Regional Sustainability of Logistics Efficiency in China along the Belt and Road Initiative Considering Carbon Emissions
Round 1
Reviewer 1 Report
From the present version of this manuscript, I feel that a few of revision required. Several of my concerns are listed as follows:
1. Introduction needs to be improved. In addition, the final part of the introduction should explain the structure of the manuscript.
2. About logistics evaluation indicators and influencing factors, what are the reasons for choosing? It's not clear.
3. The manuscript should explain the basis of model selection, that is, why SBM-DEA model and Tobit model are selected.
4. The fifth part section is not highlighted the innovations of this manuscript. Your research should be compared with completed studies.
5. The fifth section provides conclusion, but there is no suggestions, and there is no outlined future work.
6. There are errors in the format of references.
Author Response
Response to Reviewer 1 Comments
Dear reviewer:
Hello, thank you very much for your careful review of our paper in your busy schedule and for your constructive suggestions. We have carefully studied your valuable suggestions and comments, and have revised and improved the paper according to your suggestions and comments (the modified part of the paper is marked out with the "Track Changes" function in Word). The specific responses are as follows:
Point 1: Introduction needs to be improved. In addition, the final part of the introduction should explain the structure of the manuscript.
Response 1: Thank you very much for this valuable suggestion. We completely agree with your suggestion. The previous introduction is too extensive. As suggested,we divide introduction into two new sections: Introduction and literature review.And we added the structure of the manuscript in the final part of the introduction. This is in the fourth paragraph of the Introduction, lines 27-48 of page 2 in the Word's " Track Changes " model.
Specific modifications:
The research of this paper is divided into seven parts. The first part is the introduction. It introduces the research background of this paper in detail, proposes the research topic and explains the significance of this paper. The second part illustrates the problems of regional logistics efficiency along the "Belt and Road" at present, and collates the existing references to determine the research object of this paper and explore the suitable research method.The third part presents the research model. The IPCC carbon emission calculation model, the principal component analysis, the SBM model, the Malmquist model and the Tobit model are introduced to lay the foundation for the subsequent empirical analysis.Part 4 is the measurement and analysis of regional logistics efficiency along the route. This chapter firstly constructs the index system and conducts data processing, and then measures the indicators related to the efficiency of the logistics industry along the route under the SBM model and Malmquist model, and evaluates and analyzes the efficiency of the logistics industry along the route from two directions: static and dynamic, and three perspectives: overall, regional and provincial. Part 5 is the study of the factors influencing the efficiency of logistics industry. This chapter firstly sorts out the possible influencing factors of low-carbon logistics efficiency, determines the set of influencing factors, constructs a Tobit regression model of low-carbon logistics efficiency based on this, and investigates the mechanism and degree of influence of each factor through empirical analysis. Part 6 presents the practical and theoretical implications and suggestions of this paper.Part 7 presents the conclusions to promote the improvement of low-carbon logistics efficiency in the regions along the Belt and Road.
Point 2: About logistics evaluation indicators and influencing factors, what are the reasons for choosing? It's not clear.
Response 2: This comment is crucial. We apologize for not adequately describing the reasons for choosing logistics evaluation indicators and influencing factors.Based on the comment, we have added indicators’ descriptions and the reasons for choosing them.This is in the “4.1.1Construction of the indicator system”, lines 4-39 of page 8 in the Word's " Track Changes " model.
Specific modifications:
The construction of a scientific index system is the basis for scientific and efficient efficiency measurement. Therefore, based on the prior research, this paper establishes a systematic and quantifiable index system around human, capital and infrastructure investment, with the goal of obtaining higher logistics output and as little carbon emission as possible with less human, capital and infrastructure investment. The logistics evaluation index system is shown in Table 1.
(1)Mileage transported in logistics (X1): the transportation methods used in the logistics industry are mainly roads and railroads, so the sum of the construction mileage of roads and railroads is used to express this indicator to reflect the investment in infrastructure construction in the logistics industry.
(2)The number of road freight vehicles(X2): road transport as the main mode of transport, this indicator can better reflect the logistics industry in the infrastructure construction of investment.
(3)The number of postal outlets (X3): the postal industry is an important component of the logistics industry as defined in this paper, so this indicator is a better one to reflect the investment in infrastructure construction in the logistics industry.
(4)Fixed asset investment in logistics (X4): this indicator can well reflect the investment in capital factors in logistics industry.
(5)Employment in logistics industry (X5): the employment in this industry mainly includes employees in railroad, road, water, aviation, pipeline, loading and unloading, and transport agency, warehousing and postal industry, which can better reflect the input of the logistics industry to labor factors.
The output of the logistics industry is the final output generated by the consumption of labor, assets and infrastructure; therefore, the construction of the output indicator system should consider not only the expected output reflecting the logistics performance and development, but also the unintended output generated due to the impact of logistics activities on the environment.
(1)Gross value of logistics industry (Y1): the gross value of logistics industry in each province along the region can well measure the economic output of logistics industry.
(2)Carbon emissions from logistics (Y2): Under the low-carbon constraint, this paper considers the eight main energy sources used in the logistics industry and follows the guidance of the Guidelines for National Greenhouse Gas Emissions Inventories issued by the IPCC to calculate the carbon emissions of each province and city along the route region, which is used to measure the environmental output of the route region.
Point 3: The manuscript should explain the basis of model selection, that is, why SBM-DEA model and Tobit model are selected.
Response 3: We found this comment is important and useful. As suggested, we have added the explanations of why SBM-DEA model and Tobit model are selected.They are on lines 46 and 1-6 of page 5-6 and lines 2-8 of page 7 in Word's " Track Changes " mode.
Specific modifications:
Explanations of why SBM-DEA model is selected(lines 45-47 and 1-4 of page 5-6):When the logistics industry is considered as a whole, as a non-radial DEA model, the slack variable-based measurement (SBM) model can effectively identify inefficient resources in DMUs by identifying excess inputs and insufficient outputs in logistics efficiency to obtain the maximum slack variables. Therefore, the SBM-DEA model is selected for measurement in this paper. The expression of SBM-DEA model in this paper is as follows.
Explanations of why Tobit model is selected(lines 1-7 of page 7):As the research on the efficiency of logistics industry continues to deepen, the empirical analysis adopted in the previous studies gradually changes from a single to a comprehensive approach. The combination of DEA and Tobit model can not only measure the efficiency of logistics industry more comprehensively, but also study the specific factors influencing logistics efficiency. Based on this, this paper uses Tobit regression model to empirically analyze the correlation between logistics efficiency and the influencing factors.
Point 4: The fifth part section is not highlighted the innovations of this manuscript. Your research should be compared with completed studies.
Response 4: This suggestion is important. We completely agree with your suggestion. As suggested, in the last section, we have compared our research with completed studies. They are on lines 28-40 of page 32 in Word's " Track Changes " mode.
Specific modifications:
The research of domestic scholars on the efficiency of low-carbon logistics in regions along the "Belt and Road" has been comprehensive, and it is found that although some scholars have focused on the provinces and cities along the "Belt and Road" in China, most of the research has focused on the overall analysis or the analysis of specific provinces and cities. However, this paper quantitatively analyzes the efficiency of logistics industry in 17 provinces and cities along the "Belt and Road" in China during the period of 2006-2020, to investigate the sustainable development level of logistics industry in general, regions and specific provinces and cities, and analyze the factors affecting the efficiency of logistics industry. The paper also analyze the key factors affecting the efficiency of logistics industry, and make corresponding suggestions based on the empirical results, in order to provide reference for the sustainable and coordinated development of logistics industry in the regions along the Belt and Road.
Point 5: The fifth section provides conclusion, but there is no suggestions, and there is no outlined future work.
Response 5: We found this suggestion is important and useful. Therefore,in the discussion section, we have added suggestions, and pointed out the limitations and future research directions of this paper.They are in the lines 14-54 of page 31 and lines 1-26 of page 32 in Word's " Track Changes " mode.
Specific modifications:
Based on the above findings, this paper makes the following suggestions:
(1)Adhere to low-carbon concept and promote green logistics.According to the calculation and analysis results of carbon emissions of logistics industry along the route, it is clear that the rapid development of logistics industry is accompanied by the negative impact on ecological environment. In order to respond to the call for sustainable development in the "Belt and Road" initiative, the government should strengthen the guiding role, establish a set of effective carbon emission supervision mechanism for the logistics industry, and introduce carbon emission related laws and regulations to regulate, supervise and motivate the trinity, so as to promote the green development of the logistics industry. In addition, it should also incorporate green logistics into government planning, accelerate the development of green technologies, encourage reverse logistics and resource reuse, and implement clean energy tax subsidies and carbon emission trading systems, with a view to achieving environmental protection and resource conservation.
(2)Play a leading role and promote synergistic development.According to the results of static analysis of regional low-carbon logistics efficiency along the route, it can be seen that there is an unbalanced development of logistics industry among provinces and cities and regions. In order to narrow the regional logistics efficiency gap and achieve synergistic development, four provinces and cities, namely Fujian, Zhejiang, Inner Mongolia and Shanghai, should play a leading role, strengthen the dissemination of advanced logistics energy-saving technologies and the summary of advanced management methods, and enhance experience sharing and exchange cooperation with other provinces under the guidance of the "Belt and Road" initiative, so as to narrow the regional efficiency To reduce regional efficiency differences and promote the synergistic development of regional logistics industry along the "Belt and Road".
(3)Focus on technology research and development to enhance scale efficiency.According to the results of the dynamic analysis of low-carbon logistics efficiency in the regions along the Belt and Road, technological progress has an important role in promoting the improvement of low-carbon logistics efficiency in the regions along the Belt and Road. Therefore, it is important to continue to strengthen the upgrade of logistics technology innovation and explore the path of scale efficiency improvement. To this end, in terms of technological progress, we can focus on strengthening the close connection between "Internet+" and low-carbon logistics, introducing unmanned vehicles, sorting robots and other high-tech logistics equipment, and using efficient logistics information technology such as big data and cloud computing. In terms of expanding the scale of benefits, we can take measures to strengthen the integration and reorganization of the logistics industry, break the barriers of cooperation between various modes of transportation and enterprises, and strengthen the standardization and informationization of the logistics industry.
(4)Control energy consumption and increase government input.According to the results of the analysis of the influencing factors of logistics efficiency in the regions along the route, it can be seen that energy consumption and government expenditure have not positively influenced the efficiency of low-carbon logistics in the regions along the route. For this reason, in terms of energy consumption, a push-back mechanism can be adopted, i.e., energy consumption can be controlled through energy quota consumption in the logistics industry, while the share of clean energy consumption can be promoted jointly in terms of policy and technology, with a view to promoting the green development of the logistics industry in the coastal regions. In terms of government investment, while comprehensively increasing the investment in logistics industry, it should also pay attention to the formulation of macro-control policies, and should be appropriately tilted to less economically developed regions in the allocation, so as to accelerate the process of overall improvement of the efficiency of regional logistics industry along China's "Belt and Road".
However, our paper has the following limitations.In our paper, 17 provincial regions are selected as the research units, while in the actual study, there are certain differences in the development level of logistics industry within the same province, so the ideal research unit should be prefecture-level cities or county-level regions. However, due to the availability of data, it is difficult to collect the energy consumption in areas below the provincial level, and the provincial level is the most detailed research unit in both domestic and international studies. Therefore, it is hoped that in future research, we can enrich the relevant scientific data, change the research idea, find alternative and representative variables, further refine the research unit, and obtain more reliable and closer to the current life of the research conclusions, so as to provide heoretical guidance for the green development of regional logistics industry.
Point 6: There are errors in the format of references.
Response 6: This comment is important. We apologize for not carefully checking the format of references.After checking the template of Sustainability, we have changed the references’ format into”Author 1, A.B.; Author 2, C.D. Title of the article. Journal Name Year, Volume, page rang”.This is in “References” section, lines 1-18 and 1-56 of page 34 and 35 in the Word's " Track Changes " model.
Specific modifications:
References
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Point 7: Moderate English changes required.
Response 7: Thank you for this useful suggestion. As suggested, we have combed through the entire paper, and revised and corrected some expressions.
Thanks again to you for your careful review of our research paper during your busy schedules!
Reviewer 2 Report
The paper entitled Research of regional logistics efficiency in China along the Belt and Road Initiative considering carbon emissions deals with the actual topic. Suggestions for paper improvement are below:
· The paper should be better aligned with the aim and scope of the journal. The paper must be more in the context of sustainability.
· The word “sustainability” is not used in the title, abstract, or keywords,…
· Sustainability must be highlighted and emphasized throughout the paper (all parts Abstract, keywords, introduction, problem description, methodology, etc).
· Introduction is too extensive. Introduction, problem description, and literature review should be written separately.
· Divide this section into two new sections: Introduction and Problem description with literature review
· Explain in more detail the steps shown in Figure 1 (page 4, lines 1 to 8).
· Observed period is questionable.
· It will be very useful if you can extend observed period. (In 2022 very important are 2020 and 2021, particularly because of the pandemic) .
· The separate section Practical and theoretical implications (or Discussion) is missing.
· Conclusion section is not on a satisfactory level. It is not written in a scientific manner (Do not use bullets and numbering).
· Clearly state your unique research contributions in the conclusion section. The authors need to clearly provide several solid future research directions (this confirms a bad relationship with the gaps in the literature).
· Limitations of this research should be provided in the last section.
· Technical problems:
- use the instructions for the authors
- A lot of blank pages.
· Scientific and practical contributions must be explicitly stated.
Suggested references:
Andrejić M., Kilibarda, M. Pajić, V., (2021). Measuring efficiency change in time applying malmquist productivity index: a case of distribution centres in Serbia. FACTA UNIVERSITATIS, Series Mechanical Engineering, 19 (3), 499-514.
Qin, W.; Qi, X. Evaluation of Green Logistics Efficiency in Northwest China. Sustainability 2022, 14, 6848. https://doi.org/10.3390/su14116848
Zhao, W.; Qiu, Y.; Lu, W.; Yuan, P. Input–Output Efficiency of Chinese Power Generation Enterprises and Its Improvement Direction-Based on Three-Stage DEA Model. Sustainability 2022, 14, 7421. https://doi.org/10.3390/su14127421
Author Response
Response to Reviewer 2 Comments
Dear reviewer:
Hello, thank you very much for your careful review of our paper in your busy schedule and for your constructive suggestions. We have carefully studied your valuable suggestions and comments, and have revised and improved the paper according to your suggestions and comments (the modified part of the paper is marked out with the "Track Changes" function in Word). The specific responses are as follows:
Point 1: The paper should be better aligned with the aim and scope of the journal. The paper must be more in the context of sustainability.
Response 1: Thank you very much for this valuable suggestion. We should clarify that our study focuses on the regional logistics efficiency under the carbon emission constraint, which aims to provide suggestions for the sustainable development of regional logistics.As suggested, we have added research background on sustainability to the abstract and introduction, they are on lines 10-13,43 of page 1 and lines 1-3 of page 2 in Word's " Track Changes " mode.
Specific modifications:
Abstract: The Belt and Road Initiative puts higher requirements for the logistics industry. As one of the most energy-comsuming industries, logistics is a high-carbon emission industry. Its impact on the environment cannot be ignored. In this context, how to respond to the "Belt and Road" under the concept of sustainable development, to promote the logistics industry to achieve "low consumption, low emissions, high efficiency" of regional sustainability, has become the most important development of China's logistics industry.
Introduction:Therefore, reducing energy consumption and carbon emissions in the logistics industry is of great significance for promoting regional sustainable development and low-carbon economy[3].
From the perspective of low carbon economy and regional sustainability, this paper responds to the "One Belt and One Road" strategy, selects 17 provinces and cities along the "One Belt and One Road" in China as the research object, and divides them into four regions
Point 2: The word “sustainability” is not used in the title, abstract, or keywords,…
Response 2: We found this comment is important and useful. As suggested, to be more relevant to the main theme of the journal,we used the word “sustainability” in the title, abstract and keywords. They are on lines 2-3,11-13, 27of page 1 in Word's " Track Changes " mode.
Specific modifications:
Title: We have changed the title into”Regional sustainability of logistics efficiency in China along the Belt and Road Initiative considering carbon emissions”
Abstract: The Belt and Road Initiative puts higher requirements for the logistics industry. As one of the most energy-comsuming industries, logistics is a high-carbon emission industry. Its impact on the environment cannot be ignored. In this context, how to respond to the "Belt and Road" under the concept of sustainable development, to promote the logistics industry to achieve "low consumption, low emissions, high efficiency" of regional sustainability, has become the most important development of China's logistics industry.
Keywords:Logistics efficiency; Regional sustainability; Carbon emissions; Belt and Road; SBM model; Tobit model
Point 3: Sustainability must be highlighted and emphasized throughout the paper (all parts Abstract,keywords, introduction, problem description, methodology, etc).
Response 3: Thank you very much for this valuable suggestion.As suggested, in addition to the inclusion of sustainability in the title, abstract, keywords and introduction, we also added a description of the importance of sustainability in the problem description, methodology and conclusion of our study. They are on lines 45-49 of page 3; lines 10-12 of page 4; lines 5-8 of page 5;lines 32-38 of page 32 in Word's " Track Changes " mode.
Specific modifications:
Literature review(problem description): In addition, a two-stage DEA model combining the SBM model and Tobit regression is proposed to further analyze the macro drivers of low-carbon logistics efficiency and provide scientific references for the formulation of relevant policies, the improvement of logistics industry quality, and the application of green development and sustainable development concepts.
With the vision of sustainable development in the "Belt and Road" strategy, some scholars have started to consider the impact of carbon emissions when measuring the efficiency of logistics industry.
Methodology and models: In our study, eight types of logistics energy are used to estimate the carbon emissions of the logistics industry in China along the "Belt and Road" to contribute to the sustainable development of the regions along the route.
Conclusions: However, this paper quantitatively analyzes the efficiency of logistics industry in 17 provinces and cities along the "Belt and Road" in China during the period of 2006-2020, to investigate the sustainable development level of logistics industry in general, regions and specific provinces and cities, and analyze the factors affecting the efficiency of logistics industry. The paper also analyze the key factors affecting the efficiency of logistics industry, and make corresponding suggestions based on the empirical results, in order to provide reference for the sustainable and coordinated development of logistics industry in the regions along the Belt and Road.
Point 4: Introduction is too extensive. Introduction, problem description, and literature review should be written separately.Divide this section into two new sections: Introduction and Problem description with literature review.
Response 4: This comment is crucial. As suggested, we divide the previous Introduction into two new sections: Introduction and Problem description with literature review.They are on lines 30-43 of page 1; lines 1-55 of page 2; lines 1-55 of page 3; lines 1-19 of page 4 in Word's " Track Changes " mode.
Specific modifications:
- Introduction
Greenhouse gas emissions, represented by carbon dioxide, exceed the ecological loading capacity and are the direct cause of global warming [1]. To cope with the increasing atmospheric carbon concentration and sound the alarm for environmental protection, in 2018, the IPCC released the Special Report on Global 1.5°C Warming, which proposed two targets: temperature rise control target and carbon emission control target.In 2013, China emitted 10 billion tons of carbon dioxide, accounting for 27.8% of global carbon emissions; in 2019, China's primary energy consumption generated carbon emissions increased by 3.2% from the previous year [2]. The "Belt and Road" strategy has promoted the economic and trade development along its routes, and the logistics industry has played a role in promoting the economic and trade development. As a high carbon emission industry, the logistics industry has a negative impact on the ecological environment due to its high energy consumption and emission characteristics. Therefore, reducing energy consumption and carbon emissions in the logistics industry is of great significance for promoting regional sustainable development and low-carbon economy[3].
From the perspective of low carbon economy and regional sustainability, this paper responds to the "One Belt and One Road" strategy, selects 17 provinces and cities along the "One Belt and One Road" in China as the research object, and divides them into four regions, takes carbon emissions from logistics industry as non-expected output, measures the efficiency of logistics industry, and conducts comparative analysis in three dimensions: overall, regional and provincial, and gives suggestions for efficiency improvement, which helps to further clarify the concept of logistics efficiency, enriches the theoretical system of low carbon logistics efficiency and sustainable development to a certain extent, and improves the research content and method of logistics industry efficiency measurement [4].
With the rapid development of the "Belt and Road" construction, China's logistics industry is facing unprecedented favorable conditions, while the large gap in the development of the logistics industry among the provinces and cities along the route has caused certain impact on the development of cooperation between the regions. This paper considers the non-expected output indexes in the evaluation system, understands the difference of carbon emissions of logistics industry in each province and city along the route through empirical analysis, and evaluates the low-carbon logistics efficiency in the route from two perspectives: static and dynamic, which provides scientific and effective data support and theoretical basis for the low-carbon development of logistics industry along the "Belt and Road" route. It also provides a scientific and effective data support and theoretical basis for the low-carbon development of the logistics industry along the "Belt and Road", and provides a decision basis for the government to formulate regional emission reduction plans and implement the carbon emission assessment system.
The research of this paper is divided into seven parts. The first part is the introduction. It introduces the research background of this paper in detail, proposes the research topic and explains the significance of this paper. The second part illustrates the problems of regional logistics efficiency along the "Belt and Road" at present, and collates the existing references to determine the research object of this paper and explore the suitable research method.The third part presents the research model. The IPCC carbon emission calculation model, the principal component analysis, the SBM model, the Malmquist model and the Tobit model are introduced to lay the foundation for the subsequent empirical analysis.Part 4 is the measurement and analysis of regional logistics efficiency along the route. This chapter firstly constructs the index system and conducts data processing, and then measures the indicators related to the efficiency of the logistics industry along the route under the SBM model and Malmquist model, and evaluates and analyzes the efficiency of the logistics industry along the route from two directions: static and dynamic, and three perspectives: overall, regional and provincial. Part 5 is the study of the factors influencing the efficiency of logistics industry. This chapter firstly sorts out the possible influencing factors of low-carbon logistics efficiency, determines the set of influencing factors, constructs a Tobit regression model of low-carbon logistics efficiency based on this, and investigates the mechanism and degree of influence of each factor through empirical analysis. Part 6 presents the practical and theoretical implications and suggestions of this paper.Part 7 presents the conclusions to promote the improvement of low-carbon logistics efficiency in the regions along the Belt and Road.
- Literature review
Measuring the efficiency of the logistics industry first requires the selection of logistics efficiency evaluation indicators, and the research on logistics efficiency evaluation indicators is relatively mature. Existing studies tend to select investment indicators from three main perspectives: human, material and financial resources [5], and output indicators from both quantitative and qualitative aspects [6], and the input and output indicators used in past studies include the number of employees in the logistics industry, road mileage and traffic flow, the stock of fixed assets in the logistics industry [7], the number of postal outlets and the added value of the logistics industry [8]. However, in the past, when conducting logistics efficiency studies, only capital and labor factors were often considered, and environmental indicators were rarely considered [9]. In the past few years, under the pressure to restore green and sustainable development, the logistics industry has been given the requirement to reduce energy consumption and carbon emissions. Therefore, in recent years, carbon emission indicators have become undesired outputs in efficiency evaluation [10], and performance evaluation indicator systems considering carbon emissions in the logistics industry have started to be established [11]. To avoid the subjectivity of indicator selection, principal component analysis can be used for dimensionality reduction to obtain comprehensive and objective indicators, and then efficiency evaluation and analysis can be performed [12]. Shuai Bin and Du Wen first proposed to combine DEA and PCA for comprehensive analysis and quantitative assessment of the logistics industry [13]. This combined approach was widely used to measure logistics efficiency [14], and several studies obtained objective evaluation results from it, thus proving the scientificity and feasibility of this combined approach [15].
DEA has the advantages of being able to calculate data directly without pre-estimating parameters, unifying indicator units, or determining indicator weights compared with other efficiency evaluation methods. Therefore, it is more suitable for solving complex multiple-input and multiple-output efficiency assessment problems [16], and it has been widely used in logistics efficiency assessment at present. Many scholars have assessed logistics efficiency from different perspectives, such as the assessment of national logistics efficiency based on global perspective [17], the assessment of port logistics efficiency [18], the study of sustainability of national logistics efficiency [19], the study of logistics performance evaluation [20], and so on.
However, with the in-depth study of DEA models, the traditional radial DEA models, represented by the BCC and CCR models, suffer from the problem of neglecting efficiency improvement, which may lead to inaccurate calculation results. Therefore, in 2001, Tone made some modifications to the DEA model and proposed a non-radial SBM model with the addition of slack variables. The SBM-DEA model considering undesired output can clarify the significance of slack in both undesired and desired output and define the optimal efficiency as maximizing desired output and minimizing undesired output for a fixed input level [21].
There are several current examples of SBM-DEA models applied in port logistics efficiency studies as well as in other areas.Tan et al. used the Super-SBM model to assess sustainable logistics efficiency in China considering inputs, desired and undesired outputs [22]; Qian Qiming et al. assessed regional differences in green economy efficiency in China and compared the CCR and SBM models under DEA [23]. It was found that the SBM-DEA model was more applicable and distinguishable in the evaluation; Wang Zhaofeng et al. used the SBM-DEA model to study the spatial and temporal differences of carbon emission efficiency and its influencing factors in Hunan [24].
In addition, a two-stage DEA model combining the SBM model and Tobit regression is proposed to further analyze the macro drivers of low-carbon logistics efficiency and provide scientific references for the formulation of relevant policies, the improvement of logistics industry quality, and the application of green development and sustainable development concepts.For example, Liu et al. used the SBM-DEA model and Tobit model to study the logistics efficiency and its influencing factors in the middle reaches of Yangtze River economic zone, indicating that the combination of SBM-DEA and Tobit model is a reliable method to reflect the efficiency of logistics industry [25].
The "One Belt, One Road" initiative has been attracting much attention since its introduction. Many scholars have used the DEA method to measure and analyze the efficiency of logistics industry in countries and regions along the "Belt and Road". Wang Qinmei et al. used DEA model to obtain the comprehensive technical efficiency of different countries and provinces, and decomposed it into pure technical efficiency and scale efficiency to evaluate the efficiency of logistics industry in the core region of "One Belt and One Road" [26]. Zheng Xiujuan used DEA to evaluate the efficiency of the logistics industry in the national, eastern, central and western regions [27]. Meng Kui used a three-stage DEA to investigate the logistics efficiency in the central region of China under the dual constraints of energy consumption and carbon emissions [28]. Xueqing Zhang used DEA model to analyze the changes of regional logistics efficiency along the "Belt and Road", regional differences and their main causes [29].
With the vision of sustainable development in the "Belt and Road" strategy, some scholars have started to consider the impact of carbon emissions when measuring the efficiency of logistics industry. Among them, Yao Shanji et al. used the DEA model and Malmquist index model to measure and analyze the efficiency of logistics industry in each province along the "Belt and Road" region in both static and dynamic aspects [30]. Zhang Yunning et al. used a three-stage DEA model to measure the logistics efficiency in the Yangtze River protection area, and used the Tobit model to investigate the degree of influence of environmental factors on the efficiency of the logistics industry, taking carbon emissions into account [31]. Carbon logistics efficiency was measured and dynamically analyzed using the Malmquist index model [32].
Point 5: Explain in more detail the steps shown in Figure 1 (page 4, lines 1 to 8).
Response 5: Thank you very much for this valuable suggestion.It is important for an article to have a careful description of its mechanism. As suggested, we have explained in more detail the steps shown in Figure 1.They are on lines 21-30 of page 4 in Word's " Track Changes " mode.
Specific modifications:
In our study, based on the priors, firstly, the non-desired output, i.e. carbon emission, in the index system is calculated by using IPCC coefficient method. Secondly, the principal component analysis is used to extract the principal components and use them as input indicators, and after the two input indicators and two output indicators obtained, the static and dynamic analysis of the low carbon logistics efficiency along the regional route are substituted into the SBM-DEA model and Malmquist model respectively, and finally, the Tobit model was used to conduct regression analysis on four influencing factors: level of economic development , energy consumption, industrial structure and government expenditures. The main research models and their applications are shown in Figure 1.
Point 6: Observed period is questionable. It will be very useful if you can extend observed period. (In 2022 very important are 2020 and 2021, particularly because of the pandemic) .
Response 6: Thank you for this useful comment. As recommended, we have extended the observed period to 2020.However, we are unable to update the data in the article to 2021 as the data for 2021 needs to be checked in the Statistical Yearbook for 2022.On the bright side, the data updated to 2020 still contributes much to the reliability of this paper.Here are the figures and tables in our study updated to 2020.
Specific modifications:
Figure 3. Average value of carbon emissions from logistics in the regions along the route, 2006-2020.
Figure 4. Average carbon emissions from logistics along the route, 2006-2020.
Figure 5. Average carbon emissions in the four regions along the route, 2006-2020
Table 4. Carbon Emissions from Logistics Industry in the Belt and Road Region, 2006-2020 (million tons).
|
Provinces |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
|
Liaoning |
2490.01 |
2783.84 |
2786.40 |
2933.89 |
3023.40 |
3272.49 |
3466.98 |
3214.66 |
3526.86 |
3644.12 |
3732.07 |
3805.43 |
3753.95 |
3732.40 |
3714.25 |
|
Jilin |
586.92 |
784.35 |
971.34 |
977.44 |
1024.01 |
1046.07 |
1963.77 |
1155.91 |
1277.92 |
1339.99 |
1287.04 |
1252.39 |
1074.25 |
1075.08 |
1087.97 |
|
Heilongjiang |
1075.81 |
1086.17 |
926.75 |
1087.65 |
1059.87 |
1854.87 |
1030.07 |
2101.57 |
2250.80 |
2326.68 |
2382.82 |
2139.61 |
1835.72 |
1850.39 |
1858.23 |
|
Inner Mongolia |
1491.27 |
1704.70 |
1973.51 |
2253.77 |
2551.10 |
2842.93 |
3321.03 |
2393.24 |
2406.62 |
2473.34 |
1619.43 |
1644.87 |
1581.40 |
1624.19 |
1630.94 |
|
Shanxi |
764.60 |
978.31 |
1276.96 |
1559.97 |
1715.15 |
1849.56 |
1895.16 |
1456.78 |
1526.61 |
1495.35 |
1297.12 |
1299.61 |
1435.28 |
1402.87 |
1427.51 |
|
Gansu |
163.02 |
504.71 |
538.74 |
573.86 |
628.60 |
669.77 |
749.66 |
1019.83 |
1035.93 |
975.82 |
960.55 |
975.26 |
904.36 |
912.45 |
921.82 |
|
Qinghai |
82.09 |
148.40 |
184.10 |
205.26 |
229.50 |
245.76 |
249.70 |
261.45 |
280.53 |
304.56 |
350.19 |
394.34 |
439.51 |
454.76 |
447.39 |
|
Ningxia |
255.62 |
271.33 |
265.65 |
272.25 |
298.81 |
309.26 |
330.22 |
340.97 |
360.48 |
373.91 |
372.31 |
364.53 |
314.74 |
345.53 |
339.62 |
|
Xinjiang |
905.78 |
933.39 |
964.43 |
931.03 |
996.06 |
1090.37 |
1238.27 |
1496.10 |
1563.54 |
1864.77 |
1976.94 |
2096.73 |
2107.14 |
2077.86 |
2089.74 |
|
Guangxi |
1087.21 |
1195.45 |
1235.84 |
1395.12 |
1515.30 |
1624.58 |
1759.37 |
1369.78 |
1727.90 |
1792.26 |
1840.32 |
2004.02 |
2057.82 |
1981.58 |
1994.03 |
|
Yunnan |
1215.46 |
1316.16 |
1349.34 |
1383.75 |
1734.80 |
1853.82 |
1979.23 |
1875.10 |
2120.47 |
2052.24 |
2147.28 |
2192.06 |
2462.00 |
2679.65 |
2730.49 |
|
Chongqing |
713.34 |
879.07 |
999.94 |
940.31 |
1147.59 |
1236.80 |
1454.85 |
1609.49 |
1506.00 |
1792.18 |
1910.12 |
2007.15 |
1785.96 |
1844.84 |
1828.62 |
|
Shanghai |
3278.77 |
3718.26 |
3824.43 |
3856.29 |
4061.23 |
3926.56 |
3995.97 |
3992.21 |
3979.72 |
4172.96 |
4643.43 |
5083.26 |
4974.33 |
5166.33 |
5190.59 |
|
Zhejiang |
1699.55 |
1879.96 |
2039.74 |
2095.77 |
2262.76 |
2454.25 |
2574.43 |
2687.77 |
2735.81 |
2903.22 |
2906.10 |
3017.48 |
2938.64 |
2737.75 |
2876.51 |
|
Fujian |
811.20 |
947.99 |
1247.03 |
1378.70 |
1514.29 |
1638.63 |
1695.52 |
1762.48 |
1908.95 |
2009.84 |
2139.08 |
2261.94 |
2401.37 |
2589.35 |
2604.98 |
|
Guangdong |
3769.04 |
4153.52 |
4478.50 |
4693.50 |
5186.08 |
5453.00 |
5670.24 |
5357.07 |
5605.38 |
5840.51 |
6550.22 |
6641.18 |
6767.50 |
6839.40 |
6821.47 |
|
Hainan |
310.02 |
339.44 |
496.29 |
562.82 |
610.54 |
644.92 |
653.60 |
598.27 |
563.51 |
588.75 |
564.28 |
598.24 |
578.06 |
590.36 |
581.44 |
Table 5. Carbon emissions from logistics in the four regions along the Belt and Road, 2006-2020 (million tons).
|
Region |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
|
Northeast Region |
4152.7 |
4654.4 |
4684.5 |
4999.0 |
5107.3 |
6173.4 |
6460.8 |
6472.1 |
7055.6 |
7310.8 |
7401.9 |
7197.4 |
6663.9 |
6657.9 |
6679.4 |
|
Northwest Region |
3662.4 |
4540.8 |
5203.4 |
5796.1 |
6419.2 |
7007.7 |
7784.0 |
6968.4 |
7173.7 |
7487.8 |
6576.6 |
6775.3 |
6782.4 |
6817.7 |
6824.8 |
|
Southwest Region |
3016.0 |
3390.7 |
3585.1 |
3719.2 |
4397.7 |
4715.2 |
5193.5 |
4854.4 |
5354.4 |
5636.7 |
5897.7 |
6203.2 |
6305.8 |
6506.1 |
6492.8 |
|
Southeast Region |
9868.6 |
11039.6 |
12086.0 |
12587.1 |
13634.9 |
14117.4 |
14589.8 |
14397.8 |
14793.4 |
15515.3 |
16803.1 |
17602.1 |
17659.9 |
17923.2 |
18047.5 |
Table 10. Effective provinces and municipalities for efficiency under the CCR model and SBM model.
|
|
CCR model |
SBM model |
||
|
Year |
Effective provinces and cities |
Number of cases |
Effective provinces and cities |
Number of cases |
|
2006 |
Fujian, Guangdong, Hainan, Ningxia, Shanghai |
5 |
Gansu, Shanghai, Fujian |
3 |
|
2007 |
Fujian, Guangdong, Hainan, Ningxia, Shanghai |
5 |
Shanghai, Fujian |
2 |
|
2008 |
Fujian, Guangdong, Hainan, Ningxia, Shanghai |
5 |
Shanghai, Fujian |
2 |
|
2009 |
Fujian, Guangdong, Ningxia |
3 |
Fujian |
1 |
|
2010 |
Fujian, Guangdong, Ningxia, Shanghai, Zhejiang |
5 |
Shanghai, Fujian |
2 |
|
2011 |
Fujian, Guangdong, Ningxia, Zhejiang |
4 |
Fujian |
1 |
|
2012 |
Fujian, Guangdong, Hainan |
3 |
Fujian |
1 |
|
2013 |
Fujian, Guangdong, Inner Mongolia, Ningxia |
4 |
Inner Mongolia, Fujian |
2 |
|
2014 |
Fujian, Guangdong, Hainan, Ningxia, Zhejiang |
5 |
Fujian |
1 |
|
2015 |
Fujian, Guangdong, Ningxia |
3 |
Fujian |
1 |
|
2016 |
Fujian, Guangdong, Ningxia |
3 |
Fujian |
1 |
|
2017 |
Fujian, Guangdong, Ningxia |
3 |
Fujian |
1 |
|
2018 |
Guangdong, Ningxia, Zhejiang |
3 |
Inner Mongolia, Zhejiang |
2 |
|
2019 |
Fujian, Guangdong, Ningxia, Zhejiang |
4 |
Inner Mongolia, Shaanxi, Zhejiang, Fujian |
4 |
|
2020 |
Fujian,Guangdong,Ningxia, Zhejiang |
4 |
Inner Mongolia,Zhejiang, Fujian |
3 |
Figure 6. Average value of logistics efficiency by provinces and municipalities along the route region in all years, 2006-2020.
Table 9. Results of measuring the overall efficiency value of the logistics industry in the provinces along the route region, 2006-2020.
|
Provinces |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
|
Liaoning |
0.4577 |
0.4216 |
0.4460 |
0.5347 |
0.5766 |
0.6434 |
0.6540 |
0.7121 |
0.6615 |
0.6602 |
0.4006 |
0.3666 |
0.4559 |
0.3202 |
0.3427 |
|
Jilin |
0.3449 |
0.3246 |
0.3523 |
0.4047 |
0.3936 |
0.4269 |
0.3418 |
0.4238 |
0.3979 |
0.3384 |
0.3186 |
0.3083 |
0.4567 |
0.2556 |
0.2890 |
|
Heilongjiang |
0.2720 |
0.2497 |
0.2675 |
0.3100 |
0.3067 |
0.2903 |
0.3367 |
0.2979 |
0.2915 |
0.2573 |
0.2455 |
0.2359 |
0.3259 |
0.1336 |
0.1837 |
|
Average value |
0.3582 |
0.3320 |
0.3553 |
0.4165 |
0.4256 |
0.4535 |
0.4441 |
0.4779 |
0.4503 |
0.4187 |
0.3216 |
0.3036 |
0.4128 |
0.2364 |
0.2718 |
|
Inner Mongolia |
0.4819 |
0.4383 |
0.5181 |
0.6507 |
0.6205 |
0.6923 |
0.6846 |
1.0000 |
0.7227 |
0.4716 |
0.4545 |
0.3612 |
1.0000 |
1.0000 |
1.0000 |
|
Shanxi |
0.3189 |
0.2681 |
0.2757 |
0.3242 |
0.3140 |
0.3376 |
0.3446 |
0.3702 |
0.3055 |
0.2789 |
0.2797 |
0.2725 |
0.3737 |
1.0000 |
1.0000 |
|
Gansu |
1.0000 |
0.2392 |
0.2646 |
0.2646 |
0.2385 |
0.2777 |
0.2735 |
0.2825 |
0.1899 |
0.1571 |
0.1342 |
0.1282 |
0.1621 |
0.1852 |
0.1927 |
|
Qinghai |
0.1446 |
0.1159 |
0.1021 |
0.1245 |
0.1349 |
0.1352 |
0.1289 |
0.1264 |
0.1260 |
0.1146 |
0.1020 |
0.0944 |
0.0970 |
0.0952 |
0.0929 |
|
Ningxia |
0.3113 |
0.2790 |
0.3105 |
0.6281 |
0.7424 |
0.8039 |
0.4952 |
0.7507 |
0.6066 |
0.5011 |
0.4569 |
0.3813 |
0.4413 |
0.2825 |
0.3168 |
|
Xinjiang |
0.1901 |
0.1658 |
0.1700 |
0.1904 |
0.1734 |
0.1803 |
0.1963 |
0.2495 |
0.2487 |
0.2357 |
0.2178 |
0.2294 |
0.3669 |
0.3058 |
0.3173 |
|
Average value |
0.4078 |
0.2510 |
0.2735 |
0.3638 |
0.3706 |
0.4045 |
0.3538 |
0.4632 |
0.3666 |
0.2932 |
0.2742 |
0.2445 |
0.4068 |
0.4781 |
0.4866 |
|
Guangxi |
0.3272 |
0.3084 |
0.3463 |
0.3617 |
0.4042 |
0.4668 |
0.4354 |
0.5166 |
0.4657 |
0.4423 |
0.4235 |
0.4235 |
0.5854 |
0.3167 |
0.3778 |
|
Yunnan |
0.1519 |
0.1382 |
0.1444 |
0.1140 |
0.1076 |
0.1120 |
0.1159 |
0.1317 |
0.1185 |
0.1068 |
0.1012 |
0.0998 |
0.3988 |
0.2844 |
0.3021 |
|
Chongqing |
0.3812 |
0.3046 |
0.3226 |
0.3943 |
0.3534 |
0.3835 |
0.3707 |
0.3482 |
0.3800 |
0.3242 |
0.3235 |
0.3129 |
0.4136 |
0.3029 |
0.2958 |
|
Average value |
0.2868 |
0.2504 |
0.2711 |
0.2900 |
0.2884 |
0.3208 |
0.3073 |
0.3322 |
0.3214 |
0.2911 |
0.2827 |
0.2787 |
0.4659 |
0.3013 |
0.3252 |
|
Shanghai |
1.0000 |
1.0000 |
1.0000 |
0.5485 |
1.0000 |
0.5512 |
0.5368 |
0.4766 |
0.4514 |
0.4193 |
0.4028 |
0.3929 |
0.5539 |
0.3415 |
0.3628 |
|
Zhejiang |
0.7898 |
0.7378 |
0.7713 |
0.7930 |
0.8319 |
0.8348 |
0.7439 |
0.8044 |
0.8207 |
0.7514 |
0.7474 |
0.7546 |
1.0000 |
1.0000 |
1.0000 |
|
Fujian |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
1.0000 |
0.8638 |
1.0000 |
1.0000 |
|
Guangdong |
0.6016 |
0.5434 |
0.5758 |
0.6603 |
0.6336 |
0.6501 |
0.6614 |
0.6674 |
0.5956 |
0.5500 |
0.5314 |
0.5246 |
0.5826 |
0.3971 |
0.3276 |
|
Hainan |
0.4158 |
0.3959 |
0.3941 |
0.3514 |
0.3413 |
0.3536 |
0.3594 |
0.3804 |
0.4574 |
0.3460 |
0.3123 |
0.3404 |
0.3342 |
0.2520 |
0.2247 |
|
Average value |
0.7614 |
0.7354 |
0.7482 |
0.6706 |
0.7614 |
0.6779 |
0.6603 |
0.6658 |
0.6650 |
0.6133 |
0.5988 |
0.6025 |
0.6669 |
0.5981 |
0.5830 |
Point 7: The separate section Practical and theoretical implications (or Discussion) is missing.
Response 7: We found this suggestion is crucial. For an article,practical and theoretical implications are very important. Therefore, we added a discussion section which including practical and theoretical implications. They are in lines 34-53 of page 30 and lines 1-13 of page 31 in Word's " Track Changes " mode.
Specific modifications:
Theoretical implications: From the perspective of low carbon economy, this paper responds to the strategy of "One Belt and One Road", selects 17 provinces and cities along the "One Belt and One Road" in China as the research object, and divides them into four regions, takes carbon emissions from logistics industry as non-expected output, measures the efficiency of logistics industry, and conducts comparative analysis in three dimensions: overall, regional and provincial, and gives suggestions for efficiency improvement, which helps to further clarify the concept of logistics efficiency, enriches the theoretical system of low carbon logistics efficiency and sustainable development to a certain extent, and improves the research content and method of logistics industry efficiency measurement.
Practical implications: With the rapid development of the "Belt and Road" construction, China's logistics industry is facing unprecedented favorable conditions, while the large gap in the development of the logistics industry among the provinces and cities along the route has caused certain impact on the development of cooperation between the regions. This paper considers the non-expected output indexes in the evaluation system, understands the difference of carbon emissions of logistics industry in each province and city along the route through empirical analysis, and evaluates the low-carbon logistics efficiency in the route from two perspectives: static and dynamic, which provides scientific and effective data support and theoretical basis for the low-carbon development of logistics industry along the "Belt and Road" route. It also provides a scientific and effective data support and theoretical basis for the low-carbon development of the logistics industry along the "Belt and Road", and provides a decision basis for the government to formulate regional emission reduction plans and implement carbon emission assessment system.From the study results, the development level of low-carbon logistics in each province along the "Belt and Road" in China is uneven, and the logistics efficiency of most provinces has not reached the optimal level, so there is great room for improvement and development potential. From 2006 to 2020, the overall low-carbon logistics efficiency of China's regions along the Belt and Road shows an upward trend. Among them, technological progress has a driving effect on the growth of low-carbon logistics efficiency, while the low scale efficiency and pure technical efficiency form a certain obstacle to the growth of low-carbon logistics efficiency.The level of economic development and industrial structure have positive effects on the improvement of low-carbon logistics efficiency in the regions along the route, while energy consumption and government expenditure are negatively related to low-carbon logistics efficiency.
Point 8: Conclusion section is not on a satisfactory level. It is not written in a scientific manner (Do not use bullets and numbering).
Response 8: Thank you very much for this valuable suggestion. It is important to written article in a scientific manner. As suggested, we have rewritten conclusion section and deleted the bullets and numbering.They are in lines 28-51 of page 32 and 1-53 of page 33.
Specific modifications:
7.Conclusion
The research of domestic scholars on the efficiency of low-carbon logistics in regions along the "Belt and Road" has been comprehensive, and it is found that although some scholars have focused on the provinces and cities along the "Belt and Road" in China, most of the research has focused on the overall analysis or the analysis of specific provinces and cities. However, this paper quantitatively analyzes the efficiency of logistics industry in 17 provinces and cities along the "Belt and Road" in China during the period of 2006-2020, to investigate the sustainable development level of logistics industry in general, regions and specific provinces and cities, and analyze the factors affecting the efficiency of logistics industry. The paper also analyze the key factors affecting the efficiency of logistics industry, and make corresponding suggestions based on the empirical results, in order to provide reference for the sustainable and coordinated development of logistics industry in the regions along the Belt and Road. The following conclusions are drawn from the empirical analysis of this paper:
According to the measurement results of carbon emissions from logistics industry along the route, from 2006 to 2020, the energy consumption and carbon dioxide emissions of 17 provinces and cities along the "Belt and Road" in China show an increasing trend year by year, while the energy intensity and carbon emission intensity show a decreasing trend, indicating that the green level of logistics industry in China has improved in recent years due to technological improvement and national policies. However, energy consumption and carbon emissions are still on the rise, and the environmental pressure brought by the logistics industry is still great, so there is still a long way to go to promote the green development of the logistics industry.
According to the static evaluation results of logistics industry efficiency along the route, from 2006 to 2020, the efficiency values of logistics industry in most provinces and cities along the route did not reach the effective state, which indicates that the overall low carbon logistics efficiency level of all provinces and cities along the "Belt and Road" in China is low. The average value of low-carbon logistics efficiency in the regions along the belt and road shows fluctuations, and since the pure technical efficiency is relatively stable, it can be seen that the main factor affecting the overall efficiency is the scale efficiency; from the regional point of view, the logistics industry efficiency in the four regions along the belt and road is ranked from high to low in the southeast, northeast, northwest and southwest, and the southeast has obvious advantages, while the other three regions are close to each other, indicating that a good economic and logistics industry foundation has a great influence on the efficiency of logistics industry; from the perspective of specific provinces and cities, the highest value of low-carbon logistics efficiency of provinces and cities along the route region is 1.0000, and the lowest value is only 0.0944, with large differences in logistics efficiency values among provinces, which illustrates the uneven development of logistics industry in provinces and cities along the route region from 2006 to 2020.
According to the results of the dynamic evaluation of the efficiency of the logistics industry in the regions along the route, on the whole, although there are slight fluctuations in each decomposition index of the average value of the MI index in the calendar years of the regions along the route from 2006 to 2020, the average values of MI, TC, EC, PEC and SEC in the regions along the route are still greater than 1, indicating that China's "One Belt, One Road The overall development of low-carbon logistics in the regions along the "Belt and Road" is positive. By region, the mean values of MI indexes of the regions along the Belt and Road are, in descending order, Southeast, Southwest, Northeast and Northwest, and the MI indexes are all greater than 1, indicating that the low-carbon logistics efficiency in all four regions shows an upward trend. The TC index of all four regions is higher than the EC index, which indicates that the growth of logistics industry efficiency in all four regions is mainly due to the progress of logistics technology, and also indicates that the four regions should focus on the improvement of regional scale efficiency when exploring the efficiency improvement path of logistics industry. From the perspective of specific provinces and cities, except for Qinghai, the average value of MI from 2006 to 2020 is greater than 1, and the highest of them is Fujian Province, which shows that most of the provinces and cities along the region have continued to grow in low-carbon logistics efficiency. The average value of TC index in Qinghai province is greater than 1 while the average value of PEC and SEC index is less than 1, indicating that the overall decline in logistics efficiency in Qinghai province is the result of the combination of scale efficiency and pure technical efficiency.
From the results of the study on the factors influencing the efficiency of the logistics industry in the regions along the Belt and Road, on the whole, four indicators, namely the level of economic development, energy consumption, industrial structure and government expenditure, have a significant effect on the low-carbon logistics efficiency in the regions along the Belt and Road. Among them, economic development level and industrial structure have a positive effect on low-carbon logistics efficiency, while energy consumption and government expenditure are negatively related to low-carbon logistics efficiency. By region, the level of economic development is negatively related to low-carbon logistics efficiency in the northeast, has a facilitating effect on low-carbon logistics efficiency in the northwest, and has no significant effect on low-carbon logistics efficiency in the southeast and southwest; energy consumption is positively related to low-carbon logistics efficiency in the northeast, has an adverse effect on low-carbon logistics efficiency in the southeast, and has no significant effect; industrial structure has a positive contribution to low carbon logistics efficiency in the northwest and southwest regions, and no significant effect for the northeast and southeast regions; government expenditure is negatively related to low carbon logistics efficiency in the northwest and southeast regions, and has no significant effect for the northeast and southwest regions.
Point 9: Clearly state your unique research contributions in the conclusion section. The authors need to clearly provide several solid future research directions (this confirms a bad relationship with the gaps in the literature).
Response 9: We found this suggestion is important. Therefore,in the conclusion section, we have added a paragraph to state your unique research contributions.In addition, we have pointed out the limitations and future research directions of this paper in the discussion section.They are in the lines 16-40 of page 32 in Word's " Track Changes " mode.
Specific modifications:
Conclusion Section(state contributions): The research of domestic scholars on the efficiency of low-carbon logistics in regions along the "Belt and Road" has been comprehensive, and it is found that although some scholars have focused on the provinces and cities along the "Belt and Road" in China, most of the research has focused on the overall analysis or the analysis of specific provinces and cities. However, this paper quantitatively analyzes the efficiency of logistics industry in 17 provinces and cities along the "Belt and Road" in China during the period of 2006-2020, to investigate the sustainable development level of logistics industry in general, regions and specific provinces and cities, and analyze the factors affecting the efficiency of logistics industry. The paper also analyze the key factors affecting the efficiency of logistics industry, and make corresponding suggestions based on the empirical results, in order to provide reference for the sustainable and coordinated development of logistics industry in the regions along the Belt and Road.
Discussion section(limitations and future research directions): However, our paper has the following limitations.In our paper, 17 provincial regions are selected as the research units, while in the actual study, there are certain differences in the development level of logistics industry within the same province, so the ideal research unit should be prefecture-level cities or county-level regions. However, due to the availability of data, it is difficult to collect the energy consumption in areas below the provincial level, and the provincial level is the most detailed research unit in both domestic and international studies. Therefore, it is hoped that in future research, we can enrich the relevant scientific data, change the research idea, find alternative and representative variables, further refine the research unit, and obtain more reliable and closer to the current life of the research conclusions, so as to provide theoretical guidance for the green development of regional logistics industry.
Point 10: Limitations of this research should be provided in the last section.
Response 10: This suggestion is important. We completely agree with your suggestion. As suggested, in the discussion section, we have pointed out the limitations and future research directions of this paper. They are on lines 16-26 of page 32 in Word's " Track Changes " mode.
Specific modifications:
However, our paper has the following limitations.In our paper, 17 provincial regions are selected as the research units, while in the actual study, there are certain differences in the development level of logistics industry within the same province, so the ideal research unit should be prefecture-level cities or county-level regions. However, due to the availability of data, it is difficult to collect the energy consumption in areas below the provincial level, and the provincial level is the most detailed research unit in both domestic and international studies. Therefore, it is hoped that in future research, we can enrich the relevant scientific data, change the research idea, find alternative and representative variables, further refine the research unit, and obtain more reliable and closer to the current life of the research conclusions, so as to provide theoretical guidance for the green development of regional logistics industry.
Point 11: Technical problems: use the instructions for the authors and a lot of blank pages.
Response 11: Thank you for your careful review.This suggestion is useful. As suggested, we have carefully read the instructions for the authors,and corrected lots of errors that inconsistent with the template.And the blank pages are deleted as well.
Point 12: Scientific and practical contributions must be explicitly stated.
Response 12: We found this suggestion is crucial.We added a discussion section which including scientific and practical contributions. They are in lines 34-53 of page 30 and lines 1-13 of page 31 in Word's " Track Changes " mode.
Specific modifications:
Scientific Contributions: From the perspective of low carbon economy, this paper responds to the strategy of "One Belt and One Road", selects 17 provinces and cities along the "One Belt and One Road" in China as the research object, and divides them into four regions, takes carbon emissions from logistics industry as non-expected output, measures the efficiency of logistics industry, and conducts comparative analysis in three dimensions: overall, regional and provincial, and gives suggestions for efficiency improvement, which helps to further clarify the concept of logistics efficiency, enriches the theoretical system of low carbon logistics efficiency and sustainable development to a certain extent, and improves the research content and method of logistics industry efficiency measurement.
Practical Contributions: With the rapid development of the "Belt and Road" construction, China's logistics industry is facing unprecedented favorable conditions, while the large gap in the development of the logistics industry among the provinces and cities along the route has caused certain impact on the development of cooperation between the regions. This paper considers the non-expected output indexes in the evaluation system, understands the difference of carbon emissions of logistics industry in each province and city along the route through empirical analysis, and evaluates the low-carbon logistics efficiency in the route from two perspectives: static and dynamic, which provides scientific and effective data support and theoretical basis for the low-carbon development of logistics industry along the "Belt and Road" route. It also provides a scientific and effective data support and theoretical basis for the low-carbon development of the logistics industry along the "Belt and Road", and provides a decision basis for the government to formulate regional emission reduction plans and implement carbon emission assessment system.From the study results, the development level of low-carbon logistics in each province along the "Belt and Road" in China is uneven, and the logistics efficiency of most provinces has not reached the optimal level, so there is great room for improvement and development potential. From 2006 to 2020, the overall low-carbon logistics efficiency of China's regions along the Belt and Road shows an upward trend. Among them, technological progress has a driving effect on the growth of low-carbon logistics efficiency, while the low scale efficiency and pure technical efficiency form a certain obstacle to the growth of low-carbon logistics efficiency.The level of economic development and industrial structure have positive effects on the improvement of low-carbon logistics efficiency in the regions along the route, while energy consumption and government expenditure are negatively related to low-carbon logistics efficiency.
Point 13: Suggested references:
Andrejić M., Kilibarda, M. Pajić, V., (2021). Measuring efficiency change in time applying malmquist productivity index: a case of distribution centres in Serbia. FACTA UNIVERSITATIS, Series Mechanical Engineering, 19 (3), 499-514.
Qin, W.; Qi, X. Evaluation of Green Logistics Efficiency in Northwest China. Sustainability 2022, 14, 6848. https://doi.org/10.3390/su14116848
Zhao, W.; Qiu, Y.; Lu, W.; Yuan, P. Input–Output Efficiency of Chinese Power Generation Enterprises and Its Improvement Direction-Based on Three-Stage DEA Model. Sustainability 2022, 14, 7421. https://doi.org/10.3390/su14127421
Response 13: We found this suggestion is important and useful. Reading through the studies gave us a deeper understanding, which was helpful in writing our paper. In addition, the results of these studies on sustainability fit well with our theme. Therefore, we have cited it. They are in the lines 31-33 of page 17 and lines 32-34 of page 28 in Word's " Track Changes " mode.
Specific modifications:
Each province has a high pure technical efficiency, but significant regional differences in comprehensive technical efficiency and scale efficiency are apparent[44,45].
The higher the efficiency of the logistics system, the greater the savings in regional logistics energy consumption can be achieved[47].
44.Qin, W.; Qi, X. Evaluation of Green Logistics Efficiency in Northwest China. Sustainability 2022, 14, 6848.
45.Zhao, W.; Qiu, Y.; Lu, W.; Yuan, P. Input–Output Efficiency of Chinese Power Generation Enterprises and Its Improvement Direction-Based on Three-Stage DEA Model. Sustainability 2022, 14, 7421.
47.Andrejić M., Kilibarda, M. Pajić, V. Measuring efficiency change in time applying malmquist productivity index: a case of distribution centres in Serbia. Series Mechanical Engineering, 2021, 499-514.
Point 14: English language and style are fine/minor spell check required.
Response 14: Thank you for this useful suggestion. As suggested, we have combed through the entire paper, and revised and corrected some expressions.
Thanks again to you for your careful review of our research paper during your busy schedules!
Round 2
Reviewer 1 Report
The authors made clear revisions to the manuscript. I have no other suggestions.
Reviewer 2 Report
The authors made a great effort. The corrections are in line with my suggestions.
