Consumer Orientation and Market-Driven Strategies for Promoting Low-Carbon Innovation in Supply Chains: Pathways to Sustainable Development

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dears Authors

In the review process of article 3386357 it should be stated that the competences that I am competent to evaluate some aspects related to the content of this study. The topic addressed in this study is relevant to the contemporary scientific debate. However, in the development of this study they can be pointed out as limitations or lack in the results.

Issues related to supply chain macro-processes are cited by profiles of chain participation type, but it is not clear what the contribution of each stage would be to carbon neutral products and service innovation. Another point that I believe needs to be improved is the treatment of particularities in business models, which I consider an open window for materialized actions on carbon neutral products and service innovation issues.

To help improve the result of this excellent study, the following points should be worked on through the answers to the questions formulated below:

1.         In the authors' introduction they state that:

“Existing studies show that manufacturers face significant technical and cost-related challenges when adopting low-carbon technologies. Retailers also need to better understand consumer behavior and employ more sophisticated marketing strategies to effectively promote low-carbon products [3].”

Question 1:

Which studies support this situation described, since only one study was cited?

2.         In the selection of the method-model, the Stackelberg game theory model is used. The arguments for the selection of the problem-solving strategy are missing.

Question 2:

What are the reasons that justified the selection of this model?

Question 3:

To what extent does the Stackelberg game theory model represent a mathematical modeling advantage compared against other methodologies?

3.         In the presentation of the research variables it is worth asking:

Question 4:

What were the sources justifying the research variables?

Question 5:

Why was theory of measurement among the research variables not presented?

Despite the limitations, the research offers valuable contributions in terms of modeling for the context of the supply chain in a carbon neutral economy model. The weighting given to the factor of coordination of business efforts for sustainable development benefits is noteworthy.

Regards

Author Response

1.1. “Existing studies show that manufacturers face significant technical and cost-related challenges when adopting low-carbon technologies. Retailers also need to better understand consumer behavior and employ more sophisticated marketing strategies to effectively promote low-carbon products [3].”

Question 1:

Which studies support this situation described, since only one study was cited?

Response 1：Thank you for your constructive feedback. We have added 3 citations to support this argument to make the argument more complete. Please refer to 1 Introduction (p.2, Line 53-56).

1.2. In the selection of the method-model, the Stackelberg game theory model is used. The arguments for the selection of the problem-solving strategy are missing.

Question 2:

What are the reasons that justified the selection of this model?

Response 2：Thank you for your constructive feedback. We've added the following in response to this question:

1)Applicability of the Stackelberg model in the supply chain: Illustrates how the model reflects hierarchical dynamics in the supply chain by emphasizing the structure of upstream firms as leaders and downstream firms as followers.

2)Relevance of low carbon innovation: Discusses how the Stackelberg model can be applied to market-driven strategies to drive low carbon innovation, especially when upstream firms influence downstream firms' behaviour by choosing environmentally friendly product specifications.

3）Reasonableness of model selection: The Stackelberg model is explained in detail why it is appropriate in this study, especially its ability to capture the interdependence of decision making and leadership dynamics at different stages in the supply chain. Please refer to 2. Literature review (p.2-3, Line 95-135).

Question 3:

To what extent does the Stackelberg game theory model represent a mathematical modeling advantage compared against other methodologies?

Response 3：Thank you for your constructive feedback. We have supplemented and explained it in 2.1. For example:：One of the key advantages of thismodel is its ability to effectively handle sequential decision-making, where the leader in the market makes a decision first, and then the follower adjusts its behavior based on this decision[12].

1.3. In the presentation of the research variables it is worth asking:

Question 4:

What were the sources justifying the research variables?

Response 4：Thank you for your constructive feedback. We have revised part 3.3 to explain the source of the study variables.Please refer to 3.2. research variable (p.2-3, Line 265-290).

Question 5:

Why was theory of measurement among the research variables not presented?

Response 5：Thank you for your constructive feedback. The study did not measure the study variables. This study focuses on the conceptual relationship between decision variables and outcomes in the Stackelberg game framework, rather than on operational or statistical measures of these variables. The study assumes that consumer and market-related parameters (such as price sensitivity and carbon reduction sensitivity) have been adequately captured from the existing literature, so no further in-depth measurement theory is required.

Thus, while measurement theory may be relevant for future research, especially when it comes to accurately quantifying consumer behavior or operationalizing models in real-world Settings, it is not the main focus of this study. This research focuses on dynamic interactions and strategic behavior in supply chains, with an emphasis on low-carbon innovation.

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors

Congratulations for this well-developed paper. The research problem is original and the methodology is good but I detect some weaknesses and I suggest to work on it so that the manuscript will be more clear:

- The conclusion section is too long. We should reformulate it for more clarity.

- Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. I don't see any description  about how we collect data and sample?? why including the informed consent statement?

- In the discussion section please avoid to include mathematical formulas.

All the best 

Author Response

Comment 1:The conclusion section is too long. We should reformulate it for more clarity.

Response 1：Thank you for your constructive feedback. We have revised the 6.conclusion, which has been revised, to make the article clearer.Please refer to p.20-21,Line 766-826.

Comment 2: Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. I don't see any description  about how we collect data and sample?? why including the informed consent statement?

- In the discussion section please avoid to include mathematical formulas.

Response 2：Thank you for your constructive feedback. Mathematical formulas have been removed from the discussion section. And revise the discussion section again. Make the discussion more thorough. At the same time, the Data Availability Statement is modified.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. Insufficient Justification of the Theoretical Framework

The article employs Stackelberg game theory as its primary analytical framework to study centralized and decentralized decision-making models. However, it lacks a thorough discussion on the suitability of Stackelberg game assumptions, such as the leader-follower dynamic, which might not always hold true in real-world supply chain interactions.

Suggestions:Include a dedicated section to elaborate on the assumptions of Stackelberg game theory and justify its applicability in low-carbon supply chain management.

Discuss alternative theoretical frameworks, such as asymmetric information games or dynamic game models, to enhance the robustness and relevance of the analysis.

 

2. Ambiguity Between Consumer Orientation and Market Strategies

The article treats consumer orientation and market strategies as independent drivers of low-carbon innovation but fails to address their potential interplay. For instance, consumer sensitivity to carbon reduction (α) and sensitivity to low-carbon marketing (β) are likely interrelated, yet their synergistic effects are not explored.

Suggestions:Add an analysis of the interplay between consumer orientation and market strategies, focusing on how simultaneous increases in α and β influence supply chain profitability.

Employ interaction effect modeling and numerical simulations to quantify their combined impact.

 

3. Oversimplification of Cost Dynamics

The study assumes constant production and operational costs, focusing solely on the costs of low-carbon production and marketing. This oversimplification overlooks real-world dynamics, such as fluctuations in resource availability, regulatory changes, and technological advancements, which may significantly affect costs.

Suggestions:Revise the assumptions to incorporate a dynamic cost model, introducing adjustment factors based on external environmental changes.

Conduct a sensitivity analysis to evaluate the robustness of the conclusions under different cost dynamics scenarios.

 

4. Neglect of Policy Environment Analysis

While the article highlights consumer demand and market strategies, it overlooks the critical role of policy interventions in driving low-carbon innovation in supply chains. Tax incentives, subsidies, and regulations often play a decisive role in shaping corporate decisions.

Suggestions:Add a section analyzing the impact of policy interventions on centralized and decentralized decision-making models, such as the effects of carbon taxes or green subsidies.

Utilize policy simulation models to quantify the influence of varying policy intensities on supply chain profits and carbon reduction efforts.

 

5. Lack of Empirical Validation

The study relies heavily on theoretical models and numerical simulations but lacks empirical evidence from real-world corporate data. This gap limits the practical applicability and credibility of the conclusions.

Suggestions:Recommend incorporating real-world case studies or industry data to validate the theoretical findings in the future research section.

If feasible, cite existing case studies from the literature to provide preliminary empirical support for the proposed models.

 

The article presents an innovative topic with a solid theoretical foundation and meaningful numerical simulations. However, the applicability of the Stackelberg game framework, assumptions about cost dynamics, and the lack of empirical evidence require further attention. Strengthening the analysis of the interplay between consumer orientation and market strategies, incorporating the role of policy environments, and validating the findings with empirical data would significantly enhance the article's academic rigor and practical relevance.

Author Response

Comment 1： Insufficient Justification of the Theoretical Framework

The article employs Stackelberg game theory as its primary analytical framework to study centralized and decentralized decision-making models. However, it lacks a thorough discussion on the suitability of Stackelberg game assumptions, such as the leader-follower dynamic, which might not always hold true in real-world supply chain interactions.

Suggestions:Include a dedicated section to elaborate on the assumptions of Stackelberg game theory and justify its applicability in low-carbon supply chain management.

Discuss alternative theoretical frameworks, such as asymmetric information games or dynamic game models, to enhance the robustness and relevance of the analysis.

Response 1：Thank you for your valuable suggestions, which have helped us a lot. In 2.2, we have reelaborated the applicability of Stackelberg game theory for this study and its interpretation of asymmetric information games.2. Ambiguity Between Consumer Orientation and Market Strategies.Please refer to p.2-3,Line 77-135.

Comment 2：The article treats consumer orientation and market strategies as independent drivers of low-carbon innovation but fails to address their potential interplay. For instance, consumer sensitivity to carbon reduction (α) and sensitivity to low-carbon marketing (β) are likely interrelated, yet their synergistic effects are not explored.

Suggestions:Add an analysis of the interplay between consumer orientation and market strategies, focusing on how simultaneous increases in α and β influence supply chain profitability.

Employ interaction effect modeling and numerical simulations to quantify their combined impact.

Response 2：Thank you for your valuable suggestions, which have helped us a lot. We have Add an analysis of the interplay between consumer orientation and market strategies, focusing on how simultaneous increases in α and β influence supply chain profitability, Please refer to 4.2.3(p.17, . Line 630-651).

Comment 3：Oversimplification of Cost Dynamics

The study assumes constant production and operational costs, focusing solely on the costs of low-carbon production and marketing. This oversimplification overlooks real-world dynamics, such as fluctuations in resource availability, regulatory changes, and technological advancements, which may significantly affect costs.

Suggestions:Revise the assumptions to incorporate a dynamic cost model, introducing adjustment factors based on external environmental changes.

Conduct a sensitivity analysis to evaluate the robustness of the conclusions under different cost dynamics scenarios.

Response 3： Thank you for your valuable suggestions, which have helped us a lot. Given the limitations of assuming constant production and operating costs, this study revises the relevant assumptions and introduces a dynamic cost model. The new model takes into account factors that adjust costs due to changes in the external environment, including: resource availability, such as fluctuations in the supply of raw materials required for low-carbon production may affect production costs. Policy changes, such as government policies such as carbon taxes or subsidies for low-carbon innovation, can increase or reduce operating costs for manufacturers and retailers. Technological advances, such as the adoption of advanced technologies, may raise costs initially, but reduce costs in the long run due to improved efficiency. Please refer to 3.3.2( p.6 ,Line 265-290 )

Comment 4：Neglect of Policy Environment Analysis

While the article highlights consumer demand and market strategies, it overlooks the critical role of policy interventions in driving low-carbon innovation in supply chains. Tax incentives, subsidies, and regulations often play a decisive role in shaping corporate decisions.

Suggestions:Add a section analyzing the impact of policy interventions on centralized and decentralized decision-making models, such as the effects of carbon taxes or green subsidies.

Utilize policy simulation models to quantify the influence of varying policy intensities on supply chain profits and carbon reduction efforts.

Response 4：Thank you for your valuable suggestions, which have helped us a lot. We have supplemented this part, considering the effect of green subsidies, and constructed an equilibrium model to solve the equilibrium decision of manufacturers and retailers by solving the Stackelberg game.Please refer to p.10-11, Line 441-457) .

Comment 5： Lack of Empirical Validation

The study relies heavily on theoretical models and numerical simulations but lacks empirical evidence from real-world corporate data. This gap limits the practical applicability and credibility of the conclusions.

Suggestions:Recommend incorporating real-world case studies or industry data to validate the theoretical findings in the future research section.

If feasible, cite existing case studies from the literature to provide preliminary empirical support for the proposed models.

The article presents an innovative topic with a solid theoretical foundation and meaningful numerical simulations. However, the applicability of the Stackelberg game framework, assumptions about cost dynamics, and the lack of empirical evidence require further attention. Strengthening the analysis of the interplay between consumer orientation and market strategies, incorporating the role of policy environments, and validating the findings with empirical data would significantly enhance the article's academic rigor and practical relevance.

Response 5：Thank you for your valuable comments. We have added the verification of the results of this study by relevant literatures in the discussion. For example：The results of this study validate Su's research. Su's research on the impact of consumer preferences on low-carbon decision-making and coordination strategies in supply chains confirms that centralized strategies can effectively improve carbon emission reduction level and market competitiveness by optimizing resource allocation and strengthening supply chain collaboration. In addition, the study also shows that consumer preference for low-carbon products significantly drives low-carbon investment and marketing efforts of manufacturers and retailers, thus forming a positive feedback loop between supply chain innovation and consumer behavior, consistent with the conclusions of this study. this limitation and research direction are emphasized in the future research.Please refer to p.18-21,Line 654-658,689-698,748-751.812-826.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors

congratulations again for this good contribution. 
I see that the paper has been improved.

Good luck 

Author Response

Comment 1：Dear authors

congratulations again for this good contribution. 
I see that the paper has been improved.

Good luck 

Response 1：Thank you for your advice and encouragement. I will continue to work hard in my research field. I wish you a smooth work.