Green Transformation of Enterprises and the Bullwhip Effect: Empirical Evidence from Listed Companies in China
Round 1
Reviewer 1 Report (Previous Reviewer 4)
Comments and Suggestions for AuthorsThe article “Towards a Paradigm of Proximity Economy for Competitive and Resilient Cities and Territories” conceptualizes the Proximity Economy as a human-centered development model based on short value chains, local production, and social cohesion, aiming to enhance resilience and sustainability in response to global crises.
Despite its conceptual richness, the article lacks empirical validation, overrelies on policy narratives, and fails to critically engage with the trade-offs and practical constraints of implementing proximity-based models across diverse territorial contexts.
The introduction outlines the urgency of new economic paradigms but relies heavily on policy rhetoric without providing a clear research question or methodological approach. It frames proximity as a solution to multiple crises yet lacks empirical justification for its systemic feasibility or priority over other alternatives.
The multidimensional definition of proximity (geographical, cognitive, social, institutional) is thorough but descriptive. It fails to engage critically with tensions among dimensions or the limits of their co-existence. The section reads more like a theoretical overview than a conceptual framework with testable propositions.
While the strategic objectives (sustainability, resilience, quality of life) are compelling, they are asserted rather than demonstrated. The policy examples (e.g., the EU Green Deal, Cyprus circular economy plan) are well-selected but remain illustrative, lacking comparative evaluation or evidence of effectiveness. The discussion misses nuance on policy transferability across regions.
The contrast between urban and rural proximity applications is insightful, but the section overlooks infrastructural and institutional constraints, especially in under-resourced rural regions. The historical reflection is informative but lacks engagement with contemporary critiques of localized development (e.g., equity, efficiency, scalability).
Economic, environmental, and social contributions are detailed, but they are largely hypothetical or drawn from secondary data. The impact indicators proposed are useful but not applied, making the framework more aspirational than actionable. Trade-offs (e.g., cost, scalability, inequality) are acknowledged only superficially.
The case studies (OpenDot, Grööntüügs, Križevci Solar Roofs, Materialenbank) are rich in narrative but weak in analytical depth. They showcase local innovations without providing comparative benchmarks, replicability assessments, or critical discussion of failures and barriers. The reliance on anecdotal successes risks idealizing the model.
The conclusion reiterates potential benefits without addressing structural tensions, such as globalization vs. localization or standardization vs. specificity. The policy trade-offs section is conceptually sound but lacks prioritization or actionable guidance for policymakers. The integration with EU-level frameworks is presented more as an aspiration than a practical roadmap.
Author Response
Q1: The article “Towards a Paradigm of Proximity Economy for Competitive and Resilient Cities and Territories” conceptualizes the Proximity Economy as a human-centered development model based on short value chains, local production, and social cohesion, aiming to enhance resilience and sustainability in response to global crises. Despite its conceptual richness, the article lacks empirical validation, overrelies on policy narratives, and fails to critically engage with the trade-offs and practical constraints of implementing proximity-based models across diverse territorial contexts.
Response：
Thank you very much for taking the time to review our manuscript and for your valuable efforts in providing feedback. We greatly appreciate your professional input and the attention dedicated to our work.
After carefully reading the review comments, we noticed that the content primarily focuses on the concept of the “Proximity Economy,” which appears to be unrelated to the subject of our submitted manuscript, titled “Green Transformation of Enterprises and the Bullwhip Effect: Empirical Evidence from Listed Companies in China.”
We kindly and respectfully suggest that there may have been an error during the review upload process. We sincerely appreciate your time and understanding, and we look forward to receiving the appropriate feedback relevant to our submission.
Author Response File: 
Author Response.pdf
Reviewer 2 Report (Previous Reviewer 3)
Comments and Suggestions for AuthorsThis manuscript aims to examine the relationship between green transformation and the bullwhip effect. It will investigate whether green transformation can effectively mitigate the bullwhip effect and identify the specific pathways and mechanisms through which green practices influence this phenomenon. In doing so, this study will provide valuable insights for enterprises to make informed decisions regarding their green transformation efforts and offer recommendations to policymakers for designing more targeted strategies to support supply chain stability and sustainable development. In more details, this study utilizes data from Chinese A-share listed enterprises from 2008 to 2022, employing a multiple linear regression model alongside robustness and endogeneity tests to investigate the mechanism through which corporate green transformation alleviates the bullwhip effect. The empirical results indicate that a one-unit increase in the green transformation leads to a significant 0.073-unit reduction in the bullwhip effect. Mechanism analysis further reveals that green transformation mitigates the bullwhip effect by enhancing supply chain information sharing, strengthening organizational resilience, and improving managerial effectiveness. Heterogeneity analysis shows that the impact of green transformation on the bullwhip effect varies significantly depending on ownership structure and industry characteristics.
The manuscript, in general, is well written. It could be better organized, especially the tables. The results seem original, and the methods used seem suitable. The introduction section describes and contextualizes the present work with the previous research on the topic in a suitable way. I suggest adding a section of discussion to discuss the results in brief before the conclusion section. Also, the authors are advised to eliminate the unnecessary references that are not strongly related to the current work.
Author Response
Q1: The manuscript, in general, is well written. It could be better organized, especially the tables. The results seem original, and the methods used seem suitable. The introduction section describes and contextualizes the present work with the previous research on the topic in a suitable way. I suggest adding a section of discussion to discuss the results in brief before the conclusion section. Also, the authors are advised to eliminate the unnecessary references that are not strongly related to the current work.
Response：
(1) Improve the organization of the tables
We have optimized the table content by uniformly presenting control variables as "Controls." These modifications enhance the clarity and readability of the tables while preserving the integrity of the information presented.
(2) Add a brief discussion section before the conclusion
We sincerely thank the reviewer for their valuable suggestion. In response, we have added a new discussion section (Section 5.1) that provides a deeper reflection on the study's findings and offers a broader interpretation of the results.
The added discussion section elaborates on the novel contributions of this study in linking corporate green transformation with the bullwhip effect, particularly emphasizing the role of green transformation in mitigating supply chain volatility. We have identified three key mediating mechanisms—improved information sharing, enhanced organizational resilience, and more efficient management practices—that contribute to the reduction of the bullwhip effect. Additionally, we discuss the heterogeneity of the impact of green transformation across different organizational contexts, with a focus on the moderating effects of ownership type and capital intensity.
In addition, we have enhanced the discussion regarding the 4.2.Baseline Regression and 4.5.Pathway Mechanism Testing. These additions further strengthen the manuscript's persuasiveness and logical coherence, ensuring a more comprehensive interpretation of the results.
The relevant modifications can be found in section “5.1.Discussion”.
5.1.Discussion
This study enhances the understanding of the impact of corporate green transformation on supply chain dynamics, specifically focusing on its role in mitigating the bullwhip effect. While much of the existing literature has explored the causes and mitigation strategies of the bullwhip effect, this research is one of the few to examine the potential stabilizing role of green transformation. The findings indicate that green transformation, as proxied by green invention patents, can reduce the bullwhip effect by improving coordination, enhancing the efficiency of information transmission, and fostering more adaptive supply chain responses under uncertain conditions.
The study identifies three key mediating mechanisms through which green transformation influences the bullwhip effect: improved information sharing, enhanced organizational resilience, and more efficient management practices. These mechanisms operate independently, allowing firms to target specific aspects of green transformation to strengthen their supply chain stability. This extends the existing literature, which has largely concentrated on the economic or reputational benefits of green transformation, by emphasizing its role in fostering systemic coordination within supply chains.
Additionally, the research highlights the importance of contextual factors, particularly the moderating effects of ownership type and capital intensity. The results suggest that the effectiveness of green transformation varies across different types of organizations. State-owned enterprises and capital-intensive firms, for instance, may be better equipped to leverage green innovations due to greater institutional resources and absorptive capacities. These insights provide a more nuanced view of how organizational context influences the strategic value of green transformation, enriching the ongoing discourse on heterogeneity in environmental practices.
From a practical standpoint, the findings offer valuable implications for both corporate managers and policymakers. Enterprises, particularly those in complex supply networks, should view green transformation not only as a compliance measure but as a strategic tool to reduce supply chain volatility. Policymakers should focus on providing targeted incentives and infrastructure to support the adoption of green technologies, particularly for firms that face resource constraints, ensuring that green transformation can be effectively scaled across different industries.
In conclusion, this study establishes green transformation as a key factor in mitigating the bullwhip effect and stabilizing supply chains. The research contributes to the growing body of knowledge on sustainable supply chain management, underscoring the importance of integrating environmental strategies with operational efficiency in future supply chain practices.
(3) Remove unnecessary references not directly related to the study
We sincerely thank the reviewer for this helpful suggestion. In response, we carefully re-evaluated the reference list and removed 12 entries that were deemed to have limited relevance to the core topic of our study. Additionally, we added 3 references to enhance the theoretical foundation and empirical context of the manuscript. These adjustments have improved the focus and coherence of the literature review section. The revised reference list is now more concise and better aligned with the objectives of the study. At present, the manuscript cites a total of 51 references.
Author Response File: Author Response.pdf
Reviewer 3 Report (Previous Reviewer 2)
Comments and Suggestions for AuthorsAccept in present form
Author Response
We sincerely appreciate the reviewer’s time and effort in evaluating our manuscript. We are grateful for the positive feedback and are pleased that the current version of the paper is acceptable for publication. Thank you again for your thoughtful review and support.
Reviewer 4 Report (Previous Reviewer 1)
Comments and Suggestions for AuthorsYour paper offers valuable insights as it addresses an important and timely research question regarding the relationship between corporate green transformation and the bullwhip effect. However, I would encourage you to follow the following recommendations which would assist you in further enhancing the academic rigor and contribution of your study.
In Section 3.3 (“Empirical Methodology”), you mention that both fixed-effect and random-effect models were considered, but the final choice was OLS regression. The justification for this choice is not sufficiently explained, especially since the Hausman test suggested that random effects could be appropriate. Moreover, please kindly note that OLS may not adequately address potential endogeneity issues (e.g., reverse causality between green transformation and the bullwhip effect). For this reason, please consider further discussing potential sources of endogeneity and focus on explaining why alternative methods like instrumental variables (IV), Generalized Method of Moments (GMM), or fixed-effects models were not chosen.
The operationalization of concepts such as "supply chain information sharing," "organizational resilience," and "management quality" is not fully represented in the empirical model, despite the fact that the three hypotheses (H1, H2, and H3) are explicit. Although they are not directly measured, these mechanisms are essential to your arguments throughout the study. Think about addressing this discrepancy or providing an explanation for their exclusion from the regression analysis's mediating variables list.
Even though your findings consistently show a negative relationship between green transformation and the bullwhip effect (e.g., Table 5), I am afraid that the discussion section of your paper remains descriptive rather than analytical.
Furthermore, despite being mentioned in the introduction, other theories and the circumstances in which a green transformation could exacerbate supply chain instability are not sufficiently included in the conversation. Lastly, more precise contextualization and supporting data are needed for the interpretation of control variables, such as the positive correlation between profit and the bullwhip effect.
Comments on the Quality of English LanguageEnglish language find, however it requires revision for conciseness and clarity.
Author Response
Q1: In Section 3.3 (“Empirical Methodology”), you mention that both fixed-effect and random-effect models were considered, but the final choice was OLS regression. The justification for this choice is not sufficiently explained, especially since the Hausman test suggested that random effects could be appropriate. Moreover, please kindly note that OLS may not adequately address potential endogeneity issues (e.g., reverse causality between green transformation and the bullwhip effect). For this reason, please consider further discussing potential sources of endogeneity and focus on explaining why alternative methods like instrumental variables (IV), Generalized Method of Moments (GMM), or fixed-effects models were not chosen.
Response：
We sincerely appreciate the reviewer’s thoughtful comment regarding the empirical methodology. In response, we have substantially revised Section 3.3 to provide a clearer justification for the choice of Ordinary Least Squares (OLS) regression in our baseline analysis. While OLS offers a statistically consistent and interpretable estimation under classical assumptions, we acknowledge that it may not fully address potential endogeneity issues, such as reverse causality and sample selection bias.
To mitigate these concerns and enhance the robustness of our empirical identification, we have incorporated two complementary approaches in the robustness checks. First, we adopt the two-stage least squares (2SLS) method to address potential reverse causality, using instrumental variables for green transformation. Second, we apply the Heckman two-step selection model to correct for selection bias arising from unobserved firm-level characteristics. These additional analyses strengthen the credibility of our findings and align more closely with the reviewer’s valuable suggestion.
The relevant modifications can be found in section “3.3. Empirical Methodology”.
3.3. Empirical Methodology
To examine the impact of green transformation on the bullwhip effect, Ordinary Least Squares (OLS) regression is employed as the primary estimation method. Under classical assumptions, OLS provides the Best Linear Unbiased Estimator, relying on key conditions such as linearity, zero conditional mean of the error term, homoscedasticity, and the absence of perfect multicollinearity. Diagnostic tests confirm that these assumptions are largely satisfied by the dataset, making OLS a statistically reliable and interpretable approach for baseline estimation.
However, while OLS provides consistent and interpretable baseline estimates under standard assumptions, it does not fully address potential endogeneity concerns, particularly those arising from reverse causality and sample selection bias. In this study's context, green transformation may be both a cause and a consequence of supply chain volatility, introducing reverse causality. Additionally, firms engaged in green transformation may possess unobserved characteristics, such as superior managerial capabilities, fewer financial constraints, or better access to policy support, all of which simultaneously influence supply chain stability and lead to sample selection bias. If these endogenous factors remain unaccounted for, causal inference could be compromised, resulting in biased estimates.
To address these concerns and strengthen the robustness of the results, subsequent robustness checks incorporate two complementary econometric approaches. First, the two-stage least squares (2SLS) method is applied to mitigate reverse causality by using instrumental variables for green transformation. Second, the Heckman two-step selection model is employed to correct for sample selection bias arising from non-random firm characteristics. These additional estimation strategies improve the empirical identification and provide more credible evidence regarding the stabilizing effect of green transformation on supply chain dynamics.
Q2: The operationalization of concepts such as "supply chain information sharing," "organizational resilience," and "management quality" is not fully represented in the empirical model, despite the fact that the three hypotheses (H1, H2, and H3) are explicit. Although they are not directly measured, these mechanisms are essential to your arguments throughout the study. Think about addressing this discrepancy or providing an explanation for their exclusion from the regression analysis's mediating variables list.
Response：
We sincerely thank the reviewer for the valuable suggestion. In response, we have further clarified the measurement of the mediating variables—“supply chain information sharing,” “organizational resilience,” and “management quality”—in Section 3.2 (“Variable Setting”). To improve transparency and consistency, these mediating variables have also been included and clearly defined in Table 3. Variable Definitions and described in Table 4. Descriptive Statistics.
Q3: Even though your findings consistently show a negative relationship between green transformation and the bullwhip effect (e.g., Table 5), I am afraid that the discussion section of your paper remains descriptive rather than analytical. Furthermore, despite being mentioned in the introduction, other theories and the circumstances in which a green transformation could exacerbate supply chain instability are not sufficiently included in the conversation.
Response：
In response to your comments, we have further elaborated on the empirical results in Sections 4.2 (“Baseline Regression”) and 4.5 (“Pathway Mechanism Testing”). Additionally, we have included a more detailed discussion on the potential challenges where green transformation could exacerbate supply chain instability. We believe these revisions enhance the analytical depth of the manuscript and provide a more comprehensive discussion of the theoretical implications, addressing the concerns raised in your feedback.
Q4: Lastly, more precise contextualization and supporting data are needed for the interpretation of control variables, such as the positive correlation between profit and the bullwhip effect.
Response：
We have revised the discussion of control variables to provide deeper theoretical and contextual explanations, particularly regarding the observed positive correlation between profitability and the bullwhip effect. Specifically, the updated manuscript now includes the following elaborations:
Regarding the control variables, the coefficient of Profit is significantly positive, indicating a positive correlation between profit margins and the bullwhip effect. This finding suggests that firms with higher profit margins may be more inclined to adopt expansionary production and inventory strategies in anticipation of continued growth in demand [44]. In particular, within the context of China’s manufacturing sector, profitability is often viewed as a signal of market opportunity, prompting firms to increase output levels and place larger upstream orders. These aggressive adjustments, aimed at maximizing short-term gains, can inadvertently lead to greater variability in order volumes and amplify fluctuations across the supply chain. Furthermore, higher profitability may reduce firms’ sensitivity to cost risks, weakening internal constraints on overproduction and speculative inventory buildup [45], thereby intensifying the bullwhip effect.
The coefficient of Tangible is significantly negative, suggesting that firms with a higher proportion of tangible assets are less prone to experiencing severe bullwhip effects. Such firms, often operating in asset-heavy industries, typically exhibit more structured production systems, better inventory planning capabilities, and greater operational stability, all of which help dampen supply chain volatility.
The coefficient of Man is also significantly negative, indicating that a higher proportion of male executives on the board is associated with a smaller bullwhip effect. This may reflect gender-based behavioral tendencies, where male executives are more likely to adopt rational, risk-averse decision-making styles, thereby avoiding excessive or reactive production adjustments.
The coefficient of Subsidy is significantly negative, suggesting that a greater ratio of government subsidies to revenue contributes to a reduction in the bullwhip effect [46]. Financial support from the government eases firms’ operational pressures, allowing for more stable production planning and reducing the need to overcompensate for demand uncertainties.
The coefficient of Payable is significantly positive, implying that a higher accounts payable ratio is associated with a stronger bullwhip effect. This may indicate tighter liquidity or weaker supplier relationships, both of which can introduce disruptions in order cycles and inventory management, thus amplifying fluctuations along the supply chain.
Finally, the coefficient of Gfrl is significantly negative, supporting the conclusion that green finance pilot policies play a role in mitigating the bullwhip effect. These policies promote sustainable practices and long-term coordination within supply chains, thereby enhancing operational resilience and reducing order variability.
[44] Rajagopalan, S.; Swaminathan, J. M. A coordinated production planning model with capacity expansion and inventory management. Management Science, 2001, 47(11): 1562-1580.
[45] Chan, Y. C.; Wang, W. K.; Lu, W. M. The effects of overproduction on future firm performance and inventory write‐downs. International Transactions in Operational Research, 2021, 28(6): 3493-3512.
[46] Gao, J.; Gao, Y.; Guan, T.; Liu, S.; Ma, T. Inhibitory influence of supply chain digital transformation on bullwhip effect feedback difference. Business Process Management Journal, 2024, 30(1), 135-157.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report (Previous Reviewer 4)
Comments and Suggestions for AuthorsThe authors have made a significant effort to revise the manuscript, which is now in a condition suitable for acceptance and publication.
Author Response
We are truly grateful for your thoughtful comments and positive feedback. Thank you for recognizing our revisions. We are pleased that the manuscript is now considered suitable for publication.
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsYour article is relevant as it addresses a critical aspect in today's world, namely the intersection of green transformation and supply chain management. It offers valuable insights into mitigating the bullwhip effect in the context of sustainable business practices.
However, I would encourage you to follow the following recommendations to further strengthen the robustness of your research paper.
While the study provides valuable insights into how green transformation mitigates the bullwhip effect, the research design could benefit from more detailed explanations. Specifically, focus on clarifying the rationale behind the selection of control variables and the use of certain statistical techniques (e.g., OLS regression). This would help you enhance the methodological rigor of the article. In order to strengthen the study's transparency, It would be advisable to include a more thorough justification for data exclusion criteria (such as omitting financial companies or ST-listed firms).
Comments on the Quality of English LanguageThe manuscript's language could be improved to ensure clearer communication and flow of ideas. Some sentences are overly complex, making it difficult for readers to follow key arguments.
Author Response
Dear Reviewers,
Firstly, we sincerely express our gratitude for the valuable comments you provided on our manuscript. Your feedback and suggestions are of great significance to the improvement of our research quality and manuscript level. Below are our specific responses to each of the comments you raised. Revisions in the text are highlighted in red to indicate additions.
We are very grateful for your permission to resubmit a revised version of the manuscript and we appreciate your time and consideration.
Q1. While the study provides valuable insights into how green transformation mitigates the bullwhip effect, the research design could benefit from more detailed explanations. Specifically, focus on clarifying the rationale behind the selection of control variables and the use of certain statistical techniques (e.g., OLS regression). This would help you enhance the methodological rigor of the article. In order to strengthen the study's transparency, It would be advisable to include a more thorough justification for data exclusion criteria (such as omitting financial companies or ST-listed firms).
Response：
(1) Regarding the selection of control variables
We sincerely appreciate the reviewer’s valuable comments on the selection of control variables. In the revised manuscript, we have provided a more detailed explanation of the rationale behind the selection of control variables. Additionally, we have categorized these variables according to different levels: the entrepreneur level, the firm level, and the ownership structure level. The relevant modifications can be found in section “3.3 Empirical Methodology”.
Specifically, the control variables at the firm level include company size (Size), operating cash flow ratio (Cash), return on assets (ROA), revenue growth rate (Growth), and debt ratio (Debt). These variables primarily reflect characteristics such as firm size, financial condition, and operational efficiency, which directly impact the firm's ability to implement green transformation and the effectiveness of such efforts. At the entrepreneur level, the control variables include dual roles (Dual), financial background (Background), and the average age of management (Age). The dual roles may lead to centralized decision-making, which can influence the adoption of green transformation and the firm’s response to supply chain disruptions. Executives with a financial background may be more inclined to adopt strict financial controls, which could result in overly conservative supply chain management strategies, thereby exacerbating the bullwhip effect, as they may focus on short-term financial goals and overlook the long-term impact of supply chain fluctuations. The average age of management reflects the team’s experience and inclination towards innovation, with younger management teams potentially being more willing to adopt emerging green transformation strategies. At the ownership structure level, the control variables include ownership balance (Balance) and institutional investor shareholding ratio (Institutional). A balanced ownership structure can bring diverse perspectives to decision-making, influencing the firm’s decisions and execution in the green transformation process. Institutional investors, who are typically concerned with long-term investment returns and ESG (environmental, social, and governance) issues, may encourage green transformation and influence supply chain management strategies.
(2) Regarding the justification for using OLS regression
To study the impact of green transformation on the bullwhip effect, we first considered both fixed-effect and random-effect models and conducted a Hausman test. The test results failed to reject the null hypothesis, suggesting that the random-effect model might be appropriate. However, further diagnostic tests revealed that our dataset did not fully satisfy the key assumptions required for the random-effect model. Therefore, based on these findings, we opted for the Ordinary Least Squares (OLS) regression method. OLS regression provides the Best Linear Unbiased Estimator (BLUE) under standard assumptions, including linearity, zero conditional mean of the error term, homoscedasticity, and no perfect multicollinearity. Diagnostic checks confirmed that our dataset satisfies these fundamental OLS assumptions, making OLS a suitable and statistically robust method for estimating the impact of green transformation on the bullwhip effect. The relevant modifications are reflected in section “3.3 Empirical Methodology”.
(3)A more comprehensive explanation of the data exclusion criteria (e.g., exclusion of financial companies or ST-listed companies) is required
Thank you for your valuable suggestion regarding the clarification of our data exclusion criteria. In the revised manuscript, we have provided a more comprehensive explanation of the rationale behind the exclusion of financial companies, ST-listed companies, and samples with incomplete data.
Exclusion of Financial Companies: We excluded financial companies due to their distinct financial structures and regulatory frameworks, which are markedly different from non-financial firms. Including them could have introduced significant comparability issues, making it difficult to draw valid conclusions in the context of this study. We have now clarified this reasoning in the revised manuscript.
Exclusion of ST-listed Companies: Companies labeled as "ST" (Special Treatment) typically face financial distress, such as consecutive losses and high debt ratios, which could severely skew the results. We excluded these firms to ensure that our analysis focuses on companies with more typical performance, thus enhancing the robustness of our findings.
Exclusion of Incomplete Data: We also removed samples with missing or inconsistent data to maintain the integrity and reliability of the dataset. This step ensures that our results are based on complete and accurate information.
Q2.The manuscript's language could be improved to ensure clearer communication and flow of ideas. Some sentences are overly complex, making it difficult for readers to follow key arguments.
Response：
Thank you for your valuable feedback regarding the language clarity in our manuscript. We appreciate your suggestion to improve the flow of ideas and ensure clearer communication. In the revised manuscript, we have carefully reviewed and simplified complex sentences to enhance readability and clarity. We have also restructured some sections to ensure that the key arguments are presented more logically and are easier for readers to follow. We believe these revisions will significantly improve the overall readability and effectiveness of the manuscript. Thank you again for your insightful suggestion.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis paper investigates Green Transformation of Enterprises and the Bullwhip Effect: Empirical Evidence from Listed Companies in China. The following comments are intended to improve the paper before final submission:
1. This paper emphasises the impact of ‘green transformation’ on the ‘bullwhip effect’, but it does not provide a clear introduction to the concept of ‘green transformation’ that you propose. And do all green transformation behaviours reduce the bullwhip effect? Do green technologies, resource efficiency, supply chain transparency, etc. have a consistent impact on the bullwhip effect?
2. Would it not be more appropriate to exchange the places of paragraphs 1 and 2 in the introduction?
3. Does it make sense to place the last three paragraphs of the introduction, which are more of a narrative of the innovations of this study, at the end of the introduction?
4. Please check the formulas in the text. Formulas 3, 4, 6, etc. are BEW errors on the left side.
5. Is there a sample selection bias in the study? For example, are there specific industry or firm characteristics that cause the relationship between GT and BWE to appear significant? If the study sample is relatively homogeneous (e.g., limited to firms in specific industries, sizes, or regions), the results may not be broadly representative.
6. Is the meaning of line 266 GT in the text consistent with that in table 2? This is important for the later interpretation of the data in the second row of Tables 7, 8 and 9.
7. The value of VIF is not observed in Table 3.
8. Formulas 3 and 4 are duplicated in the text, and it is suggested that one of them be retained.
9. In section 3.1, the screening resulted in 24,406 samples, but no description appears in the text of where the 24,411 samples in column 2 of table 3 came from.
10. The authors mention that the absolute values of the correlations between the variables are all below 0.5, and that on the face of it, multicollinearity is not a significant problem. However, it is important to note that low correlation coefficients do not completely exclude the problem of multicollinearity. Especially when multiple control variables or high dimensional data are included, the interaction effect between variables may not be directly reflected in a single correlation coefficient. It is recommended to further check for multicollinearity between factors by, for example, calculating the variance inflation factor (VIF).
11. In section 4.2, column 2 of table 5 adds a set of control variables to the original model to ensure the robustness of the results. However, do the authors take into account all possible influences? For example, industry differences, firm size, firm financial status, macroeconomic fluctuations, and other factors that may affect the bullwhip effect. If the control variables do not cover all relevant factors, it may lead to omitted variable bias, which may affect the accuracy of the results.
12. A regression model has been used in the paper and the methodology is correct from the results, but the R2 values are all well below 1. Explain the feasibility of this methodology.
13. The conclusion needs to be revised. There is a lot of repetition and it is recommended that the conclusions be streamlined.
Author Response
Dear Reviewers,
Firstly, we sincerely express our gratitude for the valuable comments you provided on our manuscript. Your feedback and suggestions are of great significance to the improvement of our research quality and manuscript level. Below are our specific responses to each of the comments you raised. Revisions in the text are highlighted in red to indicate additions.
We are very grateful for your permission to resubmit a revised version of the manuscript and we appreciate your time and consideration.
Q1.This paper emphasises the impact of ‘green transformation’ on the ‘bullwhip effect’, but it does not provide a clear introduction to the concept of ‘green transformation’ that you propose. And do all green transformation behaviours reduce the bullwhip effect? Do green technologies, resource efficiency, supply chain transparency, etc. have a consistent impact on the bullwhip effect?
Response：
Thank you for the reviewer’s valuable comments. We have provided a more detailed explanation of the concept of “green transformation” in the revised manuscript. We clearly point out that green transformation involves behaviors such as the adoption of green technologies, improvements in resource efficiency, and enhanced supply chain transparency. However, the impact of different green transformation behaviors on the bullwhip effect may not be consistent. Green technologies can help improve product traceability and information sharing, reducing demand fluctuations. In contrast, improving resource efficiency may lead to cost-cutting measures with varying effects, influencing inventory management practices across firms. Furthermore, while enhanced supply chain transparency can help reduce information asymmetry, data monopolization in practice may hinder information flow, potentially exacerbating the bullwhip effect. We also discuss how the high costs associated with green technologies could discourage some firms, leading to uneven implementation of green practices across the supply chain, which in turn creates new challenges in information transmission and decision-making.
The relevant modifications can be found in section “1. Introduction”.
Q2. Would it not be more appropriate to exchange the places of paragraphs 1 and 2 in the introduction?
Response：
We agree with the reviewer's suggestion. Exchanging the positions of Paragraph 1 and Paragraph 2 in the introduction will make the writing more logically coherent. By first elaborating on the challenges China faces in terms of supply chain disruptions and the bullwhip effect, we can then better introduce the concept of green transformation as a potential solution. This new order will help readers understand the problem background first, and thus be more receptive to the proposed solution. In the revised manuscript, we swap these two paragraphs to improve the overall structure of the introduction.
The relevant modifications can be found in section “1. Introduction”.
Q3. Does it make sense to place the last three paragraphs of the introduction, which are more of a narrative of the innovations of this study, at the end of the introduction?
Response：
We understand the reviewer's concerns. In the revised version, the research innovations will be presented in the 5.Conclusion section.
Q4. Please check the formulas in the text. Formulas 3, 4, 6, etc. are BEW errors on the left side.
Response：
Thank you for the reviewer’s comments. We have carefully checked the formulas in the text and found that formulas 3, 4, 6, etc. indeed contain BEW errors (on the left side). We have corrected these formulas and modified them to BWE. We appreciate your careful review, and we will correct these errors in the revised manuscript and resubmit it.
Q5. Is there a sample selection bias in the study? For example, are there specific industry or firm characteristics that cause the relationship between GT and BWE to appear significant? If the study sample is relatively homogeneous (e.g., limited to firms in specific industries, sizes, or regions), the results may not be broadly representative.
Response：
Thank you for the reviewer's comments. Regarding the issue of sample selection bias, we understand your concerns. To ensure the wide representativeness of the research results, our sample selection covers a wide range of industries such as manufacturing, information technology, and wholesale and retail, excluding the financial industry.
Considering the possibility of sample selection bias, in the robustness test section, we adopted the Propensity Score Matching (PSM) and Entropy Balancing methods. After being processed by PSM and Entropy Balancing, the coefficients and significance of the key variables remained basically stable, further demonstrating the reliability of the results. These methods can effectively eliminate the influence of potential factors such as industry, firm size, or region on the results.
We further describe these robustness test methods in detail in the revised manuscript and provide the corresponding analysis results. Thank you again for your valuable comments.
The relevant modifications can be found in section “3.1. Data Sources” and “4.4. Robustness Test”.
Q6. Is the meaning of line 266 GT in the text consistent with that in table 2? This is important for the later interpretation of the data in the second row of Tables 7, 8 and 9.
Response：
Thank you for pointing out this crucial issue. We have carefully re-examined the definition of GT in the text. In response to your comment, we have rewritten the definition of GT as "The comprehensive indicator system for corporate green transformation".
Q7. The value of VIF is not observed in Table 3.
Response：
Thank you for the reviewer’s comments. We have listed the VIF values in Table 6, and all VIF values are below 5, indicating that there is no serious multicollinearity issue. We will add a further explanation in the revised manuscript to clarify this point more clearly.
The relevant modifications can be found in section “4.1. Descriptive Statistics”.
Q8. Formulas 3 and 4 are duplicated in the text, and it is suggested that one of them be retained.
Response：
Thank you for the reviewer’s comments. We have noticed that Formulas 3 and 4 are duplicated in the text. We will retain one of the formulas and make the necessary adjustments in the revised manuscript to avoid repetition. Thank you again for your thorough review.
Q9. In section 3.1, the screening resulted in 24,406 samples, but no description appears in the text of where the 24,411 samples in column 2 of table 3 came from.
Response：
Thank you for the reviewer’s comments. We have noticed that the sample selection resulted in 24,406 samples, but the source of the 24,411 samples mentioned in Column 2 of Table 3 was not explained. We will revise the text to clarify the source of the sample count in the table and ensure consistency in the numbers. Thank you again for your thorough review.
The relevant modifications can be found in section “3.1. Data Sources”.
Q10. The authors mention that the absolute values of the correlations between the variables are all below 0.5, and that on the face of it, multicollinearity is not a significant problem. However, it is important to note that low correlation coefficients do not completely exclude the problem of multicollinearity. Especially when multiple control variables or high dimensional data are included, the interaction effect between variables may not be directly reflected in a single correlation coefficient. It is recommended to further check for multicollinearity between factors by, for example, calculating the variance inflation factor (VIF).
Response：
Thank you for the reviewer’s comments. We agree that low correlation coefficients do not completely exclude the possibility of multicollinearity, especially when multiple control variables or high-dimensional data are involved, as the interaction effects between variables may not be directly reflected in a single correlation coefficient. To further check for multicollinearity, we have calculated the Variance Inflation Factor (VIF) and presented the results in the table 6. All VIF values are below 5, indicating that multicollinearity is not a significant issue. Thank you for your suggestion, and we have included the relevant analysis and explanation in the revised manuscript.
Q11. In section 4.2, column 2 of table 5 adds a set of control variables to the original model to ensure the robustness of the results. However, do the authors take into account all possible influences? For example, industry differences, firm size, firm financial status, macroeconomic fluctuations, and other factors that may affect the bullwhip effect. If the control variables do not cover all relevant factors, it may lead to omitted variable bias, which may affect the accuracy of the results.
Response：
We would like to express our sincere gratitude for your insightful comments. Regarding the potential bias in the selection of control variables, we have made the following supplementary measures in the robustness tests:
Firstly, we controlled for the interaction effects of industry and year. This approach helps to alleviate the impact that industry-specific differences may have on our results. By doing so, we can better isolate the relationship between the variables of interest, accounting for the unique characteristics and trends within each industry over time.
Secondly, we took into account the influence of macroeconomic environmental policies and incorporated this factor into the robustness tests. Given the significant impact of macro policies on corporate behavior and the overall economic environment, it is crucial to consider these elements to ensure the robustness of our findings.
Finally, we added additional control variables in the robustness tests, including the Inventory Turnover Ratio (In_Turnover), Accounts Payable Ratio (Ac_Payable), Investment Inefficiency (In_Investment), Government Subsidies (Subsidy), and Regional Cultural Values (Culture).
Q12. A regression model has been used in the paper and the methodology is correct from the results, but the R2 values are all well below 1. Explain the feasibility of this methodology.
Response：
Thank you for your valuable comments. We fully understand your concerns about the relatively low R² values in our regression model. In the fields of economics and social sciences, especially when dealing with complex situations involving multiple control variables and highly heterogeneous data, a low R² value does not necessarily mean that the model is infeasible or ineffective.
In our research, we use a regression model to analyze the impact of green transformation on the bullwhip effect. The economic phenomena we study are extremely complex and are jointly influenced by numerous factors. For example, apart from the two main variables of green transformation and the bullwhip effect, factors such as industry - specific characteristics, firm - level strategic decisions, and macro - economic fluctuations all interact and affect the final outcome. Some of these numerous influencing factors are difficult to measure precisely or fully capture in the model, which limits the explanatory power of the model. Despite the low R² value, our model still effectively controls for these various factors and accurately estimates the impact of green transformation on the bullwhip effect. The coefficients of our key variables are statistically significant, indicating that the model can reveal the relationships we are interested in. For instance, previous studies may also have encountered situations with low R² values.
（1）Aobdia, D.; Lin, C. J.; Petacchi, R. Capital market consequences of audit partner quality. The Accounting Review, 2015, 90(6): 2143-2176.
（2）Bills, K. L.; Cobabe, M.; Pittman, J.; et al. To share or not to share: The importance of peer firm similarity to auditor choice. Accounting, Organizations and Society, 2020, 83: 101115.
Q13. The conclusion needs to be revised. There is a lot of repetition and it is recommended that the conclusions be streamlined.
Response：
Thank you for the reviewer’s constructive feedback. We have noticed that the conclusion section did contain some repetitive content. Based on your suggestion, we have simplified and streamlined the conclusion, removing unnecessary parts to make it more concise and clear. Additionally, we have added sections “5.2.Theoretical contributions”, “5.3.Managerial implications” and “5.4.Limitations and future research” which further enrich the discussion in the conclusion. We believe these additions improve the overall structure and clarity of the conclusion. Thank you again for your helpful comments.
The relevant modifications can be found in section “5.Conclusion and suggestion”.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThis study, based on data from A-share listed companies in China between 2008 and 2022, explores the mechanisms through which corporate green transformation mitigates the bullwhip effect. The findings reveal that green transformation significantly alleviates the bullwhip effect. Pathway analysis indicates that green transformation reduces supply chain volatility by enhancing supply chain information sharing, innovation capabilities, and management practices. Further empirical analysis shows that the mitigation effect of green transformation on the bullwhip effect is more pronounced in non-state-owned enterprises, technology-intensive industries, firms with a higher proportion of board members with environmental backgrounds, and those with lower financing constraints. This study also offers theoretical support and practical guidance for promoting corporate green transformation and optimizing supply chain management strategies.
The idea of the manuscript is interesting, but the methodology needs to be improved. The following comments can help to do so.
- In the introduction it is important to mention the previous studies in literature that are most related to the present work and how this study continues or improves these studies.
- Table 1 needs to be better organized.
- The authors should state why they choose the approach of Bray and Mendelson. if it is not the only approach, it is convenient to compare the chosen approach with the other ones.
- I believe that a section to explicitly discuss the outcomes needs to be added.
- In the conclusions' section, it would be beneficial to explicitly highlight the relationship between the results and their potential impact on future research, including suggestion of open research questions.
Author Response
Dear Reviewers,
Firstly, we sincerely express our gratitude for the valuable comments you provided on our manuscript. Your feedback and suggestions are of great significance to the improvement of our research quality and manuscript level. Below are our specific responses to each of the comments you raised. Revisions in the text are highlighted in red to indicate additions.
We are very grateful for your permission to resubmit a revised version of the manuscript and we appreciate your time and consideration.
Q1. In the introduction it is important to mention the previous studies in literature that are most related to the present work and how this study continues or improves these studies.
Response：
Thank you for the reviewer’s valuable comments. In response to the suggestion to mention the previous studies most related to the present work in the introduction and explain how this study continues or improves these studies, we have added this section in the revised manuscript. In the introduction, we discuss previous research related to "green transformation" and the "bullwhip effect," highlighting the gaps in these studies, particularly the lack of research on the impact of green transformation on the bullwhip effect. We further clarify how this study fills this gap by providing new empirical evidence on the role of green transformation in supply chain management.
The relevant modifications can be found in section “1. Introduction”.
Q2. Table 1 needs to be better organized.
Response：
Thank you for the reviewer’s valuable comments. In response to the suggestion that "Table 1 needs to be better organized," we have reorganized and optimized the table. In the revised manuscript, the layout of the table is clearer, and we have provided a detailed description of how the indicators for corporate green transformation were selected.
The relevant modifications can be found in section “3.2. Variable Setting”.
Q3.The authors should state why they choose the approach of Bray and Mendelson. if it is not the only approach, it is convenient to compare the chosen approach with the other ones.
Response：
Thank you for the reviewer’s valuable comments. Regarding the choice of the Bray and Mendelson method, we have provided a detailed explanation of this method in the revised manuscript. The main reason for selecting the Bray and Mendelson method in this study is its widespread recognition in the academic community and its theoretical foundation based on the principles of supply and demand economics, which effectively captures the characteristic of the bullwhip effect - the amplification of demand fluctuations as they propagate upstream in the supply chain. Additionally, the method is highly adaptable and can be applied in various economic contexts, particularly with good operability in the complex and dynamic Chinese market.
The relevant modifications can be found in section “3.2. Variable Setting”.
Q4. I believe that a section to explicitly discuss the outcomes needs to be added.
Response：
Thank you for the reviewer’s valuable comments. In response to the reviewer’s suggestion to add a dedicated section to explicitly discuss the outcomes, we have included sections "5.2 Theoretical Contributions" and "5.3 Managerial Implications" in the revised manuscript under the "5. Conclusion and Suggestion" section. In these sections, we provide a detailed discussion of the theoretical contributions of this study and its implications for management practice. We believe that these added sections better articulate the study's findings and offer valuable insights for future research.
The relevant modifications can be found in section “5.Conclusion and suggestion”.
Q5. In the conclusions' section, it would be beneficial to explicitly highlight the relationship between the results and their potential impact on future research, including suggestion of open research questions.
Response：
Thank you for the reviewer’s valuable feedback. In response to your suggestion to explicitly highlight the relationship between the results and their potential impact on future research, we have added a new section "5.4 Limitations and future Research" in the conclusion. In this section, we discuss the limitations of the study and propose several avenues for future research.
5.4. Limitations and future research
Although this study yields valuable insights, it inevitably has certain limitations.
First, this research focuses solely on Chinese A-share listed companies, which may limit the generalizability of the findings. Different countries and regions have unique economic conditions and regulatory environments, and the characteristics of Chinese A-share listed companies may not fully reflect the situation in other areas. Therefore, future research could consider expanding the scope of the study to include companies from other regions or conduct cross-national comparative studies to more comprehensively assess the global applicability of the findings. Second, this study employs a multiple linear regression model to analyze the relationship between green transformation and the bullwhip effect. However, this relationship may be influenced by some unobserved factors, such as external market conditions. Future studies could incorporate interdisciplinary econometric methods to delve into these potential factors, thereby providing a more precise understanding of the underlying mechanisms linking green transformation and the bullwhip effect. Third, environmental policies and supply chain dynamics are constantly evolving. This study primarily focuses on the current state; however, future research could further explore the long-term impact of green transformation on supply chain performance. Such studies would be better suited to adapt to the changing external environment and provide more forward-looking insights for corporate decision-making and policy formulation.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsThis study examines how green transformation affects the bullwhip effect in firms, identifying innovation capability and internal control quality as partial mediators while exploring heterogeneity across ownership structures, industry types, and board environmental backgrounds.
However, it has drawbacks, particularly in its reliance on specific proxies, potential endogeneity issues, and limited generalizability across different economic contexts.
The abstract presents several weaknesses, including a lack of specificity regarding the study's theoretical contributions beyond confirming that green transformation mitigates the bullwhip effect. Additionally, it does not sufficiently explain the methodology, failing to mention the regression models or robustness checks used. The findings are overgeneralized, as the claim that green transformation significantly alleviates the bullwhip effect lacks quantification or discussion of effect size. Furthermore, while the abstract references policy implications, it does not outline concrete regulatory or managerial recommendations.
The introduction contains repetitive statements about the bullwhip effect, with multiple paragraphs reiterating its definition and causes, making the section longer than necessary. While it highlights the benefits of green transformation, it does not adequately address potential risks, such as data monopolization and high costs associated with green technologies. Additionally, the introduction does not provide a well-supported research gap, as it asserts that the relationship between green transformation and the bullwhip effect is underexplored without offering a critical review of existing literature. Finally, although the introduction references China’s 14th Five-Year Plan, it does not clearly link specific policies to corporate decision-making regarding the bullwhip effect.
The literature review and hypothesis development section lacks a strong theoretical foundation, as it states that green transformation enhances supply chain transparency but does not ground this claim in frameworks like Transaction Cost Economics or the Resource-Based View. The hypotheses are weakly developed, with minimal explicit connections to prior literature. The section also relies on overly general arguments, such as the claim that green technologies like IoT and big data improve forecasting, without addressing inefficiencies such as data misinterpretation or delays in technological adoption.
The data sources section is underdeveloped, as it does not justify the choice of the 2008–2022 sample period in relation to economic trends or policy changes. Additionally, the process of data cleaning is insufficiently detailed, lacking statistics on how much data was removed at each step. There is no discussion on whether the final dataset is representative of the broader A-share market or if selection biases are introduced. Furthermore, the study does not conduct robustness checks to address survivorship bias or missing data concerns.
In terms of variable selection, the justification for the dependent variable is unclear, as the study uses the Bray and Mendelson approach to measure supply and demand fluctuations without discussing its applicability to the Chinese market. The mathematical notation in equations is not adequately explained, with key variables introduced abruptly. The green transformation indicator system relies on entropy weighting but does not discuss why this method is preferable to alternatives like PCA or DEA. Endogeneity remains a significant issue, as green transformation may be endogenous to firm performance, yet the study does not employ instrumental variable techniques to address this. Additionally, the selection of some indicators, such as operating costs and selling expenses, lacks a clear rationale in relation to green transformation.
The empirical methodology section has several weaknesses, including a lack of details on model specification. The study employs OLS regression without explaining whether fixed or random effects were tested or why OLS was deemed appropriate. Control variables, such as financial background and institutional investor ownership, are included without sufficient justification regarding their relevance to green transformation. Although the text mentions industry and year fixed effects, it does not specify how they were implemented. The study also fails to discuss potential omitted variable bias, as unobservable factors like firm culture or government subsidies might influence the results. Finally, there is no mention of robustness tests, alternative regression models like 2SLS or panel GMM, or sensitivity analyses to validate the findings.
The mediation analysis faces several limitations. The study uses green patent applications as a proxy for innovation capability, but this does not fully capture firms' overall innovation efforts, and other indicators like R&D expenditure could provide a more comprehensive view. There are also causality concerns, as the study assumes that green transformation increases innovation capability without accounting for potential reverse causality. Additionally, the mediation analysis does not test alternative pathways that may influence the relationship between green transformation and the bullwhip effect. Similar issues arise in the analysis of internal control quality, where the study uses the DIBO internal control index as a proxy but does not address whether this metric fully captures firm-specific internal control mechanisms. There is also a potential overlap between internal control quality and innovation capability as mediators, yet the study does not examine their interactions. The study lacks robustness checks using alternative measures of internal control quality, which weakens the validity of its findings.
The heterogeneity analysis has several shortcomings. The classification of firms into state-owned and non-state-owned enterprises is overly simplistic, as it does not consider within-group variations such as firm size, industry regulations, or government support programs. The study does not examine how government policies might influence the effectiveness of green transformation in state-owned enterprises. Similarly, the distinction between technology-intensive and non-technology-intensive industries does not fully capture differences in business models, supply chain structures, or regulatory pressures.
Regarding the environmental background of the board, the study assumes that board members’ environmental expertise influences green transformation decisions but does not analyze whether this effect varies based on board size, independence, or diversity. There are endogeneity concerns, as firms that prioritize green transformation may actively seek to appoint board members with environmental expertise, creating potential reverse causality. Additionally, the study does not include a longitudinal analysis to assess how the influence of environmentally focused board members evolves over time or aligns with broader corporate governance trends.
A structural weakness of the study is the lack of a dedicated discussion of findings section, which limits the depth of interpretation and contextualization of the results. The absence of a discussion hinders the ability to connect empirical results to theoretical implications, practical applications, and broader economic considerations.
Author Response
Dear Reviewers,
Firstly, we sincerely express our gratitude for the valuable comments you provided on our manuscript. Your feedback and suggestions are of great significance to the improvement of our research quality and manuscript level. Below are our specific responses to each of the comments you raised. Revisions in the text are highlighted in red to indicate additions.
We are very grateful for your permission to resubmit a revised version of the manuscript and we appreciate your time and consideration.
Q1. The abstract presents several weaknesses, including a lack of specificity regarding the study's theoretical contributions beyond confirming that green transformation mitigates the bullwhip effect. Additionally, it does not sufficiently explain the methodology, failing to mention the regression models or robustness checks used. The findings are overgeneralized, as the claim that green transformation significantly alleviates the bullwhip effect lacks quantification or discussion of effect size. Furthermore, while the abstract references policy implications, it does not outline concrete regulatory or managerial recommendations.
Response：
Thank you for the reviewer’s valuable comments. In response to the feedback on the abstract, we have made several revisions:
Theoretical Contributions: In the revised abstract, we have clarified the theoretical contributions of the study. In addition to confirming that green transformation mitigates the bullwhip effect, we emphasize the integration of green development theory and supply chain management theory, highlighting the theoretical innovation in understanding how green transformation impacts supply chain management.
Methodology: We have provided a more detailed explanation of the methodology in the revised abstract, specifying the use of a multiple linear regression model and robustness checks. Additionally, we mention the sensitivity and endogeneity tests conducted to ensure the reliability of the findings.
Quantification and Discussion of Results: The abstract now includes a quantifiable description of the findings, stating that for each one-unit increase in the green transformation index, the bullwhip effect is significantly reduced by 0.397 units. We also elaborate on the path analysis conducted to explain the mechanisms through which green transformation mitigates the bullwhip effect.
Policy and Managerial Recommendations: We have outlined concrete regulatory and managerial recommendations in the revised abstract. Specifically, we suggest that government regulatory bodies strengthen policy guidance and support, encourage enterprises to prioritize green transformation, and adopt differentiated strategies based on the characteristics of different types of companies to achieve supply chain stability and sustainable development.
These revisions aim to address the reviewer’s concerns and enhance the clarity and comprehensiveness of the abstract. We appreciate the reviewer’s thoughtful suggestions and the opportunity to improve the manuscript.
The relevant modifications can be found in section “Abstract”.
Abstract: Against the backdrop of increasing economic downward pressure in China, disruptions in certain segments of the supply chain have exacerbated the bullwhip effect, severely undermining the stability of supply chains and posing a threat to the healthy development of the real economy. This study, based on data of Chinese A-share listed enterprises between 2008 and 2022, employs a multiple linear regression model and conducts sensitivity and endogeneity tests to explore the mechanism by which enterprises' green transformation alleviates the bullwhip effect. The results show that for each one-unit increase in the green transformation index, the bullwhip effect is significantly reduced by 0.397 units. Path analysis reveals that green transformation mitigates the bullwhip effect by enhancing supply chain information sharing, improving innovation capabilities, refining management practices, and alleviating earnings volatility. Heterogeneity analysis indicates that the mitigating effect of green transformation on the bullwhip effect is more pronounced in non-state-owned enterprises, technology-intensive industries, and enterprises with a higher proportion of board members with an environmental background. This study not only expands the integration of green development theory and supply chain management theory but also provides theoretical support and practical guidance for promoting corporate green transformation and optimizing supply chain management strategies. Specifically, the study suggests that government regulatory bodies strengthen policy guidance and support, encourage enterprises to prioritize green transformation, and adopt differentiated strategies based on the characteristics of different types of companies to achieve supply chain stability and sustainable development.
Q2. The introduction contains repetitive statements about the bullwhip effect, with multiple paragraphs reiterating its definition and causes, making the section longer than necessary. While it highlights the benefits of green transformation, it does not adequately address potential risks, such as data monopolization and high costs associated with green technologies. Additionally, the introduction does not provide a well-supported research gap, as it asserts that the relationship between green transformation and the bullwhip effect is underexplored without offering a critical review of existing literature. Finally, although the introduction references China’s 14th Five-Year Plan, it does not clearly link specific policies to corporate decision-making regarding the bullwhip effect.
Response：
We appreciate the reviewer’s valuable feedback on the introduction section. In response to the concerns raised, we have made the following revisions:
Reduction of Repetitive Statements: We have streamlined the introduction by removing repetitive statements about the bullwhip effect. The definition and causes of the bullwhip effect are now presented more concisely to avoid redundancy and improve the overall flow of the section.
Incorporation of Potential Risks: We have added a discussion of the potential risks associated with green transformation, such as data monopolization and the high costs associated with adopting green technologies. This addition addresses the concerns regarding the lack of balance in the introduction, providing a more comprehensive view of the potential challenges as well as the benefits of green transformation.
Clarification of the Research Gap: We have provided a more detailed critical review of existing literature in the introduction to better support the research gap. While existing studies have explored various aspects of the bullwhip effect, we have highlighted that the role of green transformation in mitigating the bullwhip effect remains underexplored. This further justifies the need for our study.
Link to China’s 14th Five-Year Plan: We have expanded on the relevance of China’s 14th Five-Year Plan for Ecological and Environmental Protection by more clearly linking the policy to corporate decision-making. Specifically, we discuss how the policy provides enterprises with direction and support in aligning their green transformation efforts with broader national goals. We also emphasize the need for more specific guidance to enterprises in leveraging these policies effectively.
These revisions aim to address the reviewer's concerns and improve the clarity, balance, and relevance of the introduction. We are grateful for the reviewer’s insights, which have contributed to enhancing the manuscript.
The relevant modifications can be found in section “1. Introduction”.
- Introduction
In the era of green and low-carbon development, green transformation represents a promising strategy for enhancing the resilience and efficiency of supply chains [1,2,3]. Green transformation not only fosters environmental protection and sustainable development but also significantly improves supply chain efficiency and flexibility through technological innovation and management optimization [4]. Notably, China’s 14th Five-Year Plan for Ecological and Environmental Protection has explicitly emphasized the acceleration of green and low-carbon development, with deepening green supply chain management identified as a key component of the national strategy. This strategic focus provides enterprises with clear policy support and direction, encouraging them to optimize their supply chain structures and operational models in response to increasingly stringent environmental policies.
Currently, China faces a range of challenges stemming from both internal and external factors. International trade frictions, fluctuations in energy and raw material prices, geopolitical risks, and domestic economic pressures have disrupted supply chains, exacerbating the bullwhip effect and severely impacting supply chain stability and the healthy development of the real economy [5,6,7]. Disruptions in production and logistics have progressively distorted demand information along the supply chain, leading to an increasing mismatch between upstream enterprises and actual demand. The bullwhip effect, initially introduced by Forrester [8] and later empirically validated by Lee et al. [9], refers to the amplification and lagging of demand information as it propagates upstream in the supply chain. This distortion results in inventory overstocking, overcapacity, or supply shortages, contributing to resource misallocation and supply-demand imbalances [10,11,12]. Therefore, effectively mitigating the bullwhip effect and restoring supply chain stability are crucial for sustaining economic growth.
Considerable research has been devoted to understanding the bullwhip effect [13,14,15]. Its causes are multifaceted, including inaccurate and delayed information, irrational decision-making by supply chain members [16,17], and the structural characteristics of supply chains [18]. Longer supply chain tiers and more complex network str uctures increase the number of information transmission links and the time required for information propagation, thereby facilitating the bullwhip effect [19]. Market uncertainties, such as rapid shifts in consumer preferences and economic volatility, further exacerbate this phenomenon [20,21]. To mitigate the bullwhip effect, strategies such as strengthening information sharing, establishing long-term stable partnerships, and optimizing inventory management have been proposed, with collaborative demand forecasting showing particular promise in improving forecast accuracy [22,23,24].
However, while extensive research has been conducted on the causes, mechanisms, and mitigation strategies of the bullwhip effect, a critical gap remains regarding the role of green transformation in mitigating this phenomenon. Despite its growing prominence, green transformation—defined as the integration of environmental considerations into supply chain processes—has not been sufficiently explored in the context of its potential to address supply chain inefficiencies, particularly the bullwhip effect. Green transformation, in this context, involves integrating environmental considerations into all aspects of the supply chain, including the adoption of green technologies, promotion of resource efficiency, and enhancement of supply chain transparency. However, it is important to note that not all green transformation behaviors may have the same effect on the bullwhip effect. For instance, the impact of green technologies, resource efficiency improvements, and enhanced transparency may vary, and it remains unclear whether these factors always lead to a reduction in the bullwhip effect. While green transformation holds significant potential to mitigate the bullwhip effect by improving transparency and the efficiency of information flow, there are also associated risks. For example, the sharing of green data may be hindered by data monopolization, where certain supply chain members hoard valuable data for their own benefit, disrupting the overall information flow and potentially exacerbating the bullwhip effect. Furthermore, the high cost of adopting green technologies may discourage some enterprises from fully implementing green transformation, leading to inconsistent levels of green practices across the supply chain and potentially creating new bottlenecks in information transmission and decision-making.
Moreover, while China’s 14th Five-Year Plan for Ecological and Environmental Protection advocates for green supply chain management, there is a lack of clarity regarding how these policies can be practically applied by enterprises to mitigate the bullwhip effect. Enterprises need more specific guidance on how to leverage policy support to optimize supply chain operations within the framework of green transformation and reduce the impact of the bullwhip effect.
This study aims to address this research gap by comprehensively investigating the relationship between green transformation and the bullwhip effect. It will explore whether green transformation can effectively mitigate the bullwhip effect and identify the specific pathways and mechanisms through which green practices influence this phenomenon. In doing so, the study will provide valuable insights for enterprises to make informed decisions in their green transformation efforts and offer recommendations for policymakers to design more targeted strategies to support supply chain stability and sustainable development.
The relevant modifications can be found in section “2. Theoretical Analysis and Hypothesis”.
- 2. Theoretical Analysis and Hypothesis
In the current economic environment, enterprises face intense market competition and increasing environmental pressures. Green transformation, as an emerging business paradigm, has gained significant attention from firms seeking sustainable development. The bullwhip effect, characterized by amplified fluctuations in supply chains due to information asymmetry, inaccurate demand forecasting, and overreactions, severely undermines supply chain stability. Corporate green transformation has the potential to mitigate this effect through various mechanisms, thereby enhancing the overall efficiency and resilience of the supply chain. Drawing on established theoretical frameworks, such as transaction cost economics and resource-based theory, this study investigates how green transformation can alleviate the bullwhip effect through supply chain information sharing, green technological innovation, improvements in internal control quality, and reduced profitability volatility.
2.1. The Role of Green Transformation and Supply Chain Information Sharing in Mitigating the Bullwhip Effect
From the perspective of transaction cost economics, information asymmetry within supply chains leads to increased transaction costs. When supply chain members lack timely and accurate information, making rational decisions becomes challenging. This results in inefficiencies in resource allocation and increases costs related to negotiation, monitoring, and risk management. Therefore, information sharing is crucial to reducing these costs and improving supply chain performance. In line with resource-based theory, effective information sharing can be considered a valuable strategic resource. It allows firms to enhance their competitive advantage by improving their ability to adapt to market changes, respond to customer demands, and collaborate more effectively with partners.
Green transformation compels firms to place greater emphasis on information sharing and transparency within supply chain management [25, 26]. During the green transformation process, firms often adopt advanced information technologies such as the Internet of Things (IoT), big data analytics, and cloud computing. From the standpoint of transaction cost economics, these technologies significantly reduce the costs associated with information search, negotiation, and supervision [27,28]. By enabling real-time data collection and sharing across all stages of the supply chain, these technologies allow supply chain members to gain more accurate insights into market demand, production progress, and inventory status. As a result, forecasting errors and overreactions among supply chain participants are minimized, which effectively curtails the amplification of the bullwhip effect. From a resource-based view, these technologies represent unique resources that can be leveraged to create sustainable competitive advantages.
Furthermore, green transformation fosters closer collaboration among supply chain stakeholders [29, 30]. As enterprises increasingly prioritize environmental protection and the efficient use of resources, they are more inclined to establish long-term, stable partnerships with suppliers, distributors, and other upstream and downstream stakeholders [31]. These collaborative relationships, consistent with resource-based theory, are valuable strategic assets that contribute to the long-term success of the enterprise. Strengthened collaboration enhances information sharing and demand coordination, thereby mitigating the bullwhip effect. Supply chains characterized by high levels of information sharing and close collaboration among partners are more resilient to market fluctuations, significantly reducing the bullwhip effect. Stakeholders can respond promptly to shifts in market demand, preventing supply chain instability and the propagation of the bullwhip effect due to delays or distortions in information flow.
In addition, green transformation requires enterprises to consider not only the environmental attributes of their products but also the environmental impact of their production and logistics processes [32]. This necessitates the adoption of more sophisticated production scheduling and inventory management practices. Information sharing, therefore, extends beyond the enterprise itself to include upstream and downstream supply chain partners. By strengthening information sharing, enterprises can coordinate scheduling with supply chain partners, reducing production and inventory fluctuations and preventing overreactions to market demand changes within the supply chain [33].
However, the implementation of information-sharing technologies is not without challenges. The complexity and diversity of the data involved can lead to misinterpretation. Etzioni and Etzioni [34] noted that some enterprises, lacking professional data analysts and effective data interpretation methods, often misinterpret data obtained from IoT and big data analytics, resulting in inaccurate demand forecasts and supply chain disruptions. Additionally, delays in adopting these technologies may pose significant challenges. Enterprises with limited financial resources, insufficient technical capabilities, or conservative organizational cultures may struggle to implement the latest information-sharing technologies in a timely manner, which could hinder their ability to effectively mitigate the bullwhip effect [35].
Based on the above analysis, this study proposes the following hypothesis:
Hypothesis 1: Corporate green transformation significantly mitigates the bullwhip effect by enhancing supply chain information sharing.
2.2. The Role of Green Transformation and Green Technological Innovation in Mitigating the Bullwhip Effect
Drawing on dynamic capabilities theory, which emphasizes a firm's ability to adapt to and shape its external environment, green technological innovation is a critical pathway for enterprises to develop these dynamic capabilities. Green technological innovation encourages firms to adopt more efficient resource allocation strategies and optimize production processes during manufacturing [36, 37]. This not only reduces resource waste but also enhances production efficiency, thereby minimizing uncertainties and fluctuations in the production process [38]. As firms reduce production uncertainties through green technological innovation, both upstream and downstream supply chain participants are better positioned to respond more consistently to market demand changes, thus helping to mitigate the amplification of demand fluctuations within the supply chain.
The advancement of green transformation also motivates firms to implement more precise production and inventory management practices. Lean production methods, commonly associated with green transformation, help to reduce unnecessary inventory accumulation and production redundancies [39, 40]. Lean production not only enhances production flexibility but also optimizes resource allocation, enabling firms to adjust production schedules swiftly in response to market demand changes. This helps to prevent overreactions to market fluctuations that could lead to inventory imbalances [41]. By minimizing inventory accumulation, firms can effectively balance supply and demand within the supply chain, thereby mitigating the negative impacts of the bullwhip effect on operations.
However, the adoption of green technologies is not without challenges. First, there may be data misinterpretation when green technologies are used for forecasting [42]. The complex algorithms and large - scale datasets involved in green technology forecasting systems are prone to misinterpretation, which may lead to inaccurate production plans and inventory imbalances. Second, delays in technology adoption can prevent enterprises from fully realizing the benefits of green technology innovation [43]. Enterprises that lag behind in the adoption of green technologies are more vulnerable to the bullwhip effect. Despite these challenges, green technology innovation can fundamentally mitigate the bullwhip effect caused by market demand fluctuations by improving production efficiency and optimizing resource allocation [44]. Through green transformation, enterprises can achieve a win-win situation, bringing both environmental benefits and economic benefits. At the same time, they can establish a more precise and efficient supply chain management framework to cope with market uncertainties.
Based on the above analysis, this study proposes the following hypothesis:
Hypothesis 2: Corporate green transformation significantly mitigates the bullwhip effect by promoting green technological innovation.
2.3. The Role of Green Transformation and Internal Control Quality in Mitigating the Bullwhip Effect
From the perspective of principal-agent theory, information asymmetry within an enterprise can give rise to agency problems, which, in turn, may negatively impact the firm’s operational efficiency and stability. Green transformation, by enhancing the quality of a firm's internal control system, can effectively address these issues, particularly in areas such as resource utilization, production efficiency, and environmental compliance [45, 46]. A robust internal control system helps reduce information asymmetry between different levels of the organization, thereby lowering agency costs and ensuring the smooth functioning of various operational processes [47]. This improvement in internal control capabilities allows the firm to manage production processes more precisely, thereby avoiding overproduction and resource waste. By ensuring better control over raw materials and closely monitoring production, the firm can significantly reduce production volatility [48]. This reduction in volatility directly influences the response mechanisms of both upstream and downstream supply chain participants, mitigating the transmission of market demand fluctuations throughout the supply chain and, consequently, lowering the bullwhip effect.
Furthermore, green transformation impels enterprises to adopt a more comprehensive and proactive approach to risk management. During the process of enterprises' green transformation, they are better able to identify and address environmental risks, policy uncertainties, and market demand fluctuations [49, 50]. Enterprises with a strong internal control system and effective risk management capabilities are more resilient to market risks, thus making the supply chain more stable. This enhanced risk management ability enables enterprises to respond more effectively to external changes and avoid supply chain disruptions caused by overreactions to market fluctuations. Especially as environmental regulations become increasingly stringent, enterprises can stabilize production processes and minimize the interference of external factors on the supply chain by improving the quality of internal control, ensuring compliance operations, and meeting environmental standards.
Based on the above analysis, this study proposes the following hypothesis:
Hypothesis 3: Corporate green transformation significantly mitigates the bullwhip effect by improving internal control quality.
2.4. The Role of Green Transformation and Internal Profitability Volatility in Mitigating the Bullwhip Effect
The role of corporate green transformation in reducing the bullwhip effect by lowering profitability volatility can be explained from multiple perspectives.
Firstly, from the perspective of resource-based theory, green transformation encourages enterprises to improve operational efficiency by adopting more efficient resource allocation strategies and optimizing production processes, which directly contributes to the stability of profits [51]. More stable profit expectations enable firms to make more rational production decisions, avoiding excessive reactions to market fluctuations, thereby effectively mitigating the occurrence of the bullwhip effect.
At the same time, dynamic capabilities theory suggests that firms must possess the ability to adapt and adjust their resources and capabilities in response to constantly changing external environments [52]. Green transformation plays a key role in helping enterprises develop this adaptive capability. By upgrading management models, enterprises can better cope with fluctuations in market demand, reducing profitability volatility caused by uncertainty. Therefore, through green transformation, firms can enhance their adaptability and production flexibility, enabling supply chain participants to respond more quickly and accurately to market changes, which in turn reduces instability in the supply chain and mitigates the bullwhip effect.
Furthermore, green transformation drives the optimization of lean production and inventory management practices. Lean production methods, by eliminating unnecessary production redundancies and inventory accumulation, allow firms to adjust production schedules more flexibly and respond more efficiently to market demand fluctuations. This optimization not only enhances production efficiency but also prevents supply chain imbalances caused by excessive reactions to market fluctuations, thereby reducing profitability volatility and further curbing the amplification of the bullwhip effect.
Based on the above analysis, this study proposes the following hypothesis:
Hypothesis 4: Corporate green transformation significantly mitigates the bullwhip effect by lowering profitability volatility.
Q3. The literature review and hypothesis development section lacks a strong theoretical foundation, as it states that green transformation enhances supply chain transparency but does not ground this claim in frameworks like Transaction Cost Economics or the Resource-Based View. The hypotheses are weakly developed, with minimal explicit connections to prior literature. The section also relies on overly general arguments, such as the claim that green technologies like IoT and big data improve forecasting, without addressing inefficiencies such as data misinterpretation or delays in technological adoption.
Response：
We sincerely appreciate your detailed feedback on the literature review and hypothesis development section of our manuscript. Your comments have been instrumental in helping us improve this crucial part of our study.
- Strengthening the Theoretical Foundation We acknowledge the initial lack of a strong theoretical basis. In the revised version, we've incorporated Transaction Cost Economics and the Resource-Based View more comprehensively. For example, in the context of green transformation enhancing supply chain transparency, we've explained how advanced information technologies adopted during this process reduce information - related transaction costs (TCE) and act as valuable, unique resources for competitive advantage (RBV).
- Hypothesis Development The hypotheses have been significantly enhanced. Each hypothesis is now more explicitly linked to prior literature and relevant theoretical frameworks. For Hypothesis 1 (green transformation mitigates the bullwhip effect through supply chain information sharing), we've referenced multiple studies that support the role of information sharing in reducing transaction costs and building competitive advantage. Similar improvements have been made for Hypothesis 2 (green technological innovation), Hypothesis 3 (internal control quality), and the newly added Hypothesis 4 (lowering profitability volatility).
- Addressing Over-General Arguments We've rectified the over - general nature of our previous arguments. We now discuss in detail the inefficiencies such as data misinterpretation and delays in technological adoption. We've cited relevant literature to support our points on how these inefficiencies can occur with green technologies like IoT and big data analytics, and how they can impact the mitigation of the bullwhip effect.
Q4. The data sources section is underdeveloped, as it does not justify the choice of the 2008–2022 sample period in relation to economic trends or policy changes. Additionally, the process of data cleaning is insufficiently detailed, lacking statistics on how much data was removed at each step. There is no discussion on whether the final dataset is representative of the broader A-share market or if selection biases are introduced. Furthermore, the study does not conduct robustness checks to address survivorship bias or missing data concerns.
Response：
Revised Data Sources Section：
In this study, we focus on China's A-share listed companies from 2008 to 2022. The choice of this sample period is closely tied to significant economic developments and policy changes in China during this time. The global financial crisis of 2008 had a profound impact on China’s economy, prompting substantial adjustments in both the economic environment and corporate operations. In response, the Chinese government implemented various policy measures, including proactive fiscal policies and moderately loose monetary policies, aimed at stimulating economic growth. Additionally, during this period, China made notable advancements in its environmental protection policies and sustainable development strategies, providing a unique context to examine the relationship between corporate green transformation and the bullwhip effect. Analyzing data from this period allows us to better understand how companies adapted to external economic shocks and policy shifts in their operations.
Data Cleaning Process:
The sample data was processed according to the following standards:
Exclusion of Financial Companies: Financial companies were excluded from the sample due to their distinct financial structures, business models, regulatory requirements, and risk characteristics, which significantly differ from those of non-financial companies. This exclusion was necessary as including financial companies could distort the analysis. A total of 1,044 financial company samples were removed.
Exclusion of ST Companies: Companies labeled as “ST” (Special Treatment) during the sample period were also excluded. These companies typically face significant financial distress and operational challenges, such as consecutive losses and excessive debt-to-asset ratios, which result in performance that diverges significantly from that of normal companies. Including such companies could distort the analysis. A total of 1,478 ST company samples were removed.
Exclusion of Incomplete Data: Companies with incomplete data during the sample period were removed to ensure the integrity and accuracy of the analysis. This step led to the removal of 21,083 samples with missing or inconsistent data.
After these data cleaning procedures, the final dataset consisted of 24,406 firm-year observations. To assess the representativeness of the final dataset and the potential for selection bias, the following factors were carefully considered:
Industry Distribution: The final dataset covers all non-financial industries in China, including manufacturing, consumer goods, high-tech, and services. The industry distribution of the final dataset is consistent with the overall structure of China’s A-share market, suggesting that the sample adequately reflects the industrial composition of non-financial listed companies.
Geographic Distribution: The sample includes listed companies from all 31 provinces, autonomous regions, and municipalities in China, ensuring a geographically diverse representation of business operations across the country.
Robustness Checks:
To further enhance the reliability of the study’s results, several robustness checks were conducted, including:
Propensity Score Matching (PSM): PSM was employed to control for sample selection bias and ensure that the research results are not affected by bias in sample selection.
Entropy Balancing: Entropy balancing was used to verify the representativeness of the sample and ensure that it accurately reflects the overall population across multiple dimensions.
Two-Stage Least Squares (2SLS): 2SLS was employed to address potential endogeneity issues, further enhancing the robustness of the results.
These robustness checks effectively address issues related to survivorship bias, missing data, and selection bias, ensuring the reliability and validity of the analysis.
Q5. In terms of variable selection, the justification for the dependent variable is unclear, as the study uses the Bray and Mendelson approach to measure supply and demand fluctuations without discussing its applicability to the Chinese market. The mathematical notation in equations is not adequately explained, with key variables introduced abruptly. The green transformation indicator system relies on entropy weighting but does not discuss why this method is preferable to alternatives like PCA or DEA. Endogeneity remains a significant issue, as green transformation may be endogenous to firm performance, yet the study does not employ instrumental variable techniques to address this. Additionally, the selection of some indicators, such as operating costs and selling expenses, lacks a clear rationale in relation to green transformation.
Response：
We sincerely appreciate your valuable feedback on the variable selection section of our manuscript. In response to the issues you raised, we have made the following improvements and explanations:
Rationale for the Dependent Variable. In the revised version, we have added a discussion on the applicability of the Bray and Mendelson method to the Chinese market. We clarified that this method, by quantifying the impact of supply and demand fluctuations on corporate green transformation, has certain general applicability, particularly when considering the complexity and dynamics of the Chinese market. We also explained how this method provides an accurate tool for measuring supply chain fluctuations and is applicable for assessing supply and demand relationships in the process of green transformation for Chinese enterprises.
Mathematical Notation in the Equations. We have further explained the mathematical notation in the equations, ensuring that the introduction of all key variables is sufficiently elaborated, so readers can better understand the mathematical expressions and their context.
Selection of Green Transformation Indicator System. Regarding the selection of the green transformation indicator system, we have added more discussion on the entropy weighting method in the revised version, explaining why we chose this method over alternatives like Principal Component Analysis (PCA) or Data Envelopment Analysis (DEA). The advantage of the entropy weighting method is that it can objectively assign weights, avoiding the interference of subjective human factors. Additionally, this method can effectively handle the heterogeneity and uncertainty of multiple indicators. Therefore, we believe that the entropy weighting method provides a more scientific and reasonable weight distribution when evaluating the comprehensive impact of green transformation across multiple dimensions.
Endogeneity Issues. We fully understand the potential impact of endogeneity on our research results. To address this issue, we have added a discussion on endogeneity problems, including sample selection bias and reverse causality. Specifically, we use Propensity Score Matching (PSM) and entropy balancing to solve endogeneity problems, and apply the 2SLS instrumental variable technique to resolve endogeneity concerns.
Theoretical Basis for Indicator Selection. We have further clarified the theoretical basis for the selection of indicators, particularly the relationship between operating costs, sales expenses, and green transformation. In the process of green transformation, a company's operating costs and sales expenses are key dimensions for measuring the effects of transformation and resource optimization. These indicators directly reflect the impact of green transformation on cost structure and production efficiency. Therefore, selecting these indicators helps better reveal the substantive impact of green transformation.
The relevant modifications can be found in section “3.2. Variable Setting”.
Q6. The empirical methodology section has several weaknesses, including a lack of details on model specification. The study employs OLS regression without explaining whether fixed or random effects were tested or why OLS was deemed appropriate. Control variables, such as financial background and institutional investor ownership, are included without sufficient justification regarding their relevance to green transformation. Although the text mentions industry and year fixed effects, it does not specify how they were implemented. The study also fails to discuss potential omitted variable bias, as unobservable factors like firm culture or government subsidies might influence the results. Finally, there is no mention of robustness tests, alternative regression models like 2SLS or panel GMM, or sensitivity analyses to validate the findings.
Response：
Thank you for your insightful feedback. We greatly appreciate your valuable suggestions, which have helped improve the robustness and clarity of our empirical methodology. Below are our responses to your concerns regarding the methodology, particularly the model specification, sensitivity analyses, and robustness checks.
Model Specification and Choice of OLS: We carefully considered both fixed-effect and random-effect models and conducted a Hausman test to assess the suitability of these models. The test results failed to reject the null hypothesis, suggesting that a random-effect model might be appropriate. However, after further diagnostic tests, we found that the assumptions required for the random-effect model were not fully satisfied in our dataset. Based on these findings, we decided to proceed with the Ordinary Least Squares (OLS) regression method. We believe that OLS provides the Best Linear Unbiased Estimator (BLUE) under standard assumptions, which is confirmed by our diagnostic checks. Additionally, to control for potential bias from industry and year-specific factors, we used dummy variables to include industry and year fixed effects in the empirical analysis. This method aims to eliminate potential biases from industry and year-specific factors.
The relevant modifications can be found in section “3.3. Empirical Methodology ”.
3.3. Empirical Methodology
To study the impact of green transformation on the bullwhip effect, we first considered both fixed-effect and random-effect models and conducted a Hausman test. The test results failed to reject the null hypothesis, indicating that the random-effect model might be appropriate. However, further diagnostic tests revealed that the key assumptions required for the random-effect model were not fully satisfied in our dataset.
Given these findings, the study opted for the Ordinary Least Squares (OLS) regression method, which provides the Best Linear Unbiased Estimator under standard assumptions, including linearity, zero conditional mean of the error term, homoscedasticity, and no perfect multicollinearity. Diagnostic checks confirmed that our dataset satisfies these fundamental OLS assumptions, making it a suitable and statistically robust approach for estimating the impact of green transformation on the bullwhip effect.
（3）
Specifically, BWEi,t is the independent variable, representing the bullwhip effect, with indices i and t denoting the firm and year, respectively. GTi,t is the dependent variable, representing corporate green transformation. Based on previous research [54,55], the following variables are selected as control variables: firm size (Size), operating cash flow ratio (Cash), return on assets (ROA), revenue growth rate (Growth), debt ratio (Debt), dual role (Dual), financial background (Background), ownership balance (Balance), institutional investor ownership (Institutional), and average age of management (Age). Specifically, the control variables at the firm level include company size (Size), operating cash flow ratio (Cash), return on assets (ROA), revenue growth rate (Growth), and debt ratio (Debt). These variables primarily reflect characteristics such as firm size, financial condition, and operational efficiency, which directly impact the firm's ability to implement green transformation and the effectiveness of such efforts. At the entrepreneur level, the control variables include dual roles (Dual), financial background (Background), and the average age of management (Age). The dual roles may lead to centralized decision-making, which can influence the adoption of green transformation and the firm's response to supply chain disruptions. Executives with a financial background may be more inclined to adopt strict financial controls, which could result in overly conservative supply chain management strategies, thereby exacerbating the bullwhip effect, as they may focus on short-term financial goals and overlook the long-term impact of supply chain fluctuations. The average age of management reflects the team's experience and inclination towards innovation, with younger management teams potentially being more willing to adopt emerging green transformation strategies. At the ownership structure level, the control variables include ownership balance (Balance) and institutional investor shareholding ratio (Institutional). A balanced ownership structure can bring diverse perspectives to decision-making, influencing the firm's decisions and execution in the green transformation process. Institutional investors, who are typically concerned with long-term investment returns and ESG (environmental, social, and governance) issues, may encourage green transformation and influence supply chain management strategies.
ɑ0 is the constant term; ɑ1 is the coefficient for the impact of corporate green transformation on the bullwhip effect, and it is expected to be significantly negative, indicating that corporate green transformation effectively reduces the bullwhip effect; ∑Year and ∑Industry represent year and industry fixed effects, where year and industry dummy variables are introduced into the regression model to effectively eliminate the influence of industry and year factors, ensuring the accuracy of the regression results; and ε is the random error term.
Regarding the selection of control variables. We sincerely appreciate the reviewer’s valuable comments on the selection of control variables. In the revised manuscript, we have provided a more detailed explanation of the rationale behind the selection of control variables. Additionally, we have categorized these variables according to different levels: the entrepreneur level, the firm level, and the ownership structure level. The relevant modifications can be found in section “3.3 Empirical Methodology”.
3.3. Empirical Methodology
Specifically, the control variables at the firm level include company size (Size), operating cash flow ratio (Cash), return on assets (ROA), revenue growth rate (Growth), and debt ratio (Debt). These variables primarily reflect characteristics such as firm size, financial condition, and operational efficiency, which directly impact the firm's ability to implement green transformation and the effectiveness of such efforts. At the entrepreneur level, the control variables include dual roles (Dual), financial background (Background), and the average age of management (Age). The dual roles may lead to centralized decision-making, which can influence the adoption of green transformation and the firm's response to supply chain disruptions. Executives with a financial background may be more inclined to adopt strict financial controls, which could result in overly conservative supply chain management strategies, thereby exacerbating the bullwhip effect, as they may focus on short-term financial goals and overlook the long-term impact of supply chain fluctuations. The average age of management reflects the team's experience and inclination towards innovation, with younger management teams potentially being more willing to adopt emerging green transformation strategies. At the ownership structure level, the control variables include ownership balance (Balance) and institutional investor shareholding ratio (Institutional). A balanced ownership structure can bring diverse perspectives to decision-making, influencing the firm's decisions and execution in the green transformation process. Institutional investors, who are typically concerned with long-term investment returns and ESG (environmental, social, and governance) issues, may encourage green transformation and influence supply chain management strategies.
Sensitivity Analysis: In response to your concerns about the robustness of our findings, we conducted several sensitivity tests. These tests explored the potential impact of factors such as regional environmental pressures (e.g., Yangtze River Economic Belt), industry pollution characteristics (e.g., heavy-pollution industries), and time trends. Each test showed that the results remained consistent and supported our hypotheses. These analyses help ensure the robustness of our conclusions and address potential confounding factors.
The relevant modifications can be found in section “4.3. Sensitivity Analyses”.
4.3. Sensitivity Analyses
Next, this study conducts a series of sensitivity tests to further verify the robustness of the baseline analysis results. The sensitivity tests primarily address three concerns:
(1) Regional Environmental Pressure Differences. The Yangtze River Economic Belt plays a key role in the national ecological strategy, shouldering the responsibility of protecting the ecological environment of the Yangtze River. Enterprises in this region often face stricter environmental regulations and greater social scrutiny compared to those in other regions, which motivates them to undergo green transformation at a higher level and to a greater extent. If not controlled, this could potentially interfere with the study’s results. Therefore, in the sensitivity tests, the sample of enterprises located in the Yangtze River Economic Belt is excluded for analysis. As shown in Column (1) of Table 9, the coefficient of GT remains significantly negative, and the results continue to support the research hypothesis.
(2) Industry Pollution Characteristics. Heavy-pollution industries, due to the significant environmental impact of their production activities, are often subject to early and deeper green transformation under the influence of policy guidance and social attention. This could lead to the higher level of green transformation in these firms potentially confounding the relationship between corporate green transformation and the bullwhip effect. To address this concern, the study selects a sample of enterprises from non-heavy-pollution industries for sensitivity analysis. As shown in Column (2) of Table 9, the coefficient of GT remains significantly negative, and the results continue to support the research hypothesis.
(3) Time Trends. In recent years, with the deepening of the green development concept and strong policy support, the level of corporate green transformation has shown rapid growth. This surge may cause the observed relationship between corporate green transformation and the bullwhip effect to be concentrated in the later years of the sample period, potentially affecting the generalizability of the findings. To address this concern, the sample is divided into early and late periods, and separate regression analyses are conducted. As shown in Columns (3) and (4) of Table 9, the coefficient of GT remains significantly negative, and the results continue to support the research hypothesis.
Robustness Tests: To further validate the robustness of the results, we included additional tests such as the interaction effects of year and industry fixed effects, difference-in-differences (DID) variables for carbon emission policies and green finance pilot zones, as well as using Total Factor Productivity (TFP) as a proxy for green transformation. At the same time, when adding extra control variables, we also considered factors such as government subsidies received by the firm (represented by the natural logarithm of the subsidies received) and regional cultural values (the number of Confucius Institutes in the firm's region). These tests confirmed the consistency of our findings.
The relevant modifications can be found in section “4.4. Robustness Test”.
4.4. Robustness Test
First, this paper conducts robustness tests by incorporating the interaction effects between year and industry. Specifically, the year fixed effect can control for heterogeneity across time, while the industry fixed effect can eliminate heterogeneity at the industry level. However, the impacts of these fixed effects may not be independent; there could be interaction effects. Different industries may be influenced to varying degrees at different times, or certain industries may exhibit trends distinct from others in specific years. Therefore, by introducing the interaction fixed effects of year and industry, we can uncover this potential interaction. As shown in Column (1) of Table 10, after adding the Industry*Year fixed effects, the results remain unchanged.
Second, to promote sustainable development and achieve harmonious coexistence between humans and nature, the Chinese government has introduced multiple environmental policies. In June 2013, Shenzhen took the lead in launching a carbon emission trading pilot program, followed by Beijing, Shanghai, Tianjin, Chongqing, Hubei, Guangdong, and Shenzhen. In June 2017, after discussions at the State Council executive meeting, the Chinese government decided to establish the first batch of green finance pilot zones in five provinces: Zhejiang, Guangdong, Xinjiang, Jiangxi, and Guizhou. To account for the potential impact of these policies on green transformation and the bullwhip effect, this study constructs difference-in-differences (DID) variables for the implementation of carbon emission trading policies (tpfjy) and the green finance pilot zones (gfrl), and includes them as control variables in the regression equation. The regression results controlling for the carbon emission trading policy and the green finance pilot zone policy are presented in Column (2) and Column (3) of Table 10, respectively. The coefficient of GT remains significant at the 1% level, indicating that after controlling for exogenous policies, the conclusions of this study remain robust.
Third, considering the potential measurement bias of the independent variable, this study conducts a robustness test by altering the measurement method. Specifically, the study uses total factor productivity (TFP) as a proxy for corporate green transformation, primarily because TFP provides a more comprehensive reflection of a firm's resource utilization efficiency and productivity improvement. As shown in Column (4) of Table 10, after changing the measurement method for corporate green transformation, the research findings remain robust.
Fourth, to address the potential impact of omitted variables, this study includes additional control variables for the robustness check, including inventory turnover ratio (In_Turnover), accounts payable ratio (Ac_Payable), investment inefficiency (In_Investment), government subsidies (Subsidy), and regional cultural values (Culture). As shown in Column (5) of Table 10, after adding these additional control variables, the research conclusions remain unchanged.
Endogeneity: We agree that endogeneity is an important issue and have taken measures to address it. We reduced sample selection bias by using Propensity Score Matching (PSM) and Entropy Balancing Method (EBM), and addressed potential reverse causality issues through the Two-Stage Least Squares (2SLS) approach. The results obtained from these methods continue to support our hypothesis that green transformation significantly mitigates the bullwhip effect.
The relevant modifications can be found in section “4.4. Robustness Test”.
4.4. Robustness Test
To address potential endogeneity issues, this study considers sample selection bias and reverse causality. To mitigate endogeneity arising from sample selection bias, we employ Propensity Score Matching (PSM) and Entropy Balancing Method (EBM). Specifically, PSM reduces endogeneity bias caused by sample selection by matching the treatment and control groups, while EBM further reduces the impact of endogeneity by optimizing sample weights. To resolve the reverse causality issue, we apply the Two-Stage Least Squares (2SLS) instrumental variable approach, selecting appropriate instrumental variables to eliminate interference from reversed causal relationships, thereby ensuring the validity and consistency of the estimation results.
As shown in Columns (1) and (2) of Table 11, after applying PSM, the coefficient of GT remains significantly negative, indicating that green transformation has a significant inhibiting effect on the bullwhip effect. Similarly, the research findings remain unchanged after using the Entropy Balancing Method (see Column (3) of Table 11). Overall, considering the potential endogeneity issues arising from sample selection bias, the conclusions still support the theoretical analysis.
To address the potential reverse causality issue (i.e., firms with stronger bullwhip effects may actively pursue green transformation strategies, thereby influencing the research results), we adopt the 2SLS instrumental variable approach. The instrumental variable is the average green transformation of firms within the same year, region, and industry. As shown in Columns (4) and (5) of Table 11, the coefficient of GT remains significantly negative, and the research conclusions remain consistent.
Q7. The mediation analysis faces several limitations. The study uses green patent applications as a proxy for innovation capability, but this does not fully capture firms' overall innovation efforts, and other indicators like R&D expenditure could provide a more comprehensive view. There are also causality concerns, as the study assumes that green transformation increases innovation capability without accounting for potential reverse causality. Additionally, the mediation analysis does not test alternative pathways that may influence the relationship between green transformation and the bullwhip effect. Similar issues arise in the analysis of internal control quality, where the study uses the DIBO internal control index as a proxy but does not address whether this metric fully captures firm-specific internal control mechanisms. There is also a potential overlap between internal control quality and innovation capability as mediators, yet the study does not examine their interactions. The study lacks robustness checks using alternative measures of internal control quality, which weakens the validity of its findings.
Response：
Regarding the external review comments, your response can be improved and supplemented as follows:
Proxy variable for innovation capability. Thank you for your valuable suggestions on the proxy variable for innovation capability. We have acknowledged that although using green patent applications as a proxy can measure green innovation, it may not fully capture a firm’s overall innovation activities. Therefore, we have added a discussion of R&D investment in the revised version, using it as a supplementary indicator for innovation capability. However, considering the potential bidirectional causality between green transformation and R&D investment, directly using current R&D investment as a proxy might lead to reverse causality issues. For example, green transformation may drive firms to increase R&D investment, while R&D investment itself might influence the green transformation decision. To mitigate this endogeneity problem, we use lagged R&D investment (L1.R&D) as the proxy. This approach helps break the causal link between current R&D investment and green transformation decisions, focusing the causal relationship on prior R&D decisions rather than current impacts, thus alleviating reverse causality and ensuring the results more accurately reflect the impact of R&D investment on green transformation.
Alternative paths in mediation analysis. Regarding the alternative paths in mediation analysis, as pointed out by the reviewer, in the revised version, we have further explored the mediating effects of various potential variables on the relationship between green transformation and the bullwhip effect. Initially, we examined mediating variables such as supply chain information sharing, innovation capability, and internal control quality. Now, we have further included profitability volatility as a new mediator. The choice of profitability volatility is based on several reasons. Theoretically, profitability volatility directly reflects a firm's profit stability. Green transformation can influence profitability stability by adjusting business structures, which in turn affects supply chain activities and the bullwhip effect. At the same time, aligned with dynamic capability theory, green transformation improves dynamic capabilities by reducing profitability volatility, thereby stabilizing profits and helping firms operate steadily within the supply chain, reducing the bullwhip effect. Practically, profitability is a key concern for firms, and green transformation investments impact profitability, subsequently influencing profitability volatility, which is closely related to supply chain decisions and the bullwhip effect. Furthermore, profitability volatility indirectly reflects the effectiveness of supply chain operations—if green transformation leads to supply chain optimization, it will result in stable profits; conversely, large profit volatility suggests problems in supply chain operations, manifesting as the bullwhip effect.
Proxy variable selection for internal control quality. Regarding the proxy variable for internal control quality, we have used the natural logarithm of the DIBO internal control index in our study. We acknowledge that the DIBO index has certain limitations, as it may not comprehensively capture the unique internal control mechanisms of firms. However, it is important to emphasize that this index is widely used in academic research as a reliable tool for assessing internal control quality. It provides a comprehensive view of a firm’s overall internal control environment, covering aspects such as control activities, risk assessment, and communication of information. To further enhance the reliability of our conclusions, we do not rely solely on this single proxy. In addition to the DIBO internal control index, we also introduce another variable to measure internal control quality: when a firm has deficiencies in internal control, this variable takes a value of 1, and when no deficiencies are present, it takes a value of 0. Our analysis shows that even when using different measures, the results remain consistent. This finding strongly supports that internal control quality plays a stable mediating role in the relationship between green transformation and the bullwhip effect. This not only validates the robustness of our findings but also provides a solid empirical foundation for understanding the internal relationships among these variables.
Overlap and interaction between mediating variables. In the current research, we focus on testing each mediating variable individually to clearly identify their contributions to the relationship between green transformation and the bullwhip effect. Regarding internal control quality and innovation capability, we acknowledge that there may be some potential overlap. For example, good internal control quality may create a stable and regulated environment for innovation activities, indirectly promoting innovation capability. Once innovation capability is enhanced, new management processes and technological applications may be introduced, which could, in turn, optimize existing internal control systems. However, we believe there is some rationale in our research approach of testing each mediator separately first and then progressing. When exploring the complex relationships between green transformation, the bullwhip effect, and multiple mediating variables, we think it is necessary to first establish the basic relationship between each mediator, the independent variable (green transformation), and the dependent variable (bullwhip effect). We acknowledge that this approach may have certain limitations, but at this stage, we hope it will lay a solid foundation for further in-depth research.
Q8. The heterogeneity analysis has several shortcomings. The classification of firms into state-owned and non-state-owned enterprises is overly simplistic, as it does not consider within-group variations such as firm size, industry regulations, or government support programs. The study does not examine how government policies might influence the effectiveness of green transformation in state-owned enterprises. Similarly, the distinction between technology-intensive and non-technology-intensive industries does not fully capture differences in business models, supply chain structures, or regulatory pressures.
Response：
Thank you for your valuable feedback on our paper. Your comments have made us realize that there are indeed areas in the heterogeneity analysis that could be further refined and improved. Below is our response to your main points:
Classification of state-owned and non-state-owned enterprises: We understand your concern that the classification of state-owned and non-state-owned enterprises is overly simplistic. Indeed, there is significant heterogeneity within firms, and classifying solely by ownership may not accurately reflect the diversity within the enterprises. To address this, in the revised manuscript, we have further segmented the sample and conducted a more in-depth analysis based on firm size and government subsidies for both state-owned and non-state-owned enterprises. This refinement better reveals the differences in the performance of different types of firms in the green transformation process. For example, smaller non-state-owned enterprises and those receiving more government subsidies exhibit a more pronounced suppression of the bullwhip effect in their green transformation processes. Additionally, we have included a discussion on how government policies affect the green transformation of state-owned enterprises, elaborating on how policy support can influence the transformation outcomes by alleviating financial pressure and reducing uncertainty for non-state-owned enterprises.
Classification of technology-intensive and non-technology-intensive industries: We fully agree with your point that the industry classification is overly simplistic. There are differences in business models, supply chain structures, and regulatory pressures within industries, and these variations can impact the effectiveness of green transformation. Therefore, in the revised manuscript, we have further refined the industry classification. In addition to considering technological intensity, we have also incorporated factors such as supply chain efficiency and external regulatory pressures. This allows for a more thorough representation of the heterogeneity within technology-intensive and non-technology-intensive industries. Specifically, we provide a more detailed analysis of how innovation-driven green transformation and regulatory pressures in technology-intensive industries impact the suppression of the bullwhip effect.
The relevant modifications can be found in section “4.6. Heterogeneity Analysis”.
4.6. Heterogeneity Analysis
In this section, the study further discuss the potential heterogeneity results based on differences in corporate characteristics.
The sample is first divided into state-owned and non-state-owned enterprises based on ownership type. To account for within-group differences, further subdivisions are made based on firm size and government subsidies. As shown in Table 16, the suppressive effect of green transformation on the bullwhip effect is significant only in small-scale non-state-owned enterprises and non-state-owned enterprises receiving more government subsidies.
Small-scale non-state-owned enterprises benefit from a simpler organizational structure and shorter decision-making processes, allowing them to quickly adjust strategies, change production methods, or adopt new technologies during the green transformation. Additionally, with narrower product lines, these firms can focus resources on green transformation, optimizing resource allocation, reducing waste and cost fluctuations, stabilizing operations, and effectively mitigating the bullwhip effect. These enterprises also tend to focus on niche markets and engage in differentiated competition, with green transformation serving as a competitive advantage that attracts eco-conscious consumers and enhances product value. Facing greater market competition, these firms are more motivated to transform, making the impact of green transformation on the bullwhip effect more pronounced.
In non-state-owned enterprises receiving more government subsidies, these subsidies provide direct financial support for green transformation, helping firms purchase equipment, introduce technologies, and conduct research and development. This alleviates financial pressure and reduces uncertainty and cost fluctuations during the transformation process. The subsidies not only lower transformation costs but also enhance product pricing competitiveness, stabilize market share and sales revenue, and reduce supply chain volatility. Additionally, the policy guidance and resource allocation accompanying government subsidies bring more resources and collaboration opportunities, helping establish stable supply chains and improving efficiency. The government's support sends positive signals to the market, boosting confidence and attracting more resources, which facilitates the success of green transformation, stabilizes operations, and ultimately mitigates the bullwhip effect.
However, in state-owned enterprises, the suppressive effect of green transformation on the bullwhip effect is not significant. This is due to the complex institutional and decision-making mechanisms in state-owned enterprises, which are subject to greater administrative intervention and slower decision-making processes. The performance evaluation criteria tend to focus more on traditional metrics, leading to insufficient emphasis on green transformation and weaker motivation for change. Additionally, the large scale of state-owned enterprises results in higher sunk costs and greater difficulty in transformation. Despite easier access to resources, the urgency of transformation to enhance competitiveness is less recognized due to resource dependence. Moreover, state-owned enterprises bear multiple social responsibilities, and their business objectives are more diverse. When facing conflicting goals, they often prioritize more urgent tasks, with decision-making involving numerous factors and a more cautious approach. These factors collectively impact the effectiveness of green transformation in suppressing the bullwhip effect.
Next, the sample is divided into technology-intensive and non-technology-intensive industry groups based on industry characteristics. To account for within-group differences, further subdivisions are made based on supply chain efficiency and external environmental regulatory pressures. As shown in Table 17, in the technology-intensive industry group, green transformation significantly suppresses the bullwhip effect, while in the non-technology-intensive industry group, the effect is not significant.
In technology-intensive industries, firms typically rely on advanced technologies and innovation to drive development, making them more focused on applying and improving technology during green transformation. These industries often possess higher R&D capabilities, allowing them to optimize production models and supply chain management through new technologies, thus improving efficiency, reducing resource waste, and minimizing cost fluctuations. This innovation-driven green transformation helps better cope with fluctuations in the supply chain and effectively mitigates the bullwhip effect. Moreover, technology-intensive industries are often subject to strong environmental regulatory pressures, which drive firms to pay more attention to green development. By improving environmental management and optimizing production processes, they can further reduce supply chain fluctuations and uncertainties, thereby suppressing the bullwhip effect.
In contrast, non-technology-intensive industries tend to invest less in technology R&D and innovation, and their production and supply chain management methods are more traditional. Green transformation has a relatively smaller impact on these firms because their transformation may rely more on external resources or policy support, with limited technological innovation and management optimization during the process. Therefore, regardless of supply chain efficiency or the level of environmental regulatory pressure, the effect of green transformation in non-technology-intensive industries is not as pronounced as in technology-intensive industries and fails to significantly mitigate the bullwhip effect.
Q9. Regarding the environmental background of the board, the study assumes that board members’ environmental expertise influences green transformation decisions but does not analyze whether this effect varies based on board size, independence, or diversity. There are endogeneity concerns, as firms that prioritize green transformation may actively seek to appoint board members with environmental expertise, creating potential reverse causality. Additionally, the study does not include a longitudinal analysis to assess how the influence of environmentally focused board members evolves over time or aligns with broader corporate governance trends.
Response：
Thank you for your valuable feedback on our paper. Your comments have made us realize that there are indeed some aspects that can be further refined in the analysis of the impact of board members' environmental background on green transformation decisions. Below are our responses to your main points:
Endogeneity Concerns: We fully understand your concerns regarding endogeneity. To address this issue, we have employed the Propensity Score Matching (PSM, 1:4) method in the revised version to mitigate the impact of endogeneity. We used the matched sample data to conduct the heterogeneity analysis. This approach allows us to more accurately assess the impact of board members' environmental backgrounds on green transformation, thereby providing more reliable conclusions.
Impact of Board Size, Independence, and Diversity: In the revised version, we further explore the potential differences in the impact of board members' environmental backgrounds based on board size and independence. Our analysis shows that, in firms with smaller boards and greater authority for independent directors, board members with environmental backgrounds can more effectively drive green transformation and suppress the bullwhip effect. In firms with smaller boards, decision-making processes are more efficient and internal communication is smoother, allowing the company to respond quickly to market changes and external environmental pressures. As a result, when these firms appoint board members with environmental backgrounds, these members can directly participate in the formulation and implementation of green transformation strategies, promoting the adoption of green technologies and management practices, optimizing supply chain management, reducing resource waste, and cost fluctuations, thus effectively mitigating the bullwhip effect.
Longitudinal Analysis: Although this study did not include a longitudinal analysis, we acknowledge that this could further enhance the understanding of how the influence of environmentally focused board members evolves over time. To address this, we have added more detailed discussions in the revised version regarding the role of board members' environmental backgrounds in firms with different characteristics. Specifically, we explore how these members can be more effective in promoting green transformation in firms with low financing constraints and high managerial incentives, which in turn reduces the bullwhip effect.
The relevant modifications can be found in section “4.6. Heterogeneity Analysis”.
4.6. Heterogeneity Analysis
Finally, the sample is divided based on the environmental background of the board members. It is important to note that companies that prioritize green transformation are more likely to actively seek board members with environmental expertise, which may introduce endogeneity between green transformation and the environmental background of board members. To address this issue, this study employs the Propensity Score Matching (PSM, 1:4) method during the heterogeneity analysis. This approach helps more accurately assess the impact of board members' environmental backgrounds on green transformation, mitigating estimation bias arising from the company's active selection of board members with environmental expertise, thus leading to more reliable conclusions.
As shown in Table 18, in firms with smaller boards and greater authority for independent directors, board members with environmental backgrounds can more effectively enhance the green transformation's ability to suppress the bullwhip effect. In firms with smaller boards, decision-making processes are more efficient and internal communication is smoother, enabling companies to respond quickly to market changes and external environmental pressures. Therefore, when such firms appoint board members with environmental backgrounds, these members can directly participate in the formulation and implementation of green transformation strategies, driving the adoption of innovative green technologies and management practices, optimizing supply chain management, reducing resource waste and cost fluctuations, and thus effectively mitigating the bullwhip effect. In firms with more authority for independent directors, these directors provide independent insights and decision-making advice, prompting the company to prioritize green transformation issues and make effective adjustments in areas such as compliance with environmental regulations, promoting technological innovation, and optimizing resource allocation. Independent directors with environmental expertise can leverage their knowledge to promote the implementation of environmental policies and actions, reducing uncertainties and fluctuations in the supply chain, thereby strengthening the effects of green transformation and better suppressing the bullwhip effect.
As shown in Table 19, in enterprises with low financing constraints and high managerial incentives, board members with environmental backgrounds can more effectively promote green transformation, thereby mitigating the bullwhip effect. For enterprises with low financing constraints, obtaining financial support is easier, providing sufficient funding for green transformation. With these funds, companies can invest more resources in technological innovation, optimizing green production methods, and improving supply chain management.
In enterprises with high managerial incentives, management typically places greater emphasis on the company's long-term development, creating stronger motivation to drive green transformation to enhance competitiveness and sustainable development capabilities. In such corporate environments, board members with environmental backgrounds can leverage their expertise to provide professional advice and decision-making support on environmental matters. They drive the specific implementation of green transformation strategies, optimize resource allocation, reduce uncertainties and fluctuations in the supply chain, and ultimately effectively suppress the bullwhip effect.
Q10. A structural weakness of the study is the lack of a dedicated discussion of findings section, which limits the depth of interpretation and contextualization of the results. The absence of a discussion hinders the ability to connect empirical results to theoretical implications, practical applications, and broader economic considerations.
Response：
We sincerely appreciate your insightful comment regarding the lack of a dedicated discussion section for findings in our initial manuscript. We've taken this criticism to heart and have made comprehensive revisions to address this structural weakness.
In the revised version, we've added a detailed "Conclusion and Suggestion" section, which is divided into four sub - sections: "Conclusion", "Theoretical contributions", "Managerial implications", and "Limitations and future research".
In the "Conclusion" part, we summarize the key findings of our study, highlighting how green transformation mitigates the bullwhip effect in Chinese enterprises through various means. We also point out the heterogeneity of this effect among different types of enterprises.
The "Theoretical contributions" section delves into the theoretical significance of our findings. We compare our research perspective with existing literature, emphasizing that our focus on the impact of green transformation on the bullwhip effect offers a new approach to understanding the intersection of environmental sustainability and supply chain management. Additionally, we explain how our exploration of the internal mechanisms and the influence of different characteristics enriches the current theoretical knowledge in this field.
For "Managerial implications", we provide practical guidance for both policymakers and entrepreneurs. We discuss how companies should view green transformation as a strategic approach to enhance supply chain management, and suggest that policymakers strengthen policy support and improve relevant frameworks.
Finally, in the "Limitations and future research" section, we acknowledge the limitations of our study, such as the restricted sample scope, the use of a single econometric model, and the focus on the current situation. We then propose directions for future research, including expanding the sample, adopting interdisciplinary methods, and exploring long - term impacts.
The relevant modifications can be found in section “5.Conclusion and suggestion”.
5.Conclusion and suggestion
5.1.Conclusion
This study examines how green transformation mitigates the bullwhip effect in Chinese enterprises by analyzing data from A-share listed companies between 2008 and 2022. The findings reveal that green transformation significantly reduces supply chain volatility by enhancing information sharing, fostering innovation, optimizing management practices, and mitigating profit fluctuations. This effect is particularly pronounced in non-state-owned enterprises, technology-intensive industries, and firms with board members possessing environmental backgrounds. This research provides theoretical insights into the impact of green transformation on the bullwhip effect and offers practical guidance for optimizing supply chain strategies.
5.2.Theoretical contributions
This study makes several significant theoretical contributions to the fields of green development and supply chain management.
First, existing literature predominantly focuses on the economic consequences of green transformation, such as its impact on firm performance and innovation [61,62,63,64,65]. In contrast, this study focuses on the impact of green transformation on the bullwhip effect, providing a deeper exploration of the intersection between environmental sustainability and supply chain management. This research perspective offers a novel lens for understanding the relationship between these fields and effectively bridges the gap between sustainable development and supply chain management research.
Second, this study emphasizes the internal mechanisms through which green transformation reduces supply chain volatility, specifically highlighting aspects such as enhanced information sharing, innovation promotion, improved management practices, and reduced profit volatility. This finding broadens the scope of green transformation’s effects, extending its impact beyond merely enhancing a company’s environmental reputation to also optimizing supply chain operational efficiency.
Third, this study reveals how different characteristics, such as ownership structure and industry type, influence the effectiveness of green transformation in mitigating the bullwhip effect. This finding enriches the current literature on green supply chain management [66,67,68], providing new insights into how organizational and industry-specific factors can shape the outcomes of green transformation initiatives.
5.3.Managerial implications
This study offers valuable managerial insights for policymakers and entrepreneurs. The findings indicate that green transformation not only helps improve a company's environmental reputation but also plays a key role in enhancing supply chain efficiency and stability. Companies should recognize that green transformation should not be seen solely as a means to meet environmental compliance requirements or enhance brand reputation, but rather as a strategic approach to improving supply chain management practices and strengthening overall operational resilience. In particular, for non-state-owned enterprises and companies in technology-intensive industries, green transformation can significantly mitigate the bullwhip effect by improving information sharing, fostering innovation, and enhancing internal control quality. Furthermore, companies with a higher proportion of board members with environmental backgrounds and lower financial constraints are often more effective in promoting green transformation. Such companies are better positioned to leverage the innovation and resource optimization opportunities provided by green transformation, thereby playing a more significant role in supply chain management. Therefore, companies should increase their investment in green transformation, focusing not only on environmental protection but also on enhancing their competitiveness and strengthening supply chain resilience.
Policymakers, on the other hand, should further strengthen policy support for green transformation, such as providing government subsidies and developing differentiated incentive measures. This will help companies implement green transformation tailored to their specific characteristics, thereby promoting sustainable development across society. Additionally, the government should improve relevant policy frameworks, enhance guidance on green supply chain management, and create a more favorable environment for green transformation, providing sufficient resource support for enterprises.
5.4. Limitations and future research
Although this study yields valuable insights, it inevitably has certain limitations.
First, this research focuses solely on Chinese A-share listed companies, which may limit the generalizability of the findings. Different countries and regions have unique economic conditions and regulatory environments, and the characteristics of Chinese A-share listed companies may not fully reflect the situation in other areas. Therefore, future research could consider expanding the scope of the study to include companies from other regions or conduct cross-national comparative studies to more comprehensively assess the global applicability of the findings. Second, this study employs a multiple linear regression model to analyze the relationship between green transformation and the bullwhip effect. However, this relationship may be influenced by some unobserved factors, such as external market conditions. Future studies could incorporate interdisciplinary econometric methods to delve into these potential factors, thereby providing a more precise understanding of the underlying mechanisms linking green transformation and the bullwhip effect. Third, environmental policies and supply chain dynamics are constantly evolving. This study primarily focuses on the current state; however, future research could further explore the long-term impact of green transformation on supply chain performance. Such studies would be better suited to adapt to the changing external environment and provide more forward-looking insights for corporate decision-making and policy formulation.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsAccept in present form
Author Response
Dear Reviewers,
Thank you very much for your thoughtful review and valuable comments. We are pleased to hear that our manuscript has been accepted in its present form and meets the necessary standards for publication. We appreciate your time and effort in reviewing our work, and we will continue to maintain high standards in our future research to contribute meaningfully to both academic and practical fields.
Once again, thank you for your support and guidance during the review process.
Reviewer 3 Report
Comments and Suggestions for AuthorsAll the raised concerns have been taken into account and therefore I believe that the manuscript now is in better condition and can be accepted in the present form.
Author Response
Dear Reviewer,
Thank you for your thoughtful and constructive comments. We are pleased to hear that all of the concerns raised have been addressed, and we appreciate your feedback, which has helped improve the manuscript.
We are happy that the manuscript is now in a better condition and meets the necessary standards for acceptance. Once again, thank you for your valuable input and for taking the time to review our work.
Reviewer 4 Report
Comments and Suggestions for Authors
The authors have made significant revisions, which are appreciated. However, some key concerns remain. Endogeneity issues persist, as the justification for instrumental variable selection is not fully developed, and omitted variable bias is not comprehensively addressed. The mediation analysis could be strengthened by considering interactions between mediators and alternative causal explanations. Additionally, the rationale for variable selection, particularly the bullwhip effect measurement and green transformation indicators, could be further clarified, and the theoretical and managerial contributions would benefit from more concrete empirical support.
Author Response
Dear Reviewer,
First of all, we would like to express our sincere gratitude for the insightful comments and constructive feedback you provided on our manuscript. Your valuable suggestions have played an essential role in enhancing the quality of our research and the overall strength of the manuscript. Below, we outline our specific responses to each of the points you raised. All revisions in the manuscript are highlighted in red for your convenience.
We are truly appreciative of your consideration and the opportunity to resubmit our revised manuscript. Thank you once again for your time and effort.
Q1. Endogeneity issues persist, and the justification for the selection of instrumental variables has not been fully explained.
Response：Thank you very much for your valuable comments and suggestions. We appreciate your insightful feedback on the endogeneity issue in our study. To address your concern regarding the justification for the selection of instrumental variables, we have elaborated on the rationale behind our choice in the revised manuscript. Specifically, we explain that the endogeneity problem arises from the potential bidirectional causal relationship between corporate green transformation and the bullwhip effect. To mitigate this issue, we employ the two-stage least squares (2SLS) method, using the average green transformation of peer firms in the same region and industry (excluding the focal firm) as the instrumental variable.
We have provided a detailed explanation of why this instrumental variable satisfies the exogeneity condition. The average green transformation of peer firms reflects regional and industry-wide trends that are not influenced by the individual decisions of any specific firm, ensuring that it is correlated with the target firm’s green transformation but not directly affected by its own bullwhip effect. We believe this approach effectively mitigates the risk of endogeneity, as demonstrated by the consistent results in Columns (4) and (5) of Table 12, where the coefficient of green transformation (GT) remains significantly negative.
Q2. Omitted variable bias has not been adequately addressed.
Response：Thank you for your constructive comments regarding the issue of omitted variable bias. We appreciate your feedback and have made substantial efforts to address this concern in the revised manuscript.
To mitigate the potential impact of omitted variable bias, we have incorporated control variables that account for both internal and external factors influencing the firm. Specifically, we introduced several firm-level control variables related to internal factors, including the degree of financing constraints (KZ index), tangible asset ratio (ratio of tangible assets to total assets), current ratio (ratio of current assets to current liabilities), government subsidy intensity (ratio of government subsidies to total assets), and tax rate (ratio of corporate income tax expense to total assets). As shown in Column (1) of Table 11, after including these additional control variables, our core research findings remain robust and unchanged.
Additionally, we addressed external factors at the firm level by considering the following controls:
The location of the firm, particularly whether it is situated in a provincial capital city, as large cities generally experience higher levels of economic development, marketization, and environmental regulation. This can lead to a stronger motivation for firms to undertake green transformation, potentially overstating the impact of green transformation on the bullwhip effect. To account for this, we defined a dummy variable for whether the firm is located in a provincial capital city (Capital) and included the "Capital-Industry-Year" joint fixed effects in model (3). The corresponding regression results, shown in Column (2) of Table 11, indicate that the coefficient for green transformation (GT) remains significantly negative.
To control for the influence of social values, we defined a variable (Value) representing the number of Confucius temples and Confucian schools in each province and included the "Value-Industry-Year" joint fixed effects in model (3). As demonstrated in Column (3) of Table 11, the coefficient for GT remains significantly negative, with the research conclusions remaining consistent.
Lastly, we addressed regional variation by considering the eastern region of China, where market competition is more intense and consumer demand for green products is higher. This market pressure could incentivize firms to adopt green transformation strategies earlier, affecting the relationship between green transformation and the bullwhip effect. We included a dummy variable for whether the firm is registered in the eastern region (Eastern) and incorporated the "Eastern-Industry-Year" joint fixed effects in model (3). The regression results in Column (4) of Table 11 show that the coefficient for GT remains consistent with our baseline regression, and the research conclusions remain unchanged.
Q3. The mediation analysis could be further strengthened by considering the interactions between mediators and alternative causal explanations.
Response：We appreciate the reviewer’s valuable feedback and suggestions regarding the mediation analysis.
To address potential endogeneity between green transformation and innovation capability, we have utilized a panel instrumental variable (IV2) approach, which is explained in the revised manuscript. The introduction of a time-varying instrument, constructed from the interaction between the number of listed companies in the city and the national average level of corporate digital transformation, ensures that the results are not biased by endogeneity, providing more robust empirical support.
Q4. The rationale for variable selection, particularly the measurement of the bullwhip effect, still requires further clarification.
Response：Thank you for your valuable comments and suggestions regarding the rationale for variable selection, particularly the measurement of the bullwhip effect. We appreciate your feedback and have elaborated on the measurement approach in the revised manuscript to provide further clarification.
In this study, we measure the bullwhip effect using the ratio of production volatility to demand volatility. Specifically, production volatility is defined as the standard deviation of quarterly production, while demand volatility is defined as the standard deviation of quarterly demand. By comparing these two volatilities, we quantify the amplification of production fluctuations relative to demand fluctuations in the supply chain, which captures the intensity of the bullwhip effect. If production volatility significantly exceeds demand volatility, it indicates that production fluctuations are amplified, a characteristic feature of the bullwhip effect.
To ensure the accuracy of our measurement, we account for long-term trends and seasonal fluctuations by applying logarithmic differencing transformations to both production and demand data. This approach removes time trends, enabling us to focus on short-term fluctuations and accurately assess the impact of demand volatility on production volatility, thereby revealing the presence of the bullwhip effect in the supply chain. The specific measurement method is detailed in formulas (1) and (2), and the transformation process is clearly outlined to ensure transparency and robustness in the analysis.
Q5. The rationale for variable selection, especially the selection of green transformation indicators, still requires further clarification.
Response：Thank you for your valuable comments on the rationale for variable selection, particularly regarding the selection of green transformation indicators. We greatly appreciate your feedback and have further clarified the construction and selection of the green transformation indicators in the revised manuscript to provide a clearer explanation.
In this study, green development is an intensive development model aimed at addressing environmental, resource, and developmental challenges. Corporate green development integrates green development principles into corporate strategies, striving to achieve a win-win situation for business growth, social welfare, and environmental protection. Therefore, in constructing the green transformation indicator system, we have incorporated various characteristics of corporate green development, aiming to reflect the comprehensive benefits achieved by companies during their transformation process through a multidimensional indicator framework.
Specifically, the construction of the green transformation indicator system includes the following dimensions:
Economic Performance Dimension: Given that the ultimate goal of corporate green development is still profitability, we evaluate the economic development level of the company across multiple dimensions, including profitability, operational efficiency, debt repayment capacity, and growth potential.
Social Value Dimension: Green transformation is not only a requirement for sustainable development but also an important aspect of corporate social responsibility. Therefore, we assess the company’s contributions to society and employees, reflecting its efforts in social responsibility.
Environmental Performance Dimension: Environmental benefits are one of the core aspects of green transformation. Companies should focus on environmental management and sustainable development during the transformation process. We have selected indicators such as environmental tax and ISO9001 certification to measure the company’s environmental compliance and performance in green production.
Q6. The theoretical and managerial contributions should be further strengthened with more robust empirical support.
Response：Thank you for your valuable comments and suggestions on the theoretical and managerial contributions sections.
In the theoretical contributions section, this study further clarifies the mechanisms through which green transformation affects the bullwhip effect. Path analysis is employed to reveal in detail how green transformation alleviates supply chain fluctuations through mechanisms such as enhanced information sharing, improved innovation capabilities, optimized management practices, and reduced profit volatility. These empirical results more comprehensively demonstrate how green transformation impacts supply chain stability through different pathways. I have added detailed quantification of these mechanisms and ensured that each mechanism is specifically reflected in the data analysis, thereby strengthening the theoretical support for the study.
In the managerial contributions section, we have provided more specific and actionable recommendations for different types of companies (e.g., non-state-owned enterprises, technology-intensive industries, etc.). These recommendations are based on the differentiated effects observed in the empirical analysis and provide guidance on how companies should adopt green transformation strategies tailored to their characteristics in order to maximize the reduction of the bullwhip effect. This not only enhances the managerial relevance of the study but also offers more concrete guidance for practical implementation.
Author Response File: 
Author Response.doc
Round 3
Reviewer 4 Report
Comments and Suggestions for AuthorsThe paper suffers from significant methodological weaknesses, particularly concerning endogeneity issues, which compromise the validity of its causal claims. The instrumental variable (IV) approach remains questionable as the authors fail to conduct formal statistical tests, such as weak instrument or overidentification tests, to validate its exogeneity. Additionally, potential omitted variable bias is not adequately addressed, raising concerns that unobserved factors may be influencing both the independent and dependent variables. The lack of consideration for alternative IVs further weakens the methodological rigor, as the authors do not justify why their chosen instrument is superior to other possible candidates. Without these crucial statistical checks, the proposed IV approach does not provide a reliable solution to endogeneity concerns.
The mediation analysis also remains inconclusive, further diminishing confidence in the study’s findings. The newly introduced IV lacks empirical validation, making it uncertain whether it effectively addresses endogeneity. Additionally, the authors fail to account for potential interactions between mediating variables, leaving critical relationships within the model unexplored. The absence of a discussion on alternative causal mechanisms—such as the direct effects of policy incentives or supply chain pressures—further weakens the credibility of the mediation pathway. Without a comprehensive assessment of these factors, the proposed mediating role of R&D investment in the green transformation process remains speculative rather than empirically grounded.
Another major flaw in the study is the poor justification of green transformation indicators. The selection of variables lacks both theoretical and empirical validation, and the authors fail to explain why specific indicators were chosen over others. Their conceptualization of green transformation remains vague, without a clear measurement framework to establish its dimensions. Moreover, some indicators, such as profitability and employee salaries, are not inherently tied to sustainability, raising serious concerns about construct validity. The absence of reference to established frameworks or empirical studies further diminishes the credibility of their measurement approach, making the assessment of green transformation inconsistent and unconvincing.
Finally, the theoretical and managerial contributions of the paper are weak. The theoretical claims are not supported by strong empirical evidence, as the authors do not provide robust statistical findings to substantiate their proposed mechanisms. Additionally, the managerial recommendations remain overly broad and fail to translate the study’s insights into actionable strategies. Although the authors suggest that different types of companies may experience varying effects, they do not offer specific guidance on how firms should adapt their strategies based on these findings. Furthermore, the paper does not effectively integrate statistical results into its theoretical contributions, making its overall impact limited.
Given the unresolved endogeneity issues, inconclusive mediation analysis, poorly justified indicators, and superficial contributions, the paper lacks the methodological rigor and theoretical depth necessary for publication.
Author Response
Dear Editor,
I sincerely appreciate the reviewers for taking the time out of their busy schedules to conduct a thorough and professional review of my manuscript and for providing valuable revision comments. Throughout this process, your expert guidance and patience have significantly improved the quality of the paper, for which I am deeply grateful.
For this revision, I have carefully and comprehensively addressed all the comments raised by the reviewers. To facilitate your evaluation, I have highlighted all modifications in red in the revised manuscript. I hope these clear annotations will help you quickly locate the revised sections and gain a more intuitive understanding of the improvements I have made.
In the following, I will provide detailed explanations of my revision approach and specific modifications based on the reviewers' comments to ensure that all concerns and suggestions are fully addressed. Once again, thank you for your hard work and dedication. I sincerely hope that, after this round of revisions, the manuscript will meet the publication standards of your esteemed journal.
Q1.The paper suffers from significant methodological weaknesses, particularly concerning endogeneity issues, which compromise the validity of its causal claims. The instrumental variable (IV) approach remains questionable as the authors fail to conduct formal statistical tests, such as weak instrument or overidentification tests, to validate its exogeneity. Additionally, potential omitted variable bias is not adequately addressed, raising concerns that unobserved factors may be influencing both the independent and dependent variables. The lack of consideration for alternative IVs further weakens the methodological rigor, as the authors do not justify why their chosen instrument is superior to other possible candidates. Without these crucial statistical checks, the proposed IV approach does not provide a reliable solution to endogeneity concerns.
Respond：I have carefully read your comments on the methodology section, especially your concerns about the endogeneity issues. In response to your comments, I have made revisions to the methodology section, elaborating in detail on how to improve and address these issues.
1.Re-selection of control variables to address omitted variable bias
To reduce the impact of omitted variable bias on the accuracy and robustness of the regression model, I have re-selected control variables, comprehensively considering key factors that may affect the relationship between corporate green transformation (GT) and the bullwhip effect (BWE). These include control variables across multiple dimensions such as firm size, financial conditions, market competition, and supply chain management characteristics. The inclusion of these variables effectively mitigates the issue of omitted variable bias.
The specific modifications can be found in Section 3.3. Empirical Methodology of the article.
In selecting control variables, this study comprehensively considers key factors that may influence the relationship between corporate green transformation (GT) and the bullwhip effect (BWE) to mitigate the issue of omitted variable bias and enhance the accuracy and robustness of the regression model. First, firms’ fundamental characteristics may affect their supply chain management capabilities and production decisions. Therefore, this study controls for total assets (Size), measured as the natural logarithm of total assets at the beginning of the year, and workforce size (Staff), represented by the natural logarithm of the total number of employees in the listed company. Additionally, firms’ financial conditions and asset structures may influence the bullwhip effect, prompting the inclusion of profitability (Profit), calculated as (operating revenue - operating costs) / operating revenue, and the proportion of tangible assets (Tangible), measured as (total assets - net intangible assets - net goodwill) / total assets, to capture the stability of firms' asset structures.
Furthermore, the gender composition of executives may influence corporate decision-making styles and risk preferences. To account for this, the proportion of male members in the board of directors, supervisors, and senior management (Man) is included to examine its potential impact on supply chain management and green transformation strategies. The market competition environment is also a crucial determinant of corporate decision-making. To address this, the Lerner index (Power) is incorporated to measure firms’ market monopoly power, where higher values indicate greater market competitiveness. Additionally, government subsidies may influence corporate green investments and supply chain management decisions. Therefore, the ratio of government subsidies to operating revenue (Subsidy) is included as a control variable to isolate the effect of government support on firms' behavior.
Regarding supply chain management characteristics, this study includes accounts receivable turnover (Receivable) and accounts payable turnover (Payable), measured as the ratio of operating revenue to the ending balance of accounts receivable and the ratio of operating costs to the ending balance of accounts payable, respectively. These variables account for firms’ liquidity management within the supply chain. Moreover, supply chain concentration (Concentration), calculated as (the proportion of procurement from the top five suppliers + the proportion of sales to the top five customers) / 2, is incorporated to reflect firms' dependence on key suppliers and customers, which may influence the bullwhip effect.
Beyond internal firm characteristics, external environmental factors also play a crucial role in shaping corporate green transformation and supply chain management. To capture the impact of government environmental expenditure, this study includes the ratio of regional environmental protection expenditure to regional GDP (Environment). Additionally, in June 2017, the Chinese government established the first batch of green finance pilot zones in Zhejiang, Guangdong, Xinjiang, Jiangxi, and Guizhou following discussions at a State Council executive meeting. To account for the potential impact of this policy, a difference-in-differences variable for the implementation of the green finance pilot zone policy (Gfrl) is introduced. Finally, considering that cultural factors may influence firms' long-term strategic choices, regional cultural values (Value) are controlled for, represented by the natural logarithm of the number of Confucian academies in the region, to examine the potential impact of Confucian culture on corporate green transformation.
In summary, this study incorporates control variables across multiple dimensions, including firm characteristics, financial conditions, market competition, supply chain management, government policies, and regional culture, to minimize the risk of omitted variable bias and enhance the explanatory power of the regression model. While the inclusion of these factors captures key determinants influencing the relationship between corporate green transformation and the bullwhip effect, the possibility of unobserved factors remains, which future research may further explore.
In the robustness test section, this study further examines the potential issue of omitted variables to enhance the reliability and robustness of the research findings. Below is a detailed explanation of the modifications made:
(1)Introduction of Internal Control Variables
Several control variables have been added to comprehensively consider the internal and external factors that may influence the relationship between corporate green transformation (GT) and the bullwhip effect (BWE). Regarding internal factors, new control variables such as the management expense ratio (Fee), debt-to-asset ratio (Debt), environmental information disclosure quality (Quality), net tangible asset ratio (Tangible), and the ratio of regional science and technology expenditure per ten thousand yuan (Science) have been included. These variables effectively capture potential factors at the firm level, helping mitigate the impact of omitted variable bias. The regression results indicate that after adding these additional internal firm-level control variables, the research conclusions remain unchanged, further demonstrating the robustness of the model.
(2)Control for External Environmental Factors
This study also considers external environmental factors that may influence the relationship between corporate green transformation and the bullwhip effect. Regarding the size of the city, it is argued that large cities typically exhibit higher economic development and marketization levels, along with stricter environmental regulations, which can enhance a firm's willingness to undergo green transformation. Therefore, firms located in large cities may be more proactive in pursuing green transformation, potentially overestimating its impact on the bullwhip effect. To address this, a dummy variable "Capital" was introduced to indicate whether the firm is located in a provincial capital, and "Capital-Industry-Year" joint fixed effects were included in Model (3). The regression results show that the coefficient for green transformation (GT) is significantly negative, suggesting that the negative relationship between green transformation and the bullwhip effect is not affected by the size of the city.
(3)Consideration of Regional Market Pressure
Given that the eastern region of China is at the forefront of economic openness and market competition, with stronger consumer demand for green products, this market pressure may prompt firms to adopt green transformation strategies earlier, influencing the relationship between corporate green transformation and the bullwhip effect. To account for this, a dummy variable "Eastern" was introduced to indicate whether the firm is registered in the eastern region, and "Eastern-Industry-Year" joint fixed effects were included in Model (3). The regression results show that the coefficient for green transformation (GT) is consistent with the baseline regression, further validating the robustness of the research conclusions.
In summary, this study strengthens the robustness test by considering omitted variables and incorporating both internal and external control variables. By thoroughly controlling for internal and external factors, the study ensures the reliability of the conclusions, further validating the negative relationship between corporate green transformation and the bullwhip effect.
The specific modifications can be found in Section 4.4. Robustness Test of the article.
2.Instrumental Variables (IV) Selection and Testing
Thank you for your valuable feedback. Regarding the instrumental variable (IV) approach, I have addressed the concern about the consideration of other potential instruments and the justification for selecting the current instrument. In this study, to address the endogeneity issue between corporate green transformation (GT) and the bullwhip effect (BWE), I selected the interaction term between the number of fixed-line telephones per hundred people in 1984 across regions and the lagged number of internet users nationwide as the instrumental variable. This method follows the approach used by Tao et al. and utilizes historical telecommunications data from 1984 as an instrument. I believe this instrumental variable is particularly suitable due to several reasons:
Exogeneity: The historical telecommunications infrastructure is unlikely to be directly influenced by current corporate decisions. Instead, it impacts corporate behavior indirectly through mechanisms like information dissemination and technology diffusion, which are fundamental for corporate green transformation.
Long-Term Impact: The level of communication technology development in 1984 may have a long-term impact on the technological upgrading paths of local industries, potentially fostering clusters of technology-based companies and enhancing green innovation capabilities. This makes the historical telecommunications data a relevant and exogenous instrument. Comparison with
Other Instruments: Compared to industry-average variables, this historical data is an "exogenous historical variable," unaffected by current corporate decisions, which reduces the simultaneity bias and strengthens the robustness of the estimation results. Furthermore, since the selected IV's original data is in cross-sectional form and cannot directly be used for panel data analysis, I applied a time-varying approach to create a panel instrumental variable, following the methodology of Nunn and Qian. The time-varying interaction of the previous year's national green technology patent applications and the fixed-line telephones per ten thousand people in 1984 was used to construct the panel IV. This modification improves the explanatory power of the IV, better reflecting the competitive advantage of regions in green innovation.
The results from using this IV demonstrate that both the coefficient of corporate green transformation (GT) and the bullwhip effect (BWE) remain robust. In columns (3) and (4) of Table 11, the first-stage IV coefficient is significantly positive, and the Cragg-Donald Wald F-statistic (119.50) is well above the critical value (16.38 at the 10% significance level), confirming that the instrument is not weak. The Kleibergen-Paap rk LM statistic (139.36) rejects the null hypothesis at the 1% significance level, indicating that the model is correctly identified. Moreover, the second-stage regression confirms that green transformation (GT) significantly reduces the bullwhip effect, thus supporting the robustness of the findings.
The specific modifications can be found in Section 4.4. Robustness Test of the article.
In this study, there may be an endogeneity issue between corporate green transformation and the bullwhip effect. On one hand, corporate green transformation may alleviate the bullwhip effect by improving supply chain management, enhancing information transparency, and optimizing resource allocation, thus reducing information lag and overreaction within the supply chain. However, the bullwhip effect itself could also drive companies to undergo green transformation. Specifically, demand fluctuations and supply chain instability may encourage companies to increase investment in green patent research and development to enhance the adaptability of the supply chain and mitigate the impact of fluctuations on the company. In this case, the bullwhip effect could promote green transformation, especially when companies face supply chain challenges, where green innovation may become a strategy to cope with uncertainty and enhance market stability. Therefore, the bullwhip effect may exacerbate market fluctuations, increase the number of green patents, and further promote corporate green transformation.
To address this endogeneity issue, the most commonly used method is to identify a suitable instrumental variable and use Two-Stage Least Squares (2SLS) to estimate the results, which can provide more consistent and reliable estimates. This study uses the interaction term of the number of fixed telephones per hundred people in each city in 1984 and the lagged number of Internet users nationwide as the instrumental variable. Specifically, drawing on the method of Tao et al., the historical postal and telecommunications data from 1984 in each city are used as an instrumental variable for corporate green transformation. On the one hand, as the Internet is a continuation of traditional communication technologies, the local historical telecommunications infrastructure will influence the subsequent application and diffusion of green technologies due to factors such as technological level and usage habits, which in turn affects corporate green transformation. Corporate green transformation relies on information access, technology dissemination, and supply chain coordination, and the early telecommunication infrastructure laid the foundation for the flow of information and technological diffusion. Furthermore, the postal and telecommunications infrastructure from 1984 represents the level of communication technology development in various regions at that time, and these differences may have long-term impacts on the technological upgrading paths of local industries. Regions with advanced communication networks are more likely to form clusters of technology-based enterprises, which in turn enhances the green innovation capabilities of companies. Therefore, historical telecommunications data may only indirectly influence current corporate behavior through channels such as information dissemination and industrial upgrading, rather than directly determining whether companies undergo green transformation, thus meeting the exclusion restriction. Moreover, compared to industry average instrumental variables, historical telecommunications infrastructure is an "exogenous historical variable" that is not influenced by current corporate decisions, thus effectively reducing potential simultaneity bias between corporate behavior and the instrumental variable, improving the robustness of the estimation results. It should be noted that the selected instrumental variable's original data is in cross-sectional form, and cannot be directly used for panel data econometric analysis. Referring to the method used by Nunn and Qian for this issue, a time-varying variable is introduced to construct a panel instrumental variable. Specifically, the interaction term of the previous year’s national green technology patent application volume and the number of telephones per ten thousand people in each city in 1984 is used to construct the instrumental variable for green transformation in that year. The increase in national green technology patent applications represents the trend of green technology development across the entire economy, and its interaction with historical telecommunications infrastructure can more accurately reflect the relative competitive advantage of different regions in green innovation, enhancing the explanatory power of the instrumental variable. The regression results using this instrumental variable show that both the coefficient of corporate green transformation and the bullwhip effect are significant, further verifying the robustness of the previous results.
From the results in columns (3) and (4) of Table 11, it can be seen that the regression coefficient of the first-stage instrumental variable (IV) is significantly positive at the 1% level, and the Cragg-Donald Wald F-statistic for the weak instrument test is 119.50, significantly greater than the critical value of 16.38 at the 10% significance level, indicating the absence of weak instruments. The Kleibergen-Paap rk LM statistic is 139.36, and the null hypothesis is rejected at the 1% level, indicating that there is no issue of under-identification with the instrument. The coefficient of the second-stage green transformation (GT) is significantly negative at the 1% level, indicating that after addressing endogeneity, the conclusions remain robust.
Furthermore, as some listed companies have not applied for green patents, sample self-selection bias may occur, which could affect the validity of the estimated results. Therefore, this study uses the Heckman two-stage model for estimation. In the first-stage Probit regression, whether a company has a green patent (Dum_GT) is used as the dependent variable to estimate the inverse Mills ratio (IMR). In the second-stage regression, IMR is added to the model (3) to control for potential self-selection bias. The results in column (6) of Table 11 show that the estimated coefficient of GT remains significantly positive, indicating that after considering sample self-selection, the research conclusions remain robust, suggesting that potential self-selection bias does not substantially affect the core conclusions of this paper.
Q2.The mediation analysis also remains inconclusive, further diminishing confidence in the study’s findings. The newly introduced IV lacks empirical validation, making it uncertain whether it effectively addresses endogeneity. Additionally, the authors fail to account for potential interactions between mediating variables, leaving critical relationships within the model unexplored. The absence of a discussion on alternative causal mechanisms—such as the direct effects of policy incentives or supply chain pressures—further weakens the credibility of the mediation pathway. Without a comprehensive assessment of these factors, the proposed mediating role of R&D investment in the green transformation process remains speculative rather than empirically grounded.
Respond：
1.Theoretical Foundation and Robustness of Research Findings
In this revision, we have further expanded the theoretical discussion, particularly by elaborating on the theoretical basis for selecting supply chain information sharing, organizational resilience, and management quality as mediating variables, based on dynamic capabilities theory and organizational resilience theory. We argue that dynamic capabilities theory and organizational resilience theory provide a strong framework for understanding how enterprises can mitigate the bullwhip effect in the process of green transformation by enhancing information sharing, improving organizational adaptability, and strengthening management capabilities. Dynamic capabilities theory emphasizes a firm's ability to quickly integrate and adjust resources in response to market changes, while organizational resilience theory focuses on a firm’s adaptability when facing external shocks. Management quality is closely related to decision-making efficiency, resource allocation capabilities, and information flow efficiency, all of which play an important role in mitigating the bullwhip effect.
When selecting these three pathways, we considered their core role in mitigating the bullwhip effect during green transformation:
(1) Supply Chain Information Sharing: Supply chain information sharing is a key pathway to mitigate the bullwhip effect. The bullwhip effect is essentially caused by information asymmetry and feedback delays, which amplify demand fluctuations. Supply chain information sharing enables all stages of the supply chain to obtain accurate market demand information in a timely manner, reducing errors in demand forecasting and preventing inventory backlog and overproduction. During the green transformation process, companies need to optimize resource allocation and production planning to support sustainable development goals. In this process, information sharing can effectively reduce coordination costs between stages, improve the responsiveness of the supply chain, and thereby mitigate the bullwhip effect. Supply chain information sharing enables companies to respond based on more accurate market demand, enhancing dynamic capabilities and adaptability, which further supports the framework derived from dynamic capabilities theory and organizational resilience theory.
(2) Organizational Resilience: Organizational resilience refers to a company's ability to adapt and recover when facing external shocks. Green transformation is itself a process involving various changes, and enterprises face challenges and uncertainties in technology, market, and policy areas during this transformation. Organizational resilience theory posits that firms with high resilience can flexibly respond to these challenges, maintaining business stability through continuous adjustment and optimization. Therefore, organizational resilience can help companies better adapt to the changes brought about by green transformation and mitigate the bullwhip effect resulting from these changes. Organizational resilience also helps companies strengthen their ability to respond to changes in the external environment, which is closely related to the rapid resource integration capabilities highlighted in dynamic capabilities theory, helping companies effectively reduce supply chain fluctuations during green transformation.
(3) Management Quality: Management quality directly influences a company's decision-making efficiency, resource allocation capabilities, and information flow efficiency. The higher the management quality, the stronger a company’s ability to respond to market changes, and the faster and more accurately it can make decisions and execute them. During green transformation, improvements in management quality can help companies implement transformation strategies more efficiently, optimize resource allocation, and ensure coordination across production and supply chain stages. High-quality management plays a crucial role in reducing redundant processes and improving efficiency, which in turn helps mitigate the bullwhip effect. The enhancement of management quality is closely linked to organizational resilience, as it helps companies make faster and more effective decisions when faced with the complex challenges of green transformation.
2.Interaction Between Mediating Variables
In response to the reviewer’s suggestion, we further examined whether there are interactions between the mediating variables and whether they can be analyzed as independent mediating mechanisms. To this end, we introduced interaction terms of the mediating variables into the baseline regression model for testing. Specifically, we constructed interaction terms for each pair of mediating variables and incorporated them into the main regression model to investigate whether there are significant synergistic effects between the different mediating variables. The regression results in Table 15 show that the coefficients of all interaction terms are not significant, indicating that there are no strong interaction effects between the different mediating variables. This suggests that, within the framework of this study, the mediating paths are relatively independent. Furthermore, we conducted a multicollinearity test on the regression model, and the results showed that the variance inflation factors (VIF) for all variables are below 5, indicating that the model is not significantly affected by multicollinearity. Therefore, treating the mediating variables as independent mechanisms for further analysis is justified, allowing for a systematic exploration of the impact of green transformation on the bullwhip effect through different pathways, and providing more comprehensive empirical evidence.
3.Direct Effects of Policy Incentives and Supply Chain Pressure
We appreciate the reviewer’s attention to alternative causal mechanisms such as policy incentives and supply chain pressure. In response to this concern, we integrated several key variables into the model to more comprehensively consider the direct effects of policy incentives and supply chain dynamics on the bullwhip effect. Specifically, we included the ratio of government subsidies to operating revenue (Subsidy), a differentiated variable for the implementation of green financial pilot policies (Gfrl), accounts receivable turnover (Receivable), accounts payable turnover (Payable), and supply chain concentration (Concentration) as control variables. The inclusion of these variables helps us better control for the direct effects of policy incentives and supply chain dynamics on the bullwhip effect, thus improving the accuracy and robustness of the study. Additionally, in the robustness check, we performed a subgroup analysis by dividing the sample into two groups based on whether they received government subsidies. If green transformation still effectively reduces the bullwhip effect in the group without government subsidies, this further confirms that the mitigating effect of green transformation on the bullwhip effect remains significant even in the absence of policy incentives, suggesting that policy incentives did not significantly impact the conclusions of our study.
The specific modifications can be found in Section 2. Theoretical Analysis and Hypothesis and 4.5. Pathway Mechanism Testing of the article.
- Theoretical Analysis and Hypothesis
2.2. The Role of Green Transformation and Organizational Resilience in Mitigating the Bullwhip Effect
Corporate green transformation can significantly enhance organizational resilience. Organizational resilience refers to a company's ability to rapidly adjust, adapt, and maintain stable operations when facing changes in the external environment or internal shocks. Green technological innovation enhances a company's adaptability and resilience by improving its resource integration capacity and flexibility in responding to market fluctuations, thereby boosting its ability to handle unexpected events.
Firstly, corporate green transformation promotes the upgrading of green technologies, enabling companies to maintain efficient operations when facing environmental policy changes and market demand fluctuations. Green technologies typically possess strong sustainability and adaptability, which means companies can more quickly perceive changes in the external environment and adjust production and supply chain operations accordingly, reducing the impact of external uncertainties [36]. Driven by green technologies, companies not only improve resource utilization in production but also optimize supply chain management, reducing supply chain fluctuations caused by slow responses or information asymmetry, thus effectively alleviating the bullwhip effect [37]. At this stage, companies exhibit stronger dynamic capabilities, maintaining supply chain stability through continuous innovation and adjustment, thereby reducing the impact of the bullwhip effect.
Secondly, corporate green transformation also enhances resource allocation capabilities, allowing companies to flexibly adjust resource use when facing market demand fluctuations, raw material supply disruptions, or other unforeseen situations, thereby avoiding excessive dependence on a single supplier or production process [38]. This flexibility effectively reduces overreaction caused by information asymmetry, thereby mitigating risks in the supply chain. During the implementation of green technological innovation, companies typically strengthen supply chain transparency, enabling more timely and accurate responses from upstream and downstream parties to market demand changes, thus improving coordination and responsiveness within the supply chain [39]. According to Dynamic Capabilities Theory, by enhancing resource allocation and mobilization capabilities, companies can quickly adapt to external market changes, strengthen organizational resilience, and reduce instability factors within the supply chain.
Finally, green transformation enhances a company's sustainable competitive advantage, allowing it to maintain stable operations under long-term environmental pressures [40]. With increasingly stringent global environmental policies, companies can meet societal and market demands for environmental protection through green technological innovation while improving their strategic adaptability and flexibility—core elements of organizational resilience. When facing challenges such as environmental regulation changes, market demand fluctuations, and resource shortages, green technological innovation provides companies with sustained and stable competitive advantages. By reducing resource waste and production costs, companies can further enhance supply chain flexibility and adaptability, mitigating risks arising from supply chain disruptions or market fluctuations.
Therefore, corporate green transformation improves organizational resilience, effectively reducing the negative impact of the bullwhip effect. Green technological innovation not only helps companies better cope with external environmental changes but also alleviates supply chain instability triggered by market fluctuations through optimized resource allocation and flexible production adjustments. During the green transformation process, the dynamic capabilities of companies are significantly enhanced, providing a solid foundation for tackling challenges in a complex and ever-changing market environment.
However, despite the general positive impact of green transformation on organizational resilience, there are cases where the transformation may not achieve the expected results. Firstly, the high upfront costs of green technological innovation may impose financial pressure on companies in the short term, affecting their ability to respond to unexpected events. Secondly, the implementation of green technologies may encounter technical challenges or adaptation barriers, preventing companies from making quick adjustments. Furthermore, over-reliance on a single green technology could reduce a company's flexibility in responding to market changes. Additionally, external environmental uncertainties and insufficient policy support may affect the effective application of green technologies, thereby impacting organizational resilience.
Based on the above analysis, this study proposes the following hypothesis:
Hypothesis 2: Corporate green transformation significantly mitigates the bullwhip effect by promoting organizational resilience.
2.3. The Role of Green Transformation and management quality in Mitigating the Bullwhip Effect
The green transformation of enterprises is not only a strategic choice to respond to environmental changes but also enhances the management capabilities of the enterprise, thereby effectively alleviating the bullwhip effect. During the green transformation process, enterprises often introduce advanced green technologies, management concepts, and process optimizations, which help improve the ability to allocate resources, decision-making efficiency, and information flow, thus enhancing the enterprise's ability to respond to complex market environments and ultimately reducing volatility and instability in the supply chain.
First, the green transformation promotes the upgrading of management concepts and methods [41]. In the process of implementing green transformation, enterprises often introduce more sustainable development concepts and strategies, improving management models. The resource dependence theory suggests that the external environment and resource dependencies of an enterprise have a profound impact on its internal management and decision-making [42]. Green transformation enables enterprises to use energy and raw materials more efficiently, reduce waste, improve production efficiency, and strengthen supply chain management to minimize supply chain fluctuations caused by resource shortages or misallocation [43, 44]. As management models are optimized, the enterprise's adaptability and decision-making efficiency are enhanced, enabling more timely and precise responses to market demand fluctuations, thereby reducing the bullwhip effect.
Second, the green transformation helps strengthen collaboration with suppliers, distributors, and other stakeholders, enhancing the overall stability of the supply chain [45]. During the promotion of green technology innovation, enterprises often need to establish closer cooperation with upstream and downstream supply chain partners to ensure the implementation of green production standards and compliance with environmental protection requirements. This close cooperation not only helps stabilize raw material supply and production arrangements but also enhances the flexibility of resource allocation, reducing uncertainties caused by supply chain disruptions or market demand fluctuations [46]. For example, in green supply chain management, enterprises may adopt Vendor-Managed Inventory (VMI) or long-term strategic cooperation agreements to share green technology standards, production plans, and inventory information, thereby jointly optimizing resource allocation with supply chain partners and reducing the volatility in supply chain operations, thus alleviating the bullwhip effect.
Finally, the green transformation helps enterprises strengthen risk management and response capabilities [47]. As enterprises gradually improve their management systems during the green transformation process, they are not only better equipped to respond to environmental regulation changes but can also optimize supply chain management and risk control mechanisms to effectively identify and avoid potential risks. Green transformation enhances an enterprise's ability to anticipate potential fluctuations in the supply chain, enabling proactive measures to prevent supply chain disruptions or resource wastage due to unexpected events [48]. A well-established risk management system helps enterprises quickly adjust production and resource allocation in the event of market demand fluctuations, raw material supply interruptions, or other unforeseen situations, reducing excessive reactions caused by information asymmetry, and further mitigating the bullwhip effect.
Therefore, the green transformation of enterprises, by improving management levels, helps optimize resource allocation, enhance information flow efficiency, and strengthen risk management capabilities, thereby effectively alleviating fluctuations in the supply chain and reducing the impact of the bullwhip effect. Green transformation not only brings sustainable development advantages to enterprises but also, by enhancing management levels, improves their ability to respond to market and environmental changes, thereby reducing instability in the supply chain.
However, although the green transformation is generally considered to optimize enterprise management and enhance supply chain stability, in some cases, it may also bring unexpected challenges and even exacerbate the bullwhip effect. On the one hand, enterprises promoting green technology innovation may face issues such as insufficient technological maturity or low market acceptance, leading to a decrease in production efficiency or an increase in costs, thus affecting the overall coordination of the supply chain. On the other hand, green transformation often involves cross-departmental and cross-enterprise cooperation, and poor communication or coordination can lead to information asymmetry or management errors, which could increase supply chain volatility. Lastly, external policy uncertainties, such as changes in carbon trading systems or environmental protection subsidies, could put additional operational pressure on enterprises during the green transformation process, affecting their stability.
Based on the above analysis, this study proposes the following hypothesis:
Hypothesis 3: Corporate green transformation significantly mitigates the bullwhip effect by improving management quality.
4.5. Pathway Mechanism Testing
Previous literature suggests that the higher the degree of relationship closeness and stability between large customers and suppliers, the higher the interdependence between the two parties, which is conducive to the transmission and sharing of both public and private information within the supply chain. Based on this, the degree of relationship closeness between supply chain firms (Supply) is constructed to measure the level of supply chain information sharing. Specifically, Supply is based on the ratio of accounts payable to total assets. To eliminate the impact of industry characteristics and bargaining power, a regression is performed on the accounts payable ratio with respect to firm nature, size, and industry, and the residuals are then used as a proxy for the degree of relationship closeness between firms. As shown in the first column of Table 12, green transformation significantly reduces the bullwhip effect of firms. The second step is to regress model (4) to examine the impact of green transformation on supply chain information sharing. As indicated in the second column of Table 12, the coefficient for GT is significantly positive, suggesting that green transformation significantly enhances supply chain information sharing. The third step is to verify whether supply chain information sharing plays a mediating role in the process through which green transformation reduces the bullwhip effect. According to the results shown in the third column of Table 12, when Supply is included in the model, the coefficient for GT remains significantly negative, but its value decreases, indicating that supply chain information sharing plays a partial mediating role. In conclusion, the mediating effect test supports the finding that green transformation improves supply chain information sharing and reduces the bullwhip effect.
Based on dynamic capabilities theory, this study aims to construct an organizational resilience index from two dimensions: rebound resilience and overtaking resilience. Rebound resilience refers to the ability of an organization to bounce back to its original state or functionality after enduring challenges or setbacks. Drawing from existing literature on corporate resilience, four indicators are used to comprehensively evaluate rebound resilience: the quick ratio, embedded redundant resources, non-embedded redundant resources, and return on equity. Overtaking resilience refers to an organization not only recovering from setbacks but becoming even stronger, representing the company's growth capacity. Following the approach of Ping and Nan [55], the study selects indicators that measure a company's growth potential: year-on-year growth rates of total assets, operating revenue, and net profit. The data for the above indicators are standardized, and the average values are used to calculate the comprehensive organizational resilience index, denoted as "Resilience."
As shown in the first column of Table 13, green transformation significantly reduces the bullwhip effect of firms. The next step is to regress model (4) to examine the impact of green transformation on organizational resilience. As indicated in the second column of Table 13, the coefficient for GT is significantly positive, suggesting that green transformation significantly improves organizational resilience. Finally, we test whether organizational resilience plays a mediating role in the process through which green transformation reduces the bullwhip effect. According to the results in the third column of Table 13, when organizational resilience (Resilience) is included in the model, the coefficient for GT remains significantly negative, but its value decreases, indicating that organizational resilience partially mediates the relationship between green transformation and the bullwhip effect.
This study draws on the approach of Sun et al. [56] to measure IME (Management Efficiency Indicator) based on management expenses. Specifically, the following steps are taken: First, management expenses are regressed against total number of employees, operating income, cost markup, industry, and year to obtain the corresponding residual values. Then, these residual values are ranked by industry, and the mean value of companies within the top decile is taken as the industry management efficiency frontier (ME). Next, the residual values are divided by ME to obtain the management efficiency indicator (ME) for each company. Finally, the negative value of ME is taken to obtain the positive indicator of management efficiency, IME. The higher the IME, the higher the level of management efficiency in the company.
As shown in the first column of Table 14, green transformation significantly reduces the bullwhip effect. The next step involves regression model (4) to examine the impact of green transformation on management quality. As presented in the second column of Table 14, the coefficient of GT is significantly positive, indicating that green transformation significantly reduces the management quality of firms. Finally, we test whether management quality plays a mediating role in the relationship between green transformation and the reduction of the bullwhip effect. According to the results in the third column of Table 14, when management quality (EV) is included in the model, the coefficient of green transformation remains significantly negative but decreases in magnitude, suggesting that management quality partially mediates the effect of green transformation in mitigating the bullwhip effect.
To further verify whether the mediator variables are interrelated and can be analyzed as independent mediating mechanisms, this study introduces interaction terms of the mediator variables in the baseline regression model for testing. Specifically, interaction terms between pairs of mediator variables are constructed and incorporated into the main regression model to examine whether there are significant synergistic effects among different mediator variables. The results in Table 15 show that the regression coefficients of all interaction terms are not significant, indicating that there is no strong interactive effect between the different mediator variables. This suggests that the mediating paths are relatively independent within the framework of this study. Furthermore, a multicollinearity test was conducted on the regression model, and the results show that the variance inflation factors (VIFs) of all variables are less than 5, indicating that the model is not significantly affected by multicollinearity issues. Therefore, it is reasonable to treat the mediator variables as independent mechanisms for further analysis, systematically revealing how green transformation impacts the bullwhip effect through different paths and providing more detailed empirical evidence.
Q3.Another major flaw in the study is the poor justification of green transformation indicators. The selection of variables lacks both theoretical and empirical validation, and the authors fail to explain why specific indicators were chosen over others. Their conceptualization of green transformation remains vague, without a clear measurement framework to establish its dimensions. Moreover, some indicators, such as profitability and employee salaries, are not inherently tied to sustainability, raising serious concerns about construct validity. The absence of reference to established frameworks or empirical studies further diminishes the credibility of their measurement approach, making the assessment of green transformation inconsistent and unconvincing.
Respond：Thank you for your detailed and valuable feedback. We greatly appreciate your insightful comments, particularly regarding the justification and measurement of green transformation indicators. Based on your feedback, we have revised the measurement approach and made substantial improvements to the paper, which we summarize below:
(1)Justification of Green Transformation Indicators
We acknowledge the need for a more rigorous justification of the indicators used to measure corporate green transformation. After carefully considering your comments, we have decided to revise our measurement framework. The previous use of an evaluation system was indeed insufficiently grounded in theoretical and empirical validation. To address this, we have now chosen to use the number of green patent applications as a key indicator of corporate green transformation.
Green patents reflect both the resource investments in green innovation and their tangible outcomes, making them a more direct and effective measure of a firm's green transformation. Moreover, we believe the number of green patent applications provides a more timely and accurate reflection of the firm's progress, as the transition to the application stage is often a significant milestone in the green innovation process. This adjustment enhances the theoretical and empirical grounding of our study.
(2)Refinement of Conceptualization and Measurement
In response to your comments on the vague conceptualization of green transformation, we have refined the definition and measurement framework. By using green patent applications as a measure, we aim to provide a clearer and more direct link to a firm’s green transformation efforts. This approach is both conceptually sound and practically relevant, as green patents are widely recognized as a meaningful indicator of a firm's engagement in sustainable innovation. Additionally, we have processed the data by adding one to the raw values and taking the natural logarithm, a standard method to mitigate skewness and ensure the robustness of the statistical results. This adjustment strengthens the validity and reliability of the measurement.
(3)Empirical Reassessment and Consistency of Results
Given the change in our measurement approach, we revisited the empirical analysis to ensure consistency and robustness. We conducted new empirical tests based on the revised indicator—green patent applications. While the measurement framework has changed, the basic results of the study remain largely unchanged. In addition to revising the measurement approach, we also made corresponding revisions to the related content in the paper to align with the new indicator and provide clearer explanations. The robustness of our findings has been confirmed, and we believe the new approach addresses your concerns while maintaining the integrity of the study's conclusions.
The specific modifications can be found in Section 3.2. Variable Setting of the article.
Independent variables. Corporate green transformation is a continuous and evolving dynamic process. Green patents, as a reflection of both resource investment in green innovation and the resulting outcomes, serve as an indicator of a firm's green transformation level to a certain extent [50]. Compared to the number of granted green patents, the number of green patent applications can more promptly and accurately capture the latest progress in green innovation [51], as the entry of a green patent into the application stage often signifies that the firm has achieved a milestone in its green transformation. Accordingly, this study employs the number of green patent applications as a measure of corporate green transformation [52]. In constructing this measure, the study processes the raw data by adding one and taking the natural logarithm to mitigate the impact of data skewness.
Q4.Finally, the theoretical and managerial contributions of the paper are weak. The theoretical claims are not supported by strong empirical evidence, as the authors do not provide robust statistical findings to substantiate their proposed mechanisms. Additionally, the managerial recommendations remain overly broad and fail to translate the study’s insights into actionable strategies. Although the authors suggest that different types of companies may experience varying effects, they do not offer specific guidance on how firms should adapt their strategies based on these findings. Furthermore, the paper does not effectively integrate statistical results into its theoretical contributions, making its overall impact limited.
Respond：Thank you for your valuable feedback on our paper. We have made detailed revisions based on your comments, which are highlighted below:
(1)Strengthening Theoretical Contributions
We have expanded the theoretical contributions section of the paper. First, we clearly articulated the novelty of our study in exploring the relationship between corporate green transformation and the bullwhip effect. This is a new perspective in the literature, which mainly focuses on the impact of green transformation on corporate economic performance and high-quality development, but few studies have explored its effect on the bullwhip effect and the underlying mechanisms. Our empirical analysis, based on data from Chinese A-share listed companies from 2008 to 2022, provides robust statistical findings. The regression results indicate that for each unit increase in corporate green transformation, the bullwhip effect decreases by 0.073 units, and this finding remains robust across several robustness tests, including the instrumental variable approach, propensity score matching, and the Heckman selection model.
Additionally, we conducted a pathway analysis to explore the mechanisms through which green transformation mitigates the bullwhip effect. We identified three main channels: (1) enhanced information sharing, (2) improved management quality, and (3) increased organizational resilience. We quantified the mediating effects and verified their relative independence, thus strengthening the integration of empirical results into our theoretical contributions.
(2)Making Managerial Implications More Specific
In the managerial implications section, we translated the research findings into actionable strategies for policymakers and corporate managers. We included specific, quantitative insights, such as how every unit increase in the green transformation index leads to a significant reduction of 0.073 units in the bullwhip effect. Based on this, we provide detailed, industry-specific recommendations for firms to integrate green transformation into their strategic planning. For instance, non-state-owned enterprises (NSOEs) are advised to use advanced supply chain management software like SAP Ariba or Oracle to establish real-time information exchange platforms, while state-owned enterprises (SOEs) should push for internal management reforms and streamline decision-making structures.
We also tailored recommendations for capital-intensive enterprises and diversified firms, addressing their unique challenges in the green transition process. These suggestions help firms optimize their strategies for green transformation and supply chain efficiency.
(3)Integrating Statistical Results into Theoretical Contributions
We have further integrated empirical findings into the theoretical contributions. We emphasized how green transformation impacts the bullwhip effect through the mechanisms of information sharing, management optimization, and organizational resilience. The study’s robust statistical analysis not only strengthens the theoretical claims but also offers concrete insights for policymakers and business leaders in the fields of sustainability and supply chain management.
(4)Enhancing the Practicality of Managerial Recommendations
We have further elaborated on how different types of companies should adapt their strategies based on the research findings. Specific guidance was provided for non-state-owned enterprises, state-owned enterprises, and capital-intensive companies. For example, for small-scale SOEs, we recommend government support through dedicated funds for green technology R&D, while for large SOEs, internal management reforms should be prioritized. Moreover, we provided detailed recommendations on leveraging government policies and incentives to support the green transition in both SOEs and NSOEs.
The specific modifications can be found in Section 5.Conclusion and suggestion of the article.
5.2.Theoretical contributions
This study makes several significant theoretical contributions to the fields of green development and supply chain management.
This study explores the relationship between corporate green transformation and the bullwhip effect from a novel perspective. Existing literature primarily focuses on the impact of green transformation on corporate economic performance and high-quality development [57,58,59,60,61], yet relatively few studies examine how green transformation influences the bullwhip effect and its underlying mechanisms. Based on data from Chinese A-share listed companies from 2008 to 2022, this study employs a multiple linear regression model for empirical analysis and conducts robustness tests to ensure the reliability of the results. The regression results indicate that for each unit increase in corporate green transformation, the bullwhip effect significantly decreases by 0.073 units. This finding remains robust across a series of robustness tests, including the instrumental variable approach, propensity score matching, and the Heckman selection model, further strengthening the credibility of the causal inference. This discovery not only confirms the positive role of green transformation in enhancing corporate environmental performance but also unveils its profound implications for supply chain management, expanding the research perspective at the intersection of environmental sustainability and supply chain dynamics.
Furthermore, to gain deeper insights into the mechanisms through which corporate green transformation mitigates the bullwhip effect, this study conducts a pathway analysis and identifies three main channels: (1) enhanced information sharing, (2) improved management quality, and (3) increased organizational resilience. The results demonstrate that corporate green transformation fosters information sharing between firms and their supply chain partners, effectively reducing supply chain fluctuations caused by information asymmetry. The mediating effect of information sharing accounts for 2.7% of the total effect (Z=-2.898, p=0.04), indicating its critical role in this process. Additionally, green transformation optimizes corporate management practices (mediating effect=3.9%, Z=-4.507, p<0.01) and enhances organizational resilience (mediating effect=6.8%, Z=-3.843, p<0.01), further stabilizing the supply chain and improving operational efficiency. To verify the independence of these mediating variables, this study introduces interaction terms among them in the regression analysis. The results show that all interaction terms are statistically insignificant, suggesting that there is no significant synergy among different mediators, thereby supporting the relative independence of each mediating pathway.
This study makes several theoretical contributions. First, it expands the scope of green transformation research by extending its impact to the field of supply chain management, providing robust empirical evidence. Second, it explicitly reveals the mechanisms through which green transformation alleviates the bullwhip effect via information sharing, management practice optimization, and organizational resilience enhancement, enriching the intersection of green management and supply chain dynamics research. Finally, by incorporating multiple robustness tests, this study enhances the credibility of its findings and integrates statistical results with theoretical contributions, offering valuable insights for policymakers and corporate managers in formulating more effective sustainability and supply chain management strategies.
5.3.Managerial implications
This study provides policymakers and corporate managers with practical and actionable guidance. The findings reveal that corporate green transformation not only enhances environmental reputation but also serves as a core driver for improving supply chain efficiency and stability. Firms should recognize that green transformation is not merely about meeting environmental compliance requirements or shaping a positive brand image. Instead, it is a strategic initiative crucial for optimizing supply chain management and strengthening overall operational resilience.
Empirical evidence shows that for every unit increase in the corporate green transformation index, the bullwhip effect significantly decreases by 0.073 units. This quantitative result intuitively illustrates the positive impact of green transformation on supply chain stability. Based on this, firms should integrate green transformation into their core strategic planning, incorporating it into top-level strategy design and embedding green principles across all aspects of corporate operations.
In the process of green transformation, non-state-owned enterprises (NSOEs) should adopt mature supply chain management software, such as SAP Ariba or Oracle Supply Chain Management, to establish real-time information exchange platforms. Additionally, firms should implement a monthly information evaluation mechanism, assessing information transmission based on quantitative indicators like data accuracy and information latency. Based on these evaluations, companies should hold dedicated meetings each month to formulate process optimization plans, implementing them in the following month to continuously enhance supply chain information flow efficiency. By sharing supply chain information in real time, firms can better manage inventory, optimize production schedules, and mitigate the bullwhip effect.
For state-owned enterprises (SOEs), the marginal impact of green transition on the bullwhip effect is generally lower than that of NSOEs due to unique organizational structures, decision-making mechanisms, and market environments. The complex hierarchical decision-making structure, prolonged transformation process, and relatively low market competition pressure in SOEs reduce their internal motivation for green transition. Therefore, the government should actively promote internal management reforms within SOEs, particularly large-scale enterprises. Specifically, SOEs should streamline existing decision-making hierarchies, eliminate unnecessary intermediaries, and establish specialized green transition decision-making committees led by senior executives. After initiating these reforms, the introduction of market competition mechanisms and the establishment of green transition performance evaluation metrics, including improvements in the bullwhip effect, will further incentivize enterprises to accelerate their green transition efforts.
For small-scale SOEs, although decision-making processes are relatively flexible, resource constraints often hinder green transition efforts. To address this, the government should establish dedicated funds, providing clear application guidelines and processes to ensure timely financial support for firms. These funds should be allocated specifically for green technology R&D, environmentally friendly process innovation, and the acquisition of green equipment. Furthermore, the government should organize professional advisory teams to conduct in-depth assessments of firms' actual conditions and assist them in developing tailored green transition strategies.
Regarding government subsidies, policies should be optimized to provide increased, targeted subsidies to SOEs with limited financial support, ensuring that additional funding is precisely allocated to green technology R&D and supply chain green transformation. Simultaneously, tax incentives should be introduced post-policy implementation to alleviate corporate financial burdens. For SOEs receiving high subsidies, the government should establish a financial supervision platform to monitor fund allocations in real time, ensuring that all funds are dedicated to green transition projects. This approach will foster efficient green development and enable SOEs to fully leverage their advantages in the green transition.
Capital-intensive enterprises face significant challenges in green transition, primarily due to high capital investment requirements and fixed-cost pressures, which weaken their ability to mitigate the bullwhip effect. To address this, firms should develop detailed green technology R&D investment plans and adopt a phased investment approach based on project timelines and expected returns. For instance, BYD strategically allocated initial investments to battery production equipment and fundamental research in its electric vehicle battery technology development, later adjusting investments based on R&D outcomes to ensure efficient capital utilization and technological breakthroughs.
In the production process, firms should integrate intelligent manufacturing management systems, such as Manufacturing Execution Systems (MES), leveraging big data analytics to monitor equipment performance and raw material consumption. This proactive approach enables firms to predict equipment failures and supply risks in advance, optimizing production planning and enhancing supply chain efficiency.
In mature markets, capital-intensive firms should accelerate their green transition efforts by increasing investments in green technology R&D to enhance the market competitiveness of green products. For firms operating under low environmental regulatory pressures, adopting sustainable development principles and incorporating green transition into their core strategies is essential. Establishing internal evaluation mechanisms will further facilitate green transition initiatives. Conversely, firms under high environmental regulatory pressures should seize this opportunity to strengthen green supply chain management by integrating green standards across all stages, from raw material procurement to product sales. This approach will enhance corporate environmental reputation and competitive advantage. Additionally, government policies should play a guiding role in encouraging firms to advance green technology R&D and improve supply chain structures, reinforcing the positive impact of green transition on supply chain management.
Diversified firms encounter complex management and coordination challenges during green transition, limiting its effectiveness in mitigating the bullwhip effect. To address this, firms should reassess their green transition strategies to ensure alignment with overall business objectives.
First, firms should establish cross-departmental expert teams, prioritizing individuals with multi-industry green transition experience. These teams should formulate a three-to-five-year green transition strategic plan tailored to the characteristics of different business segments. During the planning phase, firms should evaluate and prioritize resource allocation using resource allocation models (e.g., the ABC classification method) to ensure efficient resource distribution.
In terms of financing, firms should optimize internal fund allocation by establishing centralized fund management systems that enable real-time monitoring and allocation of financial resources. Firms with high financing constraints should focus on enhancing capital efficiency and exploring diversified financing channels, such as green bonds and green industry funds. Collaborating with government-led green industry funds can further support green transition projects. Conversely, firms with low financing constraints should increase investments in green technology R&D, establish green technology innovation centers, and attract top industry talent to drive green transition efforts.
Regarding institutional investors, firms should strengthen engagement by organizing regular meetings to report progress on green transition initiatives. Leveraging the resources and influence of institutional investors can further propel green transition. If a firm has a low proportion of institutional investors, management should shift its decision-making perspective from short-term profitability to long-term sustainability. Establishing a Green Transition Strategy Committee, directly led by senior executives, will ensure that green transition strategies are implemented without being influenced by short-term financial pressures. This approach will help mitigate management complexity and resource allocation challenges, enhance the effectiveness of green transition, and ultimately reduce the bullwhip effect.
Author Response File: Author Response.doc
