A method for evaluating the development status of port-type logistics hub industrial clusters

Abstract

The present invention discloses a method for evaluating the development status of a port-type logistics hub industrial cluster, including obtaining industries and their output values, employed population and economic development indicators of sub-regions and large regions; using a location entropy algorithm to calculate the location entropy value of the industry; judging the degree of industrial agglomeration; using a grey correlation model to analyze and judge the correlation between the sub-regional industrial cluster and the logistics hub, and judging whether the sub-regional industrial cluster belongs to a port-type logistics hub industrial cluster; forming a logistics hub economic development level index matrix X in T years with the selected sub-regional industrial cluster indicators, selecting the economic development level index matrix Y corresponding to the large region in T years, and using a two-way Malmquist index model to quantitatively analyze and calculate the coordination of the logistics hub economy and the economic development of the large region in T years. The present invention comprehensively considers the industrial economic factors of the hub and the city where it is located, and the industry identification is more accurate, and the correlation analysis is more reliable, which provides an important basis for selecting the industrial cluster development model.

Description

Technical Field

[0001] The invention relates to a method for evaluating the development status of a port-type logistics hub industrial cluster, and belongs to the technical field of logistics planning. Background Art

[0002] Logistics hubs are the fulcrums where multiple modes of transportation and multiple resource elements converge, and they are closely related to regional economic and industrial development. In December 2018, the National Development and Reform Commission and the Ministry of Transport jointly issued the "National Logistics Hub Layout and Construction Plan", aiming to build a number of demonstration logistics hubs to jointly create a low-cost, high-efficiency national logistics network system, promote the concentration of logistics resources, solve the problem of weak distribution functions of traditional logistics nodes, and improve logistics operation efficiency. Among different types of logistics hubs, port-type logistics hubs show an outstanding high-speed development trend. At present, China has nearly 800 ports, which are important nodes connecting with international channels and effectively support the development of my country's export-oriented economy. In the first batch of 23 national logistics hub construction lists released in 2019, port-type logistics hubs occupy 9 seats, which means that the construction and development of port-type logistics hubs are of great strategic significance at the national level.

[0003] However, the construction of port-type logistics hubs in my country is still in its initial stage, and the development of industrial clusters in logistics hubs is still in the exploration and planning stage, especially in terms of high-value businesses, which still show obvious shortages and have very limited contributions to the regional economy. Hub economy is a very new concept, and its related theoretical research is still in the exploratory stage. In the context of developing hub economy, there is still no practical analytical method for the development of industrial clusters based on logistics hubs. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a method for evaluating the development status of port-type logistics hub industrial clusters. Relying on the relevant theories of logistics hubs and industrial clusters, a dynamic coordinated development evaluation model of logistics hubs and regional industrial clusters is proposed, and the development logic between industrial clusters and logistics hub economies is further clarified, thereby providing certain ideas and directions for the development of industrial clusters in national logistics hubs.

[0005] The present invention specifically adopts the following technical solutions to solve the above technical problems:

[0006] A method for evaluating the development status of a port-type logistics hub industrial cluster comprises the following steps:

[0007] S1. The district or county where the port-type logistics hub is located is taken as the sub-region, and the prefecture-level city where it is located is taken as the large region. The industries and their output values, employed population and economic development indicators of the sub-region and large region are obtained respectively;

[0008] S2. Based on the obtained sub-regional and large-regional industries and their output values, the location entropy value of the industry is calculated using the location entropy algorithm; the degree of industrial agglomeration is determined based on the calculated industrial location entropy value to determine whether the industry can be developed into an industrial cluster in the sub-region;

[0009] S3. Use the grey correlation model to analyze and determine the correlation between the sub-regional industrial cluster and the logistics hub: Calculate the correlation between the industry in the industrial cluster and the logistics hub in the sub-region determined in step S2, and determine whether the sub-regional industrial cluster belongs to the port-type logistics hub industrial cluster based on the calculated correlation, and select the sub-regional industrial clusters that belong to the port-type logistics hub;

[0010] S4. The indicators of the sub-regional industrial clusters belonging to the port-type logistics hub selected in step S3 are used to form the logistics hub economic development level indicator matrix X in T years. The economic development level indicators of the corresponding large regions in T years are selected to form the matrix Y. The two-way Malmquist index model is used to quantitatively analyze and calculate the coordination between the logistics hub economy and the economic development of the large regions in T years.

[0011] Further, as a preferred technical solution of the present invention, the step 2 is specifically:

[0012] Use the location entropy algorithm to calculate the location entropy value of the industry:

[0013]

[0014] Where: i represents the i sub-region, j represents the j industry, E ij represents the output value of industry j in sub-region i; E i Represents the total industrial value of the sub-region; E j represents the output value of industry j in the large region, and E represents the total value of the industry in the large region;

[0015] According to the calculated location entropy value, the degree of industrial agglomeration is judged: when LQ>1, it indicates that industry j has a high degree of agglomeration in sub-region i and can develop into an industrial cluster; when LQ<1, it indicates that industry j has a low degree of agglomeration in sub-region i and cannot form an industrial cluster.

[0016] Furthermore, as a preferred technical solution of the present invention, the step 3 uses a grey correlation model to analyze and determine the correlation between sub-regional industrial clusters and logistics hubs, including:

[0017] Select the output value of the industry in the industrial cluster within T years that the sub-region determined in step S2 can develop into a sub-sequence, and use the numerical value of the logistics hub development level indicator within the corresponding T years to form a parent sequence, put the sub-sequence in front and the parent sequence in the back to form a matrix x′, and perform dimensionless normalization to obtain the matrix x; calculate the sequence difference based on the sub-sequence and the parent sequence in the matrix x, and obtain the maximum sequence difference and the minimum sequence difference; and, based on the calculated correlation coefficient of each sub-sequence and the corresponding element of the parent sequence, calculate the mean value of the correlation between the industry and the logistics hub in the sub-regional industrial cluster;

[0018] By calculating the average value of the correlation between industries and logistics hubs within the sub-regional industrial cluster, it is judged whether the sub-regional industrial cluster belongs to a port-type logistics hub industrial cluster.

[0019] Furthermore, as a preferred technical solution of the present invention, the step 3 uses a grey correlation model to analyze and determine the correlation between sub-regional industrial clusters and logistics hubs, specifically:

[0020] Select the sub-region calculated in step S2 that can develop into a subsequence of the output value of industries in the industrial cluster with location entropy value LQ>1 within T years, where there are m+1 industries, represented by 0 to m respectively;

[0021] The parent sequence is composed of the values ​​of the logistics hub development level indicators in the corresponding T years, where there are n-(m+1) types of logistics hub development level indicators, represented by m+1 to n respectively;

[0022] The subsequence is placed in front and the parent sequence is placed in the back to form a matrix x′:

[0023]

[0024] where x′0(T)…x′ m (T) represents the output value of each industry in the industrial cluster in T years; x′ m+1 (T)…x′ n (T) represents the corresponding value of each logistics hub development level indicator in T years;

[0025] Perform dimensionless normalization to obtain the matrix x:

[0026]

[0027] Among them, x0(T)…x m (T) represents the output value of each industry in the industrial cluster in T years after dimensionless standardization; x m+1 (T)…x n (T) represents the value of each logistics hub development level indicator corresponding to the dimensionless standardization in T years;

[0028] When h=0,1,…,m, for each value of h, calculate the sequence difference:

[0029] Δ hi (t)=|x h (t)-x i (t)| i=m+1,…,n; t=1,2,…T

[0030] where x h (t) and x i (t) represent the dimensionless normalized values ​​of h and the corresponding industry in year t, respectively;

[0031] The maximum sequence difference and the minimum sequence difference are obtained as follows:

[0032]

[0033] And calculate the correlation coefficient between each subsequence and the corresponding element of the parent sequence:

[0034]

[0035] Where ρ is the resolution coefficient, 0<ρ<1;

[0036] And, calculate the mean of the correlation between industry and logistics hub:

[0037]

[0038] The judgment is made based on the mean calculation results of the correlation between the industry and the logistics hub. When the resolution coefficient ρ takes a certain value, if the calculated mean of the correlation is greater than the set threshold, the industrial cluster is considered to belong to the port-type logistics hub industrial cluster, otherwise it is considered that the industrial cluster does not belong to the port-type logistics hub industrial cluster.

[0039] Furthermore, as a preferred technical solution of the present invention, in step 3, the industrial output value, number of enterprises, fixed asset investment, cargo throughput, container throughput and ship docking volume of the logistics hub that can represent the development level of the port hub within the corresponding T years are selected to form a parent sequence.

[0040] Furthermore, as a preferred technical solution of the present invention, in step S4, the total output value, regional GDP, public fiscal budget revenue, fixed asset investment, and total foreign trade of sub-regional industrial clusters are selected to form the logistics hub economic development level indicator matrix X in year T.

[0041] Further, as a preferred technical solution of the present invention, the step S4 adopts a bidirectional Malmquist index model based on a directional distance function to quantitatively analyze and calculate the coordination, specifically:

[0042] Taking the economic development level index matrix X of the logistics hub as input and the economic development level index matrix Y of the large region as output, the Malmquist index M0 is calculated for each year and its mean value for the year T is calculated.

[0043]

[0044] Where (x t ,y t ) represents the input and output in year t, (x t+1 ,y t+1 ) represents the input and output in year t+1, and They represent the distance functions of the tth year and the t+1th year, i.e., the distance from the previous year; It represents the distance function of the +1th year under the premise of satisfying the production possibility set of the tth year, It represents the distance function of the year under the premise of satisfying the production possibility set of the year t+1;

[0045] in:

[0046]

[0047]

[0048]

[0049] Where β is a constant, (g x ,g y )≥0 and (g x+1 ,g y+1 )≥0 is the given direction vector, S t is (x t ,y t )'s complete works, S t+1 is (x t+1 ,y t+1 )'s complete works;

[0050] Taking the economic development level indicator matrix Y of the corresponding large region as input and the economic development level indicator matrix X of the logistics hub as output, calculate the Malmquist index M0 for each year and calculate its average value in year T

[0051]

[0052] Among them, (y t ,x t ) represents the input and output in year t, (y t+1 ,x t+1) represents the input and output in year t+1, and Respectively represent the distance function of the year and the t+1th year, that is, the distance from the previous year, It represents the distance function of the +1th year under the premise of satisfying the production possibility set of the tth year, It represents the distance function of the year under the premise of satisfying the production possibility set of the year t+1;

[0053] in:

[0054]

[0055]

[0056]

[0057] Where β is a constant, (g y ,g x )≥0 and (g y+1 ,g x+1 )≥0 is the given direction vector, P t is(y t ,x t )'s complete works, P t+1 is(y t+1 ,x t+1 )'s complete works;

[0058] Calculate coordination The closer the calculated coordination degree μ is to 1, the better the coordination between the logistics hub and the dynamic economic development of the city where it is located.

[0059] The present invention adopts the above technical solution to produce the following technical effects:

[0060] The method for evaluating the development status of the port-type logistics hub industrial cluster of the present invention is an evaluation method for evaluating the coordination degree of the dynamic development of the logistics hub and the hinterland regional economy, which includes the acquisition of research data, including indicators such as the output value of the main industries of the port-type logistics hub and the hinterland city where it is located, the employed population, etc.; using the location entropy algorithm to identify the industrial clusters in the region; using grey correlation analysis to determine the correlation between the industrial clusters and the logistics hub; selecting industries with higher correlation, using the bidirectional Malmquist index model based on the directional distance function to calculate the coordination degree, and determining the development coordination degree of the logistics hub and the hinterland city economy.

[0061] Therefore, the method of the present invention comprehensively considers the industrial economic factors of the port hub and the city where it is located, making industry identification more accurate and correlation analysis more reliable, providing an important basis for selecting the development model of industrial clusters, helping to optimize and shape the industrial division of labor system, guide the good layout of industrial clusters, and improve the development efficiency of industrial clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flow chart of the method for evaluating the development status of the port-type logistics hub industrial cluster of the present invention.

[0063] Figure 2 It is a schematic diagram of the geographical location of Taicang port-type logistics hub according to an embodiment of the present invention.

[0064] Figure 3 This is the change in location entropy of the three types of service industries in the embodiment of the present invention. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described below in conjunction with the accompanying drawings.

[0066] like Figure 1 As shown, the present invention relates to a method for evaluating the development status of a port-type logistics hub industrial cluster, which specifically includes the following steps:

[0067] S1. The district or county where the port-type logistics hub is located is taken as the sub-region, and the prefecture-level city where it is located is taken as the large region. The industries and their output values, employed population and economic development indicators of the sub-region and large region are obtained respectively.

[0068] S2. Based on the obtained sub-regional and large-regional industries and their output values, the location entropy value of the industry is calculated using the location entropy algorithm; the degree of industrial agglomeration is determined based on the calculated industrial location entropy value to determine whether the industry can be developed into an industrial cluster in the sub-region.

[0069] Among them, the location entropy algorithm is used to calculate the location entropy value of the industry:

[0070]

[0071] In the formula: i represents the i sub-region, j represents the j industry, E ij represents the output value of industry j in sub-region i; E i Represents the total industrial value of the sub-region; E j represents the output value of industry j in the large region, and E represents the total value of the industry in the large region.

[0072] According to the calculated location entropy value, the degree of industrial agglomeration can be judged: when LQ>1, it indicates that industry j has a high degree of agglomeration in sub-region i and can develop into an industrial cluster; when LQ<1, it indicates that industry j has a low degree of agglomeration in sub-region i and cannot form an industrial cluster.

[0073] S3. Use the grey correlation model to analyze and determine the correlation between the sub-regional industrial cluster and the logistics hub: select the sub-region determined in step S2 to develop into the industrial cluster industry output value within T years to form a sub-sequence, and use the numerical value of the logistics hub development level indicator within the corresponding T years to form a parent sequence, and put the sub-sequence in front and the parent sequence in the back to form a matrix x', and perform dimensionless standardization to obtain the matrix x; calculate the sequence difference based on the sub-sequence and the parent sequence in the matrix x, and obtain the maximum sequence difference and the minimum sequence difference; and, based on the calculated correlation coefficient of each sub-sequence and the corresponding element of the parent sequence, calculate the mean correlation between the industry in the sub-regional industrial cluster and the logistics hub; based on the calculated mean correlation between the industry in the sub-regional industrial cluster and the logistics hub, determine whether the sub-regional industrial cluster belongs to a port-type logistics hub industrial cluster, and the specific process is as follows:

[0074] Select the sub-region calculated in step S2 that can develop into a subsequence of the output value of industries in the industrial cluster with location entropy value LQ>1 within T years, where there are m+1 industries, represented by 0 to m respectively;

[0075] The parent sequence is composed of the values ​​of the logistics hub development level indicators in the corresponding T years, where there are n-(m+1) types of logistics hub development level indicators, represented by m+1 to n respectively;

[0076] Put the subsequence first and the parent sequence last to form the matrix x′:

[0077]

[0078] where x′0(T)…x′ m (T) represents the output value of each industry in the industrial cluster in T years; x′ m+1 (T)…x′ n (T) represents the corresponding value of each logistics hub development level indicator in T years;

[0079] Perform dimensionless normalization to obtain the matrix x:

[0080]

[0081] Among them, x0(T)…x m (T) represents the output value of each industry in the industrial cluster in T years after dimensionless standardization; x m+1 (T)…x n (T) represents the value of each logistics hub development level indicator corresponding to the dimensionless standardization in T years;

[0082] When h=0,1,…,m, for each value of h, calculate the sequence difference:

[0083] Δ hi (t)=|x h (t)-x i (t)| i=m+1,…,n; t=1,2,…T

[0084] where x h (t) and x i (t) represent the dimensionless normalized values ​​of h and the corresponding industry in year t, respectively;

[0085] The maximum sequence difference and the minimum sequence difference are obtained as follows:

[0086]

[0087] And calculate the correlation coefficient between each subsequence and the corresponding element of the parent sequence:

[0088]

[0089] Where ρ is the resolution coefficient, 0<ρ<1. The smaller ρ is, the greater the difference between the correlation coefficients and the stronger the discrimination ability. Usually ρ is 0.5.

[0090] Calculate the mean of the correlation between industry and logistics hub:

[0091]

[0092] The judgment is made based on the mean calculation result of the correlation between industries and logistics hubs in sub-regional industrial clusters. When the resolution takes a certain value, such as 0.5, if the calculated mean calculation result of the correlation between industries and logistics hubs is greater than the set threshold of 0.6, then the industrial cluster is considered to belong to a port-type logistics hub industrial cluster, otherwise it is considered that the industrial cluster does not belong to a port-type logistics hub industrial cluster, thereby screening out sub-regional industrial clusters belonging to port-type logistics hubs.

[0093] S4. The indicators of the sub-regional industrial clusters belonging to the port-type logistics hubs selected in step S3 are used to form the logistics hub economic development level indicator matrix X in T years. The economic development level indicators of the large regions in T years are selected to form the matrix Y. The two-way Malmquist index model is used to quantitatively analyze and calculate the coordination between the logistics hub economy and the economic development of the large region in T years, so as to determine the development coordination between the logistics hub and the urban economy of the large region, as follows:

[0094] First, preferably, the total output value of the cluster industries (billion yuan) screened out through the industrial agglomeration analysis in step S2 and the industrial correlation analysis in step S3 can be used as one of the indicators, and other indicators: regional GDP (billion yuan), public fiscal budget revenue (billion yuan), fixed asset investment (billion yuan), total foreign trade (billion yuan)) can be used to form the economic development level indicator matrix X of the logistics hub in year T.

[0095] Then, the coordination degree is calculated using the bidirectional Malmquist index model based on the directional distance function:

[0096] Taking the economic development level index matrix X of the logistics hub as input and the economic development level index matrix Y of the large region as output, the Malmquist index M0 is calculated for each year and its mean value for the year T is calculated.

[0097]

[0098] Where (x t ,y t ) represents the input and output in year t, (x t+1 ,y t+1 ) represents the input and output in year t+1, and They represent the distance functions of the tth year and the t+1th year, i.e., the distance from the previous year; It represents the distance function of the +1th year under the premise of satisfying the production possibility set of the tth year, It represents the distance function of the year under the premise of satisfying the production possibility set of the year t+1;

[0099] in:

[0100]

[0101]

[0102]

[0103] Where β is a constant, (g x ,g y )≥0 and (g x+1 ,g y+1 )≥0 is the given direction vector, S t is (x t ,y t )'s complete works, S t+1 is (x t+1 ,y t+1 )'s complete works;

[0104] Taking the economic development level indicator matrix Y of the corresponding large region as input and the economic development level indicator matrix X of the logistics hub as output, calculate the Malmquist index M0 for each year and calculate its average value in year T

[0105]

[0106] Among them, (y t ,x t ) represents the input and output in year t, (y t+1 ,x t+1 ) represents the input and output in year t+1, and Respectively represent the distance function of the year and the t+1th year, that is, the distance from the previous year, It represents the distance function of the +1th year under the premise of satisfying the production possibility set of the tth year, It represents the distance function of the year under the premise of satisfying the production possibility set of the year t+1;

[0107] in:

[0108]

[0109]

[0110]

[0111] Where β is a constant, (g y ,g x )≥0 and (g y++1 ,g x+1 )≥0 is the given direction vector, P t is(y t ,x t )'s complete works, P t+1 is(y t+1 ,x t+1 )'s complete works;

[0112] Calculate coordination The closer the coordination degree μ is to 1, the better the coordination between the logistics hub and the dynamic economic development of the city where it is located.

[0113] Therefore, the method of the present invention comprehensively considers the industrial economic factors of the port hub and the city where it is located, the industry identification is more accurate, the correlation analysis is more reliable, and it can effectively evaluate the coordination degree of the dynamic development of the logistics hub and the regional economy.

[0114] In order to verify that the method of the present invention can effectively evaluate the dynamic coordinated development of logistics hubs and regional industrial clusters, a verification example is given for illustration.

[0115] This verification example selects Taicang port-type logistics hub as the sample hub, Taicang as the sub-region, and Suzhou as the large region. Figure 2 This is a schematic diagram of the geographical location of the Taicang port-type logistics hub in an embodiment of the present invention. The evaluation is performed according to the method for evaluating the development status of the port-type logistics hub industrial cluster of the present invention. The specific steps are as follows:

[0116] S1. Obtain the data needed for subsequent operations through relevant yearbooks, that is, obtain the industries and their output values, employed population and economic development indicators of sub-regions and large regions:

[0117] Data required for the location entropy algorithm: output value and number of employees in each industry in Taicang and Suzhou;

[0118] Data required for grey correlation model analysis: industrial output value of Taicang City, number of enterprises in Taicang Port Area, fixed asset investment in Taicang Port Area, cargo throughput of Taicang Port, container throughput of Taicang Port, and number of ships docked at Taicang Port;

[0119] The data required for the two-way Malmquist index model analysis include: GDP, industrial cluster output value, public fiscal budget revenue, fixed asset investment, and total foreign trade of Taicang and Suzhou.

[0120] S2. Use the location entropy algorithm to calculate the degree of industrial agglomeration:

[0121] According to the output value of major industries in Taicang and Suzhou ij , calculate the location entropy value LQ. When LQ>1, it indicates that industry j has a high degree of agglomeration in Taicang City and can develop into an industrial cluster;

[0122]

[0123] The location entropy values ​​of three service industries, namely, leasing and business, wholesale and retail, and accommodation and catering, were calculated over the years. The results are as follows: Figure 3 It can be seen that the location entropy of leasing and business, wholesale and retail, and accommodation and catering is increasing year by year, indicating that the agglomeration degree of these three types of service industries is gradually increasing, and the scale of the cluster has been initially formed.

[0124] Taking the location entropy value LQ=1 as the critical value, 20 industries with a relatively strong degree of agglomeration were divided for further discussion, including petroleum fuel processing, papermaking, instrument manufacturing, medicine, chemical manufacturing, transportation, warehousing and postal services, leasing and commerce, wholesale and retail, accommodation and catering.

[0125] S3. Use the grey correlation model to analyze and determine the correlation between industrial clusters and logistics hubs:

[0126] The output values ​​X1 to X2 of industries that can be developed into industries with location entropy values ​​LQ>1 in the industrial cluster in step S2 are 20 The subsequences are formed, and the parent sequence is composed of the Taicang port hub development level indicators Y1~Y6 (Taicang port area industrial output value above designated size Y1, Taicang port area enterprise number Y2, Taicang port area fixed asset investment Y3, Taicang port cargo throughput Y4, Taicang port container throughput Y5 and Taicang port ship docking volume Y6). The subsequences before and after the parent form the matrix x′, and the dimensionless normalization process is performed to obtain the matrix x:

[0127]

[0128] Calculate the sequence difference according to the matrix x, find the maximum sequence difference and the minimum sequence difference; take the resolution coefficient ρ = 0.5, calculate the correlation coefficient between each subsequence and the corresponding element of the parent sequence, and calculate the mean value of the correlation between each industry in Taicang City and the Taicang port-type logistics hub:

[0129]

[0130]

[0131] According to the mean calculation results of the correlation between various industries in Taicang City and the Taicang port-type logistics hub, when the resolution coefficient ρ = 0.5, it is generally believed that when the calculated mean of the correlation is greater than the set threshold of 0.6, it can show a strong correlation. Therefore, it is believed that the 15 industries with a mean calculation result of the correlation greater than 0.6 can form industrial clusters relying on the Taicang port-type logistics hub, and then play the role of industrial cluster development. The compact hub industry cluster phenomenon of the Taicang port-type logistics hub is obvious, the strongly correlated hub industry cluster has begun to develop, and the extensible hub industry cluster has not yet formed.

[0132] S4. Based on the industrial clusters with a strong correlation with the logistics hub determined in step S3, a two-way Malmquist index model is used to conduct a dynamic coordination analysis of the economic indicators of the Taicang port-type logistics hub and Suzhou City:

[0133] Taking 4 years as 4 decision-making units (2015-2018), 5 inputs (Taicang GDP, industrial cluster output value, public fiscal budget revenue, fixed asset investment, and total foreign trade) and 5 outputs (Suzhou GDP, industrial cluster output value, public fiscal budget revenue, fixed asset investment, and total foreign trade) were selected to calculate the annual Malmquist productivity index M0 and calculate the average value. Swap the input and output terms, calculate the Malmquist productivity index M0 for each year again, and calculate the average The results are as follows:

[0134]

[0135] If M0>1, it means that the efficiency has improved compared with last year, that is, the dynamic coordination of economic development between Taicang and Suzhou is better; the data in the table preliminarily show that the dynamic coordination of economic development between the two sides is not stable.

[0136] Further calculation of coordination The calculation results are as follows:

[0137] years μ 2016 / 2015 0.965 2017 / 2016 0.725 2018 / 2017 0.762 average value 0.817

[0138] The closer the calculated coordination degree μ is to 1, the better the coordination between the hub economy and the city economy. From the operation results, it can be seen that the value of μ is close to 1, indicating that the current economic development of Taicang Port-type Logistics Hub is generally coordinated with the overall economic development of Suzhou. The economic development of Taicang Port-type Logistics Hub has a relatively positive impact on the economic development of Suzhou. At the same time, Suzhou's regional economy has strong support for Taicang Port-type Logistics Hub Economy. The coordination degree has decreased in the past two years, mainly because Suzhou's support for Taicang Port has been relatively weakened, and the coordination state is not stable.

[0139] In summary, the method of the present invention comprehensively considers the industrial economic factors of the hub and the city where it is located. The industry identification is more accurate and the correlation analysis is more reliable. It provides an important basis for selecting the development model of industrial clusters, helps to optimize and shape the industrial division of labor system, guide the good layout of industrial clusters, and improve the development efficiency of industrial clusters.

[0140] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for evaluating the development status of a port-type logistics hub industrial cluster, characterized in that: The steps include: S1. The district or county where the port-type logistics hub is located is taken as the sub-region, and the prefecture-level city where it is located is taken as the large region. The industries and their output values, employed population and economic development indicators of the sub-region and large region are obtained respectively; S2. Based on the sub-regional and large-regional industries and their corresponding output values ​​obtained in step S1, the location entropy value of the industry is calculated using the location entropy algorithm; the degree of industrial agglomeration is determined based on the calculated industrial location entropy value to determine whether the industry in the sub-region can be developed into an industrial cluster; S3. Use the grey correlation model to analyze and determine the correlation between the sub-regional industrial cluster and the logistics hub: Calculate the correlation between the industry in the industrial cluster and the logistics hub in the sub-region determined in step S2, and determine whether the sub-regional industrial cluster belongs to the port-type logistics hub industrial cluster based on the calculated correlation, and select the sub-regional industrial clusters that belong to the port-type logistics hub; The above step 3 is specifically as follows: Select the sub-region calculated in step S2 that can develop into a subsequence of the output value of industries in the industrial cluster with location entropy value LQ>1 within T years, where there are m+1 industries, represented by 0 to m respectively; The parent sequence is composed of the values ​​of the logistics hub development level indicators in the corresponding T years, where there are n-(m+1) types of logistics hub development level indicators, represented by m+1 to n respectively; The subsequence is placed in front and the parent sequence is placed in the back to form a matrix x': where x'0(T)…x' m (T) represents the output value of each industry in the industrial cluster in T years; x' m+1 (T)…x' n (t) represents the value of each logistics hub development level indicator in year T; Perform dimensionless normalization to obtain the matrix x: Among them, x0(T)…x m (T) represents the output value of each industry in the industrial cluster in T years after dimensionless standardization; x m+1 (T)…x n (T) represents the value of each logistics hub development level indicator corresponding to the dimensionless standardization in T years; When h=0,1,…,m, for each value of h, calculate the sequence difference: Δ hi (t)=|x h (t)-x i (t)|i=m+1,…,n;t=1,2,…T where x h (t) and x i (t) represents the dimensionless normalized values ​​of the industries corresponding to h and i in year t; The maximum sequence difference and the minimum sequence difference are obtained as follows: And calculate the correlation coefficient between each subsequence and the corresponding element of the parent sequence: Where ρ is the resolution coefficient, 0<ρ<1; And, calculate the mean of the correlation between industry and logistics hub: According to the calculation results of the mean value of the correlation between the industry and the logistics hub, when the resolution coefficient ρ takes a certain value, if the calculated mean value of the correlation is greater than the set threshold, the industrial cluster is considered to belong to the port-type logistics hub industrial cluster, otherwise it is considered that the industrial cluster does not belong to the port-type logistics hub industrial cluster; S4. The indicators of the sub-regional industrial clusters belonging to the port-type logistics hub selected in step S3 are used to form the logistics hub economic development level indicator matrix X in T years, and the economic development level indicators of the large regions in T years are selected to form the matrix Y. The two-way Malmquist index model is used to quantitatively analyze and calculate the coordination between the logistics hub economy and the economic development of the large regions in T years; The above step S4 is specifically as follows: Taking the economic development level index matrix X of the logistics hub as input and the economic development level index matrix Y of the large region as output, the Malmquist index M0 is calculated for each year and its mean value for the year T is calculated. Where (x t ,y t ) represents the input and output in year t, (x t+1 ,y t+1 ) represents the input and output in year t+1, and They represent the distance functions of year t and year t+1, i.e., the distance from the previous year; It represents the distance function of the year t+1 under the premise of satisfying the production possibility set of the year t, It represents the distance function in year t under the premise of satisfying the production possibility set in year t+1; in: Where β is a constant, (g x ,g y )≥0 and (g x+1 ,g y+1 )≥0 is the given direction vector, S t is (x t ,y t )'s complete works, S t+1 is (x t+1 ,y t+1 )'s complete works; Taking the economic development level indicator matrix Y of the corresponding large region as input and the economic development level indicator matrix X of the logistics hub as output, calculate the Malmquist index M0 for each year and calculate its average value in year T Among them, (y t ,x t ) represents the input and output in year t, (y t+1 ,x t+1 ) represents the input and output in year t+1, and Respectively represent the distance function of the tth year and the t+1th year, that is, the distance from the previous year, It represents the distance function of the year t+1 under the premise of satisfying the production possibility set of the year t, It represents the distance function in year t under the premise of satisfying the production possibility set in year t+1; in: Where β is a constant, (g y ,g x )≥0 and (g y+1 ,g x+1 )≥0 is the given direction vector, P t is(y t ,x t )'s complete works, P t+1 is(y t+1 ,x t+1 )'s complete works; Calculate coordination The closer the calculated coordination degree μ is to 1, the better the coordination between the logistics hub and the dynamic economic development of the city where it is located.

2. The method for evaluating the development status of port-type logistics hub industrial clusters according to claim 1 is characterized in that: The step 2 is specifically as follows: Use the location entropy algorithm to calculate the location entropy value of the industry: Where: i represents the i sub-region, j represents the j industry, E ij represents the output value of industry j in sub-region i; E i Represents the total industrial value of the sub-region; E j represents the output value of industry j in the large region, and E represents the total value of the industry in the large region; According to the calculated location entropy value, the degree of industrial agglomeration is judged: when LQ>1, it indicates that industry j has a high degree of agglomeration in sub-region i and can develop into an industrial cluster; when LQ<1, it indicates that industry j has a low degree of agglomeration in sub-region i and cannot form an industrial cluster.

3. The method for evaluating the development status of port-type logistics hub industrial clusters according to claim 1 is characterized in that: The step 3 uses the grey correlation model to analyze and determine the correlation between the sub-regional industrial clusters and the logistics hubs, including: Select the output value of the industry in the industrial cluster within T years that the sub-region determined in step S2 can develop into a sub-sequence, and use the numerical value of the logistics hub development level indicator within the corresponding T years to form a parent sequence, put the sub-sequence in front and the parent sequence in the back to form a matrix x', and perform dimensionless normalization to obtain the matrix x; calculate the sequence difference based on the sub-sequence and the parent sequence in the matrix x, and obtain the maximum sequence difference and the minimum sequence difference; and, based on the calculated correlation coefficient of each sub-sequence and the corresponding element of the parent sequence, calculate the mean value of the correlation between the industry and the logistics hub in the sub-regional industrial cluster; By calculating the average value of the correlation between industries and logistics hubs within the sub-regional industrial cluster, it is judged whether the sub-regional industrial cluster belongs to a port-type logistics hub industrial cluster.

4. The method for evaluating the development status of port-type logistics hub industrial clusters according to claim 2 is characterized in that: In step 3, the industrial output value, number of enterprises, fixed asset investment, cargo throughput, container throughput and ship docking volume of the logistics hub that can represent the development level of the port hub in the corresponding T years are selected to form a parent sequence.

5. The method for evaluating the development status of port-type logistics hub industrial clusters according to claim 1 is characterized in that: In step S4, the total output value, regional GDP, public fiscal budget revenue, fixed asset investment, and total foreign trade of the sub-regional industrial clusters are selected to form the economic development level indicator matrix X of the logistics hub in year T.

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