0.7<KMO<0.8, suitable for 

principal component analysis; 0.6<KMO<0.7, reluctantly 

accepted to do principal component analysis; KM0 < 0.6 is 

not suitable for principal component analysis [9]. The 

calculation results are shown in Table 3, from which it can be 

seen that the KMO value is 0.720, which is greater than 0.6; 

the value of Bartlett's spherical test statistic is 191.418, and 

the p-value obtained from the analysis is 0.000, which is less 

than 5% of the significance level. In conclusion, it shows that 

it is suitable for principal component analysis. 

4.1.3. Determination of principal components 
SPSS was used to analyze the data to get the eigenroot and 

cumulative variance contribution rate (see Table 4), according 

to the principle that the eigenvalue is greater than 1 and the 

cumulative variance is greater than 70%, it means that the 

components are useful and able to carry out the subsequent 

analysis. After principal component analysis, this paper can 

extract 2 principal components from the logistics 

development level rating system. 

 

 

 

 

 

 

 

 



 

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Table 4. Total Variance Explained 

ingredient Initial eigenvalue Extract the sum of squares and load 

 add up the total % of variance Cumulative % add up the total % of variance Cumulative % 

1 6.502 72.239 72.239 6.502 72.239 72.239 

2 1.218 13.536 85.775 1.218 13.536 85.775 

3 0.812 9.027 94.802    

4 0.273 3.035 97.837    

5 0.091 1.016 98.853    

6 0.083 0.922 99.775    

7 0.012 0.130 99.904    

8 0.008 0.088 99.992    

9 0.001 0.008 100.000    

 

Table 5. Component Matrix 

 ingredient 

 1 2 

Zscore (X1: number of legal entities in transportation (thousands)) 0.971 -0.026 

Zscore (X2: Road freight volume (one hundred million tons)) 0.381 -0.709 

Zscore (X3: Number of employed persons (10,000)) 0.943 0.085 

Zscore (X4: average wage (10,000RMB)) 0.898 -0.018 

Zscore (X5: GDP per capita (10,000RMB )) 0.962 0.071 

Zscore (X6: Internet broadband access ports (10,000)) 0.976 0.074 

Zscore (X7: total exports and imports of goods (one hundred million yuan)) 0.953 0.108 

Zscore (X8: Growth in investment in fixed assets (excluding agriculture) over the previous year (%)) -0.054 0.827 

Zscore (X9: R&D spending (one hundred million yuan)) 0.964 0.034 

4.1.4. Interpretation of principal components 
The first principal component contains a large amount of 

indicator information, with a variance contribution rate of 

72.239%, including the number of legal entities in 

transportation, the number of employed persons, the average 

wage, the GDP per capita, the Internet broadband access ports, 

the total amount of goods imported and exported, and the 

R&D expenditures; and the second principal component, with 

a variance contribution rate of 13.536%, contains the amount 

of highway freight transported and the investment in fixed 

assets (excluding farm households) over the previous year's 

Growth. 

4.2. Empirical Analysis 

4.2.1. Composite score calculation 
The scores of the principal components were calculated 

using SPSS as well as Table 5: 

F1=0.971/sqrt(6.502)*ZX1+0.381/sqrt(6.502)*ZX2+0.943/s

qrt(6.502)*ZX3+0.898/sqrt(6.502)*ZX4+0.962/sqrt(6.502)*

ZX5+0.976/sqrt(6.502)*ZX6+0.953/sqrt(6.502)*ZX7-

0.054/sqrt(6.502)*ZX8+0.964/sqrt(6.502)*ZX9 

F2=-0.026/sqrt(1.288)*ZX1-

0.709/sqrt(1.288)*ZX2+0.085/sqrt(1.288)*ZX3-

0.018/sqrt(1.288)*ZX4+0.071/sqrt(1.288)*ZX5+0.074/sqrt(

1.288 )*ZX6+0.108/sqrt(1.288)*ZX7+0.827/sqrt(1.288)*ZX

8+0.034/sqrt(1.288)*ZX9 

The scores of the two principal components are obtained, 

and then the variance explained ratio is used as a weight to 

calculate the composite score for each city, i.e. 

F=0.72239*F1+0.13536*F2             

4.2.2. Composite Score Ranking 
Finally, the comprehensive scores of the 14 cities involved 

in the Beijing-Tianjin-Hebei city cluster can be obtained, 

according to the order of the scores from the largest to the 

smallest, as shown in the table below: 

 

Table 6. Composite Score Ranking Table 

rankings municipalities aggregate 

score 

1 Beijing, capital of People's Republic of 

China 

5.46 

2 Tianjin 2.5 

3 Tangshan prefecture level city in Hebei 0.4 

4 Shijiazhuang prefecture level city in 

Hebei 

0.02 

5 Cangzhou prefecture level city in Hebei -0.37 

6 Langfang prefecture level city in Hebei -0.45 

7 Baoding prefecture level city in Hebei -0.48 

8 Handan prefecture level city in Hebei -0.55 

9 Qinhuangdao prefecture level city in 

Hebei 

-0.67 

10 Chengde prefecture level city in Hebei -1.02 

11 Anyang prefecture level city in Henan -1.07 

12 Xingtai prefecture level city in Hebei -1.25 

12 Zhangjiakou prefecture level city in 

Hebei 

-1.25 

13 Hengshui prefecture level city in Hebei -1.26 

5. Conclusions and Recommendations 

5.1. Conclusion 

As can be seen from Table 6, there is still a big gap between 

the 14 cities in the Beijing-Tianjin-Hebei city cluster, which 

roughly shows that the core cities represented by Beijing and 

Tianjin have better comprehensive scores, while other cities 

with smaller economies have worse comprehensive scores. 

Beijing's comprehensive score ranks first. As the core city of 

Beijing-Tianjin-Hebei coordinated development, Beijing's 

logistics development strategy needs to be closely centered on 

the strategic positioning of the capital and the needs of 



 

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people's livelihood. With its superior economic strength and 

policy advantages, Beijing has demonstrated strong 

leadership in the logistics sector, topping the list in terms of 

comprehensive logistics development, significantly 

outperforming other cities in the region. This leading position 

not only supports the city's daily operations, but also 

contributes to the improvement of residents' quality of life. 

However, despite the diversified trends in enterprise-level 

logistics planning in Beijing, some challenges remain, such as 

the integration and management of some logistics resources 

and services that have yet to be strengthened. Tianjin, with an 

overall score of 2.5 points, comes in second place, showing 

strong strength. Tianjin not only supports Beijing's function 

relocation, but also promotes its own logistics development. 

Although the secondary industry is still dominant, the tertiary 

industry, especially port logistics, is developing rapidly, but 

despite this, there is still a gap compared with international 

ports such as Hong Kong Port. In addition, Tianjin faces the 

challenge of a shortage of highly educated logistics 

professionals, and needs to strengthen its talent support in 

order to promote the continued prosperity of the logistics 

industry. Tangshan and Shijiazhuang are leading the way in 

logistics development in Hebei, with composite scores of 0.4 

and 0.02, respectively. Tangshan leads Hebei in GDP based 

on its resource-based industries and labor strengths, and its 

easy access to transportation is also conducive to logistics, 

while Shijiazhuang has become a major logistics hub in the 

north, thanks to its early transportation infrastructure and 

accelerated circulation after reforms and opening up. Both are 

in the process of transformation and upgrading, and it is 

expected that the tertiary industry, especially the logistics 

industry, will accelerate the development, but the third-party 

logistics has yet to be upgraded. The rest of the cities in Hebei 

and Anyang are lagging behind in logistics development, with 

small economies and insufficient self-driven power. Although 

Langfang and Qinhuangdao have developed, they still lack in 

industrial upgrading and talent introduction, and their 

logistics foundation is weak. The gap with Beijing and Tianjin 

is large, internal development is limited, and the role in 

Beijing-Tianjin-Hebei integration is less than that of 

subordinate cities in the Yangtze River Delta. 

5.2. Recommendations 

This paper evaluates the logistics development level of 14 

cities in the Beijing-Tianjin-Hebei city cluster by using 

principal component analysis to get the comprehensive score 

of each city and puts forward suggestions for improvement 

accordingly. 

In terms of policy, it has strengthened policy orientation 

and optimized the macro environment to build a policy 

cornerstone for the coordinated development of logistics in 

Beijing, Tianjin and Hebei. Refine the specifications for the 

construction of logistics parks, commercial bases and ports to 

improve operational efficiency. Digging deep into the 

advantages of local characteristics, precisely planning 

Zhangjiakou, Xingtai and other marginal cities, combining 

industrial characteristics and natural resources, promoting the 

integrated development of logistics and related industries, 

narrowing the gap with the core cities, and jointly promoting 

the synergistic upgrading of regional logistics. 

From the perspective of logistics enterprises, strengthen the 

core competitiveness of logistics enterprises, as the key node 

of Beijing-Tianjin-Hebei logistics development, need to 

actively innovate technology, improve the level of service, 

combined with the characteristics of each city to formulate a 

personalized development strategy. Promote industrial 

upgrading, transfer of traditional logistics, the application of 

modern technology, to achieve the staggered and 

complementary development. In the face of the epidemic and 

market fluctuations, logistics enterprises should transform 

into learning organizations, optimize the allocation of human 

resources, anticipate trends, flexibly adjust their strategies 

and enhance environmental adaptability. 

From the perspective of talent management, the 

introduction of highly skilled personnel and the strengthening 

of talent support is the key to activating urban vitality and 

driving transformation and upgrading. The national "14th 

Five-Year Plan" emphasizes the importance of talents in the 

innovation system and industrial transformation. Beijing-

Tianjin-Hebei cities should take advantage of the talent policy, 

not only to absorb the labor force, but also to focus on senior 

logistics talents, to accelerate the process of logistics 

modernization with talent as a wing. 

Acknowledgment 

This paper has received support from my two teachers. I 

am also very grateful to my classmates for their help. In 

addition, I am very thankful to the anonymous reviewers for 

their valuable comments, which have helped improve the 

quality of this paper. 

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