Carbon Finance and Dynamic Capital Structure Adjustment

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The topic, linking carbon finance to dynamic capital structure adjustment, has potential, but the manuscript in its current form falls short on conceptual clarity, construction of key variables, econometric design, and written presentation. Substantial revision is needed before the work reaches publication standard.

1. Framing, scope and contribution

The framing of “carbon finance” and its link to capital structure is overly broad and often imprecise. At various points carbon finance is described as a market instrument, a policy regime, a derivative market, and an umbrella for green finance. This creates confusion when you later define a provincial carbon finance development index and treat it as the core explanatory variable. You need a tight, operational definition aligned with the index you construct, and you need to keep this definition stable across the paper.

The claimed contribution, namely the study of carbon finance and dynamic capital structure adjustment, is not sufficiently separated from existing work on environmental regulation, carbon trading, and financing constraints. Many passages in the literature review summarise known results without explaining where your study differs in question, data, or method. The review should be shorter, more selective, and organised around clear gaps that lead to H1-H3. At present the hypotheses read as direct restatements of the narrative rather than as testable, novel propositions.

You also need to clarify the population of interest. In one place you refer to “China’s A-share listed companies” and elsewhere to “state-owned listed companies” only. The sample description and all tables must be consistent on whether you include all A-shares or only SOEs. If the study focuses on SOEs, this has strong implications for interpretation and generalisability, which need explicit discussion.

2. Construction of the carbon finance development index

The Carbon Finance Development Index (CFDI) is central to your argument, yet its construction is only sketched at high level. Table 1 lists five “Level 2” dimensions with single “Level 3” indicators, but the economic logic of some indicators is weak or even reversed. For example:

• “Green credit ratio” is defined as interest expenditure of six energy-intensive industries over total interest expenditure. This appears to measure the share of lending to high-carbon sectors, not green credit. If your intention is to capture a shift away from such sectors, the sign and interpretation of this indicator need careful explanation and possibly inversion.

• “Carbon lending intensity” is defined as carbon emissions divided by total social finance. Again, higher values point to greater emissions per unit of finance, which conflicts with the notion of more advanced carbon finance.

You need to provide a full methodological appendix or subsection: list all raw indicators, data sources, transformations, standardisation procedure, entropy method formulae, and weighting scheme. Show at least basic diagnostics of the index (distribution across provinces, time trend, correlation with established measures of carbon market activity or green finance). At present readers have to trust an index whose properties are unclear, which weakens all subsequent inference.

3. Measurement of key firm-level variables

Several core variables are not defined with sufficient precision.

• The target leverage ratio Lev* is said to come from Equation (2), but you do not report the full estimation results for this stage or the controls used. Readers need to see the first-stage regression for Lev*, at least in an appendix, with coefficient estimates and fit measures.

• The definition of AbsDev in Table 2 and the descriptive statistics raise alarms. Lev and Lev* both lie roughly between 0 and 0.6, yet AbsDev has a mean of 0.619 and a maximum above 2. This suggests either a scaling error, an incorrect formula, or typographical mistakes in the table. You must recheck code and reporting for AbsDev and reconcile these numbers.

• The KZ index is imported as a measure of financing constraints, but the paper does not provide its formula, the variables employed in the Chinese context, or any discussion of its suitability for Chinese listed firms over 2014–2024. Given the debate around different constraint indices (KZ, SA, WW), you should at least justify your choice and test robustness with an alternative measure.

• The dummy for green innovation (GI) is internally inconsistent. In the definition, GI takes the value 1 when the ratio of green utility models is below the median, yet in the text you speak of “enterprises with green innovation capabilities” and later interpret GI=1 as “low” or “high” innovation in different places. You must settle this coding, harmonise text and equations, and ensure that the discussion of H2b uses the correct direction of the effect.

4. Econometric specification and endogeneity

The econometric framework is the main weak point. Several aspects require revision.

First, the partial adjustment model is inherently dynamic and typically estimated using methods designed for dynamic panels (for example system GMM). You appear to estimate Equation (3) and (4) with fixed effects and firm-clustered standard errors only. This ignores the dynamic panel bias that arises when lagged dependent variables enter the right-hand side, especially with T around 10. You need either to adopt an appropriate GMM estimator or to show, with simulations or references, why the bias is negligible in your setting.

Second, the difference-in-differences (DID) component is not convincing in its current form. You treat 2013 as the “pilot year” and define a single DID term, but you do not:

• Show any pre-trend analysis for treated versus control provinces.
• Discuss staggered adoption across pilot regions and the risk of biased TWFE estimates in such settings.
• Clarify whether you use firm-level or province-level treatment coding once firms list or move between regions.

Without these checks, the DID tables read more as an auxiliary regression than a clean identification strategy. You should either strengthen the DID design with proper event-study graphs and alternative estimators or substantially downplay the causal language when discussing these results.

Third, the instrumental variable strategy using the mean provincial carbon finance level raises exogeneity concerns. If provincial average carbon finance is built from the same data as your index, this instrument is close to a mechanical transformation of the endogenous regressor. You need to clarify whether the “mean” excludes the focal province or firm and to justify why this variable affects firm-level adjustment speed only through its effect on the province’s carbon finance index. Otherwise the exclusion restriction is doubtful.

5. Interpretation of results and economic meaning

The results sections present many significant coefficients but spend little effort on economic interpretation. For example, the key coefficient CF*TLev in Table 4 is reported as 0.407, yet you do not translate this into an adjustment speed or a change in leverage gap in percentage points for a realistic change in CF. Readers need effect sizes in levels, not only significance levels.

Similarly, the cross-sectional tests in Table 8 and mediation tests in Table 9 are described in general terms (“more pronounced”) without any quantitative summary. You should provide:

• Marginal effects or predicted adjustment speeds for low versus high carbon finance provinces.
• Separate panels or figures showing how adjustment speed varies across debt groups and green innovation groups.
• A clear numerical statement of how much financing constraints and financing costs mediate the CF effect, for example proportion of total effect.

The policy section then extrapolates strong claims from these coefficients. Phrases such as “confirms their reliability” or “has a significant incentivising effect” are too strong given the methodological limitations and relatively modest R² values. Please calibrate the language and align the conclusions tightly with what the models can support.

6. Policy recommendations and alignment with results

The policy recommendations occupy a large part of the paper and often read like a general essay on carbon markets and green finance. Many statements are only loosely linked to your specific empirical findings. For an academic article, you should focus this section on the mechanisms you have actually tested: adjustment speed, financing constraints, and financing costs.

Concretely, you should:

• Remove generic statements on central bank digital currencies, blue bonds, and other instruments unless you can connect them to your index or to measurable variables in the analysis.
• Shorten and tighten the recommendations, and clearly separate what is supported by your estimates from broader normative proposals.
• Reflect limitations such as the SOE focus, the Chinese institutional setting, and the period studied when making policy claims.

7. Language, style and presentation

The English language needs extensive revision by a professional proof-reader or a fluent co-author. Many sentences are long, contain awkward word order, or mix tenses. There are also typographical errors (for example “deṇgērate”, “fonanctions”, “afeelits”, “vanvbn”), inconsistent capitalisation (“Carbon Finance”, “Green Finance”, “Cost”), and misuse of words (“maturity conversion method” without explanation, “reverse pressure” in a confusing way). These issues hinder comprehension and will distract readers.

You should go through the manuscript line by line to:

• Shorten sentences and remove repeated phrases.
• Standardise terminology, especially for the main constructs (carbon finance, green finance, green innovation, leverage, adjustment speed).
• Ensure that all symbols in the equations are defined immediately and consistently.

8. Required revisions

For the manuscript to move forward, I see the following as minimum requirements:

• Provide a rigorous and transparent construction of the carbon finance index, including a methodological annex and at least one robustness check with an alternative index or subset of indicators.
• Correct all variable definitions and internal inconsistencies (SOE vs all A-share firms, GI coding, AbsDev values, KZ index).
• Re-estimate the dynamic models using an appropriate method for dynamic panels or, if you stay with FE, demonstrate with strong argument why bias is not a concern.
• Re-work the DID and IV sections or substantially reduce causal claims linked to them.
• Strengthen the interpretation of coefficients with clear economic magnitudes and, where possible, graphical presentations.
• Undertake thorough language editing and structural tightening, including a shorter and more focused literature review and policy section.

Only after these issues are addressed would the contribution on carbon finance and capital structure be clear and convincing enough for publication.

 

Author Response

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Author Response File: Author Response.doc

Reviewer 2 Report

Comments and Suggestions for Authors

The authors of the manuscript Carbon Finance and Dynamic Capital Structure Adjustment address a highly relevant topic in the current context of the transition toward a low-carbon economy, examining an area that remains insufficiently explored in the literature—namely, the impact of carbon finance on the dynamic adjustment of corporate capital structure. The paper has strong applied value, as it highlights how the development of carbon markets can influence corporate financial stability and the efficiency of resource allocation, offering important insights into the mechanisms through which environmentally oriented regulations shape microeconomic decision-making. However, given the originality of the topic, we recommend that the authors clarify already in the abstract what the study’s specific innovative contributions are compared with the existing literature and how these contributions create a multiplier effect for future research, as this aspect is not sufficiently emphasized in the current form. Furthermore, we suggest reducing the similarity index to below 10%, compared to the current level of 30%.

The concepts and bibliographic references are relevant and properly integrated, citing studies that explore capital structure dynamics, green finance, and environmental regulation. Additionally, the authors included in the Introduction a distinct “Literature Review” subsection in which they systematically present the key foundational studies and recent research related to carbon finance and capital structure adjustment, as well as the rationale for the present study. This approach helps clearly identify gaps in the existing literature and delineate the original contributions of the manuscript.

The research methodology is presented appropriately, with the authors using an extensive dataset of Chinese A-share listed companies covering the period 2014–2024, constructing a provincial carbon finance development index, and applying a partial adjustment model to measure the speed of capital structure adjustment. Nevertheless, for greater methodological clarity, it would be advisable for the authors to specify more precisely the instruments associated with the econometric models and the stages of the analytical process, so that the research can be rigorously understood and replicated. Providing a detailed description of how the carbon finance index was constructed would further strengthen the methodological consistency of the study.

The results are clearly presented, showing that carbon finance has a significant positive effect on the speed of capital structure adjustment, with this effect being stronger among firms with higher levels of green innovation and greater indebtedness. The authors also identify the underlying mechanisms of this relationship, namely the alleviation of financing constraints and the reduction of financing costs. However, for a more comprehensive presentation of the results, we recommend including two or three additional tables summarizing key data, econometric estimates, and robustness tests, in order to facilitate the identification of the authors’ original and innovative contributions to the scientific literature.

The conclusions are appropriately formulated and highlight that carbon finance helps firms remain closer to their optimal capital structure, thereby enhancing financial stability. Nonetheless, given the applied orientation of the study, we recommend that the authors clearly articulate the limitations of the research and outline future research directions.

In conclusion, we commend the research team for the relevance of the topic, the contribution to the scientific literature, and the rigor of the empirical analysis, and we recommend revising the manuscript in accordance with the observations included in this report to enhance its scientific consistency and academic impact.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

An interesting manuscript about carbon finance and dynamic capital structure in China. The abstract provided is too long, it is better to focus on what is important: relevance, purpose, method and main conclusion. The introduction links different things, it talks about carbon finance and microenterprise, it returns to what is recorded in the title. It is not very clear, the motives for choosing the period, it is 2014-2024. The purpose of the manuscript and what the research questions are are not clear. The literature analysis was conducted on a completely different topic, because the authors plan to examine carbon finance but nowhere is it explained what this concept is, what is the difference with green finance or climate finance. There is a lack of discussion, the research results are not interpreted taking into account the research of other authors, although the authors themselves declared in the introduction that their research complements the literature in at least several aspects. Conclusions must be answers to the research questions and hypotheses raised, and policy implications are also needed.

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Paper significatly improved

Reviewer 3 Report

Comments and Suggestions for Authors

Hello,

Thank you implemented updates.