Pathways to Improve Energy Conservation and Emission Reduction Efficiency During the Low-Carbon Transformation of the Logistics Industry

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper deals with an interesting topic, but it is seemingly aimed at a very small and select group of readers -- the average reader of Sustainability will, like me, have very little idea what is going on here.  The troubles begin immediately:  "energy conservation performance" and "emission reduction performance" compared to what?  Controlling for what?  I would expect the industry composition of a province to be crucial to this discussion, but this topic is not even mentioned until section 5, and then only in passing.  I do not understand what I am supposed to take from Figure 2, what the results mean in Table 3 and the second Figure 2 (should this be 3?), what is fs-QCA, or why Tables 5 and 6 lead to discussions of the 4 models.  All in all, this paper seems to reflect a lot of careful analysis, but most Sustainability readers will understand very little.

Minor points:

The issue regarding increased efficiency leading to greater consumption (around footnote 4) could be expanded upon.  If society wants to discourage consumption, the price mechanism is highly recommended.

The discussion at the end of section 2 suggests the relevance of the "environmental Kuznets curve" literature, which is not mentioned.

Comments on the Quality of English Language

The quality of the English is pretty good; nevertheless it could use one more round of editing by a native speaker.

Author Response

1. ["energy conservation performance" and "emission reduction performance" compared to what?  Controlling for what?]
   Response: Thank you for your insightful questions. In our study, energy conservation performance and emission reduction performance are evaluated relative to the optimal efficiency frontier derived from the NDDF-DEA model. This allows us to compare the performance of different provinces in minimizing energy consumption and reducing emissions while maintaining logistics output. We have rewritten the abstract section so that readers can better understand the article. “As the green transformation of the logistics industry continues to deepen, logistics enterprises are adopting emission reduction technologies to lower carbon emissions due to the industry's high carbon footprint. However, inefficient energy utilization may lead to a rebound effect, increasing overall energy consumption. Based on the NDDF-DEA method, this paper develops an efficiency measurement model that incorporates energy structure, denoted as F1, and compares its performance with traditional green logistics efficiency models. Given that energy conservation and emission reduction are central to the industry's green transition, this study further examines their impact on the sustainable development of logistics using the newly constructed efficiency measurement model.”
2. [I would expect the industry composition of a province to be crucial to this discussion, but this topic is not even mentioned until section 5, and then only in passing.]
    Response: Thank you for your thoughtful feedback. We've added a talk about industrial structure in section 2. As follows, “The industrial structure has a significant impact on logistics demand and industry development. As industrial structures optimize and upgrade, production methods and technologies continue to evolve, leading to changes in logistics demand. The transition from traditional manufacturing to high-tech industries and the expansion of the service sector reshape logistics patterns, promoting more efficient and environmentally friendly logistics solutions. In regions dominated by heavy industries, logistics tends to focus on bulk transportation and raw material supply chains, whereas areas with a higher share of the tertiary industry require more advanced, service-oriented logistics solutions. The proportion of the tertiary industry’s output value in the total regional output value serves as a key indicator for measuring IS.”
3. [I do not understand what I am supposed to take from Figure 2, what the results mean in Table 3 and the second Figure 2 (should this be 3?), what is fs-QCA, or why Tables 5 and 6 lead to discussions of the 4 models.]
   Response: Thank you for your feedback. We apologize for the numbering error—the reference to the "second Figure 2" should indeed be Figure 3. Figure 2 analyzes the relationship between various dimensions and the logistics industry from the TOE perspective, serving as the basis for selecting appropriate indicators in the fs-QCA analysis. Table 3 compares the logistics efficiency results obtained from our proposed efficiency measurement model with those from traditional models, highlighting the advantages of incorporating energy structure considerations. Figure 3 illustrates the differences between energy conservation performance and emission reduction performance, providing further insights into their inconsistencies.We have added an explanation of fs-QCA in the text. As follows,” Fuzzy-set qualitative comparative analysis (fs-QCA) is an effective method that integrates both qualitative and quantitative approaches, making it well-suited for examining the effects of different strategies across multiple influencing factors”.

   In our study, the fs-QCA method follows five key steps:

   Sample Selection – Identifying core conditions influencing logistics efficiency based on theoretical and empirical considerations.

   Calibration – Converting raw data into fuzzy-set membership scores (ranging from 0 to 1) using predefined thresholds.

   Necessity Analysis – Examining whether any single condition is necessary for high logistics efficiency.

   Sufficiency Analysis – Identifying combinations of conditions that are sufficient to achieve high efficiency using truth table analysis.

   Robustness Testing – Checking the stability of results by adjusting calibration thresholds and testing alternative model specifications.

4. [The issue regarding increased efficiency leading to greater consumption (around footnote 4) could be expanded upon. If society wants to discourage consumption, the price mechanism is highly recommended.]
   Response: Thank you for your insightful comment. The section around footnote 4 aims to highlight the potential inconsistency between energy conservation and emission reduction efforts. Since this paper focuses on green logistics efficiency and does not address price mechanisms, we have not included discussions related to pricing strategies. We appreciate your suggestion and will consider it for future research.
5. [The discussion at the end of section 2 suggests the relevance of the "environmental Kuznets curve" literature, which is not mentioned.]
   Response: Thank you for your helpful comment. You are correct that the manuscript touches on the concept of the "Environmental Kuznets Curve." We have expanded the discussion on this topic and included relevant references to provide a clearer explanation and strengthen the argument.

 

 

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

1. The abstract must contain: objective, methodology and results found. These items are not clearly stated in the article, especially the objective.
2. 2. The concluding chapter must include a discussion in which the results of this research will be compared with the results of previous studies dealing with the same issue. The limitations of the methodology must be explicitly stated in the conclusion of the work.

3.Compared to the existing literature, what are the empirical findings of this paper different and the same with them?

4. 4. The content of the policy recommendations is too Chinese and does not reflect the global perspective. And it is not concise enough, it is recommended to further condense.

5.The manuscript would benefit greatly from thorough proofreading to address numerous language-related issues and typos.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

1. [The abstract must contain: objective, methodology and results found. These items are not clearly stated in the article, especially the objective.]
   Response: Thank you for your advice. We changed the abstract and focused it to reflect the purpose of the study. “Therefore, this study aims to investigate the consistency between energy conservation and emission reduction in the logistics industry to provide insights for the green transformation of the logistics industry.”
2. [The concluding chapter must include a discussion in which the results of this research will be compared with the results of previous studies dealing with the same issue. The limitations of the methodology must be explicitly stated in the conclusion of the work.]
   Response: Thank you for your thoughtful review. To address this, we discuss the differences from previous studies in Section 6.1 and have added a new section at the end as described in Section 6.3. (e.g., “Despite its contributions, this study has certain limitations. First, while it analyzes the impact of energy structure changes on logistics efficiency from a macro perspective, it does not provide precise quantitative values for specific energy structure adjustments. Second, although the fs-QCA provides valuable insights into different pathways for improving green logistics efficiency, its results are highly dependent on the selection of conditions and thresholds.”)
3. [Compared to the existing literature, what are the empirical findings of this paper different and the same with them?]
   Response: Thank you for your careful reading and insightful suggestions. We review the literature of previous similar studies and summarize their empirical results in Table 1. The detailed conclusions of this paper are presented and the similarities and differences between the empirical results of this paper and previous studies are highlighted in Section 6.1. (e.g., “Consistent with previous studies on provincial logistics efficiency in China, this paper finds that the eastern regions performed better than the central and western regions. Earlier studies integrated energy conservation and emission reduction into logistics efficiency as a single measure. In contrast, this paper differentiates between ECP and ERP.”)
4. [The content of the policy recommendations is too Chinese and does not reflect the global perspective. And it is not concise enough, it is recommended to further condense.]
   Response: Thank you very much for your valuable feedback. This study is based on empirical research in the provincial areas of China. However, we have revised the policy recommendations in section 6.2 to incorporate a global perspective and improve conciseness.
5. [The manuscript would benefit greatly from thorough proofreading to address numerous language-related issues and typos.]
   Response: Thank you for your suggestion. We have carefully proofread the manuscript and corrected language issues and typos to improve clarity and readability.

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript presents a valuable analysis of energy conservation and emission reduction efficiency in China's logistics industry, employing NDDF and fs-QCA methodologies. The following clarification will enhance the quality of the article.

1. Strengthen the justification for applying the TOE framework to logistics. Explicitly link technological, organizational, and environmental factors to the logistics context.
2. Fix minor errors (e.g., "Consist" vs. "Consistency" in Table 5, inconsistent use of "DMU" vs. "DMUs").
3. Ensure all figures (e.g., kernel density plots) are clearly labeled and described. Cross-check table numbering (e.g., Table 3 references "M1" and "M2" but labels are inconsistent in the text). Check specially 2017 to 2021.
4. How do choose 30 provinces? Clarify how the analysis of 30 provinces aligns with best practices for this method.
5. Elaborate on how adjustments to case frequency, PRI consistency, and calibration affected results. Include sensitivity analysis tables or appendices to demonstrate stability.
6. Standardize abbreviations (e.g., "Cons" is undefined in some sections) and citation formats. 

Comments on the Quality of English Language

Engage a professional proofreader to address grammatical inconsistencies and improve flow. 

Reduce jargon (e.g., "weak disposability") or define terms explicitly for non-specialist readers.

Ensure uniform formatting, abbreviations, and citation styles.

The manuscript is academically sound but requires minor revisions to polish language mechanics and enhance readability

Author Response

1. [Strengthen the justification for applying the TOE framework to logistics. Explicitly link technological, organizational, and environmental factors to the logistics context.]
   Response: We sincerely appreciate your insightful feedback, which has helped us refine our analysis. We have modified the description of the means in Section 2 to analyze the linkage between the TOE framework as a whole and the logistics industry. In addition, we added separate dimensions to our subsequent analysis related to the logistics industry. As follows,” In China's traditional logistics industry, short-term economic interests take precedence over environmental concerns. The logistics system is a complex structure with multiple inputs and outputs. However, previous studies have primarily adopted linear or single factor approaches, failing to capture the complex interdependencies. Thus, based on Technology-Organization-Environment (TOE), we present a configuration framework to find influencing factors about the consistency of energy conservation and emission reduction in the logistics industry, as shown in Figure 2. The TOE framework integrates technological, organizational, and environmental to drive sustainable development in the logistics industry. Using fs-QCA, we identify configurational pathways for the sustainable development of the logistics industry, ultimately supporting its sustainable development.”
2. [Fix minor errors (e.g., "Consist" vs. "Consistency" in Table 5, inconsistent use of "DMU" vs. "DMUs").]
   Response: Thank you for your careful review. We have corrected these minor errors, ensuring consistency in terminology throughout the manuscript.
3. [Ensure all figures (e.g., kernel density plots) are clearly labeled and described. Cross-check table numbering (e.g., Table 3 references "M1" and "M2" but labels are inconsistent in the text). Check specially 2017 to 2021.]
   Response: Thank you for your valuable feedback. We have added more detailed explanations for the figures and tables and corrected previous inconsistencies to improve clarity and accuracy.
4. [How do choose 30 provinces? Clarify how the analysis of 30 provinces aligns with best practices for this method.]
   Response: Thank you for your insightful comment. “The selection of the study area is consistent with previous logistics efficiency research, and the sample size of 30 provinces meets the methodological applicability.”” Due to the lack of data, the analysis of Xizang, Hong Kong, Macao and Taiwan is not included.” This approach is consistent with previous studies using similar methods to ensure comparability and representativeness in the analysis.].
5. [Elaborate on how adjustments to case frequency, PRI consistency, and calibration affected results. Include sensitivity analysis tables or appendices to demonstrate stability.]
   Response: Thank you for your valuable comments and suggestions. We have added a related description in Section 5.5, as follows “Increasing the case frequency makes the findings more conservative, ensuring that only well-supported configurations are retained. Lowering PRI consistency includes more causal paths and Variations in calibration thresholds influence the final configuration path.” We have added Table 7 to show the results of the Robust analysis.
6. [Standardize abbreviations (e.g., "Cons" is undefined in some sections) and citation formats.]Response: Thank you for your suggestion. We have standardized abbreviations throughout the manuscript and ensured consistency in citation formats. We have reviewed the Abbreviations at the end of the article to add the missing items.
7. [Reduce jargon (e.g., "weak disposability") or define terms explicitly for non-specialist readers. Ensure uniform formatting, abbreviations, and citation styles.]Response: Thank you for your valuable feedback. We have removed the term "weak disposability" and replaced it with a more straightforward explanation to ensure that the content is accessible to non-specialist readers (e.g., Non-radial DEA introduces relaxation variables to assess efficiency more comprehensively, which effectively overcome this challenge). Additionally, we have ensured uniform formatting, consistent use of abbreviations, and adherence to citation styles throughout the manuscript.

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I appreciate the authors' attention to my previous comments.  The paper is improved.  However, I still have 3 concerns.

First, what exactly is the overall goal here?  The paper title refers to "energy conservation" and "pollution reduction efficiency".  But then the abstract refers to "energy efficiency", "energy saving efficiency", and "logistics efficiency".  If I understand correctly, the goal is to measure "logistics efficiency", which is defined as one minus ECP (Energy Conservation Performance) minus ERP (Emission Reduction Performance), the latter two measured controlling for industrial output and with the "desirable" output freight volume.  I do not see any background or theoretical discussion as to why maximizing this measure of efficiency is more socially desirable than one that, for example, weighted ECP and ERP differently.

(An aside:  As I think I hinted at in my earlier report, I regard the sentence in the abstract reading "However, inefficient energy utilization may lead to a rebound effect, increasing overall energy consumption" as incomplete or at least misleading.  Increased energy efficiency, by lowering costs, my lead to increasing overall energy consumption as well.  This is why a better path to sustainability may be via energy (and pollution) prices, not standards.)

Second, I am not persuaded that the energy and emission measures are particularly accurate, or useful.  As I understand it, province-level consumption of various energy sources are converted into coal-equivalents through some (unspecified) "conversion factors".  I assume these conversion factors are based on something like average BTU content.  But these different energy sources have vastly different emissions:  fuel oil much higher than coal, natural gas much lower than coal.  (By the way, is there no hydro or wind power?)  So BTU-based conversion factors would not be useful in converting coal equivalents into carbon emissions via the IPCC 2006 reports.  If my understanding is wrong, the paper needs a more detailed explanation of the creation of the emissions variable.  If my understanding is right, this mis-measure of the crucial dependent variable may reduce the contribution of the paper.

Finally, on a lesser point:  How do the "managerial implications" at the end of the paper depend on the empirical results that precede them?  Could one have made these recommendations without the empirical exercise, or with different results?

Author Response

Comment1: The abstract should better emphasize the relationship between energy conservation, emission reduction, and overall logistics efficiency. The terminology should also be simplified for clarity.

Response1:

We thank the reviewer for pointing out this important issue. In response, we have thoroughly revised the abstract to reduce the use of technical terminology and better clarify the relationship between logistics industry green transformation and logistics efficiency. In industrial sectors, it is recognized that emission-reduction devices often increase energy consumption, leading to inconsistencies between energy conservation and emission reduction. Focusing solely on one aspect usually results in suboptimal overall efficiency. This phenomenon has been empirically validated in the study titled “Consistency and Influencing Factors of Industrial Energy Efficiency and Emission Reduction Efficiency in China.”

Motivated by this insight, our study aims to examine whether such inconsistency exists in the logistics sector during its green transformation, and whether pursuing only energy conservation or emission reduction can lead to high overall efficiency. To capture this, we introduced a consistency index calculated as Cons = 1 – (ECE – ERE), where ECE and ERE represent energy conservation efficiency and emission reduction efficiency, respectively. A Cons value closer to 1 indicates higher consistency between the two, corresponding to higher overall efficiency—consistent with the logic of DEA efficiency where 1 is optimal. (For clarity, we have renamed the original ECP and ERP indicators to ECE and ERE.)

By comparing overall logistics efficiency with ECE and ERE, we found that when the two are aligned, the overall efficiency is optimal. In contrast, greater inconsistency leads to significantly lower logistics efficiency. These findings support the argument that evaluating green transformation in the logistics sector requires a dual focus on both energy conservation and emission reduction.

Additionally, in response to the reviewer’s concern regarding price-related variables, we would like to clarify that product price can be used as either an input or output variable in evaluating efficiency or cost-effectiveness, but it is not a core component of the DEA methodology. The essence of DEA lies in measuring efficiency through the ratio of inputs to outputs, and price is merely one of the many possible variable selections.

Comment2: Please provide more details on how energy consumption and carbon emissions were calculated. Also, clarify why clean energy sources such as hydropower and wind energy were not included.

Response:

Thank you for this insightful comment. We have revised the manuscript to provide more detailed explanations.

Energy Consumption. Energy consumption was converted using standard coal conversion coefficients published in the China Energy Statistical Yearbook. These coefficients have been included in the updated version of Table 2 to ensure transparency.

Carbon Emissions: Carbon emissions were estimated following the methodology outlined by the IPCC (2006), which is widely adopted in the literature. The emission formula and necessary factors, including calorific value, oxidation rate, and carbon content, are clearly presented in the revised manuscript. The emission factor (EF) is calculated as the product of these parameters for each energy type, and the results are shown in Table 2.

Clean Energy: As this study focuses on the provincial-level logistics industry in China, we relied on statistical data that do not separately report clean energy usage (e.g., hydropower, wind, or solar energy) for this sector. Therefore, these sources were not included in our carbon emission calculations.

Comment3: Please clarify how the managerial implications are derived from your analysis.

Response:

We appreciate the reviewer’s attention to the practical implications of our research. The managerial implications in our study are directly drawn from the results of the fuzzy-set Qualitative Comparative Analysis (fs-QCA).

The fs-QCA method combines qualitative and quantitative analysis, identifying multiple, equifinal causal configurations that lead to high logistics efficiency under green transformation. This approach allows us to analyze both successful and unsuccessful cases, derive specific transformation pathways, and isolate key factors associated with efficiency gains or losses. The results offer practical guidance for policymakers and logistics managers to formulate differentiated, condition-specific strategies to promote sustainable development in the logistics industry.

Reviewer 2 Report

Comments and Suggestions for Authors

The paper meets the publication standards of the journal

Author Response

Comments:The paper meets the publication standards of the journal.

Respond：Thank you very much for your positive evaluation and kind recognition that the paper meets the publication standards of the journal.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

                This paper is clearly written and clearly reflects a great deal of both expertise and difficult, detailed work on the part of the authors.  Nevertheless it is difficult to analyze the efficiency or effectiveness of a province’s pollution control regime without data on pollution.  The authors try to get around this lack by a careful transformation of data on usage of different sources of energy (e.g. coal, natural gas, fuel oil) with detailed generalized (i.e. non-Chinese) calculations by a United Nations agency of energy efficiency and emissions associated with each energy source (a couple of decades old, but perhaps the best available).  However, they cannot entirely escape the implications of their lack of emissions data.  For example, suppose enterprises in one province have invested a great deal more than others in clean coal technology, scrubbers, or other forms of emission control equipment.  This would show up in the capital stock estimates used in the paper but not in the measure of emissions – one of the two crucial variables under examination.  Despite the expertise and hard work of the authors, I see no good way out of this conundrum.

Smaller points:

What is the difference between ECERE and Cons?  How does NDDF measure Non-ECERE without measuring ECE and ERE separately?  And, as I believe I asked in my earlier review, what makes ECERE a particularly good measure of “sustainability”, as is claimed repeatedly in the paper?  It seems to assume equal weights are appropriate for ECE and ERE in an ultimate index, but what is the justification for that?

Figures 1 and 2 don’t seem to me to contribute much to the paper.

Is not better proxy for GS available?  Is there no data on details of province-level government expenditures?

The summary of the Environmental Kuznets Curve seems to me to be not quite right.  I would rewrite as “On the other hand, developing regions at advancing past the beginning of the EKC focus on controlling costs and providing basic services, with environmental concerns being less important.”

As I believe I noted in my earlier review, it seems to me that the “Managerial implications” section of the Conclusions could have been – and perhaps was – written before or independent of the analysis presented by the paper.

Author Response

Q1: This paper is clearly written and clearly reflects a great deal of both expertise and difficult, detailed work on the part of the authors. Nevertheless it is difficult to analyze the efficiency or effectiveness of a province’s pollution control regime without data on pollution.  The authors try to get around this lack by a careful transformation of data on usage of different sources of energy (e.g. coal, natural gas, fuel oil) with detailed generalized (i.e. non-Chinese) calculations by a United Nations agency of energy efficiency and emissions associated with each energy source (a couple of decades old, but perhaps the best available).  However, they cannot entirely escape the implications of their lack of emissions data.  For example, suppose enterprises in one province have invested a great deal more than others in clean coal technology, scrubbers, or other forms of emission control equipment.  This would show up in the capital stock estimates used in the paper but not in the measure of emissions – one of the two crucial variables under examination.  Despite the expertise and hard work of the authors, I see no good way out of this conundrum.

Answer：Thank you so much for your valuable advice. But we should make some explanations.

Firstly, the logistics activities are involved in multiple stages of the supply chain, and carbon emission data is difficult to obtain, especially as there is no official statistical data on carbon emissions from provincial-level logistics industries.

Secondly, the proposed method in this paper remains widely used in emission accounting and is recognized in China's logistics industry research. Furthermore, recent papers published in Sustainability have also adopted this method for their carbon emission calculations. e.g. Guo, Y.; Wu, X.; Ding, H.; Tian, Z. Spatial Influence of Digital Economy on Carbon Emission Efficiency of the Logistics Industry across 30 Provinces in China. Sustainability 2024, 16, 8086. https://doi.org/10.3390/su16188086.

Ye, C.; Chen, N.; Weng, S.; Xu, Z. Regional Sustainability of Logistics Efficiency in China along the Belt and Road Initiative Considering Carbon Emissions. Sustainability 2022, 14, 9506. https://doi.org/10.3390/su14159506.

Gan, W.; Yao, W.; Huang, S. Evaluation of Green Logistics Efficiency in Jiangxi Province Based on Three-Stage DEA from the Perspective of High-Quality Development. Sustainability 2022, 14, 797. https://doi.org/10.3390/su14020797.

The study has not accounted for the impact of clean technology investment costs on total expenditures. We acknowledge that by aggregating all fixed assets as capital inputs without distinguishing whether they stem from technological investments or other contributing factors.

Thirdly, based on NDDF-DEA model, we constructed a new model to investigate the impact of energy, labor, capital inputs, and CO₂ emissions on contemporaneous technological advancement and managerial efficiency within each decision-making unit (DMU). Further, the findings determine whether such production practices can achieve more improvements in technology and efficiency.

Q2:What is the difference between ECERE and Cons?  How does NDDF measure Non-ECERE without measuring ECE and ERE separately?  And, as I believe I asked in my earlier review, what makes ECERE a particularly good measure of “sustainability”, as is claimed repeatedly in the paper?  It seems to assume equal weights are appropriate for ECE and ERE in an ultimate index, but what is the justification for that?

Answer：Thank you so much for your valuable advice.

(1) "ECERE" and "Cons" are shown in the table below:

Index	Formula	Meaning		Calculation method	Direction Vector
ECERE	1-Non-ECERE	Non-ECERE is the combined proportion of compressible energy input and carbon emissions.	ECERE represents the overall logistics efficiency.	Non-ECERE is the objective function value of Model M2.	The directional vector for Non-ECERE is g = (0,0,-gE1,-gE2,-gE3,0,0,-gC).
Cons	1-(ECE-ERE)	Non-ECE represents the compressible proportion of energy inputs.	ECE=1- Non-ECE. ECE represents the energy-saving efficiency.	Non-ERE is the objective function value of Model M3.	The directional vector for Non-ERE is g=(0,0,-gE1,-gE2,-gE3,0,0,0)
		Non-ERE represents the compressible proportion of carbon emissions.	ERE=1- Non-ERE. ERE represents the emission-reduction efficiency.	Non-ECE is the objective function value of Model M4.	The directional vector for Non-ECE is g=(0,0,0,0,0,0,0,-gC)

(2) In previous literature, it was typically assumed that ECERE equals Cons, and the NDDF model was used to directly assess logistics efficiency based on this assumption. However, in practical operations, achieving emission reduction inevitably leads to additional energy consumption, indicating that the relationship between energy-saving and emission-reduction efficiencies is more complex. Therefore, we separately measure ECE and ERE to investigate their interplay, and further explore the relationship between ECERE and Cons. We have also revised the Introduction section of the main text accordingly to clarify this point for better alignment with the reviewer's suggestion.

(3) ECERE measures the green efficiency of the logistics industry, and green efficiency is closely linked to sustainability. This relationship has been supported in previous studies, such as " Ye, C.; Chen, N.; Weng, S.; Xu, Z. Regional Sustainability of Logistics Efficiency in China along the Belt and Road Initiative Considering Carbon Emissions. Sustainability 2022, 14, 9506. https://doi.org/10.3390/su14159506.", which emphasizes that improvements in green logistics efficiency are critical for promoting logistic sustainability. We have provided further explanation in the main text and cited this paper.

(4) Assigning equal weights to ECE and ERE reflects an assumption of balance between the two objectives, indicating that neither energy saving nor emission reduction is prioritized over the other. This approach ensures a neutral evaluation framework and avoids introducing bias toward either aspect. We have also added an explanation in the main text.

Q3:Figures 1 and 2 don’t seem to me to contribute much to the paper.

Answer：We sincerely thank the reviewer for the valuable comments. We have accepted the suggestion and removed Figure 1 from the manuscript. Originally, Figure 1 was intended to illustrate the flow of energy throughout the logistics chains in industry and agriculture.

Figure 2(Now called Figure 1) presents the theoretical framework based on the Technology-Organization-Environment (TOE) model, which underpins the empirical design of the fs-QCA analysis in this paper. It visually clarifies the rationale behind the selection of explanatory variables, such as the level of green technology and government support, thus enhancing the theoretical rigor of the configurational analysis. Without this figure, the connection between the TOE framework and the empirical method would be less transparent to the reader. So we still keep the figure. At the same time, we have revised the main text to clearly highlight the contribution of Figure 2.

Q4:Is not better proxy for GS available?  Is there no data on details of province-level government expenditures?

Answer：Thank you for the insightful question. Currently, there are limitations in the availability of provincial data in China. The government support (GS) data used in this study are based on publicly available sources, which are consistent with the existing data disclosure standards at the provincial level.

Moreover, most related studies have adopted similar approaches, relying on aggregated government expenditure data as proxies for GS. This practice ensures comparability and consistency across provinces, as evidenced by the approach used in the study cited in [28] of the main text. This reference has been cited in the revised manuscript to clarify the methodological consistency."

Q5:The summary of the Environmental Kuznets Curve seems to me to be not quite right.  I would rewrite as “On the other hand, developing regions at advancing past the beginning of the EKC focus on controlling costs and providing basic services, with environmental concerns being less important.”

Answer: Thank you for pointing out the issue. We agree with the reviewer’s comment and have adopted the suggested revision. The description regarding the stages of development and the prioritization of environmental concerns has been corrected to better align with the theoretical logic of the Environmental Kuznets Curve (EKC).

Q6: As I believe I noted in my earlier review, it seems to me that the “Managerial implications” section of the Conclusions could have been – and perhaps was – written before or independent of the analysis presented by the paper.

Answer: Thank you for this valuable comment. We agree with the reviewer’s observation and have revised the “Managerial Implications” section accordingly. The updated version is now more closely aligned with the analysis and findings presented in the paper, ensuring that the implications are clearly derived from our empirical results. The revised content is as follows：

1. Infrastructure-driven model based on digital integration. For regions characterized by the synergy between logistics infrastructure and digital integration (corresponding to the H1), it is essential to advance both physical and virtual networks simultaneously. Intelligent warehousing systems and smart transportation management systems are adopted to progress the intelligence level from storage to distribution. These measures enable real-time resource monitoring and optimized routing, which collectively improve energy efficiency and reduce emissions throughout logistics processes.
2. Digital economy-driven model based on green innovation. For regions primarily driven by the digital economy (corresponding to H2), the integration of digital innovation with green logistics must be deepened. Cloud computing, artificial intelligence, and blockchain technologies should be applied to develop collaborative platforms that strengthen the coordination between digital infrastructure and logistics operations. Embedding green technologies into these digital platforms enables effective carbon tracking across the supply chain, fostering a development model defined by green innovation and digital leadership.
3. Industrial economy-driven model based on clean energy. For regions grounded in industrial economies (corresponding to H3a and H3b), clean energy resources should be leveraged to drive cross-sector integration between logistics and core industries. Substituting conventional fuels with clean energy sources will accelerate the decarbonization of industrial supply chains. Establishing a fully integrated ecosystem across sectors enhances material circulation, improves system efficiency, and supports the coordinated green transformation of upstream and downstream enterprises.
4. Customized policy model based on regional advantages. For regions achieving high consistency primarily through government support (corresponding to H4), policy frameworks should be tailored to regional development characteristics. Governments should provide targeted incentives, expand green investment, and support technological upgrading to guide logistics transformation. These interventions will strengthen green infrastructure, encourage the adoption of clean technologies, and boost regional logistics resilience and sustainability.