Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors must provide a comprehensive explanation of Table 2. Currently, the table presents descriptive statistics for the variables without much details. For instance:

• The negative values of carbon emissions (-6.4653) and (-2.1132). Thus, a detailed explanation is needed for the negative coefficients observed for variables like "Carbon Emission" and "Industrial structure." For instance, what does a negative coefficient imply? What are their units? How did you measure it? 

The Introduction section should explicitly state and justify the reasons for selecting the specific research methodologies and tools employed. For instance, why was the panel data approach chosen over other types of analysis? Why was the EBM model (or the chosen DEA variant) deemed most suitable for measuring the synergistic effects? A clear rationale for these foundational decisions needs to be embedded within the introductory narrative. 

The research gap, while alluded to, requires a much more precise and more explicit articulation. The authors should elaborate on precisely what specific aspects of digital technology's impact on synergistic pollution and carbon reduction governance have been overlooked or insufficiently explored in existing literature. Merely stating that studies on "synergistic governance of carbon and pollution are still relatively limited" is insufficient. What specific gap does this study fill? For example, is it the application of EBM to this specific context, the unique combination of mediating variables, or the particular nuances of the Chinese context for these specific digital technologies?

The table numbering is inconsistent and confusing. For example, the text refers to "Table 3" when discussing benchmark regression results, but the provided table is labeled "Table 2." This lack of consistent numbering significantly hinders readability and the ability to follow the presented results. All tables and figures must be correctly and sequentially numbered throughout the manuscript.

There is a noticeable lack of coherence and logical flow between different sections and even within paragraphs. The narrative jumps between concepts without clear transitions, making it difficult to follow the authors' line of reasoning. This requires a thorough review and restructuring of the entire manuscript to ensure a smooth and logical progression of ideas.

The referencing style is inconsistent. For instance, in lines 486 and 497, references are cited in two different formats. The authors must adhere to a single, consistent referencing style throughout the entire manuscript to maintain academic rigor and clarity.

The authors explicitly mention using Data Envelopment Analysis (DEA) in their methodology (specifically the EBM model) for measuring synergistic effects. However, the manuscript lacks any tables or sections presenting the results of this DEA analysis. Furthermore, there is no clear definition of the inputs and outputs used in the DEA model, which is fundamental to understanding its application and results. This is a critical omission that needs to be addressed comprehensively.

The authors applied linear regression (specifically fixed-effects models) for digital technology as an independent variable against several dependent variables separately. It is perplexing why they did not perform a multiple linear regression where digital technology is examined simultaneously against all relevant independent variables in a single equation (or a set of simultaneous equations) to capture their combined effect and potential interactions. This would provide a more holistic and robust analysis of the technology's overall impact.

The manuscript would greatly benefit from the inclusion of graphical representations, such as scatter plots or other relevant charts. These visualizations are essential for illustrating relationships between variables, showing trends over time, and presenting the linear or non-linear associations, which would significantly enhance the clarity and impact of the findings.

The current presentation of the manuscript is highly disorganized and impractical. The significant issues related to clarity, consistency, methodological completeness, and data presentation make it extremely challenging for readers to comprehend the authors' work and its contributions. Based on these substantial shortcomings, I cannot recommend this manuscript for acceptance. It requires a complete overhaul and rigorous revision addressing all the points raised above and then I will make another deeper review. 

Author Response

 Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Researchers, the study makes a meaningful contribution to environmental governance literature, particularly at the intersection of digital technology and green development. However, several methodological, conceptual, and presentational aspects require improvement to enhance clarity, robustness, and broader relevance. 

• Improve conceptual clarity around key terms: “synergistic governance,” “virtual agglomeration,” and “digital technology.”

• Justify and possibly expand the measurement of digital technology beyond robot density.

• Discuss the limitations of using location entropy for virtual agglomeration more transparently.

• Elaborate on the exogeneity and exclusion criteria for the IV used.

• Strengthen the generalizability and policy discussion beyond China.

With revisions the paper could significantly impact both academic research and policy-making on sustainable digital development.

Author Response

 Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The article is good and up-to-date, but I would like to present some research methods to improve it.

 Hypotheses are clearly stated in the text.

The statistical significance and practical importance of the findings are clearly demonstrated.

Research Conclusions and Policy Implications section is well presented but can be improved. The authors could suggest clearer and more exciting ideas for what researchers should study next in this area. For example, instead of general ideas, they could suggest looking at specific industries or new technologies.

The authors could explain in simpler terms how things like artificial intelligence or big data actually make it easier to reduce pollution and carbon. For example, how does data help factories pollute less?

The authors could explain in simpler language how they tried to avoid confusions regarding the fact that digital tech causes less pollution, or  places with less pollution naturally adopt more digital tech and convince readers that their findings are reliable.

The authors could give more direct advice for governments and companies on what exactly they should do with digital technology to cut down pollution and carbon.

Briefly discuss if these ideas might work in other parts of the world, or if other countries have done something similar. This would make the article more relevant globally.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The primary goal of Data Envelopment Analysis (DEA) is to calculate the relative efficiency among DMUs. Failing to correctly present DEA results and clearly display this relative efficiency in a well-structured table significantly impacts the study's transparency. Therefore, the authors must present these findings in a separate and clear table within their study.

Additionally, it's crucial to clarify the reasons for using both methods together. Were the DEA results used as inputs for the regression analysis, or was there another relationship between the two approaches? A detailed explanation of how these methods were integrated will enhance the study's clarity and rigor. 

Author Response

Dear Reviewer

We sincerely thank you for your thorough and constructive review of our manuscript. Your insightful comments have been highly valuable in improving the quality of our work. In accordance with your suggestions, we have made careful revisions to the manuscript, and the updated version with tracked changes has been submitted.

 

Q1. The primary goal of Data Envelopment Analysis (DEA) is to calculate the relative efficiency among DMUs. Failing to correctly present DEA results and clearly display this relative efficiency in a well-structured table significantly impacts the study's transparency. Therefore, the authors must present these findings in a separate and clear table within their study.

Response: Thank you very much for your valuable comment. To address this issue, we have made targeted revisions and have presented the complete calculation results in the paper. Considering the large volume of data, including all of it in the main text could disrupt the coherence of the article. Therefore, we have added an appendix section, where the full set of calculation results is presented separately. A brief explanation of these results is provided in Section 4.1.1 Dependent Variable. The revised content is as follows:

 

Based on the above calculation steps, this study ultimately calculates the level of coordinated pollution reduction and carbon reduction governance over the 23-year period from 2000 to 2022. To ensure data transparency while maintaining the coher-ence of the main text, the calculation results are presented in the appendix.

 

Appendix

The level of synergistic governance of pollution and carbon reduction

year province	2000	2004	2008	2012	2016	2020	2021	2022
Beijing	0.4699	0.3857	0.2486	0.2534	0.0577	0.0107	0.0119	0.0935
Tianjin	0.7338	0.6897	0.5959	0.5701	0.1988	0.1088	0.1005	1.0263
Hebei	1.0014	0.9910	0.8752	0.9961	0.7450	0.5834	0.6451	1.0083
Shanxi	0.8820	1.0063	1.0019	0.9228	0.7781	0.6619	0.8226	1.1346
Nei Mongol	1.0435	1.0022	0.9779	1.0023	0.7046	0.7003	1.1705	1.0344
Liaoning	1.0119	0.9864	0.7961	0.7503	0.6198	0.5089	0.4612	1.0366
Jilin	0.6916	0.6070	0.5896	0.5387	0.4082	0.3177	0.2948	0.9638
Heilongjiang	0.6438	0.6812	0.7716	0.6886	0.5760	0.4667	0.3571	1.0231
Shanghai	0.6713	0.6772	0.6382	0.6729	0.7465	0.2792	1.0077	1.0917
Jiangsu	0.9794	0.9599	0.9414	0.9585	0.9120	0.8317	1.0004	1.0290
Zhejiang	0.8308	0.7461	0.7457	0.7664	0.5220	0.3332	0.3143	0.5492
Anhui	0.8127	0.8363	0.7005	0.6111	0.5569	0.4493	0.3961	0.8184
Fujian	1.0028	0.9176	0.7547	0.6334	0.6696	0.7993	0.8506	1.0285
Jiangxi	1.0023	0.8156	0.6633	0.5170	0.4334	0.3553	0.3462	0.7663
Shandong	1.0150	1.0004	1.0014	1.0052	0.9064	0.8696	0.9496	1.0626
Henan	1.0009	0.9200	0.7733	0.8100	0.6872	0.3173	0.3108	0.6507
Hubei	0.7025	0.7310	0.7271	0.6404	0.4994	0.3248	0.3303	0.7566
Hunan	0.9257	0.7977	0.7424	0.5965	0.5570	0.4113	0.3382	0.5710
Guangdong	1.0022	1.0002	1.0010	1.0031	0.8632	0.8283	1.0023	1.0262
Guangxi	1.0283	0.9226	0.6805	0.5575	0.3702	0.3505	0.3148	0.6408
Hainan	1.0093	0.8276	0.7779	0.5495	0.2767	0.1335	0.0974	1.0631
Chongqing	1.0352	0.7136	0.5983	0.4753	0.3395	0.2203	0.2540	0.8937
Sichuan	0.8029	0.7488	0.6719	0.5877	0.4782	0.5188	0.5020	0.7819
Guizhou	1.0091	0.8896	0.8913	0.7904	0.5401	0.3890	0.3556	0.6870
Yunnan	0.7968	0.6769	0.6613	0.5806	0.4407	0.4146	0.2638	0.4416
Shaanxi	0.8082	0.7816	0.8354	0.7636	0.5940	0.3228	0.2749	0.6625
Gansu	0.7695	0.7897	0.7078	0.5871	0.3730	0.2900	0.2946	0.6677
Qinghai	1.0112	1.0004	1.0015	1.0029	0.5613	0.4156	0.4096	1.0646
Ningxia	1.0290	1.0025	0.9520	0.8379	0.5593	0.4315	0.4375	1.0410
Xinjiang	0.6066	0.6357	0.6676	0.6296	0.4396	0.3408	0.3515	0.7511

 

Q2. Additionally, it's crucial to clarify the reasons for using both methods together. Were the DEA results used as inputs for the regression analysis, or was there another relationship between the two approaches? A detailed explanation of how these methods were integrated will enhance the study's clarity and rigor.

Response: Thank you very much for your valuable comment. In accordance with your suggestions, we have provided a more detailed explanation of the integration of the two methods in Section 5.1. Benchmark Results. Specifically, we first clarified the rationale for employing both regression analysis and data envelopment analysis, in order to highlight the appropriateness of combining these two approaches. Secondly, we offered a brief, structured description of the integration process to enhance the clarity of the study. The revised content is as follows:

 

To further investigate the impact of digital technology on the synergistic govern-ance of pollution and carbon reduction, this study organizes the synergistic governance data calculated using the EBM-DEA model. On this basis, a panel regression model is constructed to empirically examine the effect of digital technology. Regression analysis is adopted because it allows for the control of various confounding variables, enabling a more precise identification of the relationship between digital technology and the synergistic governance of pollution and carbon reduction, and thereby facilitates an assessment of potential causal links between them. The synergistic governance level of pollution and carbon reduction calculated using the EBM-DEA model helps to over-come the limitations of single-indicator measurements, thereby enhancing the reliabil-ity of the regression results.

The integration of methodological steps is as follows: First, a comprehensive in-dicator system is constructed from an input-output perspective, and the EBM-DEA model is employed to measure the level of synergistic governance of pollution and carbon reduction. The temporal and spatial evolution of the governance level is then analyzed. Second, the measured results are used as input data for the regression anal-ysis. Specifically, the level of synergistic governance of pollution and carbon reduction serves as the dependent variable, while the development level of digital technology is used as the core explanatory variable. Control variables include population density, policy regulation, industrial structure, economic development, and environmental in-vestment. All relevant data for the empirical analysis are compiled in the form of a panel dataset. Finally, based on the constructed panel data, the F-test and Hausman test are conducted to justify the appropriateness of using a two-way fixed effects model, which controls for both individual-specific and time-specific effects. In addition, clus-tered robust standard errors are applied to mitigate the impact of heteroskedasticity, cross-sectional dependence, and autocorrelation, thereby ensuring the robustness of the empirical results.

Following the above approach, this study conducts the regression analysis using a two-way fixed effects model that simultaneously controls for individual and time ef-fects. The regression results are presented in Table 3.

Reviewer 2 Report

Comments and Suggestions for Authors

Congradulation!

Author Response

Thank you very much for your enlightening comments and viewpoints on the manuscript. With your help, I believe the quality of the manuscript will be greatly improved. We are also very grateful for your high approval of our work during the second round of the review process. Thank you for thinking it's acceptable.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

Congratulations