Can Technological Innovation in Renewable Energy Promote Carbon Emission Efficiency in China? A U-Shaped Relationship

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. The study tackles a timely and significant issue—renewable energy innovation and its impact on carbon efficiency—which aligns with global climate change mitigation efforts. The subject of renewable energy and carbon emissions is highly relevant. The paper makes a valuable empirical contribution; however, author should strengthen its theoretical grounding, robustness checks, and policy granularity.

Potential Shortcomings & Suggestions for improvements.

1. English language phrasing lacks clarity, conciseness, and academic polish.
2. In title, the phrase "a U-shape relationship" lacks clarity and completeness. Capitalize properly and clarify the relationship.

3. Author can include empirical results in detail in the abstract to improve its clarity and relevance.

Theoretical Framework & Hypotheses

4. Missing Mechanism:Why does RETI initially reduce CEE (the downward part of the U-curve)? Is it due to high R&D costs, lag effects, or crowding out of other green investments?

Suggestion: Add a brief theoretical explanation (e.g., "Early-stage RETI may divert resources from immediate efficiency gains").

Add a paragraph explaining potential mechanisms (e.g., high upfront R&D costs, lagged adoption, or temporary resource misallocation).

Cite innovation diffusion theory (e.g., Rogers’ adoption curve) or learning-by-doing effects to ground the U-shape hypothesis.

 Data & Methodology

5. Sample Limitations:This study uses panel data from 30 provinces, municipalities, and autonomous regions in China from 2007 to 2022. However, it excludes Tibet, Hong Kong, Macao, and Taiwan. It may bias results if these regions have unique energy profiles.

Suggestion: Justify exclusions (e.g., data availability) or test robustness by including them if possible.

Endogeneity Concerns: Does RETI drive CEE, or do high-CEE regions invest more in RETI?

Suggestion: Address with Granger causality tests or instrumental variables (IV).

6. Author can enrich the introduction by incorporating more relevant studies. The author may consider adding the following: "Role of Eco-Innovation and Financial Globalization on Ecological Quality in China: A Wavelet Analysis”, “Exploring the dynamic capability of green technology innovation for achieving sustainable development: an empirical insight from China”, “Unveiling Energy Efficiency and Renewable Electricity’s Role in Achieving Sustainable Development Goals 7 and 13 Policies”.

Policy Implications

7. Regional Specificity:Which regions are above/below the U-curve’s inflection point?

Suggestion: Map provinces by RETI/CEE phases to guide targeted policies.

Technology Transfer: Could cross-regional collaboration accelerate RETI’s positive phase?

Overemphasis on Methods, Less on Findings

8. The review details how RETI/CEE are measured but spends less time summarizing what prior studies found about their relationship.

Suggestions:

Add a dedicated subsection synthesizing empirical results (e.g., "Most studies find RETI reduces emissions, but few test nonlinearities").

Highlight conflicting evidence (e.g., Razzaq et al.’s threshold effects vs. Xu et al.’s universal reduction) to underscore your contribution.

Missing Links Between RETI and Moderators

9. The moderators in study (informatization, fiscal decentralization) are not well-connected to prior literature.

Suggestions: Cite studies on how digitalization (e.g., smart grids) or fiscal policies (e.g., local competition) interact with RETI.

Regional Heterogeneity Underdeveloped

10. Paper finds regional differences, but the literature review does not foreground this.

Suggestions: Discuss how geographic factors (e.g., resource endowments, industrial bases) shape RETI’s impact (e.g., cite Huang et al. 2024 on regional governance).

11. In Hypothesis H2, Informatization both reduces negative effects early(via optimization) and delays the inflection point (via infrastructure costs). This tension needs reconciliation.

Suggestion: Frame it as a time-lagged effect—e.g., short-term costs vs. long-term gains—and cite studies on digitalization’s lifecycle impacts (e.g., Zhou et al. 2023).

12. In Hypothesis H3, why would fiscal decentralization accelerate the inflection point? The argument focuses more on its negative role.

Suggestion: Emphasize local experimentation (e.g., pilot zones) that speeds up RETI adoption initially, but leads to inefficiencies later. Cite Oates’ "Decentralization Theorem."

 The 65% autonomy threshold is intriguing but unsourced.

Suggestion: Add a reference (e.g., "As shown by Chen & Zhang (2022), local fiscal autonomy beyond 65% correlates with...").

In Conclusion, the dual role of informatization (rightward shift and positive moderation) could confuse readers.

Suggestion: Use a two-phase framework:

• Short-term: Infrastructure costs delay inflection.
• Long-term: Digital tools (e.g., smart grids) amplify RETI’s positive effects.

13. In conclusion, the inflection points (e.g., "when RETI matures") are described qualitatively.

Suggestion: Add specific metrics (e.g., "when RETI patents exceed X% of total energy patents" or "after 5–7 years of R&D investment"). Cite similar thresholds from prior studies.

 

Comments on the Quality of English Language

1. English language phrasing lacks clarity, conciseness, and academic polish. 
2. There are several grammatical mistakes, author should take help from native speaker.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper investigates the role of renewable energy and technological innovation in improving carbon emissions efficiency in China, using a U-shaped relationship model. It explores the impact of information levels and fiscal decentralization on this relationship. Here are my comments:

1. The study uses an SFA model to measure carbon emissions efficiency. How robust is this methodology compared to alternative approaches, especially given the potential biases in stochastic frontier analysis?

2. What are the specific technological innovations in renewable energy that have contributed most to carbon emissions efficiency improvements in China?

3. How do regional differences in economic development and infrastructure impact the effectiveness of renewable energy technological innovation in reducing carbon emissions?

4. Can the U-shaped relationship between renewable energy technological innovation and carbon emission efficiency be observed in other countries, particularly those with different levels of economic development or energy consumption?

5. How can policymakers ensure that fiscal decentralization does not lead to fragmentation and inefficiency in renewable energy innovation efforts?

6. How does the integration of renewable energy technologies into existing energy infrastructures contribute to improving carbon emissions efficiency, particularly in regions with less advanced infrastructure?

7. What specific policies can be implemented to accelerate the maturity of renewable energy technologies and enhance their contribution to carbon emission efficiency?

8. What is the relationship between the cost of renewable energy technologies and their adoption rates across different provinces in China?

9. How can the findings of this study be applied to improve carbon emission efficiency in other major economies like the United States or India?

10. The study suggests fiscal decentralization can have negative effects. How do these findings reconcile with literature emphasizing its benefits (e.g., local adaptability)? Are there thresholds not explored?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

General Comment: This manuscript presents a well-structured and empirically rigorous analysis of the relationship between renewable energy technological innovation and carbon emission efficiency in China. The use of a stochastic frontier analysis (SFA) model to compute carbon emission efficiency is methodologically sound and complements the panel regression framework well. The identification of a U-shaped relationship and the inclusion of moderating factors, informatization level and fiscal decentralization, add originality and policy relevance to the research.

The manuscript can be improved as follows:

1. Clarify the contribution of the paper in the abstract and introduction by stating more explicitly what new insights it adds compared to existing literature.
2. Improve or shorten theoretical sections (like the EKC-based hypothesis rationale) by summarizing key points more succinctly to avoid redundancy with the empirical sections.
3. Define key variables earlier, such as “informatization level” and “fiscal decentralization”, and be consistent with units and measurement across sections.
4. Improve English language and grammar, especially in the conclusions and policy suggestions section, which contains minor errors and awkward phrasing. Eg carbon emission efficiency; promoting informatization development; increase in the degree; excessive decentralization emerges; scattering innovation resources and Backward (all in the conclusion)
5. Discuss policy implications more concretely, particularly how regional governments might balance decentralization with innovation efficiency.
6. Include limitations and suggestions for future research.

Comments on the Quality of English Language

Generally okay

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have improved the paper significantly; however, it still need further improvements.

1. English language writing needs improvements. There is an excessive use of passive voice and long sentences.

Suggestion: Improve language and writing style for international readability and publication quality.

2. The explanation based on EKC and “learning by doing” is mentioned, but it remains relatively shallow.

Suggestion: Authors are advised to deepen theoretical justification for the U-shape and moderating effects.

3. RETI (Renewable Energy Technological Innovation) is based on patent data, but: The paper does not differentiate between patent quality and quantity beyond referencing a diffusion/decay rate.

Suggestion: Clarify and improve measurement of RETI (include citation-based quality adjustment or patent scope if possible).

4. The review is comprehensive for Chinese literature, but it lacks in international studies.

Suggestion: Try to add some comparative context in literature review—how does China compare with other countries in renewable innovation and efficiency?

5. There is no discussion on why SFA is preferred over DEA beyond citing a few studies.

Suggestion: Report diagnostics for multicollinearity and endogeneity more clearly.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf