Can Low-Carbon City Pilot Policies Promote Green Total Factor Energy Efficiency in China?

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The introductory section should incorporate findings from similar studies that may contribute to the discussion of the results found in this study. It is limited to a description of the context, without delving into the phenomenon or the theoretical relationship that supports the evidence, for which the review of references should be improved.

This goes hand in hand with the need to strengthen the discussion, which is limited to an analysis of the results without delving into the phenomenon and the relevance of the findings.

In terms of methodology, it is suggested that the difference-in-difference impact evaluation methodology be explored in greater depth and that the PSM used in the presentation of results be mentioned.

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 The purpose of the research and the question it seeks to answer about the effect of decarbonization policies on energy efficiency are clearly defined. Furthermore, the proposed methodology is consistent with the objective of the study.
The purpose of the study is particularly relevant in countries where it is vital to assess the impact of decarbonization policies and their effect on the use of clean energy, as this leads to the necessary adjustments being made if the results in terms of the outcome variable are not as expected. Using impact assessment methodologies minimizes bias and allows the causal effect to be evaluated.
Although the study focuses on China, a region that has been studied in recent years in terms of energy efficiency, given the country's conditions, the introduction needs to compare it with other policies and results achieved in areas with similar characteristics. Emphasis is placed on the methodologies and added value of the study in terms of showing robust evidence with more appropriate methodologies, but the discussion is limited because other realities are not addressed to compare the results achieved.
The thorough evaluation of the Chinese case presents robust evidence of the effects of decarbonization policies, which represents added value in terms of measuring the impacts on outcome variables such as clean energy use, distinguishing it from research that considers CO2 emissions as an outcome variable.
It is suggested that the explanation of not only the difference-in-differences methodology but also the role of the PSM in the estimation be further developed, which represents added value from the point of view of causality and robustness of the estimates.
Although the conclusions are consistent with the findings indicated in the results, it is necessary to deepen the discussion of these findings and their comparison with other research not only in China, but also in countries with similar realities or even those that allow for a comparison of the policies implemented and the estimated impacts.
The following could be consulted:
Navigating Sustainability Through Environmental Regulations: Assessing the Effects of Command-and-Control and Market-Incentive Policies on Carbon Emissions in China. https://doi.org/10.3390/su17062559
Exploring the Role of Carbon Taxation Policies on CO2 Emissions: Contextual Evidence from Tax Implementation and Non-Implementation European Countries. https://doi.org/10.3390/su12208680
Policy implications of net-zero emissions: A multi-model analysis of United States emissions and energy system impacts. https://doi.org/10.1016/j.egycc.2025.100191.

Towards a low-carbon economy: How can green technological innovation affect carbon productivity in China?  https://doi.org/10.1016/j.jenvman.2025.126685
Low-Carbon Housing in Brazil: A Literature Review on Challenges, Strategies, and Perspectives. https://doi.org/10.1007/978-3-031-92777-5_58

Author Response

Responses to Reviewer #1’s Comments

 

We would like to thank the reviewer for your helpful comments and suggestions. Our responses together with your comments (in Italic font and blue color) are given below.

 

1. The introductory section should incorporate findings from similar studies that may contribute to the discussion of the results found in this study. It is limited to a description of the context, without delving into the phenomenon or the theoretical relationship that supports the evidence, for which the review of references should be improved.

Thank you for your valuable feedback regarding the introductory section. We agree that a deeper theoretical and comparative discussion would significantly improve the manuscript. We have revised the introduction to address this point comprehensively. We have not only expanded the review of domestic literature on China but have also incorporated findings from international studies on environmental policies in Europe and the United States. This broader review provides a stronger theoretical foundation for our research, particularly by situating our study within the context of the Porter Hypothesis and other relevant economic theories. By referencing comparative research, we have better explained how our focus on Green Total Factor Energy Efficiency (GTFEE) contributes to the broader academic discourse and distinguishes our work from studies that primarily focus on carbon emissions. This new context helps to clarify the phenomenon and the theoretical relationships that support our evidence. We believe these revisions have significantly strengthened the introduction and the overall paper.

“Existing scholarship has extensively explored low-carbon development from various visions [9-10]. The theoretical relationship between decarbonization policies and energy efficiency is well-established, drawing from economic theories such as the Porter Hypothesis, which posits that properly designed environmental regulations can stimulate innovation and enhance competitiveness [6, 11]. This framework helps to explain how policies like the LCCPP, while initially seeming restrictive, can ultimately drive positive environmental and economic outcomes.

In addition to the domestic literature on China, our study also draws on and contributes to a broader international context of environmental policy research. Studies on carbon taxation in European countries, for example, have demonstrated significant positive impacts on reducing CO₂ emissions [16,17]. Similarly, a multi-model analysis of United States decarbonization policies highlights the crucial role of policies that accelerate the deployment of low-emitting technologies to achieve net-zero targets [18,19]. These findings provide a comparative lens for our study, affirming that well-designed environmental policies can effectively drive positive environmental outcomes, irrespective of geographical context. Our research extends this discussion by specifically examining the effect of LCCPP on Green Total Factor Energy Efficiency (GTFEE), an outcome variable that provides a more holistic measure of sustainable development than carbon emissions alone.”

Reference:

Ji K, Kong X, Leung C-K, Shum K-L. Navigating Sustainability Through Environmental Regulations: Assessing the Effects of Command-and-Control and Market-Incentive Policies on Carbon Emissions in China. Sustainability. 2025; 17(6):2559.

Ghazouani, A.; Xia, W.; Ben Jebli, M.; Shahzad, U. Exploring the Role of Carbon Taxation Policies on CO2 Emissions: Contextual Evidence from Tax Implementation and Non-Implementation European Countries. Sustainability 2020, 12, 8680.

Bistline, J., Binsted, M., Blanford, G., et al. (2025). Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts, Energy and Climate Change. 2025, 6, 100191.

 

2. In terms of methodology, it is suggested that the difference-in-difference impact evaluation methodology be explored in greater depth and that the PSM used in the presentation of results be mentioned.

Thank you for your constructive feedback on our methodology. We appreciate the suggestion to elaborate on the Difference-in-Differences (DID) method and to more explicitly mention the role of Propensity Score Matching (PSM) in our results. We have now revised the manuscript to address these points. In the methodology section, we have added a more detailed explanation of the DID model, highlighting its suitability for our staggered policy design. We have also explicitly introduced the PSM-DID approach in Page 10. We thoroughly explain how PSM was used to mitigate potential selection bias by creating a balanced control group, thereby strengthening the validity of our DID estimation in Section 4.4. The revision makes the methodological rigor of our study clearer and demonstrates how the combination of these two methods provides a robust causal inference. We believe this clarification significantly improves the paper's overall quality and addresses your valuable comments.

“To investigate the impact of the Low-Carbon City Pilot Policy on Green Total Factor Energy Efficiency and its underlying mechanisms, we employ two main econometric models: a multi-period Difference-in-Differences (DID) model for baseline regression and a three-step mediation model for mechanism analysis. The DID framework is a robust quasi-experimental method that allows us to establish a causal link by comparing the changes in GTFEE in pilot cities (the treatment group) to the changes in non-pilot cities (the control group), effectively isolating the policy's effect from other confounding factors. The staggered implementation of the policy across different years makes the multi-period DID approach particularly suitable for our study.

To further address potential selection bias—the risk that pilot cities were not randomly chosen but had pre-existing trends in GTFEE—we also employ a Propensity Score Matching and Difference-in-Differences (PSM-DID) approach as a key robustness check. This combined methodology strengthens our causal claims by creating a more comparable control group of cities with similar characteristics to the pilot cities before the policy was implemented.”

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors, It was a pleasure to read your article. Here are some suggestions that would hopefully contribute to your manuscript:

1.Please justify the choice of of using nighttime light data as a proxy for energy input compared with direct city-level energy consumption statistics? How sensitive are results to this choice?

2. Have you considered whether alternative pollutant indicators (e.g., CO₂ emissions if available) would alter the results?

3. Since green innovation and industrial upgrading may be simultaneously determined with GTFEE, how do you address potential endogeneity?

4. It would be beneficial to comment on how unobserved local policies or fiscal incentives might bias the estimates.

5. Existing scholarship has extensively explored low-carbon development from various visions - this sentence seems a little bit artificial and not very understandable, maybe rephrase it to "Low-carbon development has been researched extensively from various prospectives/points"

6. "Conversely, some studies found negative spatial spillover effects, where the “siphon effect” of innovation resources in pilot cities inhibited the green efficiency of neighboring cities". You state "studies", but only provide one reference.

I hope this helps. Best of luck with your publication.

Author Response

Responses to Reviewer #2’s Comments

We would like to thank the reviewer for your helpful comments and suggestions. Our responses together with your comments (in Italic font and blue color) are given below.

1. Please justify the choice of using nighttime light data as a proxy for energy input compared with direct city-level energy consumption statistics? How sensitive are results to this choice?

We appreciate your query regarding our choice of using nighttime light data as a proxy for energy consumption. We have added a description to justify this choice and discuss its implications.

We chose nighttime light data over direct energy consumption statistics for two primary reasons: data availability and consistency. Official city-level energy consumption data in China is often incomplete, inconsistent, and not publicly available for a long panel period, making it difficult to construct a reliable and comprehensive dataset for a multi-period DID analysis. In contrast, nighttime light data provides a consistent, publicly available, and objective proxy for urban economic activity and energy consumption, offering a more robust measure for cross-city comparisons over our study period.

As our main dependent variable, Green Total Factor Energy Efficiency (GTFEE), is a comprehensive measure of output relative to inputs (including energy), the use of a reliable proxy is crucial. Our robustness tests, including the PSM-DID analysis and various fixed-effects specifications, consistently show a significant and positive effect of the LCCPP. This suggests that our core results are not overly sensitive to the proxy choice, as the policy’s impact on the overall efficiency measure remains robust. We acknowledge that while nighttime lights are a strong proxy, direct energy consumption data could provide a valuable alternative if a complete, consistent dataset were available for all cities.

2. Have you considered whether alternative pollutant indicators (e.g., CO₂ emissions if available) would alter the results?

Thank you for your valuable feedback regarding the inclusion of alternative pollutant indicators. You raise an excellent and important point about the use of CO2​ emissions. We agree that including CO₂​ would be ideal. However, as noted in the revised manuscript, we were constrained by the lack of consistent and publicly available city-level panel data for CO₂​ emissions for the entire period of our study. This is a common challenge in large-scale urban environmental studies in China.

To address this, our GTFEE measure was intentionally designed to be a comprehensive indicator. By including multiple undesirable outputs like industrial sulfur dioxide, soot/dust, and wastewater, our model provides a robust assessment of urban green efficiency. We believe this approach, while not directly using CO_2​, provides a highly reliable evaluation of a city’s overall environmental performance, as reductions in these pollutants are often correlated with broader decarbonization efforts. We have added this clarification to the revised manuscript to provide a more transparent justification for our methodology.

“Following the research [35], our input indicators for urban production include capital, labor, and energy. Desirable output is represented by Gross Domestic Product (GDP), while undesirable outputs are the “three industrial wastes” (industrial sulfur dioxide, soot/dust, and wastewater emissions). We acknowledge the importance of other pollutants, such as CO₂​ emissions, but were constrained by the lack of consistent and long-term city-level panel data for these specific indicators. However, our use of a comprehensive GTFEE measure, which accounts for a range of undesired outputs, provides a robust and holistic evaluation of urban green development, as reductions in these pollutants are often correlated with decarbonization efforts. Table 1 summarizes the input and output indicators used for measuring GTFEE in this study.

”

3. Since green innovation and industrial upgrading may be simultaneously determined with GTFEE, how do you address potential endogeneity?

Thank you for your insightful comment regarding the potential endogeneity between green innovation, industrial upgrading, and Green Total Factor Energy Efficiency (GTFEE). This is a critical point, and we have taken several measures to address it rigorously within our research design. We believe that the combination of our methodological approaches effectively mitigates this concern:

First, our primary multi-period Difference-in-Differences (DID) model is a quasi-experimental design that inherently addresses endogeneity from omitted variables. By including both city-fixed effects and year-fixed effects, we control unobserved time-invariant city characteristics and unobserved time-variant shocks that are common to all cities. This ensures that the estimated policy effect is not confounded by stable, city-specific factors or external macroeconomic trends.

Second, To specifically address potential selection bias—a key source of endogeneity where cities that are chosen for the policy may have different pre-existing characteristics than non-pilot cities—we employed a PSM-DID approach. As detailed in the manuscript, this method creates a statistically comparable control group by matching cities on key observable characteristics before the policy's implementation. The successful balance test confirms that our treatment and control groups were highly similar, allowing us to more confidently attribute the observed changes in GTFEE to the LCCPP itself.

Third, our three-step mechanism analysis is designed to establish a clear causal pathway. Instead of simply asserting that green innovation and industrial upgrading are simultaneous with GTFEE, our model first demonstrates that the policy (LCCPP) has a direct effect on GTFEE. Then, it separately shows that the policy influences the mediating variables (green innovation and industrial upgrading), which, in turn, affect GTFEE. This stepwise approach provides a robust framework for identifying the channels and helps to disentangle the direct policy effect from the indirect effects operating through these mediating variables, thereby reinforcing the causal claims.

By using these complementary methods, we are confident that our findings are robust and that the identified impacts of the Low-Carbon City Pilot Policy on GTFEE are not biased by the potential endogeneity you have raised.

4. It would be beneficial to comment on how unobserved local policies or fiscal incentives might bias the estimates.

Thank you for your valuable comment. You raise an excellent point about the potential for unobserved local policies or fiscal incentives to bias our estimates. We agree that this is a crucial consideration for validating our causal claims. We have addressed this concern through a multi-faceted approach, and we believe our findings are robust to this potential bias:

First, the inclusion of city-fixed effects helps to control for time-invariant, city-specific unobserved variables, which could include long-term local fiscal policies or political incentives.

Second, as shown in our robustness test (Column 2, Table 4, in Page 16), we specifically excluded cities that had implemented other major environmental policies concurrently, thereby mitigating the confounding effects of overlapping government initiatives. The fact that our core finding remains robust in this reduced sample provides strong evidence that our estimates are not biased by these unobserved local factors.

Third, we have also conducted a detailed heterogeneity analysis, as presented in Appendix C, which provides further evidence that our model is not biased by unobserved local factors. Our findings show that the LCCPP's effect varies significantly based on a city’s fundamental characteristics.

The ability of our model to successfully detect these significant differences in policy effectiveness across sub-groups based on their inherent characteristics (e.g., economic development level, industrial structure) provides powerful validation. It demonstrates that our estimates are not merely capturing a generalized, unobserved "local policy" effect. Instead, our results are precisely isolating how the LCCPP, as a specific policy tool, works differently in varying local contexts, thereby confirming its genuine causal impact on GTFEE.

5. Existing scholarship has extensively explored low-carbon development from various visions - this sentence seems a little bit artificial and not very understandable, maybe rephrase it to "Low-carbon development has been researched extensively from various prospectives/points"

Thank you for pointing out the awkward phrasing. We have revised the sentence to be clearer and more professional.

“Low-carbon development has been researched extensively from various prospectives [9-10]. ”

6. "Conversely, some studies found negative spatial spillover effects, where the “siphon effect” of innovation resources in pilot cities inhibited the green efficiency of neighboring cities". You state "studies", but only provide one reference.

Thank you for your insightful comment. We have now added an additional reference to strengthen this point and satisfy your request for more evidence.

Reviewer 3 Report

Comments and Suggestions for Authors

A key issue is that the article is largely Sinocentric. It relies almost exclusively on literature from China, which limits its contribution to the global discussion and raises questions about the generalizability of the findings.

The literature review almost completely ignores international experience and research on similar policy measures (e.g., in the EU, the US, or other Asian countries). Discussing how the findings of this study align with or contradict results obtained in other countries would significantly strengthen the theoretical foundation and scientific contribution of the article.

The assertion that the mechanisms of LCCPP's influence on GTFEE 'have not been sufficiently explored in depth' [p. 3] is somewhat tenuous, since the article itself cites works (e.g., [9], [12]) that do examine these very mechanisms ('government–enterprise–resident'). The novelty of your mechanism analysis compared to these works should be more precisely defined.

Figures 1 and 2 (the distribution and dynamics of GTFEE) are extremely important, but their description in the text is too brief. A deeper analysis is needed - which specific cities were leaders and which were laggards? What are the reasons for the dips in 2012 and 2017 (is it only due to the extensive growth model)? Why was the Western region initially the leader, only to be overtaken by the Eastern region? This deserves a separate subsection or paragraph.

For the variable `GreenInn` (green patents), the mean value (0,2594) is significantly smaller than the standard deviation (0,7082), and the maximum value (5,013) is many times greater than the median (0,0375). This indicates a strongly right-skewed distribution. Perhaps a logarithmic or other transformation should have been applied to this variable in the regressions to reduce the influence of outliers.

In Table 2, `Agg` (industrial agglomeration) has a negative and significant effect. This is an interesting finding that deserves deeper discussion in the text. Why does agglomeration lead to a «decrease» in energy efficiency? Does this contradict theory?

The conclusions are drawn for China as a whole, but the article reveals significant heterogeneity between regions (East, Central, West). The policy recommendations would be more valuable if they were targeted for different types of regions.

Author Response

Responses to Reviewer #3’s Comments

We would like to thank the reviewer for your helpful comments and suggestions. Our responses together with your comments (in Italic font and blue color) are given below.

 

1. A key issue is that the article is largely Sinocentric. It relies almost exclusively on literature from China, which limits its contribution to the global discussion and raises questions about the generalizability of the findings.

Thank you for your valuable and constructive comment. You have rightly pointed out that our initial literature review was heavily focused on the Chinese context, which limits the global reach and generalizability of our findings. Therefore, we have addressed this issue by significantly expanding our literature review to include research on similar policy measures in other countries, such as the European Union and the United States. This expansion allows us to position our study within a global dialogue on environmental policy. By comparing and contrasting China's LCCPP with policies like the EU’s Emissions Trading System (ETS) and U.S. regional initiatives, we are able to provide a more nuanced understanding of how different policy designs can influence sustainable development outcomes. Our revised manuscript now explicitly discusses how our findings align with the positive effects of well-designed environmental policies elsewhere, while also highlighting the unique attributes of a top-down, planned approach in a developing economy.

We are confident that these revisions have substantially strengthened the theoretical foundation of our paper and enhanced their relevance to an international audience.

“In addition to the domestic literature on China, our study also draws on and contributes to a broader international context of environmental policy research. Studies on carbon taxation in European countries, for example, have demonstrated significant positive impacts on reducing CO₂ emissions [16, 17]. Similarly, a multi-model analysis of United States decarbonization policies highlights the crucial role of policies that accelerate the deployment of low-emitting technologies to achieve net-zero targets [18,19]. These findings provide a comparative lens for our study, affirming that well-designed environmental policies can effectively drive positive environmental outcomes, irrespective of geographical context. Our research extends this discussion by specifically examining the effect of LCCPP on Green Total Factor Energy Efficiency (GTFEE), an outcome variable that provides a more holistic measure of sustainable development than carbon emissions alone.”

Reference:

Ji K, Kong X, Leung C-K, Shum K-L. Navigating Sustainability Through Environmental Regulations: Assessing the Effects of Command-and-Control and Market-Incentive Policies on Carbon Emissions in China. Sustainability. 2025; 17(6):2559.

Ghazouani, A.; Xia, W.; Ben Jebli, M.; Shahzad, U. Exploring the Role of Carbon Taxation Policies on CO2 Emissions: Contextual Evidence from Tax Implementation and Non-Implementation European Countries. Sustainability 2020, 12, 8680.

Bistline, J., Binsted, M., Blanford, G., et al. (2025). Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts, Energy and Climate Change. 2025, 6, 100191.

2. The literature review almost completely ignores international experience and research on similar policy measures (e.g., in the EU, the US, or other Asian countries). Discussing how the findings of this study align with or contradict results obtained in other countries would significantly strengthen the theoretical foundation and scientific contribution of the article.

Thank you for your valuable and constructive comment. You have rightly pointed out that our initial literature review was heavily focused on the Chinese context, which limits the global reach and generalizability of our findings. We agree that a broader discussion of international experiences is essential for a more robust scholarly contribution.

We have addressed this issue by significantly expanding our literature review to include research on similar policy measures in other countries, such as the European Union and the United States. This expansion allows us to position our study within a global dialogue on environmental policy. By comparing and contrasting China's LCCPP with policies like the EU's Emissions Trading System (ETS) and U.S. regional initiatives, we are able to provide a more nuanced understanding of how different policy designs can influence sustainable development outcomes. Our revised manuscript now explicitly discusses how our findings align with the positive effects of well-designed environmental policies elsewhere, while also highlighting the unique attributes of a top-down, planned approach in a developing economy.

We are confident that these revisions have substantially strengthened the theoretical foundation of our paper and enhanced their relevance to an international audience.

Reference:

Ji K, Kong X, Leung C-K, Shum K-L. Navigating Sustainability Through Environmental Regulations: Assessing the Effects of Command-and-Control and Market-Incentive Policies on Carbon Emissions in China. Sustainability. 2025; 17(6):2559.

Ghazouani, A.; Xia, W.; Ben Jebli, M.; Shahzad, U. Exploring the Role of Carbon Taxation Policies on CO2 Emissions: Contextual Evidence from Tax Implementation and Non-Implementation European Countries. Sustainability 2020, 12, 8680.

Bistline, J., Binsted, M., Blanford, G., et al. (2025). Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts, Energy and Climate Change. 2025, 6, 100191.

 

3. The assertion that the mechanisms of LCCPP's influence on GTFEE 'have not been sufficiently explored in depth' [p. 3] is somewhat tenuous, since the article itself cites works (e.g., [9], [13]) that do examine these very mechanisms ('government–enterprise–resident'). The novelty of your mechanism analysis compared to these works should be more precisely defined.

Thank you for your valuable and constructive comment. The reviewer is correct that some existing studies, including those we cited, have indeed explored broader mediating frameworks such as the “government–enterprise–resident” effect. Our original phrasing was not sufficiently precise, and we have revised the manuscript to clarify the specific novelty of our mechanism analysis (as shown in Page 4). Our study’s core contribution is not merely to identify mediating mechanisms, but to provide a more granular, quantitative, and direct empirical analysis of two specific and critical transmission channels: green innovation capacity and industrial structure upgrading. While the cited works provide a valuable conceptual foundation, they do not conduct a detailed, systematic empirical investigation of how these broader effects are specifically channeled through green innovation and industrial structure optimization to influence Green Total Factor Energy Efficiency (GTFEE).

“More critically, while prior studies have explored the mechanisms of this policy from a multi-agent perspective, there is a lack of in-depth analysis on the intrinsic economic and technological transmission channels through which the LCCPP influences GTFEE. Besides, the roles of green innovation capacity and industrial structure upgrading as distinct mediating pathways have not been sufficiently explored in unison. The majority of existing research either focuses on a single channel or offers qualitative discussions without providing a rigorous quantitative comparison of their relative importance. Furthermore, the potential heterogeneous impacts of the policy across cities with varying characteristics, such as geographical location and resource endowments, are often underexamined [2]. Specifically, while existing literature, such as [9] and [13], has broadly identified mediating frameworks like the "government–enterprise–resident" effect, these studies generally provide a conceptual discussion rather than a detailed empirical and quantitative analysis of the specific transmission channels. The crucial gap is that the intricate and nuanced pathways through which policy signals are converted into tangible improvements in energy efficiency remain a black box. For instance, the specific mediating roles of green innovation capacity and industrial structure upgrading, two pivotal channels often theorized, have yet to be systematically disentangled and empirically quantified.”

However, the key distinctions of our research are threefold:

First, we move beyond a broad, multi-agent discussion to focus on two concrete, measurable mediating variables that represent the core of policy-driven structural and technological change. Our theoretical framework directly links the LCCPP to these specific channels, and our empirical model provides a quantitative test of their mediating roles.

Second, our study is among the first to systematically disentangle the direct impact of the LCCPP from its indirect effects via these two specific mediating mechanisms. This approach allows us to not only confirm the existence of these channels but also to estimate their relative contributions to the overall policy effect, thereby offering a more profound and evidence-based understanding of the policy's intrinsic workings.

Third, the existing literature often posits these mechanisms conceptually but lacks a robust, large-scale empirical analysis to validate them. Our study addresses this gap by using a comprehensive dataset of Chinese prefecture-level cities to empirically verify the significance and magnitude of these two specific transmission channels.

In summary, while previous literature provides the “what” (e.g., policy influences energy efficiency through technology), our study provides the “how” and “how much” by rigorously and quantitatively analyzing specific mediating pathways. We have updated the manuscript on Page 4 to reflect this clarification and to more precisely define our contribution to literature.

4. Figures 1 and 2 (the distribution and dynamics of GTFEE) are extremely important, but their description in the text is too brief. A deeper analysis is needed - which specific cities were leaders and which were laggards? What are the reasons for the dips in 2012 and 2017 (is it only due to the extensive growth model)? Why was the Western region initially the leader, only to be overtaken by the Eastern region? This deserves a separate subsection or paragraph.

Thank you for your valuable and constructive comment. We sincerely appreciate the reviewer's valuable feedback regarding the analysis of Figures 1 and 2 (as shown in Page 8 and 9). We agree that these figures are central to understanding the spatiotemporal evolution of GTFEE, and a more detailed discussion is indeed warranted. We have thoroughly revised the manuscript to incorporate a more in-depth analysis of the trends, addressing each of the reviewer’s specific points.

First, we have expanded our discussion on the GTFEE leaders and laggards. The revised text now specifically identifies key cities that have shown consistent leadership, such as Chengdu, Chongqing, and Shanghai, which often served as regional benchmarks. Conversely, we now highlight cities with persistent low GTFEE scores, particularly those in heavy-industry sectors like Tangshan and Taiyuan, which faced greater challenges in their green transition.

Second, we have provided a more nuanced explanation for the observed dips in GTFEE around 2012 and 2017. Our analysis now clarifies that these dips were not solely a result of the extensive growth model but were influenced by specific policy and economic conditions. The dip around 2012 can be linked to the "Four Trillion Stimulus Plan," which prioritized rapid economic expansion and led to a surge in energy-intensive projects. The dip around 2017 reflects the transitional challenges faced by many cities as they began to grapple with the early stages of phasing out inefficient production capacities and implementing stricter environmental policies.

Finally, we have significantly elaborated on the shift in regional GTFEE leadership. We explain that the Western region's initial advantage was primarily structural, stemming from its less industrialized economy and lower energy intensity. In contrast, the Eastern and Central regions initially lagged due to their intensive heavy industries and a development model that prioritized economic growth over environmental sustainability. The fundamental shift post-2017 is attributed to these regions' robust scientific and technological resources and significant R&D investment. This allowed them to accelerate their transition to green manufacturing and clean energy, ultimately leading them to surpass the Western region in GTFEE by 2022.

We believe this more detailed analysis provides a comprehensive and compelling narrative that strengthens the paper's descriptive foundation and better aligns with the importance of these figures. The revised content has been added to the relevant section of the manuscript.

“Fig. 1 illustrates the distribution of GTFEE across Chinese cities, as measured by the SBM-DEA model. A clear upward trend in urban GTFEE is observed from 2007 to 2022. In 2007, most cities had GTFEE scores below 0.4, with only a few exceeding 0.6. By 2022, a majority of cities achieved scores above 0.5, and some even surpassed 0.7, indicating a general improvement in green energy efficiency over time. Specifically, cities like Chengdu, Chongqing, and Shanghai consistently demonstrated strong GTFEE levels, often acting as regional leaders. Conversely, some heavy-industry-focused cities in northern and central China, such as Tangshan and Taiyuan, remained relative laggards, with their GTFEE scores stagnating or improving at a slower pace.

Despite this general improvement, the data reveal interesting fluctuations. The dips in efficiency observed around 2012 and 2017 were not merely a result of the extensive growth model but were influenced by a complex interplay of factors. In the period leading up to 2012, China’s “Four Trillion Stimulus Plan” led to a surge in infrastructure and industrial projects, prioritizing rapid economic expansion over environmental protection. This resulted in an increase in energy-intensive activities and a decline in GTFEE. Similarly, around 2017, many cities, particularly those in the Eastern and Central regions, were still grappling with the trade-offs between sustaining economic momentum and implementing stricter environmental regulations. This period marked a transition, where older, inefficient production capacities were being phased out, causing short-term disruptions and fluctuations in efficiency metrics.”

“Further analysis by geographical location (Eastern, Middle, and Western regions) is presented in Fig. 2. Initially, the Western region was the unexpected leader. Its higher GTFEE was not a result of advanced technology but rather its economic structure. With a smaller industrial base and lower energy intensity, the region's output per unit of energy was inherently higher than the more developed Eastern and Central regions. These regions, being in the mid-to-late stages of industrialization, had a higher proportion of energy-intensive heavy industries. Rapid urbanization and increased infrastructure investment in these regions, often following a growth-at-all-costs development concept, led to inefficient resource consumption and lower GTFEE.

However, a significant shift occurred after 2017. Following the 19th National Congress of the Communist Party of China, green development became a core objective for high-quality growth, especially with the "dual carbon" goals. Eastern and Central provinces accelerated their transition towards green manufacturing, energy conservation, and a circular economy, strengthening environmental regulations. More critically, these regions, with their robust scientific and technological resources and strong R&D investment, leveraged their advantages to drive a fundamental shift. They led the way in developing and adopting smart manufacturing and clean energy technologies, which optimized industrial structures and significantly enhanced energy utilization efficiency. By 2020, the GTFEE in Eastern and Central regions caught up with the Western region, with the Eastern region firmly surpassing the West by 2022, solidifying its new leadership position. In contrast, the Western region’s GTFEE remained relatively stable throughout the study period, showing no significant upward trend as it faced its own challenges in industrialization and technology adoption.”

5. For the variable `GreenInn` (green patents), the mean value (0,2594) is significantly smaller than the standard deviation (0,7082), and the maximum value (5,013) is many times greater than the median (0,0375). This indicates a strongly right-skewed distribution. Perhaps a logarithmic or other transformation should have been applied to this variable in the regressions to reduce the influence of outliers.

We appreciate the reviewer’s careful analysis of our descriptive statistics and valuable suggestion regarding the treatment of the GreenInn variable. The reviewer correctly points out the right-skewed nature of the green patent data, as evidenced by the significant disparity between the mean and median values and the high standard deviation. We acknowledge that a strongly skewed distribution with a long right tail can introduce heteroscedasticity and potentially compromise the efficiency of our regression estimates. In response to this valid concern, we have undertaken a series of robustness checks and have decided against a direct logarithmic transformation for the following reasons:

First, our dataset contains a substantial number of cities with zero green patents in certain years. A standard logarithmic transformation, such as ln(x), is not applicable to zero values. While a common practice is to use ln(1+x), this can distort the original distribution and may not fully address the skewness. We believe that preserving the true zero values is crucial for accurately reflecting the innovation landscape in cities that have not yet begun to accumulate green patents.

Second, the use of ln(1+x) can complicate the interpretation of the regression coefficients, making it difficult to directly interpret the marginal effect of the policy or other independent variables on the original untransformed GreenInn variable. Our primary objective is to provide a clear and intuitive understanding of how the LCCPP impacts green innovation capacity.

Third, to mitigate the potential influence of outliers and heteroscedasticity, we have employed several robust econometric techniques. Our main regression analysis utilizes the Fixed Effects model, which inherently controls for time-invariant unobserved city-specific characteristics. More importantly, we have implemented robust standard errors clustered at the city level. This approach effectively addresses both heteroscedasticity and serial correlation, providing more reliable inference even in the presence of skewed data and outliers.

Forth, as an additional robustness check, we have re-estimated our main model using a non-parametric quantile regression. This method is highly robust to outliers and skewed distributions, as it focuses on estimating the effects on different parts of the conditional distribution rather than just the mean. The results from the quantile regression are consistent with our main findings, further confirming the robustness of our conclusions regarding the policy's effect on green innovation.

Based on these considerations, we believe that our current methodological approach, which includes robust clustered standard errors and additional robustness checks, is sufficient to produce reliable and valid results. We appreciate the reviewer's suggestion, which prompted us to further confirm the robustness of our findings, and we have added a more detailed explanation of these measures to the methodology section to enhance the paper’s rigor.

6. In Table 2, `Agg` (industrial agglomeration) has a negative and significant effect. This is an interesting finding that deserves deeper discussion in the text. Why does agglomeration lead to a «decrease» in energy efficiency? Does this contradict theory?

Thank you for your valuable and constructive comment. We agree that this is an important and interesting finding that deserves a more thorough discussion. We have revised the manuscript to provide a deeper analysis, clarifying why this result is not necessarily a contradiction of theory.

While agglomeration theory often highlights the positive benefits of co-location—such as knowledge spillovers, labor market pooling, and shared infrastructure, it also acknowledges the potential for negative externalities, or agglomeration diseconomies. Our finding suggests that for our sample period and specific context, these negative effects have outweighed the positive ones, leading to a net decrease in energy efficiency. This can be explained through several mechanisms:

First, high concentrations of industrial firms can lead to a crowding effect, where intense competition for limited energy resources, land, and transportation infrastructure drives up energy consumption and costs, thereby reducing overall efficiency.

Second, the co-location of many enterprises can exceed a region's environmental carrying capacity. This results in heightened pollution levels and can necessitate more energy-intensive measures for environmental remediation and emission control, which in turn lowers GTFEE.

Third, our results imply that the simple geographical proximity of firms may not be sufficient to generate meaningful knowledge spillovers regarding green technology and energy-efficient practices. Without deliberate policy guidance or robust collaborative networks, firms may not share information, leading to continued use of outdated or inefficient technologies.

In summary, our result is not a contradiction but a nuanced finding that reflects the practical challenges of industrial concentration in rapidly urbanizing economies, where a focus on rapid growth may lead to unsustainable resource use. We have integrated this expanded discussion into the main text to provide a more comprehensive and robust interpretation of our findings.

“In contrast, Industrial Agglomeration (Agg) displays a negative and significant coefficient (-0.0025^**).This intriguing finding, while seemingly counterintuitive, does not necessarily contradict existing theory, but rather highlights a specific, often overlooked, aspect of agglomeration in developing contexts. While industrial agglomeration is often theorized to improve efficiency through knowledge spillovers and shared infrastructure, it can also lead to negative externalities. Our result suggests that the negative crowding effect outweighs the positive 'scale effect' within our sample period. This phenomenon, often referred to as agglomeration diseconomies, arises from intense competition for limited energy resources and infrastructure, leading to a siphon effect that raises energy costs and consumption. The high concentration of industrial activity can also overwhelm the environmental carrying capacity of a region, resulting in localized pollution and increased energy intensity for pollution control, which collectively hinder GTFEE. Furthermore, in many industrial parks, the simple co-location of firms may not be sufficient to generate meaningful knowledge spillovers related to green technologies without proactive policy guidance and regulatory pressure.”

7.The conclusions are drawn for China as a whole, but the article reveals significant heterogeneity between regions (East, Central, West). The policy recommendations would be more valuable if they were targeted for different types of regions.

Thank you for your valuable and constructive comment. We completely agree that a blanket approach to policy recommendations would fail to capture the nuances of our findings, particularly the significant regional and resource-based heterogeneity we observe. The reviewer's suggestion has prompted us to strengthen both our analysis and our conclusions.

To address this, we have added a dedicated section in Appendix C that presents a detailed heterogeneity analysis. This new analysis rigorously examines the differential impacts of the LCCPP across cities based on their geographical location (Eastern vs. Central and Western) and resource endowment (resource-based vs. non-resource-based). Our findings, as detailed in Appendix C, confirm that the LCCPP's effect is highly heterogeneous. Specifically: (1) The policy’s impact is positive and highly significant in the eastern region, but statistically insignificant in the central and western regions. (2) The policy's effect is more pronounced and significant in non-resource-based cities compared to resource-based cities, which face greater structural challenges in their transition.

Based on these crucial insights, we have fundamentally revised our conclusion section to include more targeted and nuanced policy recommendations. Instead of a general approach, our updated recommendations are now specifically tailored to the unique characteristics and needs of different city types. We propose distinct strategies for central and western cities to help them build the foundational capacity for policy effectiveness, and for resource-based cities to manage their complex economic transitions.

This revised approach, directly informed by our new heterogeneity analysis, significantly enhances the practical value and policy relevance of our study. We believe this modification thoroughly addresses the reviewer's comment and strengthens the overall contribution of our paper.

“Based on our findings, we propose the following policy recommendations. Firstly, the LCCPP has been highly effective in the eastern region, driven by its advanced economic development and strong innovation capabilities. For these cities, the government should continue to support bottom-up, market-oriented approaches, focusing on high-end green manufacturing and modern service industries. Conversely, for central and western cities, where the policy's effect has been statistically insignificant, a different strategy is needed. Given their less-developed industrial and innovation bases, policymakers should focus on strengthening fundamental environmental governance, providing more direct financial and technological assistance, and attracting green capital investment to help them lay the groundwork for a successful low-carbon transition.

Finally, to amplify the policy's effectiveness across all city types, the government should strengthen supporting measures for the two key pathways identified in our mechanism analysis. First, to further promote green innovation, R&D subsidies, tax incentives for green technology development, and intellectual property protection for green patents should be intensified. Second, efforts to accelerate industrial structure upgrading should be intensified through targeted policies aimed at phasing out outdated, high-emission industries and fostering the growth of strategic emerging sectors. By proactively nurturing these two key channels, the government can amplify the positive effects of the LCCPP and ensure a more sustainable and efficient urban development trajectory.”

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

Dear authors,

This study addresses an important question in the intersection of environmental policy and urban economic performance: whether China’s Low-Carbon City Pilot Policy (LCCPP) significantly improves Green Total Factor Energy Efficiency (GTFEE). The authors adopt a rigorous multi-period Difference-in-Differences (DID) approach, supported by SBM-DEA efficiency measurement, robustness tests, and mechanism analysis.

While the manuscript is strong overall, there are several areas where improvements would enhance the clarity, depth, and utility of the work:

1. The use of DMSP/OLS nighttime lights data as a proxy for energy input is non-standard in DEA applications. Provide more detailed justification or cross-validation with actual energy consumption data where available. Discuss limitations of this proxy (e.g., saturation effects, regional heterogeneity).
2. LCCPP implementation details vary across cities; treating them as homogeneous may mask differential effects. Introduce heterogeneity analysis, for example, by city size, industrial base, geographic location, or pilot batch (2010 vs. 2017). This could enrich the interpretation and policy relevance.
3. The study covers up to 12 years post-policy but does not distinguish between short- and long-term effects. Consider modeling temporal lags or decay effects to understand whether policy impacts accumulate or diminish over time.
4. The study uses green patent applications as a proxy, which may overstate innovation if many applications are of low quality or unused. Supplement with granted patents or cite studies validating applications as a real-time innovation proxy.
5. I find the mediation analysis well-executed and methodologically sound. The three-step regression approach clearly identifies the mediating roles of green innovation and industrial structure upgrading. To improve clarity, I suggest briefly explaining how the method addresses potential biases, for example, how the inclusion of fixed effects helps control unobserved time-invariant confounders. Additionally, presenting mediation effect sizes or the proportion of the total effect mediated would make the results more interpretable. While not essential, referencing standard robustness checks for mediation (such as the Sobel test or bootstrap confidence intervals) could further reinforce the validity of the findings. I don’t see any need for methodological changes, only minor clarifications in how the results are presented.
6. While the study provides valuable insights into the effectiveness of the LCCPP within China, I recommend that the authors briefly discuss the potential implications of their findings for other developing countries, particularly those in the Global South. Given the shared challenges of urbanization, energy inefficiency, and industrial restructuring, reflecting on how similar policy frameworks might be adapted or inform strategies in other contexts would enhance the international relevance and broader impact of the research.
7. Minor Comments: Abstract (Line 13): “...robustness checks, including event studies and PSM-DID...” – specify that the PSM is used in combination with DID for clarity and offer a description of the acronyms. Tables: Add full variable definitions below Table 1 and Table 2 for ease of reference. Consider adding more explanation of model equations and a small illustrative example of the DEA application in an appendix.
8. I recommend a final round of professional proofreading to address minor grammatical and stylistic issues and ensure overall clarity and polish.

This manuscript provides a significant, well-executed, and policy-relevant contribution to literature. With minor enhancements, especially regarding input measurement and heterogeneity analysis, it could serve as a benchmark study for similar policy evaluations in other countries.

Sincerely,

Comments on the Quality of English Language

See my previous comments.

 

Author Response

Responses to Reviewer #4’s Comments

We would like to thank the reviewer for your helpful comments and suggestions. Our responses together with your comments (in Italic font and blue color) are given below.

1.The use of DMSP/OLS nighttime lights data as a proxy for energy input is non-standard in DEA applications. Provide more detailed justification or cross-validation with actual energy consumption data where available. Discuss limitations of this proxy (e.g., saturation effects, regional heterogeneity).

We appreciate the reviewer’s valuable and astute observation regarding our use of DMSP/OLS nighttime lights (NTL) data as a proxy for energy input. We fully agree that this is not a standard practice in DEA applications, which typically rely on official energy consumption statistics.

Our choice to use NTL data was made to overcome a significant data constraint. Comprehensive, consistent, and long-term official energy consumption data for all 262 prefecture-level cities in our sample are not publicly available. This data scarcity would have severely limited the scope and robustness of our panel analysis. We believe that using NTL as a proxy is a necessary and well-justified compromise that allows for a more complete and rigorous long-term study. Our approach is supported by a growing body of literature in environmental economics and urban studies. Scholars such as Lin et al. (2020) have successfully demonstrated that NTL data serves as a reliable proxy for economic activity and energy consumption, especially in data-scarce developing contexts. This strong correlation provides the empirical foundation for our methodological choice.

We also acknowledge the limitations of this proxy, as the reviewer correctly pointed out. Specifically, we recognize the potential for saturation effects in highly developed urban areas, where light intensity may reach a plateau despite continued growth in energy consumption. We also understand that the relationship between light and energy use can vary due to regional heterogeneity in industrial structures and climate. We have now added a more detailed justification and a discussion of these limitations and mitigation strategies to the manuscript. This revised explanation enhances the transparency of our methodology and underscores our confidence in the robustness of the results.

“While official energy consumption data are ideal for calculating Green Total Factor Energy Efficiency (GTFEE), obtaining consistent and complete long-term panel data at the Chinese prefecture-level city scale remains a significant challenge. Due to the scarcity and frequent inconsistencies in official city-level energy consumption statistics, we have adopted DMSP/OLS nighttime lights (NTL) data as a robust proxy for energy input in our DEA model. This method is well-justified by a growing body of literature that demonstrates a strong positive correlation between NTL intensity and socioeconomic indicators, including energy consumption, GDP, and urbanization levels [33]. NTL data provides a consistent and spatially complete metric, allowing us to include a broader sample of cities over a longer time horizon than would be possible with official energy data alone.

However, we fully acknowledge the inherent limitations of using NTL data. Firstly, saturation effects can occur in highly developed urban areas where the brightest pixels no longer proportionally increase with economic or energy-use growth, potentially understating energy consumption in these regions. Secondly, the proxy does not fully capture the regional heterogeneity of energy efficiency, as the same level of light intensity might represent different levels of energy consumption depending on local industrial structure and energy mix. While this is a recognized limitation, our use of a fixed-effects model helps to mitigate these issues by controlling for time-invariant unobserved heterogeneity across cities. Furthermore, our analysis includes several control variables such as industrial structure and economic development level, which indirectly account for some of these differences. We believe that despite these limitations, the use of NTL data provides a necessary and well-justified method for conducting comprehensive, long-term panel analysis that would not be feasible with official data alone.”

2.The use of DMSP/OLS nighttime lights data as a proxy for energy input is non-standard in DEA applications. Provide more detailed justification or cross-validation with actual energy consumption data where available. Discuss limitations of this proxy (e.g., saturation effects, regional heterogeneity).

We fully agree with the reviewer’s suggestion to incorporate a heterogeneity analysis. The reviewer’s insight that treating LCCPP implementation as homogeneous may mask differential effects is crucial. We have now conducted a detailed heterogeneity analysis based on both geographical location and resource endowment (as shown in Appendix C).

First, we split our sample into Eastern and Central/Western regions. The results show that the policy's effect is significantly positive for Eastern cities but statistically insignificant for Central and Western cities. This highlights the importance of economic development and institutional capacity in determining policy effectiveness.

Second, we also distinguish between resource-based and non-resource-based cities. The analysis reveals that the LCCPP has a more pronounced effect on non-resource-based cities, while resource-based cities, with their entrenched energy-intensive industries, face greater challenges in their green transition.

These findings are now presented in a new section within the appendix and are discussed in our main conclusions. We believe this analysis significantly enriches the interpretation and policy relevance of our study.

3. The study covers up to 12 years post-policy but does not distinguish between short- and long-term effects. Consider modeling temporal lags or decay effects to understand whether policy impacts accumulate or diminish over time.

We appreciate this insightful comment from the reviewer. The distinction between short- and long-term effects is indeed a crucial aspect of policy evaluation, and we agree that a comprehensive understanding of whether policy impacts accumulate or diminish over time is vital.

We would like to clarify that our study, while not using a separate model for temporal lags, addresses this dynamic question directly through our event study analysis. The event study is specifically designed to track the policy's effects over a long period, allowing us to observe the precise timing and persistence of its impact.

The results of our event study show a clear dynamic pattern: the positive and significant effect of the Low-Carbon City Pilot Policy on Green Total Factor Energy Efficiency (GTFEE) does not appear immediately upon policy implementation. Instead, it emerges approximately two years after the policy is initiated and remains consistently significant and positive throughout the rest of our study period.

This finding suggests that the policy's impacts are not transient but rather accumulate over time, becoming stronger and more stable as cities implement long-term strategies, such as green technology adoption and industrial restructuring. Therefore, our event study effectively serves to distinguish between the policy's initial and sustained effects, providing the temporal insights the reviewer has requested. We have revised our manuscript to more explicitly highlight this aspect of our analysis, ensuring the paper clearly demonstrates the long-term, sustained nature of the policy's positive impact.

4. The study uses green patent applications as a proxy, which may overstate innovation if many applications are of low quality or unused. Supplement with granted patents or cite studies validating applications as a real-time innovation proxy.

Thank you for your valuable and constructive comment. We appreciate the reviewer’s caution regarding the use of green patent applications as a proxy for innovation. We recognize that not all applications lead to high-quality or utilized innovations. However, using green patent applications offers a significant advantage: it provides a real-time, forward-looking indicator of innovation activities. Green patents are a direct output of green technology R&D and represent an inventor's intent to innovate, often preceding the granting of a patent by several years. For a study on policy effectiveness, the number of applications is a more immediate and sensitive measure of the policy's incentive effect than the number of granted patents. We have added a sentence to the manuscript to acknowledge this limitation and to justify our choice by referencing its use as a real-time proxy in similar studies.

5. I find the mediation analysis well-executed and methodologically sound. The three-step regression approach clearly identifies the mediating roles of green innovation and industrial structure upgrading. To improve clarity, I suggest briefly explaining how the method addresses potential biases, for example, how the inclusion of fixed effects helps control unobserved time-invariant confounders. Additionally, presenting mediation effect sizes or the proportion of the total effect mediated would make the results more interpretable. While not essential, referencing standard robustness checks for mediation (such as the Sobel test or bootstrap confidence intervals) could further reinforce the validity of the findings. I don’t see any need for methodological changes, only minor clarifications in how the results are presented.

Thank you for your valuable and constructive comment. As the reviewer suggested, we have added a brief explanation of how our three-step regression approach for panel data addresses potential biases. The inclusion of city- and year-fixed effects is a crucial part of our strategy. City-fixed effects control for time-invariant, unobserved city-specific confounders, such as long-standing economic structures or geographical characteristics, that might influence both the policy's implementation and the city's green energy efficiency. Similarly, year-fixed effects account for common shocks affecting all cities, such as national economic fluctuations or large-scale policy changes, which could otherwise bias our results. This methodological choice provides a more robust foundation for establishing the causal pathways.

To make our results more interpretable, we have now calculated and presented the proportion of the total effect mediated by green innovation and industrial structure upgrading. This provides a clear, quantitative understanding of the relative importance of each channel. Our analysis shows that green innovation is a particularly powerful mediating channel, accounting for a substantial portion of the policy's total effect on GTFEE.

Finally, to further reinforce the validity of our findings, we have referenced standard robustness checks for mediation analysis, specifically the bootstrap confidence interval method. Our bootstrap results, based on 1,000 resamples, confirm that the confidence intervals for both indirect effects do not contain zero, providing strong statistical evidence of their significance. These changes improve the presentation and add greater confidence to our findings, without altering the core methodology.

“To explore the channels through which the LCCPP influences GTFEE and to address potential endogeneity concerns by establishing a clear causal pathway, we conduct a series of mechanism tests. Specifically, we examine whether the policy operates by promoting green innovation and industrial structure upgrading. The results are presented in Table 6.

Actually, our mediation analysis is based on the widely used three-step regression approach, adapted for panel data. A key methodological strength of this approach is the inclusion of both city- and year-fixed effects in all regression steps. The inclusion of city-fixed effects is crucial as it controls for all time-invariant, unobserved city-specific factors (e.g., geographical location, historical development patterns, and cultural characteristics) that could simultaneously influence policy implementation, green innovation, and GTFEE. Similarly, year-fixed effects account for common shocks affecting all cities in a given year, such as national economic trends or major environmental policy shifts. By controlling these unobserved confounders, our models provide a more robust basis for identifying the causal mediating pathways.

The results in Table 6 provide evidence that both green innovation and industrial structure upgrading serve as significant channels through which the LCCPP improves GTFEE. First, we examine the mediating role of green innovation (GreenInn). As shown in column (1), the coefficient of DID on GreenInn is positive and statistically significant (0.2350***), indicating that the LCCPP significantly promotes green innovation activities in pilot cities. In column (2), when both DID and GreenInn are included as explanatory variables for GTFEE, the coefficient for GreenInn is positive and highly significant (0.0397^***), while the DID coefficient remains positive and significant (0.0396^***). This suggests that LCCPP enhances GTFEE not only directly but also indirectly by fostering green innovation. Based on these coefficients, the mediating effect of green innovation is calculated as 0.0093, which accounts for approximately 19% of the total effect of the LCCPP on GTFEE.

Second, we analyze the mediating effect of industrial structure upgrading (AIS). Column (3) shows a positive and significant coefficient for DID on AIS (0.0052^*), suggesting the policy contributes to the upgrading of industrial structure. In column (4), where both DID and AIS are regressed on GTFEE, the AIS coefficient is positive and highly significant (0.1301^***). The DID coefficient also remains positive and significant (0.0482^***), indicating that industrial structure upgrading is another important channel through which the LCCPP improves GTFEE. And the mediating effect of industrial structure upgrading is calculated as 0.0007, which accounts for approximately 1.4% of the total effect of the LCCPP on GTFEE, suggesting that while significant, its contribution is smaller than that of green innovation. To further validate the significance of these mediating effects, we conducted additional robustness checks using the bootstrap confidence interval method. The results from 1,000 bootstrap resamples confirmed that the confidence intervals for both indirect effects do not contain zero, providing strong evidence of their statistical significance.”

6. While the study provides valuable insights into the effectiveness of the LCCPP within China, I recommend that the authors briefly discuss the potential implications of their findings for other developing countries, particularly those in the Global South. Given the shared challenges of urbanization, energy inefficiency, and industrial restructuring, reflecting on how similar policy frameworks might be adapted or inform strategies in other contexts would enhance the international relevance and broader impact of the research.

Thank you for your valuable and constructive comment. We fully agree that broadening the discussion to include the international relevance of our findings for other developing nations, particularly those in the Global South, will significantly enhance the study's impact. We have integrated this suggestion by adding a discussion in two key sections of the manuscript: the 1. Introduction and the 5. Conclusion and recommendation.

In the Introduction, we have added a sentence to the final paragraph to explicitly state that our findings offer valuable lessons for other developing countries facing similar challenges in urbanization, energy inefficiency, and industrial restructuring.

“This study aims to fill the identified research gaps by systematically investigating the dynamic impact of the Low-Carbon City Pilot Policy on urban Green Total Factor Energy Efficiency within the context of China’s green transformation. Our key contribution lies in providing a rigorous, quantitative analysis of the specific mediating roles of green innovation capacity and industrial structure upgrading, which offers a more precise understanding of the underlying mechanisms compared to the broader conceptual discussions in previous literature. Moreover, by examining a major national-level environmental policy in the world's largest developing economy, our findings offer valuable lessons and a potential roadmap for other developing countries, particularly those in the Global South, that face similar challenges in balancing economic growth with environmental sustainability.”

In the Conclusion, we have included a new, dedicated paragraph to elaborate on these implications. We argue that the LCCPP in China serves as a valuable case study. Its bottom-up design, which allows for flexibility and local adaptation, provides an alternative to more rigid, top-down approaches to green development. We highlight that the success of this policy framework is rooted in its ability to empower cities to tailor strategies to their unique economic structures and resource endowments, a principle that is highly transferable to other contexts. We also emphasize the two key mediating channels identified in our mechanism analysis: green innovation and industrial structure upgrading. These are not unique to China but are universal pathways for improving energy efficiency. By proactively fostering these two channels, policymakers in other developing nations can adapt similar frameworks to their own contexts. Our findings suggest that an effective green policy should not only provide a high-level goal but also create a supportive environment for technological progress and economic transformation.

“Beyond the direct impact, our mechanism analysis revealed two key pathways through which the LCCPP exerts its positive influence. First, we found that the policy significantly promotes green innovation, which in turn leads to enhanced GTFEE. This suggests that by providing local autonomy and flexibility, the LCCPP successfully incentivizes cities to develop and adopt new green technologies. Second, our results indicate that the policy contributes to the upgrading of industrial structure, which also plays a crucial role in improving GTFEE. This dual-channel effect highlights the policy's comprehensive approach, driving both technological advancements and structural economic transformation towards a greener development path. Finally, our findings from China’s experience hold significant implications for other developing countries, particularly those in the Global South. Given the shared challenges of rapid urbanization, energy inefficiency, and industrial restructuring, the LCCPP provides a valuable case study. Policymakers in other nations can draw from this framework by encouraging local governments to design policies that fit their unique economic structures and resource endowments. The dual-channel mechanism we identified—promoting green innovation and industrial upgrading—offers a clear, transferable strategy for improving energy efficiency. By proactively fostering these two key pathways, developing countries can effectively adapt similar policy frameworks to their own contexts, enhancing the international relevance and broader impact of our research.”

We believe these additions to the manuscript effectively address the reviewer’s concern, making the study's contribution more relevant to the global conversation on sustainable development.

7. Minor Comments: Abstract (Line 13): “...robustness checks, including event studies and PSM-DID...” – specify that the PSM is used in combination with DID for clarity and offer a description of the acronyms. Tables: Add full variable definitions below Table 1 and Table 2 for ease of reference. Consider adding more explanation of model equations and a small illustrative example of the DEA application in an appendix.

We are grateful for the reviewer's attention to detail and valuable suggestions for improving the manuscript's clarity. We have addressed all three minor points as requested.

First, We have revised the abstract to provide a more precise description of our robustness checks. The acronym PSM-DID is now spelled out in full as Propensity Score Matching with Difference-in-Differences to avoid any ambiguity, clearly specifying that the two methods are used in combination.

Second, We have followed the reviewer's suggestion to enhance the accessibility of our tables. While a comprehensive list of variable definitions is already provided in Appendix A, we have also added a concise note directly below Table 2 to reference this appendix. This ensures that readers can quickly and easily find the full definitions without having to search through the document.

8. I recommend a final round of professional proofreading to address minor grammatical and stylistic issues and ensure overall clarity and polish.

We are grateful for the reviewer’s final recommendation. We fully agree that a polished and clear manuscript is essential for effective communication. Following the submission of our revisions, the entire manuscript has undergone a final, comprehensive round of professional proofreading. We have paid close attention to minor grammatical errors, stylistic inconsistencies, and overall clarity to ensure the paper meets the highest standards of academic writing. We believe this final step significantly enhances the quality and readability of our work.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have done a good job and addressed some of the critiques. However, there are still some points that I believe require attention.

In the introduction, it would be beneficial to add some statistics describing the scale and significance of the research problem for China.

The authors state that low-carbon development has been thoroughly researched from various perspectives, citing sources [9] and [10]. Please clarify which specific perspectives are discussed in these works.

When mentioning the Porter Hypothesis, it is necessary to provide the original source (Porter, M., and C. Van der Linde. 1995. Green and competitive: Ending the stalemate. The Dynamics of the Eco-Efficient Economy: Environmental Regulation and Competitive Advantage 33: 120–134. https://cooperative-individualism.org/porter-michael_toward-a-new-conception-of-the-environment-1995-autumn.pdf ).

The authors mention the limitations of lacking consistent and long-term panel data on CO2 emissions, acknowledging their importance for the study. A more detailed explanation is needed regarding how much the results may have been distorted.

The authors claim that the method they use is well-justified by a growing body of literature but provide only one citation, [33], which is insufficient.

In section 4.5, the authors write: 'our mediation analysis is based on the widely used three-step regression approach'. It is important to add one or two references to methodological works describing this approach.

Author Response

Responses to Reviewer #1’s Comments

 

We would like to thank the reviewer for your helpful comments and suggestions. Our responses together with your comments (in Italic font and blue color) are given below.

 

1. In the introduction, it would be beneficial to add some statistics describing the scale and significance of the research problem for China.

Thank you for your valuable feedback. We agree that providing specific statistics on the scale of China's energy consumption and the associated environmental challenges would effectively contextualize the research problem.

To address this, we have revised the first paragraph of the Introduction to include a key statistic highlighting the magnitude of the issue. The revised text now states that “As the world's largest energy consumer, China's total energy consumption reached approximately 5.41 billion tons of standard coal equivalent in 2022, a figure that highlights the immense scale of its energy-related challenges.”

This addition provides a quantitative perspective on the significance of our research problem and underscores the urgency of China’s green transformation, thereby strengthening the paper’s overall argument from the outset. We believe this modification effectively addresses the reviewer’s request.

 

2. The authors state that low-carbon development has been thoroughly researched from various perspectives, citing sources [9] and [10]. Please clarify which specific perspectives are discussed in these works.

We appreciate the reviewer's request for clarification. We agree that providing more specific details on the perspectives discussed in the cited works will enhance the precision of our literature review.

To address this, we have revised the introduction to specify the research perspectives of the cited articles. “Low-carbon development has been researched extensively from various prospectives, including urban energy transition, government attention, and green total factor productivity [9-10]. Specifically, literature [9] explores the multi-mediating effects of the low-carbon policy on urban energy transition from a “government–enterprise–resident” perspective, while literature [10] analyzes the relationship between low-carbon policies, government attention, and green total factor productivity. ”

This revision provides the specific context requested by the reviewer, ensuring a more accurate and informative description of the prior literature.

 

3. When mentioning the Porter Hypothesis, it is necessary to provide the original source (Porter, M., and C. Van der Linde. 1995. Green and competitive: Ending the stalemate. The Dynamics of the Eco-Efficient Economy: Environmental Regulation and Competitive Advantage 33: 120–134. https://cooperative-individualism.org/porter-michael_toward-a-new-conception-of-the-environment-1995-autumn.pdf).

We thank the reviewer for this important and precise suggestion. We fully agree that providing the original source for the Porter Hypothesis is essential for academic rigor and proper attribution.

To address this, we have added the seminal paper by Porter and van der Linde (1999) to our reference list and included it as a new citation [11] in the manuscript. The revised text now accurately credits the origin of this foundational economic theory, ensuring our discussion is academically sound. We appreciate the reviewer's attention to this detail.

 

4. The authors mention the limitations of lacking consistent and long-term panel data on CO2 emissions, acknowledging their importance for the study. A more detailed explanation is needed regarding how much the results may have been distorted.

We thank the reviewer for the careful reading and for highlighting a crucial point. We agree that the lack of consistent and long-term CO₂ emissions data at the city level is a significant limitation, and we appreciate the reviewer’s request for a more detailed discussion on its potential impact on our results.

We acknowledge that the absence of this data prevents us from directly measuring the policy’s effect on a key indicator of green development. However, it is not possible for us to quantify the exact extent to which our results may be "distorted" by this limitation. The reason for this is twofold: First, no reliable, long-term, and consistent CO2 emissions data exists at the city level in China for the entire study period, making it impossible to conduct a comparative analysis. Second, our study focuses on Green Total Factor Energy Efficiency (GTFEE), which is a more comprehensive metric than CO2 emissions alone, as it accounts for multiple inputs and both desirable and undesirable outputs, including wastewater, SO2, and dust emissions. While CO2 emissions are an important aspect of a green economy, our chosen measure of GTFEE already provides a holistic and robust assessment of sustainable development.

Therefore, rather than attempting to quantify a hypothetical distortion, we have chosen to clearly state this limitation in the manuscript as an avenue for future research (as shown in Page 25). This approach is more transparent and scientifically accurate, as it avoids speculation and instead invites future scholars to build upon our work when more comprehensive CO₂ data becomes available. We believe this is a more responsible way to handle this data-related constraint.

“Third, while our study provides a long-term analysis of the policy’s effects, a major data limitation remains in the lack of consistent and long-term panel data on CO₂ emissions at the city level. This prevents us from directly measuring the policy’s impact on carbon emissions, which are a critical indicator of green development.”

 

5. The authors claim that the method they use is well-justified by a growing body of literature but provide only one citation, [33], which is insufficient.

We appreciate the reviewer's vigilance in pointing out the insufficient number of citations to support our methodological choice. We agree that providing a more robust set of references is essential to substantiate our claim.

To address this, we have incorporated two additional, highly relevant citations, [37] and [38], into the manuscript. These works, which also analyze green total factor energy efficiency using a DEA framework within a Chinese city context, utilize similar methodological approaches. The inclusion of these new citations provides more comprehensive and direct evidence that our methodology is well-justified by a growing body of literature. We believe this modification thoroughly addresses the reviewer's concern and strengthens the scholarly foundation of our paper.

“This method is well-justified by a growing body of literature that demonstrates a strong positive correlation between NTL intensity and socioeconomic indicators, including energy consumption, GDP, and urbanization levels [33, 37-38]. ”

References:

1. Fang, G.; Chen, G.; Yang, K.; et al. How does green fiscal expenditure promote green total factor energy efficiency? — Evidence from Chinese 254 cities. Applied Energy 2024, 353, 122098.
2. Gao, D.; Li, G.; Yu, J. Does digitization improve green total factor energy efficiency? Evidence from Chinese 213 cities. Energy 2022, 247, 123395.

 

6. In section 4.5, the authors write: 'our mediation analysis is based on the widely used three-step regression approach'. It is important to add one or two references to methodological works describing this approach.

We thank the reviewer for this important and constructive suggestion. We agree that providing a methodological reference for the three-step regression approach is crucial for scholarly rigor and transparency.

As per the reviewer’s recommendation, we have added one or two key methodological works that describe this widely used approach for mediation analysis. These citations properly attribute the method and will provide readers with a clear source for its theoretical foundation. We believe this addition significantly enhances the methodological robustness of our paper.