Evaluation of linkage efficiency between manufacturing industry and logistics industry considering the output of unexpected pollutants

ABSTRACT

Applied a non-radical non-oriented Slack Based Measure (SBM) model of Data Envelopment Analysis (DEA) to measure the linkage efficiency between the two sectors from 2009 to 2016. The paper analyzes the current situation of manufacturing and logistics efficiency, presents a way to improve the linkage efficiency of manufacturing and logistics industry, and using Tobit regression to analyze the environmental factors that affect the linkage efficiency. The results show that: Totality, the two industries in the three northeast provinces show a steady development trend in the period from 2009 to 2016, but the development of the two industries in northeast China is still insufficient. Unexpected pollutant output is the main reason for the decline of manufacturing efficiency in the regions. The redundancy of input factors is the main reason that affects the efficiency decline of the logistics industry. The results of Tobit regression analysis show that the investment of science and technology and opening to the outside world have a positive influence on the efficiency of the two industries, and government consumption has a negative effect on the efficiency of the two industries. This is to correctly understand and grasp the status of two industry linkage development in three northeast provinces. And it provides a certain basis for the development policy of the two-industry linkage development.

Implications: Due to the availability of data, reference to the relevant achievements of the research on the linkage between manufacturing industry and logistics industry. The transportation industry, warehousing, and postal and telecommunications industry data are generally used as logistics industry data. Manufacturing data replaced by industrial data. The manufacturing industry is the core industry in the industrial system. Its output value accounts for more than 90% of industrial output value, so it can reflect the development trend of the manufacturing industry in general.

Introduction

The aim of the linkage of the manufacturing industry and logistics industry is to achieve better functional connectivity, operational efficiency, and integrated value performance, such as the supply chain with fast response and agile manufacturing (see, e.g., Shen Liang 2015). The manufacturing industry and logistics industry influence each other, and they both have revolved around the core interests of their respective industries and formed the relationship of industrial cooperation and interaction under the principle of reciprocal complementarity. Taking enterprises as the main body and industrial linkage as the basis to provide more efficient logistics services for manufacturing enterprises, reduces the cost of enterprises, improves the core competitiveness, and becomes the “third source of profit” for manufacturing enterprises, and finally form the cooperation of industrial activities, promoting the common development of them. In order to find out the problems of the linkage of manufacturing industry and logistics industry, the research of the linkage of both are beneficial to explore and improve the way of the joint development efficiency of the two industries, and it will improve the optimization of regional economic structure and upgrade industries, and provide new impetus to revitalize the northeastern region’s economy.

As one of the key projects of the national logistics industry adjustment and revitalization plan, the linkage between the two industries is concerned by more and more scholars and government departments, and empirical studies on the interaction between the two industries are gradually increasing. The main research methods are including the econometric model, gray relationship, input-output analysis, population evolution model, data envelope method, case study, and so on. Chen Chiping (2014) used the annual data of Hunan Province during 1978–2011, they explore the linkage development relationship between the manufacturing industry and logistics industry through cointegration analysis and Granger causality test. The results show that there is a long-term equilibrium relationship between manufacturing and logistics in Hunan Province.NieXingxin (2015) established an index system to establish a coordinated development model and a gray correlation model for the joint manufacturing industry and logistics industry in Xining City and quantitatively analyzed the development data of the two industries in Xining from 2002 to 2013. Wang Zhenzhen (2017) established a super-efficiency DEA model to measure the efficiency value of the integrated system of the manufacturing industry and the logistics industry in 2000–2013. The three northeastern provinces are the birthplace of China’s manufacturing industry. The manufacturing industry occupies the vast majority of the national economy and has basically formed a manufacturing industry system with complete categories and solid foundations. Although there are a certain number of relevant research literature, most of the existing literature is qualitative research such as policy recommendations, and a small number of quantitative studies are mainly to evaluate the efficiency of individual departments. The quantitative research on the linkage efficiency of the two industries in the three northeastern provinces is still relatively lacking.

In order to avoid the lack of traditional DEA model in using radial measurement, the model is more in line with the condition of different proportions of input and output in real production, so the non-radial measurement method is chosen. And compared with the radial measurement, non-radial measurement reflects the slack variable in input redundancy and insufficient output.⁷ Thus, adopt the super SBM model, and make a deep research on the linkage efficiency of the two industries in the three northeastern provinces, eliminating the inefficiency caused by slack. At the same time, it fills the research on the efficiency evaluation of the two departments under the existence of unexpected output, and provides reference for the improvement of the linkage efficiency of the two provinces in Northeast China, and provides theoretical guidance for the formulation of the development policies of the manufacturing and logistics industries in the three northeastern provinces, with a view to promoting the three northeastern provinces. The level of industry linkage development has improved.

The aim of the work is to build a more perfect evaluation system of the linkage efficiency between logistics industry and manufacturing industry by using the super efficiency unexpected SBM model. In the process of efficiency measurement, the carbon emission produced in the logistics operation and the industrial solid waste emission produced in the manufacturing process are taken into account, which makes the measurement results more consistent with the actual operation of the two industries, and it improves the accuracy and scientificity of efficiency measurement.

Model construction and index selection

Model construction

In the actual social production, when people’s material demand be solved, it will also produce various kinds of side-effect products, such as waste water, waste residue, waste gas, and other pollutants. This kind of unexpected output that is people want to avoid as much as possible, so the unexpected output is gradually concerned by relevant scholars. For this reason, tone proposed a new SBM model in 2003, that is, the unexpected SBM model. Just because the new SBM model actually has made some extensions on DEA, which highlights precisely the progressiveness of SBM model. The reason is that this model does not need to set the target of the optimal behavior of the production unit and preset the production function. In addition, the novelty of the model is that the result is more accurate, which is because using the radial and angular models to calculate the production units less than the relaxation variables without considering the relaxation variables. The results of this paper are calculated by software MaxDEAUltra 7.10. Tone (2002) put forward a kind of non-radial measurement DEA model, it’s to say, it’s an evaluating Decision Making Unit (DMU) method with the slacks-based measure(SBM). The SBM model adds the slack variable into the objective function, which makes the economic interpretation of the model is to maximize the actual profit. At the same time, Tone (2002) proposed a super SBM model for evaluating SBM effective DMUs, which making up for the inability to calculate the efficiency values of all DMUs. In the super SBM evaluation, the SBM model needs to evaluate the DMUs, and the effective DMUs for SBM are evaluated by the ultra-efficient SBM model. This paper introduces unexpected outputs into the super-efficient SBM, which results in an improved super-efficient SBM model which takes unexpected outputs into account. Considering DMUs are uncountable numbers, each DMU consists of input m, an expected output r₁, and some unexpected output r₂. The forms of vector are denoted as $\mathrm{x}\in {\mathrm{R}}^{\mathrm{m}}$, ${\mathrm{y}}^d\in {\mathrm{R}}^{\mathrm{r}1}$, ${\mathrm{y}}^u\in {\mathrm{R}}^{\mathrm{r}2}$, and $X$, ${\mathrm{y}}^d$ and ${\mathrm{y}}^u$ are matrixes, and the $X=[x_1,\dots ,x_n]\in R^{m\ast n}$, $Y^d=[Y_1^d,\dots ,Y_n^d]\in R^{r_1\ast n}$, $Y^u=[Y_1^u,\dots ,Y_n^u]\in R^{r_2\ast n}$, the SBM model is expressed as follows:

(1) $min\rho =\frac{1-(1/m)\sum _{i=1}^m(w_i^{-}/x_{ik})}{1+1/(r_1+r_2)(\sum _{s=1}^{r_1}(w_s^d/y_{sk}^d+\sum _{q=1}^{r_2}(w_q^u/y_{qk}^u)}$(1)

Subject to $x_{ik}=\sum _{j=1}^n{x}_{ij}{\lambda }_j+w_i^{-}$$i=1,\dots ,m$

${\lambda }_j>0$$j=1,\dots ,n$

$w_i^{-}\ge 0$$i=1,\dots ,m$

$w_s^d\ge 0$$s=1,\dots ,r_1$

Calculate the efficiency of the manufacturing and logistics industry in the three northeastern provinces from 2009 to 2016 based on the above model, and use MLLE (Manufacturing and Logistics Linkage Efficiency) to express. Owing to the characteristics of this model, the efficiency value is dimensionless. The efficiency value of region k in t years is:

(2) $MLL{E}_{kt}={\{}_{{\mathrm{\ell }}_{kt}({\rho }_{kt}=1)}^{{\rho }_{kt}({\rho }_{kt}<1)}$(2)

$k=1,2,3$, $t=2009,\dots ,2016$

The potential for industrial solid waste reduction in manufacturing and the carbon emission reduction in the logistics are calculated according to model (1). The potential for industrial solid waste reduction in area k is expressed as w⁻in model (1). The carbon emission potential of the logistics industry in area K is expressed as w^u in the model (1).

Selection of indicators

With reference to most research on the linkage efficiency of these two industries, combined with some selection principles (scientificalness, practicability, data availability, isotropy, etc.) of the indicator system, the manufacturing and logistics industry linkage development efficiency evaluation index system are constructed from the perspective of input and output. The data used in the study mainly comes from the China Statistical Yearbook, the statistical yearbooks of the provinces, the National Bureau of Statistics and the school library. Due to the availability of data, the transportation industry, warehousing and postal and telecommunications industry data are generally used as logistics industry data (Since China has not yet established a relatively complete statistical system of logistics industry, and the statistical caliber of logistics industry in each country is not the same, the model and index selection in this paper are only applicable to a single country or region with unified logistics industry data statistics). The specific selection indicators are as follows:

Empirical analysis

Estimation of the efficiency value of the manufacturing and logistics industry

Evaluate the efficiency of the manufacturing industry and logistics industry in the three northeastern provinces (Use the MAXDEA ultra software to select an input-oriented unexpected SBM model with variable scale returns, superimposed super efficiency, and yearly reference). The results are shown in Figure 1 and Table 2. Figure 1 shows that: (1) The efficiency of the manufacturing industry in the three provinces of Northeast China in 2009–2016 is generally stable. Declined slightly between 2009 and 2010; During 2010 and 2014, it began to rise steadily and reached its highest value in 2014; It fell first and then rose in 2014–2016, showing a slight fluctuation. (2) During the period of 2009–2016, the efficiency of the logistics industry in the three northeastern provinces showed a slight decline. The small increase between 2009 and 2011; Significant downward trend between 2011 and 2016; Among them, the decline was the most obvious between 2014 and 2016, and reached the lowest value in 2016. When both the manufacturing and logistics industries are effective for DEA, the two can achieve linkage development (see, e.g.,Wang Zhen-zhen 2017). (3) The deviation between the efficiency value curves of manufacturing industry and logistics industry shows that the research area fails to achieve the linkage development under the condition of considering the unexpected output, but the main industries that fail to achieve the linkage development are not the same. Among them, the low efficiency of manufacturing industry in 2009–2013 led to the failure to achieve linkage development; the low efficiency of logistics industry in 2014–2016 led to the failure to achieve linkage development. Table 2 shows: In general, the linkage efficiency of the two industries in Liaoning Province (LN)> the linkage efficiency of the two industries in Jilin Province (JL)> the linkage efficiency of the two industries in Heilongjiang Province (HLJ). Among them, the manufacturing efficiency of Liaoning Province is much higher than the other two provinces; The logistics industry in Jilin Province and Heilongjiang Province is slightly more efficient than Liaoning Province.

Table 1. Index system of manufacturing industry and logistics industry linkage efficiency evaluation

Evaluation system	Pointer type	Name of index	unit
Manufacturing system	Input indicators	The number of employees on the job in the manufacturing industry	Ten thousand people
		Total assets of industrial enterprises above designated size	100 million
	Output indicators	industrial added value	100 million
		Main business income of industrial enterprises above designated size	100 million
Logistics system	Input indicators	Number of employees in the logistics industry	Ten thousand people
		The mileage of graded highways in China	kilometers
	Output indicators	highway freight volume	tons
		Cargo turnover	Million ton-km
Unexpected output		Industrial solid waste	tons
		Carbon Emissions from Transportation	tons

Table 2. Efficiency value of the manufacturing and logistics industry

System	Manufacturing industry			Logistics industry
year	Liaoning	Jilin	Heilongjiang	Liaoning	Jilin	Heilongjiang
2009	1.13	1.04	0.49	0.70	0.90	1.12
2010	1.03	0.76	0.55	0.76	0.93	1.23
2011	1.19	0.77	0.55	0.75	0.97	1.23
2012	1.24	0.70	0.58	0.78	1.02	1.03
2013	1.49	0.56	0.54	0.77	1.02	0.94
2014	1.50	0.65	0.57	0.79	1.03	0.82
2015	1.33	0.67	0.58	0.70	0.90	0.70
2016	1.43	0.63	0.60	0.49	0.93	0.63
AVG	1.29	0.72	0.56	0.72	0.96	0.96

Figure 1. The efficiency changing of the manufacturing and logistics industry in 2009–2016

Figure 2 is the box diagram of the Northeastern manufacturing industry efficiency. From the figure, there is quite a difference in the variance of manufacturing efficiency in the northeastern provinces. Liaoning Province has the largest variance, followed by Jilin Province and Heilongjiang Province. This shows that the efficiency value of the manufacturing industry of Liaoning Province during 2009–2016 is very unstable, and the value difference between years is very large. The reason may be that Liaoning Province has experienced large economic fluctuations in recent years. As a major manufacturing province, the manufacturing industry has been hit hard. Meanwhile, Liaoning Province is also the province with the highest manufacturing efficiency value, followed by Jilin Province, and Heilongjiang Province is the smallest one. From the perspective of efficiency stability, Heilongjiang Province has the best stability, but the lowest efficiency value indicates that the manufacturing efficiency of the region is improving slowly, and the policy effect of the manufacturing industry is not obvious, so it needs to be adjusted in time.

Figure 2. Box diagram of the Northeastern manufacturing industry efficiency (Calculation based on provincial statistical data of manufacturing system in Table 1)

Figure 3 is the box diagram of the Northeastern logistics industry efficiency. From the figure, compared with the manufacturing industry, the difference in the variance of the logistics industry in the northeast provinces is relatively small. The variance of Heilongjiang Province is the most, followed by Liaoning Province and Jilin Province. This shows that the efficiency value of the logistics industry in Heilongjiang Province is very unstable, and the value difference between years is very large. Meanwhile, the Heilongjiang Province and Jilin Province are also the provinces with higher average efficiency values in the logistics industry. From the perspective of efficiency stability, Jilin Province has the best stability and high average efficiency value, indicating that the logistics industry in this region maintains a good development trend.

Figure 3. Box diagram of the northeastern logistics industry efficiency (Calculation based on provincial statistical data of logistics system in Table 1)

The improvement direction of manufacturing industry and logistics efficiency

Reasons for the loss of efficiency in the manufacturing industry and the logistics industry

According to the SBM model, when the manufacturing efficiency value>1, this shows that the manufacturing industry in this area has developed well, and the government should keep the current developing policy; meanwhile, if the efficiency value>1, the data of relaxation can reflect the reason for high-efficiency value. When the manufacturing efficiency value<1, this shows that the development of the manufacturing industry in this area has much development space, and the government should focus more on these areas. At the same time, when the manufacturing efficiency value<1, the data of relaxation can reflect the reason for the loss. The logistics industry is in the same way. The calculation results are shown in the  table 3 and table 4.

Table 3. The input and output optimization potential result of the manufacturing industry

		Input redundancy rate %		Output redundancy rate %
Province	index	number of employees	Total assets	Value-added	Business income	waste
LN	2009	25.93	0.00	0.00	0.00	0.00
	2010	6.09	0.00	0.00	0.00	−7.59
	2011	33.22	4.60	0.00	0.00	0.00
	2012	48.50	0.00	0.00	0.00	0.00
	2013	87.53	7.52	0.00	0.00	0.00
	2014	82.67	11.82	0.00	0.00	0.00
	2015	25.34	25.97	0.00	0.00	0.00
	2016	22.57	42.15	0.00	0.00	0.00
	Average	41.48	11.51	0.00	0.00	−0.95
JL	2009	−41.54	−25.67	215.47	0.00	−47.02
	2010	−101.01	−32.05	126.87	0.00	−80.64
	2011	−61.67	−37.95	96.06	0.00	−139.27
	2012	−42.51	−53.16	110.64	0.00	−109.30
	2013	−14.84	−70.15	84.43	0.00	−114.20
	2014	−12.86	−48.30	94.17	0.00	−105.02
	2015	−63.40	−34.36	128.00	0.00	−110.42
	2016	−69.34	−19.69	134.84	0.00	−123.86
	Average	−50.90	−40.16	123.81	0.00	−103.72
HLJ	2009	0.00	8.12	128.77	0.00	−43.10
	2010	−45.07	−3.26	42.24	0.00	−50.33
	2011	−43.78	−2.83	43.58	0.00	−78.28
	2012	−39.92	−20.11	45.15	0.00	−68.04
	2013	−45.51	−42.71	46.01	0.00	−58.66
	2014	−40.39	−29.87	46.79	0.00	−54.35
	2015	−35.69	−30.26	67.92	0.00	−55.38
	2016	−40.62	−33.52	60.69	0.00	−71.92
	Average	−36.37	−19.30	60.14	0.00	−60.01

Notes. Ranked according to the size of the redundancy rate: black indicates the biggest factor in promoting manufacturing, and gray indicates the smallest factor in promoting manufacturing.

Table 4. Results of optimization potential of input and output of the logistics industry

		Input redundancy rate %		Output redundancy rate %
Province	Index	Number of employees	Highway mileage	Volume of freight	Turnover	Property loss
LN	2009	−44.16	−14.98	0.00	17.92	0.00
	2010	−35.06	−13.60	0.00	3.71	0.00
	2011	−46.77	−3.35	0.00	4.60	0.00
	2012	−44.63	−0.15	0.00	3.07	0.00
	2013	−45.57	−0.66	0.00	1.70	0.00
	2014	−41.22	0.00	0.00	0.00	0.00
	2015	−59.85	−0.96	22.54	0.00	−2.58
	2016	−66.52	−36.02	9.05	0.00	−25.37
	Average	−47.97	−8.72	3.95	3.88	−3.49
JL	2009	−1.86	−17.85	0.00	0.00	0.00
	2010	−4.40	−10.13	0.00	0.00	0.00
	2011	−6.58	0.00	0.00	0.00	0.00
	2012	0.00	4.88	0.00	0.00	0.00
	2013	0.00	4.63	0.00	0.00	0.00
	2014	0.00	6.48	0.00	0.00	0.00
	2015	−19.26	0.00	0.00	0.00	−14.26
	2016	−14.91	0.00	0.00	0.00	0.00
	Average	−5.88	−1.50	0.00	0.00	−1.78
HLJ	2009	24.46	0.00	22.14	0.00	0.00
	2010	46.17	0.00	32.74	0.00	0.00
	2011	5.98	40.31	27.10	0.00	0.00
	2012	0.00	5.90	33.54	0.00	0.00
	2013	−12.13	0.00	0.00	0.00	0.00
	2014	−35.39	0.00	30.83	0.00	0.00
	2015	−53.74	−6.17	29.62	0.00	0.00
	2016	−60.26	−12.80	15.19	0.00	0.00
	Average	−10.61	3.40	23.90	0.00	0.00

Notes. Ranked according to the size of the redundancy rate: black indicates the biggest factor in promoting the logistics industry, and gray indicates the smallest factor to promote the logistics industry.

As can be seen from the above table 3, the redundancy rate of business income in the three provinces is 0, however, other inputs and the output factors are both redundancies. This shows that the business income is not the main reason for influencing the manufacturing industry efficiency of northeast China. Factors affecting the improvement of manufacturing industry efficiency mainly lies in the aspect of manufacturing value added and the number of employees. Factors affecting the decline in manufacturing efficiency are concentrated on unexpected outputs. Although the main factors affecting each year are slightly different, the overall difference is small. Among them, the overall factor affecting the efficiency improvement of manufacturing in Liaoning Province is the number of employees, and the main factor affecting the decline in efficiency is the unexpected output. The table shows that Liaoning Province should continue to maintain the input level of manufacturing and improve the efficiency of manufacturing by reducing unexpected output. The main factors affecting the improvement of the manufacturing efficiency in Jilin Province and Heilongjiang Province are both the manufacturing value added, and the main factors affecting the decline in efficiency are unexpected outputs. It shows that Jilin Province and Heilongjiang Province should increase the added value of the manufacturing industry. At the same time, it is found that the two provinces have higher input redundancy. The input factors should be reduced or the utilization rate of factors should be improved to improve the efficiency of manufacturing. Jilin Province and Heilongjiang Province should also increase the efficiency of manufacturing by reducing unexpected output.

As can be seen from the above table 4, the factors affecting the efficiency improvement of the logistics industry in the three northeastern provinces are concentrated in freight volume, road mileage, and turnover. The factors affecting the efficiency of the logistics industry are concentrated on the number of employees. Although the main factors affecting each year are slightly different, the overall difference is small. Overall, the main factor affecting the efficiency improvement of the logistics industry in Liaoning Province is the mileage of highways. The main factor affecting the decline in efficiency is the number of employees. The table shows that Liaoning Province should increase the input level of highway mileage and reduce the number of logistics employees or improve the utilization efficiency of employees to improve the efficiency of the logistics industry. The main factors affecting the decline in efficiency in Jilin Province are the number of employees, followed by unexpected output and road mileage, indicating that the input redundancy in Jilin Province is the main reason for its low efficiency. Jilin Province should reduce the investment level of the logistics industry or increase the utilization rate of input factors to improve the efficiency of the logistics industry. At the same time, it should also reduce unexpected output. The main factor affecting the efficiency improvement of the logistics industry in Heilongjiang Province is the freight volume, followed by highway mileage. The main factor affecting the decline of efficiency is the number of employees. It shows that Heilongjiang Province should also improve the efficiency of the logistics industry by reducing the number of employees or increasing the utilization rate of employees while reducing unexpected output.

Ways to improve the linkage efficiency between manufacturing industry and logistics industry

Judging from the annual efficiency comparison of the provinces in table 5, the number of employees in the manufacturing industries of Jilin Province and Heilongjiang Province is highly redundant, and both have a high potential for improvement in the number of employees. Among them, Jilin Province has the greatest potential for improvement. Deepen the reform of the state-owned enterprise system, establish a modern enterprise system, fully mobilize the enthusiasm of the invested human resources, strengthen personnel training, and increase the introduction of talents are ways to improve the utilization rate of personnel. At the same time, the total investment in Jilin Province with high potential for improvement is also highly redundant. Reducing duplication of investment and introducing high-end manufacturing to improve technology is a way to improve asset utilization. Among the unexpected outputs, the three provinces have output redundancy, of which Jilin Province and Heilongjiang Province are higher, and the improvement potential is larger, and Liaoning Province is smaller. Reducing pollutant emissions, reducing environmental pollution, and taking a sustainable green development path are ways to reduce unexpected output.

Table 5. Horizontal comparison of optimization potential of manufacturing input and output

Redundancy	Input redundancy rate %						Output redundancy rate %
Index	Number of employees			Total assets			Value-added			Business income			waste
Province	LN	JL	HLJ	LN	JL	HLJ	LN	JL	HLJ	LN	JL	HLJ	LN	JL	HLJ
2009	25.9	−41.5	0.0	0.0	−25.7	8.1	0.0	215.5	128.8	0.0	0.0	0.0	0.0	−47.0	−43.1
2010	6.1	−101.0	−45.1	0.0	−32.0	−3.3	0.0	126.9	42.2	0.0	0.0	0.0	−7.6	−80.6	−50.3
2011	33.2	−61.7	−43.8	4.6	−38.0	−2.8	0.0	96.1	43.6	0.0	0.0	0.0	0.0	−139.3	−78.3
2012	48.5	−42.5	−39.9	0.0	−53.2	−20.1	0.0	110.6	45.2	0.0	0.0	0.0	0.0	−109.3	−68.0
2013	87.5	−14.8	−45.5	7.5	−70.1	−42.7	0.0	84.4	46.0	0.0	0.0	0.0	0.0	−114.2	−58.7
2014	82.7	−12.9	−40.4	11.8	−48.3	−29.9	0.0	94.2	46.8	0.0	0.0	0.0	0.0	−105.0	−54.3
2015	25.3	−63.4	−35.7	26.0	−34.4	−30.3	0.0	128.0	67.9	0.0	0.0	0.0	0.0	−110.4	−55.4
2016	22.6	−69.3	−40.6	42.1	−19.7	−33.5	0.0	134.8	60.7	0.0	0.0	0.0	0.0	−123.9	−71.9
Average	41.5	−50.9	−36.4	11.5	−40.2	−19.3	0.0	123.8	60.1	0.0	0.0	0.0	−0.9	−103.7	−60.0

Judging from the comparison of the annual efficiency of each province in table 6, the number of employees in the three northeastern provinces is redundant. The number of employees in the logistics industry in Liaoning Province and Heilongjiang Province is highly redundant, and both have a high potential for improvement in the number of employees. Among them, Liaoning Province has the greatest potential for improvement. Deepen the reform of the state-owned enterprise system, establish a modern enterprise system, fully mobilize the enthusiasm of the invested human resources, strengthen personnel training, and increase the introduction of talents are ways to improve the utilization rate of personnel. At the same time, there is also redundancy in highway mileage and unexpected output in Liaoning Province and Jilin Province. Maintaining a moderate investment scale, not repeating construction, and making full use of existing infrastructure resources are ways to improve the utilization of highway mileage. At the same time, Liaoning Province and Jilin Province should strengthen the management of carbon emissions in the logistics industry to reduce carbon emissions.

Table 6. Horizontal comparison of optimization potential of input and output of the logistics industry

Redundancy	Input redundancy rate %						Output redundancy rate %
Index	Number of employees			highway mileage			Volume of freight			Turnover			Property loss
Province	LN	JL	HLJ	LN	JL	HLJ	LN	JL	HLJ	LN	JL	HLJ	LN	JL	HLJ
2009	−44.2	−1.9	24.5	−15.0	−17.9	0.0	0.0	0.0	22.1	17.9	0.0	0.0	0.0	0.0	0.0
2010	−35.1	−4.4	46.2	−13.6	−10.1	0.0	0.0	0.0	32.7	3.7	0.0	0.0	0.0	0.0	0.0
2011	−46.8	−6.6	6.0	−3.4	0.0	40.3	0.0	0.0	27.1	4.6	0.0	0.0	0.0	0.0	0.0
2012	−44.6	0.0	0.0	−0.2	4.9	5.9	0.0	0.0	33.5	3.1	0.0	0.0	0.0	0.0	0.0
2013	−45.6	0.0	−12.1	−0.7	4.6	0.0	0.0	0.0	0.0	1.7	0.0	0.0	0.0	0.0	0.0
2014	−41.2	0.0	−35.4	0.0	6.5	0.0	0.0	0.0	30.8	0.0	0.0	0.0	0.0	0.0	0.0
2015	−59.9	−19.3	−53.7	−1.0	0.0	−6.2	22.5	0.0	29.6	0.0	0.0	0.0	−2.6	−14.3	0.0
2016	−66.5	−14.9	−60.3	−36.0	0.0	−12.8	9.1	0.0	15.2	0.0	0.0	0.0	−25.4	0.0	0.0
Average	−48.0	−5.9	−10.6	−8.7	−1.5	3.4	3.9	0.0	23.9	3.9	0.0	0.0	−3.5	−1.8	0.0

Factors analysis based on Tobit regression

In order to study the influencing factors of the linkage efficiency between manufacturing and logistics industry, according to the existing relevant theories and references, this paper will use Tobit regression model, from the three perspectives of technology investment level, marketization level, and openness degree to analyze the impact of related variables on linkage efficiency.

Selection of environmental variables

Environmental variables are factors that affect the linkage development between the logistics industry and manufacturing industry but not within the subjective and controllable range of the sample. The following three factors are selected as environmental variables (see table 7): the level of science and technology investment, the greater the investment in science and technology, the more advanced the equipment, and this can promote the development of the logistics industry, expressed in terms of the volume of transaction of technology contracts. The higher the level of marketization means that society can effectively allocate resources and expresses it in terms of government consumption. The level of opening up to the outside world can macroscopically reflect the level of regional economic openness and can promote the linkage development between the logistics industry and manufacturing industry, expressed as the total value of imports and exports.

Table 7. Environmental variable indicator

Environment variable	Definition	Variable symbol	Unit
The investment level in science and technology	Technical contract transaction amount	TCT	10 million
Marketization level	Government consumption	GC	10 million
Degree of opening up	The total value of imports and exports	IEV	10 million

Linkage efficiency regression model and result analysis of manufacturing industry and logistics industry (In order to avoid the phenomenon of pseudo regression, improve the effectiveness of estimation parameters and make them more close to the practical significance, unit root stationarity test is carried out for variable data. According to the statistical characteristics of the data, LLC, IPS and Fisher ADF are selected as the test criteria and LLC as the judgment criteria. The results show that all variables are stable). Considering the regression model of environmental impact factor variables, the relationship between the factors affecting the linkage efficiency between manufacturing and logistics is as follows:

MLLE_it = β₀+ β₁ (TCT_it) +β₂ (GC_it) +β₃ (IEV_it) +ε_it

In the formula, MLLEit2 shows that the linkage efficiency of the manufacturing industry and logistics industry of i area in t year, β₀, β₁, β₂, β₃ are unknown correlation coefficients, and ε_it is a random error. Eviews is used to do collinearity test and unit root test, and the independent variables are TCT_it,GC_it,IEV_it. The random effect model is accepted by Hausman test. The results of Stata calculation at 95% confidence level are shown in Table 8 as follows.

Table 8. Regression result

Variable symbol	Coef.	Std.Err	z	P
TCT	0.215187	0.025881	3.13	0.02866
GC	−1.01082	0.028481	−2.25	0.00244
IEV	0.221772	0.019862	4.35	0.01686
ε	0.257731	0.279654	3.84	0.01731
Likelihood	63.26061	White value	118.28	p(waldchi2) = 0.00

Notes. For the random effect model determined by test, the absolute value of Z is greater than 1.96, the significance of variables is significant under 95% confidence level, the likelihood value and white value pass the test, and there is no heteroscedasticity.

It can be seen from the above results that, firstly, regional technology investment has a positive influence on the linkage of these two industries. The level of investment reflects the development degree of regional technology. The more developed the technology, the higher the efficiency of the linkage between these two industries. Both the manufacturing industry and logistics industry require high-tech equipment to increase industry efficiency. Therefore, the government can increase the linkage efficiency of the two industries by increasing the investment in science and technology of the region. Second, the proportion of government consumption has a negative influence on the linkage between these two industries. The level of government consumption shows regional marketization levels. Areas with relatively high government consumption are often old industrial areas with a relatively high state-owned economy. The market environment is poor, government intervention is high, and economic vitality is often insufficient. The higher the government consumption shows the lower the level of marketization. The level of marketization in regions with high government consumption is relatively low, and the efficiency of linkage between the two industries is also low. Therefore, the government can increase the proportion of the private economy and create a good business environment to improve the efficiency of the two industries in the region. Finally, the degree of openness of the region has a positive impact on the linkage between the two industries. The total value of regional import and export reflects the degree of development of the regional opening up. It is easier to introduce advanced foreign management experience and equipment in areas where the opening up is more developed. The more developed the region, the more efficient the linkage between the two industries. Therefore, the government can increase the linkage efficiency of the two industries in the region by encouraging import and export, expanding opening up, and increasing the total value of imports and exports in the region.

Conclusion and Suggestion

(1) During the period from 2009 to 2016 in the three northeastern provinces, the linkage between the two industries showed a steady development trend, but the linkage development of the two industries in the three northeastern provinces was still insufficient. (2) The manufacturing efficiency variances of the three northeastern provinces are quite different. Liaoning Province is the province with the highest efficiency value in manufacturing, while the efficiency value fluctuates the most, followed by Jilin Province, and Heilongjiang Province is the smallest. (3) In general, the linkage efficiency of the two industries in Liaoning Province > the linkage efficiency of the two industries in Jilin Province> the linkage efficiency of the two industries in Heilongjiang Province. (4) From the reasons of efficiency loss in the three northeastern provinces, although the main factors affecting in every year are slightly different, the overall difference is not large. Compared with other studies, this clarifies the reasons for the loss of efficiency in different provinces, enriches the results of the joint research on the two industries in the region, and makes a useful supplement for related research. (5) The results of Tobit regression analysis show that the science and technology input and open up of the three environmental variables have a positive impact on the linkage efficiency of the two industries, and government consumption has a negative impact on the linkage between the two industries. (6) Marketization has a positive impact on the linkage of these two industries, and is also good for solving regional environmental problems. The reason for this phenomenon may be that the marketization promotes the rational allocation of elements and greatly improves the productivity. Thus, the expansion of economic scale leads to the increase of the utilization rate of energy and resources, which will reduce the pressure on the formation of environment and ecology. (7) Low carbon and green logistics industry and high-end manufacturing industry are new economic concepts of sustainable development in the new era and new situation. They need science and technology as support, otherwise they will not be able to achieve the R & D and promotion of high-end manufacturing industry, nor the transformation and upgrading of traditional industries. There are deficiencies in the transformation of the existing three northeast provinces’ traditional industries in terms of resource reduction and pollutant emission reduction, which leads to the continuation of traditional process technology in production and the use of old production lines and old processes for processing, which aggravates the pollution. Therefore, the level of science and technology is very important to improve the efficiency of the two industries and solve the problem of high pollution in the production process.

The linkage development of the two industries is one of the key projects of the National Logistics Industry Adjustment and Revitalization Plan. It is a key development area promoted by the Northeast Region Logistics Industry Development Plan and important industrial policy for achieving coordinated development and optimizing the industrial structure. According to the research results, combined with the different levels of linkage development between the two provinces in northeastern China, in order to improve the efficiency of the linkage development of the two industries, the following policy recommendations are proposed:

(1) Improve the technical efficiency of manufacturing logistics. Utilizing the advantages of the equipment manufacturing industry in the three northeastern provinces, improve the independent research and development and production capacity of logistics equipment, and build an important national logistics technology equipment manufacturing industrial base. (2) Improve the scale degrees of the logistics industry. Vigorously promote logistics enterprises to carry out reform and restructuring through various forms, foster large-scale logistics enterprise groups with strong competitiveness, and improve the scale efficiency of the logistics industry. (3) Promote the all-around opening of the northeastern region. Make full use of coastal edge advantages and bonded logistics policies in northeastern China, and promote the international development of the manufacturing industry and logistics industry. (4) Vigorously introduce high-level talents. Provide talent and intellectual support for the development of the logistics industry in Northeast China. (5) Strengthen the construction of soft environment, optimize the business environment, and create a good market environment. (6) Inspire the vitality of the private economy and increase the proportion of other economic types of enterprises in the total industrial output value. It is imperative to innovate institutional mechanisms, accelerate the investment of private capital, and vigorously develop the private economy. (7) Improve the level of marketization and investment in science and technology. This has a positive impact on the linkage of the two industries, at the same time, it is conducive to solving regional environmental problems.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors are grateful for the financial support provided by the Natural Science Research Program of Shaanxi Province (No. 2020JQ-360).

Notes on contributors

Zheng Wen-Long

Zheng Wen-long, Ph.D. candidate, School of economics and management, Chang’an University, majoring in logistics engineering and management.

Wang Jian-Wei

Wang Jian-Wei, School of Economics and Management, Chang’an University, Xi’an, People’s Republic of China.

Zhang Shi-Qing

Zhang Shi-Qing, School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou, Henan, People’s Republic of China.

Syed Abdul Rehman Khan

Syed Abdul Rehman Khan, School of Economics and Management, Tsinghua University, Beijing, People’s Republic of China.

Jiang An-Ding

Jiang An-Ding, Institute of Soil Environment, Shaanxi Provincial Academy of Environmental Science, Xi’an, People’s Republic of China.

Yang Xu-Quan

Yang Xu-Quan, Department of Surveying and Mapping, Baoji Geotechnical Investigation & Surveying Institute, Baoji, People’s Republic of China.

Zhang Xin

Zhang Xin, Marketing Department of Dongguan Zhong Tian Electronic Technologies Co., Ltd. Xi’an Branch, Xi’an, People’s Republic of China.