Review Reports - Unveiling the Dynamic Interplay of Industrial Carbon Emissions: Insights from Quantile Time–Frequency Analysis

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

Dear Authors,

The improvements are considerable, and in my opinion, the article has been significantly enhanced.

Regarding point 1, I would suggest adding a conceptual framework or diagram to better support the theoretical justification for the integration of the BK method — although I leave this at your discretion.

Congratulations on the progress made, and I wish you much success moving forward!

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

Despite revisions, the manuscript remains conceptually overstretched and analytically thin. The authors attempt to justify their country selection by citing geopolitical relevance, but the rationale is largely descriptive and lacks empirical support. No data or citations back the claim, and other equally relevant countries are omitted without explanation, making the selection appear subjective. A more credible approach would use a data-driven or theoretically grounded method.

The discussion of geopolitical events is also weak. References to conflicts like Russia–Ukraine or Israel–Palestine are superficial, with no serious effort to show causality. The observed spillover index shifts during these periods are noted, but not analyzed using robust methods.

Some language is vague or generic. Statements like “vigilance is necessary concerning...OPEC policies and global economic pressure” resemble policy editorials, not research insights. These comments add little and don't require analysis to state. Instead, the paper should present precise, data-backed interpretations.

While the use of QVAR and Baruník–Křehlík frequency decomposition is technically solid, it is not methodologically novel. The authors also fail to link their findings to existing theory. The paper lacks a clear stance within the literature and doesn't explain whether it confirms or challenges established models. Policy recommendations, meanwhile, are either too broad or disconnected from the empirical analysis.

Figures are poorly executed. The spillover heatmaps are too small, poorly labeled, and unclear without the main text. They must be redesigned with better resolution, clear legends, and self-contained captions that tie visuals to research questions.

Grammatically, the manuscript contains frequent errors—e.g., using “Russian” instead of “Russia” and confusing “overflow” with “spillover.” Technical terms and steps in the methodology are also introduced without adequate explanation, making the paper hard to follow for non-specialists.

In short, while the technical work shows potential, the study lacks a strong narrative, clear theoretical grounding, and rigor in its empirical choices.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

Studying the effects of carbon flows in developed and developing countries can provide a more objective assessment of the impact of emissions on the environment for different countries. The authors' use of quantile autoregression methods introduces the concept of extreme quantile conditions when studying the role of carbon transfer in reducing carbon emissions. This study is relevant for the direction of sustainable "green" development, and its results will help to more rationally distribute tasks for reducing emissions. However, the phrase " Lastly, the total spillover varies over time and is influenced by sudden crises. " is too general. Such a conclusion can be made without conducting any research. This phrase should be removed from the "Abstract" section or formulated more specifically.

In the "Introduction" the authors provide a brief introduction to the series of articles devoted to the study of the relationship between carbon emissions in developed and developing countries, carbon spilovers between countries, and the impact of crises on carbon spilovers. They also provide a rationale for the importance of using the quantile autoregression method in conditions where traditional methods do not allow for an effective differentiation of the impact of different time scales on the network of spillovers and do not integrate leading industrialized countries into a single dynamic network structure for quantitatively assessing the transition of their roles in different market conditions. To justify the method used, it is desirable to provide more references indicating the problems of the applicability of traditional methods.

Section 2 presents a literature review of a number of articles devoted to the study of the interrelationships of carbon emissions across sectors and countries, the impact of tacit carbon emissions, and the carbon flows between them. The authors then state - " However, the aforementioned studies did not delve into the complex direct and indirect linkages of carbon emissions between industries, nor did they thoroughly tackle the diverse roles of nations and sectors within the complex international carbon transfer network." - which seems contradictory to the above text of the review. In the last paragraph of the section 2, the authors describe the application of the quantile approach by various researchers. They note the importance of incorporating the BK model in the frequency domain. However, it remains unclear what the advantage of the QVAR method is compared to traditional methods. What does it mean to be more resilient to outliers?

In Section 3, the authors describe the methodology of QVAR analysis. They first define the conditions for the applicability of the QVAR model. Table 1 presents the results of traditional statistical tests. Which again raises the question - Why is the quantile approach necessary if the tests allow the use of any known methods of time series analysis, including traditional ones? The second question is - Why is the J-B test not consistent across countries? Calculation of J-B using the formula JB =n/6(S^2+1/4(K-3)^2) requires different values of n to obtain the J-B presented in Table 1. A more detailed description of the data sample should be given in the section or column J-B should be excluded from Table 1.

The authors then provide a number of formulas used to analyze the connectivity of series, both in the time and frequency domains. Unfortunately, this part of the manuscript is replete with numerous typos and multiple definitions of the same quantities, which significantly complicates the understanding of the text.

The problem of inaccurate description of results is observed in Section 4. Page 9 contains an analysis of the data accumulated in table 2, but the numbers given in the text (10.51%, ) are missing from the table. In general, the data in Table 2 are consistent with each other, the sum of the TO values is equal to the sum of the FROM. However, it is impossible to understand how the data in the tables and the further text are related. In the description of Table 3, attention is paid only to the NET and TCI indices. Intuitively, one might assume that long-term data on spillovers will be smoothed due to a larger time lag. Unfortunately, the authors did not explain what they mean by extreme (up/down) market conditions and tail spillover effects. Therefore, it is unclear how the authors reach their conclusions when describing the results in Tables 4-6. Due to deviations from the normal distribution, the influence of the "tails" increases. Extreme quantiles allow to better take into account the "tails", but how this relates to the growth/decline of the market is unclear. There is no description of the results in Fig. 2, 3 in subsection 4.2. The captions to the figures are the same, although the data presented is different. The description of the results in Figures 4 and 5 presented in subsection 4.4 are generally consistent with each other, but the choice of the key dates “beginning of 2020” and “mid-2022” does not stand out very well in the figures, only for the UK in Figure 5 there are noticeable changes.

Conclusions are generally consistent with the results presented. The first key conclusion is quite general and can be made intuitively without performing complex calculations. The second one, dedicated to the EU and the UK, and the final one, dedicated to China, are made reasonably. However, the third requires a more detailed explanation in the discussion, since the connection with the selected key dates is not obvious.

The recommendations are well known and are already taken into account in many policies. However, taking into account the time-varying and asymmetric characteristics of carbon emission effects allows for a timely response to extreme changes in emissions and provides a more flexible management system. This work may be useful for formulating policies for timely emission control. The work can be published after the authors make significant edits to the manuscript. They should provide clear definitions and show the relationship of the characteristics and terms they use with real data on the dynamics of spillovers (Figure 1 is not described in the text at all. How is it related to the data in Table 2?).

Some minor notes:

P.2. p2. The sentence "In contrast to developed country markets, those with more complete industrial systems like the European Union are particularly vulnerable to the effects of extreme events." probably not consistent. "developed" should be corrected to "developing".

P.6. p4. "...fi_j (tau) is a QVAR coefficient matrix..." - Probably should be "...fi_ti(tau) is a QVAR coefficient matrix...". Otherwise, these are the coefficients of a vector of dimension p x 1. Where is mu_i(tau) in Eq. 1?

P.6. p.5. It is necessary to specify on the basis of which data the var/cov matrix is calculated. It is also not entirely clear what the i-th and j-th series mean. Is it a series composed of variables x_t? What does Psi'(tau) mean without any subscript in Eq.2?

P.6. p6. " To ensure that the row sums of theta_ij (H) add up to one " - Probably the symbol "theta" should be without the upper wave.

P.7. p2. "... the net pairwise connectedness (NPDC),..." - Probably it is should be - the net pairwise directional connectedness (NPDC),

P.7. p5. "The total directional connectedness measure (TO) gauges..." - it should probably be written - "The total directional connectedness measure (FROM) gauges..."

P.7. p9. " The lower the TCI value, the lower the market risk, while a higher value indicates the opposite." - What is "market risk" if this section describes a general methodology applicable to any connected time series.

P.7. p10. What does the subscript t next to the summation symbol in Eq.9 mean?

P.7. p11. " Integrating spectral density with generalized prediction error variance decomposition results in the frequency-specific generalized prediction error variance decomposition. " - What is "integrating spectral density", according to what formula? Probably it is "the integrated spectral density".

P.8. p1. " Additionally, the standards Formula (9) is derived from chemical treatment. " - This sentence is incomprehensible and should be completely rewritten. What are "standards", "chemical treatment" and formula (9) should be (10)? Where does Psi' disappear to in formula (10)?

P.8. p2. "... within markets," - should be removed. " The measurement quantifies the spillover magnitude..." - probably it should be - "The measure quantifies the spillover magnitude..."

P.8. p3. Definition "...the net overflow index (NET)..." causes confusion with the definition "...net effect of series i on the given network..." in Eq.7, as well as the definition "...the net spillover index...". - The same type of definitions should be used. And "markets" need to be replaced by other, more general concepts related to spillover effects. Because this methodology of connectedness can be applied to the analysis of any time series.

P.10. p2. "Table 3. frequency-domain spillover index." - There is a typo.

P.15. p1. "Notably, Figure 1 emphasizes a significant..." - It is should be " Notably, Figure 4 emphasizes a significant"

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report (New Reviewer)

Comments and Suggestions for Authors

The abstract is somewhat long and dense. It could better highlight the novelty compared to prior studies and emphasize the key policy implications in simpler terms.
* The introduction is very long (multiple paragraphs could be condensed).
* Citations sometimes cluster (e.g., multiple Wang et al.) without enough critical discussion.
* The novelty claim could be clearer and more upfront.
* How this study innovates (QVAR + BK method, multi-country comparison).
* Dataset (2019–2023, Carbon Monitor) is relevant and recent.
* Country selection is justified (covers 70% of global emissions, developed vs. developing).
* QVAR with GFEVD and BK decomposition is appropriate.
* Equations are lengthy and may overwhelm non-technical readers.
* Some notations are inconsistent (e.g., “Russian” instead of “Russia”; spacing/formatting of equations).
* Table 1 (descriptive statistics) could include mean and standard deviation for clarity.
* Clarify methodology in simpler terms before the technical details.
* Check consistency in mathematical notation and country names.
* Add more explanation on why QVAR is superior to VAR or TVP-VAR in this context.
* Results are extensive, covering time-domain, frequency-domain, quantiles, and dynamics.
* Discussion is heavily numerical; lacks economic and policy interpretation in places.
* Some results are repeated in multiple sections.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 5 Report (New Reviewer)

Comments and Suggestions for Authors

File attached.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

The topic is timely and relevant, with practical implications for both academia and industry. However, there are several issues that should be addressed to improve the quality of the submission.

Small figures in Figure 3 use the same legend. I recommend removing them from the small multiples and creating one single legend since it obstructs the view in some of the visuals when placed in every one of them.

Figure 4 is missing exes names which makes is meaningless. Same for Figures 2 and 3, Figure 6 through Figure 10.

Figure 5 is too small and blurry.

The claim “China exhibiting a stable and often negative net spillover position across market conditions, suggesting its potential as a stabilizer and safe haven during periods of systemic risk.” Needs to be further justified. How about per capita values and other variables? Tying the results to conflicts seem to be a naïve approach to such a complex dynamic.

I am not sure what extreme quantile mean here:

This highlights the importance of considering carbon transfer's role in emission reduction during extreme quantile scenarios.

I am not sure how these two sentences are connected or justified:

Despite this, the outbreak of the 2023 Israel-Palestine conflict and the 2022 Russian-Ukraine crisis pose threats to industrial transformation and upgrades. The carbon transfer between developed and developing countries has sparked disputes over environmental equity.

Are these the only two conflicts we have had in the world? How does COVID affect these? What is it relevant? I think these strong arguments must be supported by equally solid data to differentiate them from being opinions of the authors.

The source of the CO2 data must be included in the tables. For instance, here: Descriptive statistics on industrial carbon emissions in various countries.

In Table 4. frequency-domain spillover index. -- The spillover table for band: 3.14 to 0.63 Roughly corresponds to 1 days to 5 days. The word days should be day

Space is needed here: Table 8 and Figure 2illustrates

What is the source for wind database where the data came from?

The assumptions and the shortcomings of the study should be included.

The implications provided are vague. What is being proposed and how they can be implemented should be elaborated on. The conflicts caused by war or pandemics always have an impact on BAU scenarios. Can these be generalized to other scenarios and disruptions? Are there historical evidence of these?

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

The authors have significantly improved the original manuscript and revised part of the manuscript in accordance with the comments. I believe that in its current form the manuscript will be more understandable to readers and can be published without changes.

Author Response

尊敬的审稿人：

感谢您对我们题为“揭示工业碳排放的动态相互作用：分位数时频分析的见解”的手稿的来信和评论。非常感谢您的积极反馈并承认对稿件所做的改进。我们非常感谢您的时间和建设性的意见，这对于提高论文的清晰度和质量非常宝贵。

Reviewer 4 Report (New Reviewer)

Comments and Suggestions for Authors

The revision is fine with me.

Author Response

Dear reviewer:

Thank you for your kind letter and comments regarding our manuscript entitled “Unveiling the Dynamic Interplay of Industrial Carbon Emissions: Insights from Quantile Time-Frequency Analysis.” Thank you very much for your positive feedback and for acknowledging the improvements made to the manuscript. We greatly appreciate your time and constructive comments, which have been invaluable in enhancing the clarity and quality of the paper.

Reviewer 5 Report (New Reviewer)

Comments and Suggestions for Authors

Manuscript can be accepted now.

Author Response

Dear reviewer:

Round 3

Reviewer 2 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

The topic is timely and relevant, with practical implications for both academia and industry. However, several issues need to be addressed to improve the overall quality of the submission.

There are typographical errors that should be corrected. For example:

“W Although these approaches provide useful insights…” — the “W” appears to be a stray character.

“dynamics.(Diebold & Yilmaz 2012)” — there is a missing space after the period.

The paper would benefit from a thorough proofread.

Additionally, I was unable to access the link: https://www.carbonmoni-tor.org.cn/ --- it appears to be broken.

The figures remain problematic. Some are too small, and in others, the text overlaps with the data, making them hard to read.

Finally, it's unclear how the paper has been revised in response to my previous recommendations.

Author Response

We sincerely thank the reviewer for the careful reading and constructive feedback. Below, we address each point in detail:
We carefully proofread the manuscript and corrected the typographical issues noted by the reviewer.
In addition, we conducted a thorough language check to ensure clarity and accuracy throughout the paper.
We acknowledge that the link https://www.carbonmoni-tor.org.cn/ is currently inaccessible. We have replaced it with the updated and stable link: https://www.carbonmonitor.org.cn
We revised figure for readability. Adjusted font sizes and layouts to prevent overlap between text and data.
We greatly appreciate the reviewer’s detailed comments, which helped us significantly improve the quality of the paper.

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 Authors

The paper is well-written overall and clearly states its purpose: to explore carbon emissions, transfers, and spillovers between nations using quantile time-frequency analysis. The topic is timely and relevant, with practical implications for both academia and industry. However, there are several issues—both technical and conceptual—that should be addressed to improve the quality of the submission.

Formatting inconsistencies are frequent. For instance, line 37 reads “efforts(Swisher & Masters 1992)” without a space; the same issue appears in lines 51 (“transformation goals(Jiang et al.)”) and 71 (“patterns(Zhang & Pan 2023)”). These errors occur throughout the manuscript and should be corrected systematically. There are also unnecessary spaces, such as in line 56: “commodity markets (Adekoya et al. 2023).”

There are multiple instances where citations are missing. In lines 59–60, the claim that multinational corporations and industrial chains impact carbon emissions between countries requires a reference. This idea recurs in the paper but lacks refinement and clarity. Similarly, in lines 79–81, the statement about the Federal Reserve adjusting interest rates due to energy price fluctuations needs a proper citation.

The text references geopolitical conflicts multiple times (e.g., “Russo-Ukrainian conflict,” “Palestinian-Israeli conflict”) without consistently clear language. It's unclear what “Russo” refers to in this context, and the phrasing should be clarified throughout. Statements such as “the war has notably affected China’s energy and commodity markets” must be supported with analysis or data rather than asserted as fact.

The justification for the study is currently weak. It is not clear what gap in the literature this paper fills. What has previous research already covered, and what remains to be addressed? Also, what other methods have been used in similar studies, and why is the chosen method preferable? The manuscript assumes the reader understands the advantages of quantile time-frequency analysis but doesn’t explain them clearly. Acronyms like QVAR and BK must be spelled out before they appear.

The basis for country selection is also unclear—are they chosen based on GDP, emissions output, political relevance, or some other metric? This should be made explicit. The Carbon Monitor data source is referenced but not cited or described in terms of accessibility. Similarly, in Table 2, “TCI” is used without explanation. It’s also unclear whether the numbers in the table reflect lower cells or something else—this should be clarified. Figure 1 is too small and lacks detail; it does not effectively communicate the data.

Several statements are made without adequate support. For example, “factors such as the Russia-Ukraine conflict, the tightening of OPEC oil production policies, and the increasing downward pressure on the global economy have significantly changed the risk connectivity of industrial carbon emissions...” This needs to be backed by specific analysis, not stated as obvious. Finally, the policy recommendations—such as the need for stable manufacturing policy and investment in advanced manufacturing—are generic and disconnected from the paper’s findings. These are common-sense conclusions that do not directly reflect the unique contributions of the study.

In summary, while the topic is important and the methods are potentially valuable, the paper needs stronger justification, cleaner formatting, more precise language, and a tighter connection between the analysis and conclusions.

Author Response

Please see attached

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

I would like to congratulate you on the work you have carried out and on the development of your article. Below, I will share my opinions regarding the elements that I believe should be improved, as well as some positive aspects identified in your manuscript.

To keep my feedback concise and structured, I have organized my comments clearly into two categories: observations and suggestions.

General Remarks

The article proposes a timely and highly relevant study in the context of the current climate crisis, addressing the interdependencies between industrial carbon emissions across countries and the spillover effects generated under various economic and geopolitical conditions. The authors employ advanced econometric techniques — Quantile Vector Autoregression (QVAR) and the Baxter-King decomposition — to capture dynamic relationships in both the time and frequency domains.

The research theme is well aligned with the journal's objectives, and the results may have important implications for international climate policy, particularly in terms of how responsibilities for emission reduction are distributed among nations. However:

Some conclusions — such as China’s resilience in times of crisis or the UK becoming a major emitter under extreme conditions — are interesting but insufficiently linked to concrete policy implications.
Strengthening the connection between the empirical findings and their relevance for decision-makers would greatly enhance the impact of the paper.

Originality

The manuscript brings an interesting contribution by combining quantile-based analysis with a frequency perspective to examine the spillover effects of industrial carbon emissions under varying market conditions.

However, the originality of the contribution is not yet fully articulated in relation to existing literature. For example:

It is not clearly stated whether this is the first study to apply the QVAR model in this particular context.
The integration of the BK method is mentioned, but not supported by a robust theoretical rationale.

Suggestions for improvement:

Clearly state the research gap that this paper addresses.
Emphasize how your methodological and practical contributions differ from those in studies using MRIO, SNA, or other standard models.

Methodology

The methodology is rigorous and grounded in advanced econometric literature, but there are several areas that require further development:

The selection of countries included in the analysis is not justified — why were these eight countries chosen over others?
The use of lag (p=1) is said to be based on AIC/HQ criteria, but the values of these tests are not shown for comparison.
While stationarity testing is mentioned, there are no supporting charts, additional tests, or robustness checks.
The impact of structural breaks (e.g., the COVID-19 pandemic or geopolitical conflicts) is intuitively present in the results but is not explicitly discussed or modeled.

Suggestions for improvement:

You can include robustness checks using alternative quantiles (e.g., 0.1 and 0.9).
Present residual diagnostics and test the sensitivity of the results to different lag specifications.
Can introduce a discussion of structural breaks and how they may influence model interpretation.

Results and Interpretation

The tables and figures are well constructed and offer detailed insight into the results. However, the interpretation is at times too superficial or lacks nuance. For example:

The finding that the European Union transitions from a net emitter to a net recipient during crises is interesting but not explained from an economic or institutional perspective.
Similarly, the claim that “China can be considered a safe haven” requires more substantial empirical support.

Suggestions for improvement:

Provide stronger economic rationale for the observed transitions in countries’ transmission roles.
Draw clearer connections between the interpretations and the presented tables and figures to ensure narrative coherence.

Clarity of Writing

The manuscript is generally understandable, but several passages could be improved:

There are redundancies and repetitive phrasing (e.g., “spillover effect effects”).
Sentences are often too long and dense, which reduces reading fluency.
The Literature Review and Discussion sections could benefit from better structural coherence.

Suggestions for improvement:

Consider professional English language editing.
Simplify sentence structure and eliminate ambiguities for improved clarity.

Ethics and Transparency

The manuscript adheres to ethical standards in research. The data source (Carbon Monitor) is clearly stated, and the data availability statement is appropriately included.

Thank you!

Author Response

please see attached.

Author Response File: Author Response.pdf