Research on the Development of Green Logistics Under the New Quality Productivity

Technological innovation and information construction. How to develop the logistics economy, and explore the logistics development paths in the context of new economic forms has become a key topic for future research in the logistics industry. In. To measure the quality of logistics development, it is necessary to establish an effective evaluation indicator system. Therefore, this article proposes an evaluation indicator system for logistics development under new quality productivity, following the new development philosophy, and evaluates the logistics level under the new quality productivity. Currently, research on new quality productivity has made initial progress in academia, covering various aspects such as the connotation of new quality productivity[1–5], theoretical logic[6–7], and development paths[11–15]. Regarding the evaluation indicator system for new quality productivity, Sun Liwei and Guo Junhua[16] constructed fourteen evaluation indicators focusing on innovation, supported by emerging industries and talent cultivation, evaluating the new quality productivity levels across four major regions of the country. Jiang Yongmu and Qiao Zhangyuan[17] established a new quality productivity evaluation index system from six dimensions in conjunction with economic development concepts. Gong Richao[18] emphasized that the essence of new quality productivity is the enhancement of production factors, evaluating its development from the perspectives of the labor force, labor materials, and labor objects, and providing suggestions for its advancement. Ye Zhenyu and Xu Pengcheng[19] established an evaluation indicator system focusing on three main indicators, calculating the weighted average scores for national and provincial levels to rank and analyze the provincial new quality productivity results.

For the logistics industry, international scholars carried out thorough research, including the conceptual connotation of logistics[20–21], logistics evaluation[22–28], logistics optimization[29–35], and logistics forecasting[36–37]. Dong Hui[38] used the SBM-DEA model to analyze China’s logistics level from 2010 to 2021., offering suggestions for balanced regional development of logistics technology and management levels. Liu Shuai[39] based on the analysis of logistics emission efficiency in different provinces of China, the technology of logistics emission reduction is discussed, finding that digital technology promotes low-carbon logistics and proposing data-driven solutions for low-carbon logistics.

Existing literature has extensively researched new quality productivity and logistics. However, research on logistics new quality productivity remains focused primarily on its connotative significance, while understanding of its high informatization, high technological advancement, high energy efficiency, and high quality is relatively lacking. Furthermore, there has been no special research on the logistics evaluation index system under the new quality productivity. Therefore, This paper constructs a logistics evaluation index system under the new quality productivity, quantifies the quality of logistics development, and provides a reference for the development of logistics under the new concept.

2

Problem Description

The level of green logistics is directly linked to national economic development. Green logistics evaluation establishes a scientific and objective evaluation index system to quantify various aspects related to the logistics industry in numerical form. By using relevant evaluation models, logistics scores can be obtained. Evaluation indicators are selected (i = 1, 2, …, m), and data is collected to obtain specific numerical values for relevant data over time (j = 1, 2, …, n). This data can be represented intuitively using a matrix. 1 $A = [\begin{matrix} a_{11} & a_{12} & a_{13} & \dots & a_{1 n} \\ a_{21} & a_{22} & a_{23} & \dots & a_{2 n} \\ a_{21} & a_{22} & a_{23} & \dots & a_{2 n} \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ a_{m 1} & a_{m 2} & a_{m 3} & \dots & a_{m n} \end{matrix}]$

In the actual operation process, due to the difference between the data, the eigenvalues are usually different. To increase the comparability between the data, data needs to be cleaned.

3

Construction and measurement methods of evaluation index system

1)

Evaluation index system construction

The core of new quality productivity is innovation. To achieve this, it is necessary to drive industrial innovation through technological innovation, thereby forming new quality productivity. This paper categorizes the logistics new quality productivity evaluation indicators into three major aspects: technological innovation capability, digital informatization level, and environmental friendliness index.

Scientific and technological innovation stimulates industrial potential and promotes the high-quality development of the industry through product innovation and technological upgrading. As shown in Table 1, this paper measures the technological innovation capability of the logistics industry from two aspects: innovation and openness, including five indicators: A1-A5.

Research and development funding in the logistics industry (A1) provides economic support for logistics technology innovation and is a guarantee for industrial innovation. The number of logistics enterprises with R&D (A2), the number of R&D personnel in the logistics industry (A3), and The number of R&D institutions in the logistics industry (A4) injects vitality into logistics innovation, and its development scale directly affects the potential for its development. Finally, the number of imported foreign patented technology (A5) reflects China’s reliance on foreign patented technology in this field.

The new high-quality productivity improves the level of digital informatization, provides impetus for the development of logistics, optimizes the allocation of resources, and improves production efficiency. The degree of digital informatization is evaluated through five secondary indicators, as detailed in Table 2.

The coordinated development of the logistics industry is mainly reflected in the coordination of logistics development and environment - environmental collaborative governance system (A6), the coordination of logistics industry and national development - the proportion of logistics industry GDP (A7), the coordination of various links and departments of logistics - the construction level of logistics data center (A8). Sharing is reflected in the government’s policy support for the logistics industry - the perfection of the logistics information sharing policy (A9), and the information sharing degree of the logistics chain (A10).Green productivity--the essence of new high-quality productivity, achieved by disrupting traditional production models to promote sustainable development in the service sector. The development of the logistics industry should focus on greening to build a logistics model that harmonizes human activities with nature.

Among them, the logistics industry energy consumption (A11) and the carbon emission index of the logistics industry(A12) reflect the extent to which the logistics industry practices the concept of low carbon, while the solid waste utilization rate of the logistics industry (A13) and the number of air pollution control facilities in the logistics industry (A14) reflect the environmental governance capacity.

In summary, the logistics evaluation index system under the framework of new quality productivity is shown in Table 4. According to the sources of data acquisition, these indicators can be categorized into three types: The first category includes data obtained from officially published statistical yearbooks and industry development reports: A1, A2, A3, A4, A5, A11, A13, A14. The second category includes data calculated from raw index data: A7, A12, A13. The third category consists of qualitative indicators, which require experts and scholars with rich knowledge and experience in the relevant fields to quantify through scoring.

2)

Revised Evaluation Models and Methods

(1)

Combined Weighting Model

The traditional CRITIC method reflects the weight through the correlation (conflict) and contrast intensity (volatility) between indicators but does not consider the degree of dispersion between data indicators. In this paper, the CRITIC introduces the entropy weight method to calculate the entropy of indicators, so that the improved CRITIC method can fully consider the three attributes of data indicators. 2 $\bar{a_{ı}} = \frac{1}{n} \sum_{i = 1}^{n} a_{i n}$ 3 $τ_{i} = \sqrt{\frac{\sum_{i = 1}^{n} {(a_{i j} - \bar{a_{l}})}^{2}}{n - 1}}$ 4 $θ_{i} = e w m_{i} + τ_{i}$

Where ewm_irepresents the entropy value of the green logistics evaluation indicator i calculated by the entropy weighting method. 5 $γ_{i, i 1} = \frac{\sum_{i, i 1 = 1}^{m} (a_{i j} - \bar{a_{l}}) (a_{i 1 j} - \bar{a_{l 1}})}{\sqrt{\sum_{i = 1}^{m} {(a_{i j} - \bar{a_{l}})}^{2}} \sum_{i 1 = 1}^{m} {(a_{i 1 j} - \bar{a_{l 1}})}^{2}}$

Where γ_i,i1represents the correlation coefficient between the i indicator and the i1 indicator; a_ij, a_i1j represent the values of the i-th and the i1-th indicators in year j within the green logistics evaluation indicator system, respectively; and $\bar{a_{1}}$ , $\bar{a_{11}}$ represent the mean values of the i-th and the i1-th indicators, respectively. 6 $ε_{i} = \sum_{i 1}^{m} (1 - γ_{i, i 1})$ 7 $C_{i} = θ_{i} * ε_{i}$

Where Ci represents the amount of information for the i-th indicator. The greater the amount of information, the greater the impact, thereby justifying a higher allocation of weight. Through calculations, the final objective weight for the green logistics evaluation indicator i is denoted as βi. 8 $β_{i} = \frac{C_{i}}{ε_{i} \sum_{i = 1}^{m} θ_{i}}$

Establish a judgment matrix and calculate the subjective weights of the indicators using the Analytic Hierarchy Process. 9 $(X_{1}, X_{2}, \dots, X_{m}) = [\begin{matrix} x_{11} & x_{12} & x_{13} & \dots & x_{1 m} \\ x_{21} & x_{22} & x_{23} & \dots & x_{2 m} \\ x_{31} & x_{32} & x_{33} & \dots & x_{3 m} \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ x_{m 1} & x_{m 2} & x_{m 3} & \dots & x_{m m} \end{matrix}]$ x_ij represents the relative importance of indicator i to indicator j, defined such that x_ij satisfies. x_ii > 0; $x_{ij} = \frac{1}{x_{ji}}$ ; x_ii = 1.(11) 10 $\bar{w_{l}} = \sqrt[m]{\prod_{j = 1}^{m} X_{i j}}$ 11 $w_{i} = \frac{\bar{w_{l}}}{\sum_{j = 1}^{m} \bar{w_{J}}}$ 12 $λ_{m a x} = \frac{1}{m} \sum_{i = 1}^{n} \frac{{(A W)}_{i}}{W_{i}}$ 13 $C . I . = \frac{λ_{m a x} - n}{n - 1}$

Through consistency testing, we can determine whether the constructed evaluation matrix has any logical issues, specifically by assessing the value of C.R. 14 $C . R . = \frac{C . I .}{R . I .}$

The R.I. is obtained directly from a table. If the C.R. is less than 0.1, it is considered logically reasonable; otherwise, it is considered logically incorrect.

Finally, the final weights are obtained by combining the objective weights and subjective weight coefficients using game theory. Specifically, let ^α₁ represent the objective weights and ^α₂ represent the subjective weight coefficients. We define ^{α₁ + α₂ = 1}. Here, E and W represent the objective weight vector and the subjective weight vector, respectively. 15 $P = α_{1} E + α_{2} W$ 16 $m i n | | α_{1} E^{T} + α_{2} W^{T} - E^{T} - W^{T} | |_{2}$ 17 $[\begin{array}{l} E_{i} E_{i}^{T} & E_{i} W_{i}^{T} \\ W_{i} E_{i}^{T} & W_{i} W_{i}^{T} \end{array}] [\begin{array}{l} α_{1} \\ α_{2} \end{array}] = [\begin{array}{l} E_{i} E_{i}^{T} \\ W_{i} W_{i}^{T} \end{array}]$

After a series of transformations, the final objective and subjective weight coefficients are obtained by deriving the second norm. These coefficients are then used to weight the final green logistics evaluation indicators.

(2)

Combined Weighting Model

To address the issues identified in the literature regarding existing green logistics evaluations, an improved VIKOR model has been established to better suit green logistics evaluation problems. The specific analysis and directions for improvement are as follows:

Structural Defects. The VIKOR model starts from distances, where the expected value S and the regret value R represent the distances from the different indicator values of the evaluation object to the optimal indicator values. The model sums the distances of different indicators on a two-dimensional plane. However, The evaluation indicators for green logistics are interrelated. The distances to the ideal solutions are not simply linearly additive; rather, the different indicators form a spatial distance based on their interconnections. The existing model isolates the relationships between indicators, reducing the spatial distance to a simple addition or subtraction of the lateral distances between individual indicators. Consequently, S and R can only theoretically represent the relative distances between individual evaluation indicators, resulting in insufficient actual geometric transformation force. This leads to poor fitting of logistics evaluation results.

Parameter Defects. The decision-making mechanism coefficient is an important parameter in the VIKOR model and plays an important role in the calculation of the benefit ratio. Existing research often simplifies the decision mechanism coefficient to 0.5, which adapts poorly to the dynamic variability of evaluation objects. Given the high sensitivity of logistics evaluations, the results lack persuasive power. Therefore, there is significant room for parameter improvement in the VIKOR model concerning green logistics evaluation issues.

By addressing these structural and parameter defects, the improved VIKOR model aims to provide a more accurate and robust evaluation framework for green logistics.

Let A_j(j = 1,2,…, n) be the evaluation object set, A_j represent the j-th evaluation object, and C_i(1,2, …, m)be the attribute indicators, with the corresponding weight set denoted as p_i(1,2,…, m),To address the issue of high sensitivity in the data values of green logistics evaluation indicators, a three-dimensional measurement-based VIKOR optimization model is established. By incorporating the actual effect function T and the decision-making mechanism coefficient, this model aims to resolve the problem of poor fitting of evaluation results in complex data. The specific steps are as follows: 18 $z^{+} = (z_{1}^{+}, z_{2}^{+} \dots \dots z_{m}^{+})$ 19 $z^{-} = (z_{1}^{-}, z_{2}^{-} \dots \dots z_{m}^{-})$

Determine the positive and negative ideal solutions. z⁺represents the maximum value of a positive indicator over the past m-year, which is considered the optimal value for that indicator. Similarly,z^–represents the minimum value of a negative indicator, which is regarded as the optimal solution.

Calculate the three-dimensional measurement values: the group effect value S the individual regret value R, and the actual utility value T. 20 $S_{j} = \sum_{i = 1}^{m} \frac{P_{i} (z_{i}^{+} - z_{i j})}{(z_{i}^{+} - z_{i}^{-})}$ 21 $R_{j} = m a x_{i} \frac{P_{i} (z_{i}^{+} - z_{i j})}{(z_{i}^{+} - z_{i}^{-})}$ 21 $T_{j} = \frac{\sum_{i = 1}^{m} P_{i}^{2} {(z_{i}^{+} - z_{i j})}^{2}}{\sum_{i = 1}^{m} \sum_{i = 1}^{m} P_{i}^{2} {(z_{i}^{+} - z_{i}^{-})}^{2}}$ S⁺ = maxS_j; S^– = minS_j; R⁺ = maxR_j; R^– = minR_j; T⁺ = maxT_j; T^– = minT_j⚬ To determine the decision mechanism coefficient w, calculate the information entropy. 23 $H_{k} = - \frac{1}{\ln n} \times \sum_{j = 1}^{n} f_{j k} \times l n f_{j k}$

Here, it represents the weight of the k-th utility value among the j-th evaluated objects, where k={S,R,T}. 24 $f_{j k} = \frac{x_{j k}}{\sum_{j = 1}^{n} x_{j k}}$ 25 $W_{k} = \frac{1 - H_{k}}{\sum_{k = 1}^{3} (1 - H_{k})}$ 26 $Q_{i} = \frac{w_{s} (S_{j} {- S}^{-})}{S^{+} {- S}^{-}} + \frac{w_{r} (R_{j} {- R}^{-})}{R^{+} {- R}^{-}} + \frac{w_{t} (T_{j} {- T}^{-})}{T^{+} {- T}^{-}}$

Table 1.

Technological Innovation Capability Evaluation Indicators

Criteria Level	Primary Indicators	Secondary Indicators	Attributes
Technological Innovation Capability		R&D funding in the logistics industry A1	+
		Number of large-scale logistics enterprises with R&D.A2	+
	Innovation	Number of R&D personnel in the logistics industry A3	+
		Number of R&D institutions in the logistics industry A4	+
	Openness	Number of foreign patent technologies introduced A5	-

Table 2.

Evaluation Indicators for Digital Informatization Level

Criteria Level	Primary Indicators	Secondary Indicators	Attributes
Evaluation Indicators for Digital Informatization Level		Degree of Environmental Collaborative GovernanceA6	+
	Coordination	Proportion of GDP in the Logistics IndustryA7	+
		Level of Logistics Data Center ConstructionA8	+
	Sharing	Completeness of Logistics Information Sharing PoliciesA9	+
	Sharing	Degree of Information Sharing in the Logistics ChainA10	+

Table 3.

Evaluation Index of Environmental Friendliness Index

Criteria Level	Primary Indicators	Secondary Indicators	Attributes
Evaluation Index of Environmental Friendliness Index	Green	The Logistics Industry Energy ConsumptionA11	+
		Carbon emission index of logistics industry A12	+
		Solid Waste Utilization Rate in the Logistics Industry A13	+
		Number of Air Pollution Control Facilities in the Logistics Industry A14	+

Table 4.

Logistics Evaluation Index System under the New Quality Productive Forces

Criteria Level	Primary Indicators	Secondary Indicators	Attributes
Technological Innovation Capability		R&D funding in the logistics industry A1	+
		Number of large-scale logistics enterprises with R&D. A2	+
	Innovation	Number of R&D personnel in the logistics industry A3	+
		Number of R&D institutions in the logistics industry A4	+
	Openness	Number of foreign patent technologies introduced A5	-
Evaluation Indicators for Digital Informatization Level		Degree of Environmental Collaborative Governance A6	+
	Coordination	Proportion of GDP in the Logistics Industry A7	+
		Level of Logistics Data Center Construction A8	+
	Sharing	Completeness of Logistics Information Sharing Policies A9	+
	Sharing	Degree of Information Sharing in the Logistics Chain A10	+
Evaluation Index of Environmental Friendliness Index		The Logistics Industry Energy Consumption A11	+
	Green	Carbon emission index of logistics industry A12	+
	Green	Solid Waste Utilization Rate in the Logistics Industry A13	+
		Number of Air Pollution Control Facilities in the Logistics Industry A14	+

4

Case Analysis

1)

Evaluation of the Development Level of New Quality Logistics Productivity

For quantitative indicators, this paper selects national panel data from 2012 to 2021. Qualitative indicators are scored by ten experts in relevant fields. The average is calculated by removing the highest and lowest scores to evaluate the new quality of logistics productivity in China. Missing data is supplemented using interpolation methods.

Using Python, the objective and subjective weights are calculated, and the final combined weight is obtained through game theory.

According to the analysis of the logistics evaluation line chart, the logistics development level under China's new quality productivity generally shows an increasing trend, in which the group effect value and actual utility value that affect the evaluation result generally show a decreasing trend, indicating that the benefit index value of logistics new quality productivity increases year by year and gradually tends to the ideal solution, while for the cost index, the individual regret value shows an increasing trend. It shows that the development consumption of new quality productivity of logistics also presents an increasing trend. Comprehensive benefit and cost indicators, the compromise optimal value is obtained, that is, the development level of new quality productivity logistics reaches the highest in 2020.

2)

Evaluation of Traditional Logistics Development Level

Based on the experience of previous studies, this section evaluates the development level of traditional logistics by focusing on national logistics development and selecting 14 evaluation indicators such as per capita GDP, as shown in Table 9.

Through game theory, we can comprehensively assign weights to the evaluation indicators. This approach helps in understanding the strategic interactions among different stakeholders and ensures that the weights reflect the true importance and influence of each indicator in the context of traditional logistics development.

The traditional logistics level showed an overall upward trend from 2012 to 2019. However, the impact of the pandemic in 2019-2020 led to an increase in regret values and actual utility values relative to the ideal solution. Additionally, logistics cost indicators increased while benefit indicators decreased, resulting in a decline in the overall logistics level.

3)

Comparative Analysis

Due to the differences in evaluation indicators between the level of logistics development under new quality productivity and the traditional level of logistics development, it is not possible to analyze the development levels of the two in a vertical numerical comparison. Instead, it is more appropriate to analyze the differences in their development trends horizontally. Through line charts, the trends in the development of logistics under new quality productivity and traditional logistics can be effectively analyzed.

The difference between the new-quality productivity logistics level and the traditional logistics level remained at a low level from 2012 to 2017, during which the logistics industry was still in the exploratory phase and had relatively low development levels. The development of logistics and its high-quality development were relatively coordinated. From 2018 to 2021, the difference between the logistics level and high-quality logistics development became more pronounced. Specifically, from 2018 to 2019, the primary reason was that the logistics industry was steadily advancing in its exploration and achieved rapid and continuous development. Traditional logistics entered a period of rapid development, but there was not enough emphasis on new-quality productivity logistics development. Although the logistics industry developed rapidly, the quality was relatively low, and the development direction still focused on energy consumption, neglecting high-quality logistics development, leading to an imbalance in development. From 2019 to 2021, traditional logistics development was limited due to the impact of the pandemic, showing a declining trend. In contrast, new-quality productivity logistics, based on informatization and technologization, demonstrated advantages in the economic downturn environment.

Table 5.

Combined Weights

Indicator	Weight	Indicator	Weight
R&D funding in the logistics industry	0.077	Level of Logistics Data Center Construction	0.104
Number of large-scale logistics enterprises with R&D.	0.080	Completeness of Logistics Information Sharing Policies	0.043
Number of R&D personnel in the logistics industry	0.052	Degree of Information Sharing in the Logistics Chain	0.091
Number of R&D institutions in the logistics industry	0.071	The Logistics Industry Energy Consumption	0.102
Number of foreign patent technologies introduced	0.043	Carbon emission index of logistics industry	0.084
Degree of Environmental Collaborative Governance	0.084	Solid Waste Utilization Rate in the Logistics Industry	0.036
Proportion of GDP in the Logistics Industry	0.035	Number of Air Pollution Control Facilities in the Logistics Industry	0.054

The S, R, T was calculated using formulas (18) ~ (22) and the specific weights in Table 5.

Table 6.

Evaluation of New Quality Productive Logistics SRT

Years	Group Effect Value(S)	Individual Regret Value(R)	Actual Utility Value(T)
2012	0.746750551	0.136500000	0.662557547
2013	0.682208261	0.127290508	0.543409795
2014	0.598696389	0.102552243	0.436508507
2015	0.614867339	0.087197143	0.409114444
2016	0.584553040	0.082352857	0.399317391
2017	0.499201548	0.068151687	0.397362823
2018	0.557601598	0.104584677	0.485860686
2019	0.472332469	0.084569524	0.415542141
2020	0.327073343	0.058537388	0.222468460
2021	0.197739411	0.084070000	0.318927829

Table 7.

Optimal and Worst Values

S-	S+	R-	R+	T-	T+
0.197739411	0.746750551	0.058537388	0.136500000	0.222468460	0.662557547

The value of the decision-making mechanism coefficients is: w_s=0.26917, w_R=0.42053, w_T=0.3103.

Table 8.

Evaluation Results of New Quality Logistics Productivity

Years	Benefit Ratio Value (Q)	Ranking
2012	1	10
2013	0.834670916	9
2014	0.58491405	7
2015	0.490701892	6
2016	0.442801545	5
2017	0.322976099	3
2018	0.610526433	8
2019	0.411178388	4
2020	0.063410033	1
2021	0.205734809	2

Table 9.

Evaluation Indicators System for Traditional Logistics

Primary Indicator	Secondary Indicator	Attribute
Green Logistics Environment (X1)	Per Capita Gross Domestic Product (C1)	+
	Social Retail Sales (C2)	+
	Resident Consumption Expenditure (C3)	+
	Resident Disposable Income (C4)	+
	Highway Transport Route Length (C5)	+
	Freight Volume by Highway (C6)	+
Green Logistics Strength (X2)	Railway Transport Route Length (C7)	+
	Railway Freight Volume (C8)	+
	Inland Waterway Navigation Mileage (C9)	+
	Waterway Freight Volume (C10)	+
	Passenger Turnover (C11)	+
	Cargo Turnover (C12)	+
Green Logistics Level (X3)	Total Energy Consumption of Logistics Industry (C13)	-
Green Logistics Level (X3)	Carbon Emission Intensity of Logistics Industry (C14)	-

Table 10.

Combined Weights

Indicator	Combined Weight w	Indicator	Combined Weight w
Per Capita GDP	0.056	Railway Freight Volume	0.125
Total Retail Sales	0.039	Inland Waterway Navigation Length	0.268
Per Capita Consumption Expenditure	0.044	Waterway Freight Volume	0.045
Per Capita Disposable Income	0.047	Passenger Turnaround	0.092
Highway Transportation Route Length	0.035	Cargo Turnaround	0.041
Highway Freight Volume	0.062	Total Energy Consumption of Logistics Industry	0.050
Railway Transportation Line Length	0.034	Logistics Industry Carbon Emission Intensity	0.062

The evaluation results are shown as follows in Table 11.

Table 11.

Group Utility Values, Individual Regret Values, Actual Utility Values

Years	Group Effect Value	Individual Regret Value	Actual Utility Value
2012	0.6926794782977048	0.08381216275799874	0.1881485561926
2013	0.7205476930873459	0.09600960096009600	0.1821813262456
2014	0.6537981761404902	0.09200810563354571	0.1415768058347
2015	0.6266991034313488	0.08800671938117428	0.1158834034903
2016	0.5536015156307172	0.07250725072507250	0.0848030826387
2017	0.454508470030696	0.07198891151167316	0.0491690872252
2018	0.3565798706743576	0.08280828082808280	0.0266547850182
2019	0.3157077696099502	0.07185184058589770	0.0154238363018
2020	0.3962961090627796	0.15471547154715470	0.1507570898956
2021	0.25886398903613694	0.14984652394061534	0.1308793843004

Table 12.

Optimal and Worst Values

S-	S+	R-	R+	T-	T+
0.259	0.720	0.072	0.155	0.015	0.188

Decision Mechanism Coefficient Values: w_s=0.38457, w_R=0.23413, w_T=0.38129.

Table 13.

Traditional Logistics Evaluation Results

Years	Benefit Ratio Value (Q)	Sort
2012	0.7764402464613	9
2013	0.8209447358420	10
2014	0.6644035100802	8
2015	0.5738066455416	6
2016	0.4005149694325	4
2017	0.2378465154588	3
2018	0.1371442767316	2
2019	0.0473493269012	1
2020	0.6473555257813	7
2021	0.4752411025057	5

5

Conclusions

This paper constructs a new evaluation index system, evaluates the high-quality development level of logistics by improving VIKOR model, and analyzes the difference between them by comparing with traditional logistics development.

Based on the results of the evaluation, recommendations for development were made in three aspects. 1)

Science and technology. On the one hand, support the technological transformation of logistics, and develop competitive industries with new energy, new materials and artificial intelligence as the core. On the other hand, strengthen the construction of education, encourage the research and development of logistics patented technology, and cultivate professional and technical personnel.

2)

Environmental aspects. Fully stimulate the green energy innovation of the logistics industry, tap the potential of the energy conversion, actively research and develop waste gas and solid waste conversion technologies, Promote the balanced development of economy and environment. Promote the transformation and upgrading of industrial structure, and get rid of the traditional factor driven development model that relies on resource exploitation to drive economic development.

3)

Digital information. To give full play to the characteristics of new quality productivity and high informatization and digitalization, enterprises need to strengthen information transparency, form a point-to-line, line-to-network logistics information network, improve the speed of information updates, and the government needs to strengthen relevant policy support to help the development of logistics informatization.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Research on the Development of Green Logistics Under the New Quality Productivity

Jilin Zhang

Zijia Song

Published Online: Apr 11, 2025

Received: Nov 18, 2024

Accepted: Mar 17, 2025

DOI: https://doi.org/10.2478/amns-2025-0854

KeywordsLogistics, new quality productivity, evaluation index system, three-dimensional spatial measurement, improved VIKOR

© 2025 Jilin Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Logistics, new quality productivity, evaluation index system, three-dimensional spatial measurement, improved VIKOR