The Digital Transformation of the Manufacturing Industry, the Double-Factor Allocation Efficiency of the Manufacturing Industry, and Carbon Emissions: Evidence from China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript studies the relationship between the digitalization of manufacturing and carbon emissions in the context of the digital economy, and from the perspective of the efficiency of dual-factor allocation, it can reflect a certain amount of work. However, the appearance of "digital economy" in the title and introduction can easily confuse the main body of the research.

 

1. Firstly, the title of the paper is highly misleading. The digital economy is not regarded as a major variable. The manuscript studies the main research relationship between the digitalization of manufacturing and carbon emissions. Secondly, the introduction is too long and does not conduct a literature review based on the main research content. This manuscript aims to study the economic effects of digital transformation in manufacturing against the background of the digital economy. However, it devotes a considerable amount of space to introducing the impacts brought by the digital economy. The review of digital economy and carbon emission research is mixed, and the logical chain is broken. For instance, from line 112 to line 187, it fails to distinguish whether the paper explores the impact of the digital economy or the digitalization of manufacturing on carbon emissions. Meanwhile, too many Chinese policy expressions have not considered the readability for international readers.
2. The paper lacks intuitive logical expression, such as the three-level transmission framework diagram of "digitalization of manufacturing → efficiency of dual-factor allocation → carbon emissions", which uses diagrams to illustrate the logical relationship between variables and enhance the visual expression of the causal chain. Perhaps there are abundant pictures in the analysis of the current situation of facts, but the research framework diagram of the thesis is an important part that enables readers to understand it more easily.
3. The innovative points of this manuscript are not very convincing. In lines 212-232, at the theoretical level, the author mentions "enriching the research framework of the comprehensive impact of the digital economy on the efficiency of factor allocation", and the digital economy merely serves as the research background and is not the main research object of the paper. In terms of research methods, the author mentioned that "it has broken through the traditional practice of quantitatively evaluating the development level of the digital economy through an indicator system. Based on the perspective of manufacturing digitalization, it uses the industry input-output table data of the World Input-Output Database (WIOD) for calculation." Before this, was there really no scholar who had used the input-output method for manufacturing digitalization calculation?
4. In the analysis section of the theoretical hypotheses, the author put forward three research hypotheses. However, there is a lack of specific theoretical and mechanism analysis, especially for the third research hypothesis. However, heterogeneity analysis, as a further expansion of the research content, takes up too much space.
5. In terms of handling endogenous issues. It is necessary to explain in detail how to handle endogeneity problems, such as the selection of instrumental variables and model Settings. Moreover, for endogeneity and robustness tests, merely using the instrumental variable method and the GMM method with lagging terms is insufficient. The author also did not specify the parts that might be improved in future research.
6. In the conclusion section, it is necessary to combine the actual situation of China's manufacturing industry and discuss how to improve the efficiency of production factor allocation and achieve carbon reduction through digital means. This is conducive to integrating research results with practical applications. The countermeasures and suggestions are too general and lack hierarchical policy implementation. They should be in line with the research content, and policies should be proposed, classified by technology/capital/labor-intensive industries.
7. The model construction and data analysis of the mediating effect were not distinguished by subheadings in lines 692-739 of the manuscript. The overall structure and framework of the thesis are not particularly clear. The final chapter lacks "discussion“, including the limitations of the research methods and the possible improvement of future research content.
8. Lines 446 to 562 of this manuscript are too detailed in depicting the facts of the manufacturing industry, which can reflect the author's workload. However, this part is more like an analysis of the current industry situation in a thesis, still reflecting the "research background". It would be more interesting if the results of the thesis research were expressed in pictures.
9. This manuscript classified the manufacturing industry as "capital-intensive, labor-intensive, and technology-intensive", but the significance of the distinction was not reflected in the subsequent further research on carbon emissions.
10. This manuscript lacks references from the past three years. The repetition rate is relatively high.

Author Response

Response to Reviewer 1

 

This manuscript studies the relationship between the digitalization of manufacturing and carbon emissions in the context of the digital economy, and from the perspective of the efficiency of dual-factor allocation, it can reflect a certain amount of work. However, the appearance of "digital economy" in the title and introduction can easily confuse the main body of the research.

 

1. Firstly, the title of the paper is highly misleading. The digital economy is not regarded as a major variable. The manuscript studies the main research relationship between the digitalization of manufacturing and carbon emissions. Secondly, the introduction is too long and does not conduct a literature review based on the main research content. This manuscript aims to study the economic effects of digital transformation in manufacturing against the background of the digital economy. However, it devotes a considerable amount of space to introducing the impacts brought by the digital economy. The review of digital economy and carbon emission research is mixed, and the logical chain is broken. For instance, from line 112 to line 187, it fails to distinguish whether the paper explores the impact of the digital economy or the digitalization of manufacturing on carbon emissions. Meanwhile, too many Chinese policy expressions have not considered the readability for international readers.

 

Reply:

Thank you for the suggestions from the reviewers. The author fully agrees with their suggestions. We will separate the introduction and literature review sections. On the premise of not affecting the structure and research content of the article, we have removed the content about the policies issued by the Chinese government to cater to the feelings of international readers

 

2. The paper lacks intuitive logical expression, such as the three-level transmission framework diagram of "digitalization of manufacturing → efficiency of dual-factor allocation → carbon emissions", which uses diagrams to illustrate the logical relationship between variables and enhance the visual expression of the causal chain. Perhaps there are abundant pictures in the analysis of the current situation of facts, but the research framework diagram of the thesis is an important part that enables readers to understand it more easily.

Reply:

Thank you for the suggestions from the reviewers. The author fully agrees with their opinions. Therefore, the author added a diagram in the text to illustrate the logical framework of the theoretical research in this article

 

3. The innovative points of this manuscript are not very convincing. In lines 212-232, at the theoretical level, the author mentions "enriching the research framework of the comprehensive impact of the digital economy on the efficiency of factor allocation", and the digital economy merely serves as the research background and is not the main research object of the paper. In terms of research methods, the author mentioned that "it has broken through the traditional practice of quantitatively evaluating the development level of the digital economy through an indicator system. Based on the perspective of manufacturing digitalization, it uses the industry input-output table data of the World Input-Output Database (WIOD) for calculation." Before this, was there really no scholar who had used the input-output method for manufacturing digitalization calculation?

Reply:

Thank you for the opinions of the reviewing experts. The author fully agrees with the opinions of the reviewing experts. We have made modifications to the innovation of the article, removing the content of using input-output method to measure the level of digital transformation in the manufacturing industry, and further highlighting the innovation of the theoretical analysis framework and research content in this article.

“The innovation of this paper has the following aspect： From a theoretical perspective, it enriches the research framework of the comprehensive impact of the digital transformation of manufacturing industry on the factor allocation efficiency, and examines the factor allocation effect of the digital transformation of manufacturing industry from the perspective of double factors by extending the research on the efficiency of factor allocation to the field of R&D factors. In addition, this paper incorporates the digitalization of the manufacturing industry, the efficiency of factor allocation and carbon emissions into the unified theoretical analysis framework, further extends the factor allocation effect of the digital economy development to the field of carbon emissions, and comprehensively examines the comprehensive impact and internal mechanism of the digital economy development represented by the digitalization of the manufacturing industry on the factor allocation efficiency and carbon emissions in the manufacturing industry.”

 

4. In terms of handling endogenous issues. It is necessary to explain in detail how to handle endogeneity problems, such as the selection of instrumental variables and model Settings. Moreover, for endogeneity and robustness tests, merely using the instrumental variable method and the GMM method with lagging terms is insufficient. The author also did not specify the parts that might be improved in future research.

Reply:

Thank you for the opinions of the review experts. The author fully agrees with the opinions of the review experts. Due to the fact that the data used in this article is panel data at the industry level, there are relatively few endogeneity processing methods available, and it is not possible to select policy events related to digital transformation in the manufacturing industry to construct quasi natural experiments for empirical testing. This also constitutes the limitations of this article. In the final explanation of the limitations of this article, the author has provided a detailed explanation and identified it as one of the important directions for future research.

 

 

5. In the conclusion section, it is necessary to combine the actual situation of China's manufacturing industry and discuss how to improve the efficiency of production factor allocation and achieve carbon reduction through digital means. This is conducive to integrating research results with practical applications. The countermeasures and suggestions are too general and lack hierarchical policy implementation. They should be in line with the research content, and policies should be proposed, classified by technology/capital/labor-intensive industries.

Reply:

Thank you for the opinions of the reviewing experts. The author fully agrees with the opinions of the reviewing experts. However, in the author's article, the manufacturing industry is divided into labor-intensive, capital intensive, and technology intensive sub sectors, only to describe the current characteristics of the digital transformation level of China's manufacturing industry sub sectors. In the subsequent empirical testing process, heterogeneity analysis also attempted to analyze the impact of the digital transformation level of industries with different factor densities on the efficiency of dual factor allocation and even carbon emissions. However, the empirical results showed that it did not have significant heterogeneity, so this article

 

6. The model construction and data analysis of the mediating effect were not distinguished by subheadings in lines 692-739 of the manuscript. The overall structure and framework of the thesis are not particularly clear. The final chapter lacks "discussion“, including the limitations of the research methods and the possible improvement of future research content.

Reply:

Thank you for the opinions of the reviewing experts. Based on their suggestions, the author has added a section on the limitations and future prospects of this study. The specific content is as follows：

Due to factors such as the timeliness and availability of data, the limitations of this study mainly lie in the following aspects: (1) Due to the selection of double-digit industry panel data from China's manufacturing industry for empirical testing, there are relatively few endogeneity processing methods available. And the data year is relatively outdated, mainly due to the fact that the WIOD database data is only up to 2014. In future research, the author will strive to collect data on manufacturing industry segmentation in various provinces of China. If the frequency of publishing input-output table data between industries in China is increased to once a year, it is expected to use updated years and richer "provincial industry" data for empirical research. (2) In the quantitative evaluation model of factor allocation efficiency used in this article, sensitivity and robustness tests were not conducted on the selection of parameters such as the output elasticity coefficients of capital and labor, as well as the output substitution elasticity between industries. This is mainly due to the fact that the model is already a very mature mathematical model, and a large number of existing literature have been applied, which confirms that the model has strong reliability, scientificity, and robustness. However, in the future, the parameter selection and model setting of the model can still be modified and improved based on the research question and the selection of empirical objectives.

 

7. Lines 446 to 562 of this manuscript are too detailed in depicting the facts of the manufacturing industry, which can reflect the author's workload. However, this part is more like an analysis of the current industry situation in a thesis, still reflecting the "research background". It would be more interesting if the results of the thesis research were expressed in pictures.

Reply:

Thank you for the opinions of the reviewing experts. The author has presented the digital transformation characteristics of China's manufacturing industry segments through Figure 2 in the article.

 

8. This manuscript classified the manufacturing industry as "capital-intensive, labor-intensive, and technology-intensive", but the significance of the distinction was not reflected in the subsequent further research on carbon emissions.

Reply:

Thank you for the opinions of the reviewing experts. The author would like to explain this issue to the reviewing experts. This article divides the manufacturing industry into capital intensive, technology intensive, and labor-intensive industries, mainly to examine the main characteristics of digital transformation in China's manufacturing industry. In the subsequent empirical analysis process, it was found that the impact of digital transformation on the efficiency of dual factor allocation and even carbon emissions in the manufacturing industry is not heterogeneous among industries with different factor intensities. Therefore, in the heterogeneity analysis of this article, we chose not to conduct further empirical tests.

 

9. This manuscript lacks references from the past three years. The repetition rate is relatively high.

Reply:

Thank you for the suggestions from the reviewing experts. The author has added references from the past three years and made adjustments to the article's similarity rate.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Overall, this is a well-structured and policy-relevant paper that addresses a timely and important topic: the role of digitalization in improving double-factor allocation efficiency (traditional production and R&D factors) in China's manufacturing industry and its consequential impact on carbon emissions. The paper employs a novel approach by using WIOD input-output tables to measure the digital transformation at a sectoral level, integrates this with a double-factor mismatch model, and constructs an elegant empirical identification strategy to assess the mediation effect on carbon emissions.

The novelty of this manuscript lies in its unified analytical framework that integrates three components, digitalization, factor allocation efficiency, and carbon emissions, into one model. This triple-pathway mechanism has not been sufficiently explored in previous literature, particularly with the precision of the WIOD-based input metrics and sub-sectoral Chinese manufacturing data. The authors' introduction of R&D factor mismatches, in addition to production factors, adds clear value to the literature. This comprehensive view represents a significant contribution to sustainability studies, particularly those examining structural and technological drivers of decarbonization in emerging economies.

However, a few important revisions should be addressed to enhance clarity and rigor:

First, the similarity index of 42% is unacceptably high for journal submission and must be reduced. Much of this likely stem from boilerplate descriptions of models and literature; nevertheless, the authors must rephrase these sections substantially in their own words, especially the theoretical framework and empirical methods, even when citing prior work. Editors and reviewers will treat this issue seriously, and it could jeopardize the paper’s publication chances.

Second, while the empirical identification is generally sound and the authors attempt to handle endogeneity through IV-2SLS and GMM approaches, a more detailed justification of instrument validity is warranted. The use of lagged digitalization indicators is reasonable but needs further explanation regarding exclusion restrictions; why are they assumed to affect mismatch only through current digitalization? A robustness check with an alternative instrument (e.g., regional broadband penetration rates or digital infrastructure policies) would strengthen the claim.

Third, the factor mismatch measures are based on a well-referenced CES production function model; however, it remains unclear how sensitive these measures are to assumptions on parameters like σ (substitution elasticity) or α (output elasticity of capital). A sensitivity analysis around these parameter values would significantly improve robustness and credibility.

Fourth, the policy implications are thoughtful but could be expanded. Given the heterogeneity across state-owned and non-state enterprises noted in the findings, the authors should more clearly articulate targeted recommendations for SOEs, especially given their dominance in heavy-emitting industries.

Additionally, discussion of how these insights could inform China’s next Five-Year Plan or the evolving digital industrial policy landscape would be welcome. Please cite: https://doi.org/10.3390/jrfm16030199 and https://doi.org/10.1016/j.jenvman.2021.112581 to enrich your literature discussion.

Lastly, the writing is generally clear but can benefit from light proofreading to improve flow and precision in several places (e.g., tense agreement, article usage). The introduction and literature review are somewhat long; condensing these would enhance focus.

This paper is well-motivated, methodologically innovative, and offers strong potential for publication pending the above revisions. If the authors address the similarity issue seriously, reinforce instrument strength, conduct sensitivity tests, and streamline the narrative slightly, the paper will be an excellent candidate for Sustainability.

 

Author Response

Response to Reviewer 2

Overall, this is a well-structured and policy-relevant paper that addresses a timely and important topic: the role of digitalization in improving double-factor allocation efficiency (traditional production and R&D factors) in China's manufacturing industry and its consequential impact on carbon emissions. The paper employs a novel approach by using WIOD input-output tables to measure the digital transformation at a sectoral level, integrates this with a double-factor mismatch model, and constructs an elegant empirical identification strategy to assess the mediation effect on carbon emissions.

The novelty of this manuscript lies in its unified analytical framework that integrates three components, digitalization, factor allocation efficiency, and carbon emissions, into one model. This triple-pathway mechanism has not been sufficiently explored in previous literature, particularly with the precision of the WIOD-based input metrics and sub-sectoral Chinese manufacturing data. The authors' introduction of R&D factor mismatches, in addition to production factors, adds clear value to the literature. This comprehensive view represents a significant contribution to sustainability studies, particularly those examining structural and technological drivers of decarbonization in emerging economies.

However, a few important revisions should be addressed to enhance clarity and rigor:

1. First, the similarity index of 42% is unacceptably high for journal submission and must be reduced. Much of this likely stem from boilerplate descriptions of models and literature; nevertheless, the authors must rephrase these sections substantially in their own words, especially the theoretical framework and empirical methods, even when citing prior work. Editors and reviewers will treat this issue seriously, and it could jeopardize the paper’s publication chances.

 

Reply:

Thank you for the suggestions from the reviewing experts. The author has made adjustments to the article's similarity rate.

 

2. Second, while the empirical identification is generally sound and the authors attempt to handle endogeneity through IV-2SLS and GMM approaches, a more detailed justification of instrument validity is warranted. The use of lagged digitalization indicators is reasonable but needs further explanation regarding exclusion restrictions; why are they assumed to affect mismatch only through current digitalization? A robustness check with an alternative instrument (e.g., regional broadband penetration rates or digital infrastructure policies) would strengthen the claim.

Reply:

Thank you for the suggestions from the reviewing experts. As the data used by the author is industry level panel data, it is currently not possible to use a policy event symbolizing digital transformation as a quasi natural experiment for research. Therefore, the author will supplement the limitations and future prospects of this study by highlighting the shortcomings of this research. The author has added references from the past three years and addressed the issue of article duplication

 

3. Third, the factor mismatch measures are based on a well-referenced CES production function model; however, it remains unclear how sensitive these measures are to assumptions on parameters like σ (substitution elasticity) or α (output elasticity of capital). A sensitivity analysis around these parameter values would significantly improve robustness and credibility.

Reply:

Thank you for the expert review's comments. Based on the expert opinions, the author has provided an explanation of the limitations in the research section of this article, as follows:

Due to factors such as the timeliness and availability of data, the limitations of this study mainly lie in the following aspects: (1) Due to the selection of double-digit industry panel data from China's manufacturing industry for empirical testing, there are relatively few endogeneity processing methods available. And the data year is relatively outdated, mainly due to the fact that the WIOD database data is only up to 2014. In future research, the author will strive to collect data on manufacturing industry segmentation in various provinces of China. If the frequency of publishing input-output table data between industries in China is increased to once a year, it is expected to use updated years and richer "provincial industry" data for empirical research. (2) In the quantitative evaluation model of factor allocation efficiency used in this article, sensitivity and robustness tests were not conducted on the selection of parameters such as the output elasticity coefficients of capital and labor, as well as the output substitution elasticity between industries. This is mainly due to the fact that the model is already a very mature mathematical model, and a large number of existing literature have been applied, which confirms that the model has strong reliability, scientificity, and robustness. However, in the future, the parameter selection and model setting of the model can still be modified and improved based on the research question and the selection of empirical objectives.

 

4. Fourth, the policy implications are thoughtful but could be expanded. Given the heterogeneity across state-owned and non-state enterprises noted in the findings, the authors should more clearly articulate targeted recommendations for SOEs, especially given their dominance in heavy-emitting industries.

Reply:

Thank you for the suggestions from the reviewers. The author fully agrees with their suggestions. Therefore, the author revised the suggestions for state-owned manufacturing enterprises and made them more practical and informative. Specifically, as follows:

Especially for state-owned enterprises, due to the incomplete marketization of the flow and allocation of production factors in China's state-owned enterprise sector, the digital transformation of state-owned manufacturing enterprises cannot affect their factor allocation efficiency, and thus cannot have a positive effect on their carbon emissions reduction. Therefore, it is suggested that state-owned enterprise departments must strengthen market-oriented reforms and promote the free and dynamic replacement and flow of internal factors.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The article is devoted to the study of the influence of the level of industrial digitalization on the efficiency of the green economy through the mediation role  of R&D factors. The article is written competently, at the first stage the authors put forward a set of hypotheses, then empirically test them. The tools of panel analysis of data from Chinese economic sectors are used. The authors use the Cobb-Douglas production function to describe the influence of cost factors on industry output.

The article is recommended for publication with minor comments.

1. The abstract to the article is too long and not entirely informative. It is necessary to describe the methods and results. The results mainly focus on the substantive aspect of the research results. Such a substantive description of the results in the abstract, in my opinion, is not entirely appropriate. This can be reflected in the discussion of the results section.
2. Table 5 requires a more detailed explanation. What is reflected in the body of the table, what are these characteristics. Is this table based on a constructed contingency table or other type of statistical analysis?
3. What recommendations authors can provide based on the results of empirical data analysis? What management decisions are recommended at different levels of management - at the level of sectoral economic management, at the level of corporate management. What does data analysis provide for these areas of management?
4. The references should be updated to include a more detailed block of the latest information on digitalization and the experience of digital transformation of individual enterprises and sectors of the economy.

Author Response

Response to Reviewer 3

 

The article is devoted to the study of the influence of the level of industrial digitalization on the efficiency of the green economy through the mediation role  of R&D factors. The article is written competently, at the first stage the authors put forward a set of hypotheses, then empirically test them. The tools of panel analysis of data from Chinese economic sectors are used. The authors use the Cobb-Douglas production function to describe the influence of cost factors on industry output.

The article is recommended for publication with minor comments.

1. The abstract to the article is too long and not entirely informative. It is necessary to describe the methods and results. The results mainly focus on the substantive aspect of the research results. Such a substantive description of the results in the abstract, in my opinion, is not entirely appropriate. This can be reflected in the discussion of the results section.

Reply:

Thank you for the suggestions from the reviewers. The author has made deletions and improvements to the abstract content based on the opinions of the review experts.

 

2. Table 5 requires a more detailed explanation. What is reflected in the body of the table, what are these characteristics. Is this table based on a constructed contingency table or other type of statistical analysis?

Reply:

Thank you for the suggestions from the reviewers. The author has provided additional explanations for the content of Table 5, as follows:

This empirical result indicates that the improvement of the efficiency of production and R&D factor allocation between manufacturing industries will contribute to the upgrading of the manufacturing industry structure. In other words, the proportion of high polluting, high energy consuming, and high emission industries in the manufacturing sector will decrease, while the proportion of technology intensive industries is expected to increase. This will inevitably help the manufacturing industry reduce carbon dioxide emissions. In addition, technology intensive industries will gain more abundant R&D and production factors in the process of improving the efficiency of dual factor allocation between manufacturing industries, which will inevitably benefit the enhancement of independent innovation capabilities and the upgrading of production and manufacturing technologies in technology intensive industries, and this will have a positive impact on carbon emissions reduction.

 

3. What recommendations authors can provide based on the results of empirical data analysis? What management decisions are recommended at different levels of management - at the level of sectoral economic management, at the level of corporate management. What does data analysis provide for these areas of management?

Reply:

Thank you for the suggestions from the reviewers. The author has made a supplement at the end of the article to clarify the inspiration and significance of this article. Specifically, as follows:

The research conclusion of this article also has clear implications for manufacturing enterprises, that is, in the process of promoting digital transformation, manufacturing enterprises will inevitably face dynamic replacement and reconfiguration of R&D personnel and ordinary employees. Only by smoothing the path of digital transformation and adjusting the allocation of production and R&D factors can the efficiency of factor allocation be improved and the green development goals of enterprises be achieved through digital transformation.

 

4. The references should be updated to include a more detailed block of the latest information on digitalization and the experience of digital transformation of individual enterprises and sectors of the economy.

Reply:

Thank you for the suggestions from the reviewing experts. The author has supplemented the references related to this study in the past three years by searching for literature.

Yi Changjun, Zhao Xiaoyang. Does Digital Transformation Improve the Efficiency of Chinese Multinational Enterprises’ Overseas Investment [J]. China Industrial Economics,2024,(01):150-169.

Guo Jitao and Wang Zijin. The Impact of Digital Transformation on Corporate Capital Allocation Efficiency: Empirical Evidence from Listed Manufacturing Enterprises[J]. Journal of Nanjing University of Finance and Economics,2023,(03):67-76.

 

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Dear Authors,

Your paper is well written and very interesting.

1. The theme is quite interesting and well researched
2. The hypotheses are well framed
3. The analysis is well made
4. The conclusions are robust.

However, I have a few suggestions for improvement:

1. Add a managerial applications section before the conclusions. In fact, you can use part of the conclusions for this section
2. Add a Limitations and Further Research section after the conclusions.
3. Every table and graph should have a source (if made by you, write "own compilation" 

Comments on the Quality of English Language

The English should be thoroughly reviewed.

Author Response

Response to Reviewer 4

 

Dear Authors,

Your paper is well written and very interesting.

1. The theme is quite interesting and well researched
2. The hypotheses are well framed
3. The analysis is well made
4. The conclusions are robust.

However, I have a few suggestions for improvement:

1. Add a Limitations and Further Research section after the conclusions.

Reply:

Thank you for the opinions of the reviewing experts. Based on their suggestions, the author has added a section on the limitations and future prospects of this study. The specific content is as follows：

Due to factors such as the timeliness and availability of data, the limitations of this study mainly lie in the following aspects: (1) Due to the selection of double-digit industry panel data from China's manufacturing industry for empirical testing, there are relatively few endogeneity processing methods available. And the data year is relatively outdated, mainly due to the fact that the WIOD database data is only up to 2014. In future research, the author will strive to collect data on manufacturing industry segmentation in various provinces of China. If the frequency of publishing input-output table data between industries in China is increased to once a year, it is expected to use updated years and richer "provincial industry" data for empirical research. (2) In the quantitative evaluation model of factor allocation efficiency used in this article, sensitivity and robustness tests were not conducted on the selection of parameters such as the output elasticity coefficients of capital and labor, as well as the output substitution elasticity between industries. This is mainly due to the fact that the model is already a very mature mathematical model, and a large number of existing literature have been applied, which confirms that the model has strong reliability, scientificity, and robustness. However, in the future, the parameter selection and model setting of the model can still be modified and improved based on the research question and the selection of empirical objectives.

 

 

2. Every table and graph should have a source (if made by you, write "own compilation" 

Reply:

Thank you for the opinions of the reviewers. The author has made modifications according to their requirements

 

 

Author Response File: Author Response.pdf

Reviewer 5 Report

Comments and Suggestions for Authors

1. The manuscript integrates the introduction and literature review into a single part, which reduces clarity, and it is difficult to distinguish the background and determine the research gaps. The literature review would benefit from a more structured and coherent format, clearly clarifying the gaps in research and solutions.

2. This paper adopts a complex empirical method. Although the theoretical framework is reasonable, the mathematical model lacks transparency, which destroys the replicability. Specific issues include

1) The paper uses CES and Cobb-Douglas production functions interchangeably without specifically defining their respective levels of application. It seems that Cobb-Douglas is used in the micro-level modeling, while CES is used in the macro-level aggregation. This distinction should be clearly clarified in order to avoid conceptual confusion.

2) Equations 4 and 5 are mathematically correct but not well explained. A step-by-step deduction and intuitive explanation would help readers understand the realization of logic and experience.

3) The critical parameters (e.g., α=2/3, σ=1/3) are borrowed from the literature (e.g., Brandt et al., Yang), yet there is no sensitivity analysis or robustness checks provided. Considering their importance in deciding the result, this omission is very important.

4) The paper would be enhanced by a comprehensive variable definition table in the methodology chapter so that readers can keep up with the abundant mathematical content.

3. The data sources span different time ranges. For example, WIOD data through 2014 are employed to calculate digitalization indicators, whereas the remaining variables extend to 2020. The paper must explicitly mention this mismatch, discuss its implication, and justify why the coverage remains adequate to investigate current-day trends.

4. The manuscript employs inconsistent terminology. The terms factor mismatch, allocation efficiency, distribution coefficients, efficiency loss, and deviation are used without being defined consistently. The variables mis, misi, misk, and Rmis are also employed inconsistently, sometimes interchangeably, which is confusing. It is highly advisable that the authors provide a terminology guide or glossary that consistently defines all the required terms and variables.

Author Response

Response to Reviewer 5

 

1. The manuscript integrates the introduction and literature review into a single part, which reduces clarity, and it is difficult to distinguish the background and determine the research gaps. The literature review would benefit from a more structured and coherent format, clearly clarifying the gaps in research and solutions.

 

Reply:

Thank you for the suggestions from the reviewers. The author fully agrees with their opinions. We will separate the introduction and literature review of this article, and treat the literature review as an independent part to clarify the research shortcomings and deficiencies of existing literature, and further highlight the innovation of this article.

 

2. This paper adopts a complex empirical method. Although the theoretical framework is reasonable, the mathematical model lacks transparency, which destroys the replicability. Specific issues include

 

Reply:

Thank you for the suggestions from the reviewers. The author fully agrees with their opinions. The model calculation in this article is somewhat complex. If the entire calculation process is included in the article, it will affect the structural framework of the article. Therefore, the author has stated in the article that if you need to understand the specific calculation process of the model, you can contact the author for further information.

 

3. 1) The paper uses CES and Cobb-Douglas production functions interchangeably without specifically defining their respective levels of application. It seems that Cobb-Douglas is used in the micro-level modeling, while CES is used in the macro-level aggregation. This distinction should be clearly clarified in order to avoid conceptual confusion.

 

Reply:

Thank you for the suggestions from the reviewers. The author fully agrees with their opinions. We have provided a detailed explanation on why the CES function was chosen to characterize the input-output relationship between the overall output of the manufacturing industry and the output of its segmented industries, as follows:

At the same time, it is assumed that the overall output of the manufacturing industry at the national level is the CES production function of the output of each sub-sector: , which can characterize the output of manufacturing sub sectors as intermediate inputs to produce the overall output of the manufacturing industry.

 

4. 2) Equations 4 and 5 are mathematically correct but not well explained. A step-by-step deduction and intuitive explanation would help readers understand the realization of logic and experience.

 

Reply:

Thank you for the suggestions from the reviewers. The author fully agrees with their opinions. The model calculation in this article is somewhat complex. If the entire calculation process is included in the article, it will affect the structural framework of the article. Therefore, the author has stated in the article that if you need to understand the specific calculation process of the model, you can contact the author for further information.

 

5. 3) The critical parameters (e.g., α=2/3, σ=1/3) are borrowed from the literature (e.g., Brandt et al., Yang), yet there is no sensitivity analysis or robustness checks provided. Considering their importance in deciding the result, this omission is very important.

 

Reply:

Thank you for the expert review's comments. Based on the expert opinions, the author has provided an explanation of the limitations in the research section of this article, as follows:

Due to factors such as the timeliness and availability of data, the limitations of this study mainly lie in the following aspects: (1) Due to the selection of double-digit industry panel data from China's manufacturing industry for empirical testing, there are relatively few endogeneity processing methods available. And the data year is relatively outdated, mainly due to the fact that the WIOD database data is only up to 2014. In future research, the author will strive to collect data on manufacturing industry segmentation in various provinces of China. If the frequency of publishing input-output table data between industries in China is increased to once a year, it is expected to use updated years and richer "provincial industry" data for empirical research. (2) In the quantitative evaluation model of factor allocation efficiency used in this article, sensitivity and robustness tests were not conducted on the selection of parameters such as the output elasticity coefficients of capital and labor, as well as the output substitution elasticity between industries. This is mainly due to the fact that the model is already a very mature mathematical model, and a large number of existing literature have been applied, which confirms that the model has strong reliability, scientificity, and robustness. However, in the future, the parameter selection and model setting of the model can still be modified and improved based on the research question and the selection of empirical objectives.

 

6. 4) The paper would be enhanced by a comprehensive variable definition table in the methodology chapter so that readers can keep up with the abundant mathematical content.

 

Reply:

Thank you for the opinions of the reviewing experts. The author fully agrees with the opinions of the reviewing experts. The author checked the calculation part of the mathematical model and provided additional explanations for the parameters calculated by the model to ensure that all parameters appearing in the model have corresponding explanations.

“Y represents the total output,  represents the output of the ith sector. ,  is the weight of the output of sector i in the production process of total output, and its specific value can be obtained endogenously through the derivation of the later model.”

 

7. The data sources span different time ranges. For example, WIOD data through 2014 are employed to calculate digitalization indicators, whereas the remaining variables extend to 2020. The paper must explicitly mention this mismatch, discuss its implication, and justify why the coverage remains adequate to investigate current-day trends.

 

 

Reply:

Thank you for the opinions of the reviewing experts. The author fully agrees with the opinions of the reviewing experts. Based on the opinions of the review experts, we have provided relevant explanations：

As shown in Figure 2, the digitalization level of various sub sectors in China's manufacturing industry has been increasing year by year. Therefore, even though the digital transformation data of the manufacturing industry used in this article is only up to 2014, it can fully demonstrate the accelerating trend of digital transformation in China's manufacturing industry. Therefore, using this data for empirical testing, the results still have important implications for China's present and future.

 

8. The manuscript employs inconsistent terminology. The terms factor mismatch, allocation efficiency, distribution coefficients, efficiency loss, and deviation are used without being defined consistently. The variables mis, misi, misk, and Rmis are also employed inconsistently, sometimes interchangeably, which is confusing. It is highly advisable that the authors provide a terminology guide or glossary that consistently defines all the required terms and variables.

 

Reply:

Thank you for the opinions of the reviewing experts. The author fully agrees with the opinions of the reviewing experts. The author has already explained the meaning and symbols of each variable in Table 2 of the article, as follows. In the author's opinion, this is sufficient for readers to understand the meaning of each variable in the empirical testing process.

Variable Meaning	Variable symbol
Digital direct consumption factor	digi1
Digital total consumption factor	digi2
Total misallication of production factors	mis
Capital misallication	misk
Labor misallication	misl
Total misallication of R&D factors	Rmis
R&D capital misallication	Rmisk
R&D labor misallication	Rmisl

 

Author Response File: Author Response.pdf

Round 2

Reviewer 5 Report

Comments and Suggestions for Authors

I am grateful for the efforts made by the authors in revising the manuscript. After reviewing the updated edition, I would like to make the following suggestions for consideration.

1. It is suggested that more international references should be added to Chapter 1, Chapter 2, and Chapter 3 to improve the credibility of the argument. In particular, including more highly cited SSCI literature can improve the rigor of the study.
2. It would be beneficial to set up a separate section at the beginning of Chapter 4, "Data Description and Construction of Main Variable Indicators," to explain the calculation steps, procedures, and functions and provide tables or figures to guide readers in browsing the content.
3. The authors are requested to correct the incorrect chapter numbering following Chapter 4.

Author Response

I am grateful for the efforts made by the authors in revising the manuscript. After reviewing the updated edition, I would like to make the following suggestions for consideration.

1. It is suggested that more international references should be added to Chapter 1, Chapter 2, and Chapter 3 to improve the credibility of the argument. In particular, including more highly cited SSCI literature can improve the rigor of the study.

Thank you for the opinions and suggestions of the review experts. The author fully agrees with the opinions of the review experts. Therefore, this article has added some references in Chapters 1 to 3, as follows:

22. Zhang, B.; Dong, W.; Yao, J.; Cheng, X. Digital Economy, Factor Allocation Efficiency of Dual-Economy and Ur-ban-Rural Income Gap. Sustainability 2023, 15, 13514.
23. Zhang, B.; Dong, W.; Yao, J. How Does Digital Transformation of City Governance Affect Environmental Pollution: A Natural Experiment from the Pilot Policy of “National Information City for Public Service” in China. Sustainability 2022, 14, 14158.

 

1. It would be beneficial to set up a separate section at the beginning of Chapter 4, "Data Description and Construction of Main Variable Indicators," to explain the calculation steps, procedures, and functions and provide tables or figures to guide readers in browsing the content.

Thank you for the opinions and suggestions of the review experts. The author fully agrees with their opinions. Therefore, the author has adjusted the title of Chapter 4 and added a table to illustrate the construction of the main variables, as follows:

Variable Meaning	Variable symbol	calculation steps, procedures and functions
Digital direct consumption factor	digi1
Digital total consumption factor	digi2
Total misallication of production factors	mis
Capital misallication	misk
Labor misallication	misl
Total misallication of R&D factors	Rmis	RY represents the output level of research and development activities
R&D capital misallication	Rmisk
R&D labor misallication	Rmisl
CO2 emissions	lnco2	Ln(co2)
Whether it is a state-supported industry	g	g =1 if it is a state-supported industry, g=0 if it is not a state-supported industry
Industry monopoly power	mono	measured by the ratio of the industry's main business income to the main business cost.
R&D density in the industry	rd	Measured by the proportion of the output value of new products in the industry to the total output value.
The capital intensity of the industry	rcap	Measured by the proportion of the industry's net fixed assets in the total output value.
Ownership structure	own	expressed by the proportion of the total output value of state-owned enterprises in the industry to the total output value of the industry.

 

 

1. The authors are requested to correct the incorrect chapter numbering following Chapter 4.

Thank you for the opinions and suggestions of the reviewing experts. The author fully agrees with their comments and has already revised all the numbering

Author Response File: Author Response.docx