Data-Driven Resource Efficiency Evaluation and Improvement of the Logistics Industry in 30 Chinese Provinces and Cities
Abstract
:1. Introduction
1.1. Research Background
1.2. Research Review
- (1)
- Evaluation index system of the LIE
- (2)
- Measurement and evaluation of the LIE
- (3)
- Measurement and evaluation of the LIE
1.3. Research Gaps
- (1)
- The LIRE is less studied, but the LIRE can best reflect the connotations of the LISD. Existing studies primarily consider the impact of the LI on the environment to establish the evaluation index system, but it is critical to construct its evaluation index system by considering economic, social, and environmental factors comprehensively. As a result, accurately measuring and evaluating the LIRE is an urgent issue to promote the LI’s sustainable development.
- (2)
- The use of DEA models has matured in the measurement and evaluation of the LI’s efficiency, but there are various forms of DEA models. Traditional CCR and BBC models are not suitable for dealing with non-desired outputs, and the super-efficiency-SBM-non-desired output model still has some shortcomings [29]. It cannot deal with both radial and non-radial cases, and the measurement results are low and do not correspond to the actual situation. Furthermore, one of the important aspects of the evaluation is characterizing spatial evolution, and there is still a scarcity of LIRE spatial evolution research. As a result, more realistic models and methods for measuring and evaluating RE must be used to effectively analyze and optimize it.
- (3)
- In terms of suggested responses, the majority of the research findings have been made in terms of factors influencing the LI’s efficiency, and further research is needed to provide more precise policy guidance based on spatial and temporal evolution characteristics.
1.4. Research Innovations
1.5. Theoretical Value and Practical Significance
1.6. Manuscript Structure
2. Materials and Methods
2.1. Methodology Flow
2.2. Data Gathering
2.3. Data Processing
2.4. Data Model
2.4.1. Super-EBM-Undesirable Model
2.4.2. Global Malmquist–Luenberger Index Model (GML)
2.4.3. Spatial Autocorrelation Model
2.5. Data Application
3. Case Study
3.1. Case Study Background
3.2. Results
3.2.1. Static Evaluation of China’s LIRE Measurements
3.2.2. LIRE’s Dynamic Evaluation Results
3.2.3. LIRE’s Spatial Evolution Results
- (1)
- Spatial Heterogeneity Analysis
- (2)
- Spatial agglomeration analysis
3.3. Policy Recommendations
3.3.1. Increase Technology Efficiency and Strengthen Technological Innovation
3.3.2. Develop Differentiated Improvement Countermeasures and Establish a Linkage Development Mechanism
3.3.3. Promote Coordinated Economic, Social and Environmental Development from a Sustainable Perspective
3.4. Discussion and Management Insights
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Indicator Category | Indicator Name | Unit | Indicator Description | References |
---|---|---|---|---|
Input indicators of the LI resource | Number of practitioners | 10 thousand persons | Human input | [11] |
Investment in fixed assets | 100 million CNY | Capital investment | [11] | |
Number of vehicles carrying goods | 10 thousand units | Infrastructure inputs | [33] | |
Logistics network mileage | km | Infrastructure inputs | [11] | |
Internet broadband access port | 10 thousand units | Information technology input | [33] | |
Energy consumption | 104 tons of standard coal | Energy consumption | [11] | |
The expected output of the LI resources | Freight turnover | 100 million tons/km | Transport results | [11] |
Value added of the LI | 100 million CNY | Economic benefits | [11] | |
Vehicle tax revenue | 100 million CNY | Social contribution | [35] | |
The undesired output of the LI resources | CO2 emissions of the LI | 10 thousand tons | Carbon emissions | [2] |
Exhaust emissions of the LI | 10 thousand tons | Environmental pollution | [2] | |
Property damage in traffic accidents | 100 million CNY | Negative social impact | [35] |
Energy Type | Raw Coal | Coal Washing | Other Coal Washing | Coal Products | Petrol | Paraffin |
---|---|---|---|---|---|---|
Discount factor for standard coal | 0.7143 | 0.9 | 0.45 | 0.5286 | 1.4714 | 1.4714 |
Carbon emission factor | 0.7669 | 0.765 | 0.8079 | 0.7669 | 0.5571 | 0.5723 |
Energy Type | Diesel | Fuel Oil | Liquefied Petroleum Gas | Other Petroleum Products | Natural Gas | Electricity |
Discount factor for standard coal | 1.4571 | 1.4286 | 1.7143 | 1.2 | 12.143 | 1.229 |
Carbon emission factor | 0.5913 | 0.6176 | 0.5042 | 0.5857 | 0.4478 | 0.29 |
Indicators | Coal kg/t | Washed Coal kg/t | Other Washed Coal kg/t | Gasoline kg/t | Paraffin kg/t | Diesel kg/t | Fuel Oil kg/t | Natural Gas g/m3 | Liquefied Natural Gas kg/t |
---|---|---|---|---|---|---|---|---|---|
SO2 | 10.0 | 10 | 10 | 1.6 | 2.75 | 2.24 | 2.24 | 0.18 | 0.18 |
NOX | 4.0 | 4 | 4 | 16.7 | 5.09 | 9.62 | 5.84 | 1.76 | 2.1 |
PM2.5 | 0.74 | 0.74 | 0.74 | 0.125 | 0.06 | 0.31 | 0.31 | 0.17 | 0.15 |
PM10 | 1.61 | 1.61 | 1.61 | 0.25 | 1.6 | 0.31 | 0.31 | 0.24 | 0.22 |
Period | GTRECH | GPEC | GPTC | GSCH |
---|---|---|---|---|
2011–2012 | 0.986545 | 1.106506 | 0.874654 | 1.035832 |
2012–2013 | 1.031052 | 1.010661 | 1.034 | 0.993093 |
2013–2014 | 1.054622 | 1.029002 | 1.018535 | 1.01033 |
2014–2015 | 0.99586 | 0.966364 | 1.027188 | 1.01447 |
2015–2016 | 0.989223 | 1.021952 | 0.990409 | 0.984948 |
2016–2017 | 1.102431 | 1.035214 | 1.059734 | 1.011826 |
2017–2018 | 0.988786 | 1.005472 | 1.000209 | 0.995219 |
2018–2019 | 1.098424 | 1.046088 | 1.050486 | 1.008745 |
Mean | 1.030868 | 1.027657 | 1.006902 | 1.006808 |
2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
---|---|---|---|---|---|---|---|---|---|
Global Moran’s I | 0.5075 | 0.4535 | 0.4691 | 0.4042 | 0.2536 | 0.3852 | 0.3930 | 0.4647 | 0.4110 |
Z | 4.3985 | 3.9701 | 4.0962 | 3.5520 | 2.3231 | 3.3980 | 3.4510 | 4.0365 | 3.6138 |
p | 0.0000 | 0.0001 | 0.0000 | 0.0004 | 0.0202 | 0.0007 | 0.0006 | 0.0001 | 0.0003 |
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Ding, H.; Guo, Y.; Wu, X.; Wang, C.; Zhang, Y.; Liu, H.; Liu, Y.; Lin, A.; Hu, F. Data-Driven Resource Efficiency Evaluation and Improvement of the Logistics Industry in 30 Chinese Provinces and Cities. Sustainability 2022, 14, 9540. https://doi.org/10.3390/su14159540
Ding H, Guo Y, Wu X, Wang C, Zhang Y, Liu H, Liu Y, Lin A, Hu F. Data-Driven Resource Efficiency Evaluation and Improvement of the Logistics Industry in 30 Chinese Provinces and Cities. Sustainability. 2022; 14(15):9540. https://doi.org/10.3390/su14159540
Chicago/Turabian StyleDing, Heping, Yuxia Guo, Xue Wu, Cui Wang, Yu Zhang, Hongjun Liu, Yujia Liu, Aiyong Lin, and Fagang Hu. 2022. "Data-Driven Resource Efficiency Evaluation and Improvement of the Logistics Industry in 30 Chinese Provinces and Cities" Sustainability 14, no. 15: 9540. https://doi.org/10.3390/su14159540