Spatial and Heterogeneity Analysis of Environmental Taxes’ Impact on China’s Green Economy Development: A Sustainable Development Perspective

Round 1

Reviewer 1 Report

Dear author,

To improve this muniscript, please, take consideration about all recommendations and revise them:

In the form : Grammar and spelling to revise.

1. Abstract : Please reduce the abstract and follow this methodology: (Backgrownd, the purpose, Methodology/Method used, Findings, Originality/value, Implication policy, )

2. Introduction: The introduction must be improved at least two new paragraphs with cited reference studies related to your study aim.

3. Literature review: In this section, the authors must add theoritical cited update/ recent research papers (from Data bases like MDPI Journals, Elsevier, Springer, Wiley, Sage, Elmard)  related to the study  aim ( is very important). If necesssary, I can suggest some reference artilces to help you and improve/enrich the body's paper.

4. Method and Findings: 

- Please, add theoritical cited authors related to this model used  in your study ( ??? = ?? + ? ∑ ???? + ???? + ?? ? ?=1)

- Tone (2003) 170 and Cheng-Gang (2014) : Please, indicate they principal theortical and empirical findings. And why you have specially chosed this model from others?

- Control Variables : It will be more clair to indicate all these variables in TABLE.

- Table 2. Descriptive statistics of variables : Add source at the end of the table with the statistical software used.

- Empirical Results and Analysis : We did not find Hypotheses (to be supported/ rejected) in this study?

* Table 4. Global Moran's I of green economy efficiency and environmental taxes:  ( Add Source: Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 302 10%, respectively;)

* (Figure 1 and Figure 2) : More discription and make comparision test between the two figures.

* Analysis of Tax Heterogeneity : Table 6. Results of the impacts of environmental taxes on green economic efficiency ( Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 302 10%, respectively; the statistics are in parentheses.)

* Table 7. Spatial heterogeneity analysis of the effect of environmental taxes (  ( Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 302 10%, respectively; the statistics are in parentheses.)

* Table 9. Spatial heterogeneity analysis of the effect of environmental taxes on green 381 economic efficiency for the economic geography matrix ( to add : ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 302 10%, respectively; the statistics are in parentheses.)

4. Research Findings and Policy Recommendations : ( Add limitation of this study)

5. Summary CONCLUSION: missed !!

6. Reference : all new articles you will add in the introduction or in the liturature review MUST be added in this section according the model of reference of Sustainability Journal (with the DOI certainly).

7. If you need more related articles to improve the literature review we can suggested some.

 

Good luck.

The paper needs more revision :

- Grammar

- Spelling

 

Author Response

1.In the form : Grammar and spelling to revise.

Response and revision:   

        Thank you for your suggestions . After the revision, we invited a professional organization to polish the spelling of our study.

 

2. 2. Abstract : Please reduce the abstract and follow this methodology: (Backgrownd, the purpose, Methodology/Method used, Findings, Originality/value, Implication policy, )

Response and revision:

Thank you for your suggestions on the revision of this paper.We change the paper’s abstract as follows:

“Environmental taxation is an important tool used by governments to promote resource conservation and environmental protection. Given the current global constraints on resources and increasing environmental degradation, exploring how environmental taxes can effectively stimulate the development of a green economy is of utmost importance. This study utilized panel data from 30 provinces, autonomous regions, and municipalities in China, covering the period from 2006 to 2020. The research findings indicate a spatial correlation between environmental taxes and green economic efficiency in China, with the former significantly promoting the development of the latter. A heterogeneity analysis revealed varying impacts of different taxes on the efficiency of green economic development in different regions. Controlling for variables, the study results demonstrated a negative correlation between industrial structure and green economic efficiency, with a significance level of 1%. Additionally, no correlation was found between pollution control efforts and green economic benefits. The effects of different taxes on regional efficiency varied, and industrial structure exhibited a negative correlation with green economic efficiency. This study recommends strengthening intergovernmental coordination, improving tax policies, optimizing industrial structure, and enhancing the pollution control efficiency of local governments to promote China’s green economy.”

 

3. 3. Introduction: The introduction must be improved at least two new paragraphs with cited reference studies related to your study aim.

Response and revision:

Thank you for your suggestion.We have made comprehensive modifications to the introduction section of this article, and the results of the changes are shown below:

“Since the beginning of the industrial civilization era, mankind, while driving rapid global economic growth, has also accelerated the seizure of natural resources, disrupting the balance of the Earth’s ecosystem and increasingly revealing deep-seated conflicts between man and nature. According to the 2019 Global Resource Outlook report by the United Nations Environment Programme, the exploitation of natural resources has increased from 27 to 92 billion tons over the last 50 years. This has led to 90% of biodiversity loss and water scarcity and is responsible for about half of the effects of climate change. The 2022 Global Air Quality Report shows that 97.3% of the world’s population now lives in areas where air pollution exceeds health standards. Given the increase in resource and environmental problems, “developing a green economy” has become a necessary requirement for governments to break the resource and environmental constraints, accelerate the transformation of economic development, and achieve sustainable socio-economic development (Mc Afee, Kathleen, 2016).

A green economy is an economic development model that seeks to ensure both the natural environment and human well-being can coexist without causing ecological crises or social divisions resulting from the relentless pursuit of economic growth; it aims to avoid unsustainable socio-economic growth caused by the depletion of natural resources (Pierce, 1989). Achieving green economic development hinges on finding solutions to energy use inefficiency and environmental pollution that often accompany socio-economic development (Daly & Cobb, 1989). In this regard, economist BiGu pioneered the concept of “government regulation of environmental pollution through macro taxation.” This approach involves taxing emitters based on the difference between private and social costs of emissions, thereby internalizing the negative externalities of pollution. This theoretical foundation supports government intervention and management of environmental problems. Environmental taxes, as a crucial tool for the government to protect the environment and conserve resources, have multiple benefits. In the short term, they directly restrict polluters’ emission behavior and encourage rational use of environmentally friendly production materials. In the long terms, they incentivize technological innovation, leading to improved production efficiency and enhanced market competitiveness (Zhan Lei, 2022).

A well-defined environmental taxation system can effectively curb environmental destruction and excessive resource consumption, thus promoting the development of a green economy. The Chinese government has been gradually establishing a comprehensive environmental taxation system while promoting green economic transformation. This system encompasses taxes related to environmental protection, resources, urban construction and maintenance, vehicles, vehicle purchases, urban land use, and arable land occupation. Considering the competitive behaviors among local governments and regional economic development disparities, it becomes important to examine the spatial correlation between environmental taxes and the level of green economic development in each region. It is essential to determine whether the current environmental tax system in China effectively fosters the development of a green economy and whether there is heterogeneity in the impact of various environmental taxes on green economic development across regions. Clarifying these questions holds great theoretical and practical significance for the government in reforming and improving the environmental tax system while promoting the development of a green economy.”

 

 

4. 4. Literature review: In this section, the authors must add theoritical cited update/ recent research papers (from Data bases like MDPI Journals, Elsevier, Springer, Wiley, Sage, Elmard)  related to the study  aim ( is very important). If necesssary, I can suggest some reference artilces to help you and improve/enrich the body's paper.

Response and revision:

Thank you for your valuable input. In light of your recommendations, we have made amendments to the Literature Review section of our article and have incorporated recent scholarly articles with relevant theoretical citations pertaining to our research objectives. The amended content is as follows.

“Green economic efficiency serves as a significant indicator for measuring the progress of green economic development. It addresses the limitations of traditional socio-economic development, which focuses solely on increasing factor inputs without considering environmental costs. Evaluating high-quality socio-economic development now includes the consideration of green economy efficiency, as it has become a consensus for sustainable development worldwide.

Many studies have examined green economic efficiency at various spatial scales using data envelopment analysis (DEA) within the input-output framework. For example, Zhao Jinkai et al. (2021) utilized a four-stage disaggregated DEA approach, excluding the impact of external environmental variables and employed a Bootstrap-DEA model to account for random shocks. Their study focused on measuring the green development efficiency of Chinese provinces and regions. Similarly, Qianqian Geng (2023) assessed industrial green total factor productivity in China from 2004 to 2020 using the slacks-based measure (SBM) approach. Zhao PJ (2020) and Shen Y (2022) employed a similar approach to measure green economic efficiency in 30 Chinese provinces. Additionally, Fangmei Liu (2023) evaluated provincial green economic efficiency in China by employing the stochastic non-smooth data envelope (StoNED) model. Furthermore, Liu’s study examined the gradient differences in green economic efficiency among the eastern, central, and western regions of China.

The analysis of the impact mechanism of environmental tax on green economic development primarily focuses on the micro-individual level. Firstly, environmental taxes influence the production and operational behavior of enterprises, thereby promoting energy conservation and emission reduction. According to Muhammad (2021), environmental taxes encourage cleaner production in industrial enterprises, leading to sustainable economic and environmental development. Guangqiang Liu (2022) empirically examined the effects of environmental taxes on corporate environmental investment using data from Chinese listed companies between 2015 and 2019, revealing a significant increase in environmental investment due to the implementation of environmental taxes. According to Akio Yamazaki (2022), rather than diverting resources from production, environmental taxes contribute to a net increase in firm productivity by encouraging investments in environmental protection. Xu He (2022) analyzed data from a sample of listed companies in China from 2015 to 2020 and discovered that environmental tax reforms have a positive impact on corporate profitability while curbing corporate pollution behaviors. Zastempowski (2023) analyzed business survey data collected from 13 EU member states in 2014 and discovered that the implementation of environmental taxes motivated companies to replace fossil fuel with renewable energy sources.

Secondly, environmental taxes can drive firms to innovate green technologies and enhance resource efficiency. Greaker (2018) explored how environmental policies can guide firms toward technological innovation, achieving both environmental and economic benefits. Vitenu-Sackey (2021) argued that a significant increase in environmental taxes can incentivize firms to adopt green technologies, mitigating environmental pollution and improving their total factor productivity. Zhangsheng Jiang (2023) and Xiaomin Zhao (2023) demonstrated that the implementation of environmental taxes stimulated firms to engage in green innovation, consequently driving business performance. Min Fan (2022) discovered that environmental taxes indirectly contribute to regional green total factor productivity by fostering higher levels of green technological innovation among firms. Johan Albrecht (2023) confirmed that environmental taxes largely determine the adoption of energy efficiency-related technological innovations by small and medium sized-enterprises (SMEs).

Empirical studies on the impact of environmental taxes on green economic development have primarily focused on the influence of environmental regulations on green economy development. For example, Shuai S, Fan Z (2020) performed an empirical analysis using panel data from China’s regions spanning 2007 to 2018. They measured China’s green economy efficiency using a super-efficient DEA model and found a nonlinear relationship between environmental regulation and green economy efficiency. Shang Y, et al. (2022) investigated the effect of environmental regulation on circular economy performance and discovered a linear contribution of environmental regulation to it. Shen Y, Zhang X. (2022) utilized provincial-level panel data from China spanning 2004 to 2020 and employed a two-way fixed effects model to analyze the impact of environmental taxes on industrial green transformation. They found that broad environmental taxes, such as taxes on vehicles and boats, resources, and urban land use, had a significant positive impact on industrial green transformation. Syed Abdul (2022) identified a significant positive relationship between environmental policies and green total factor productivity through empirical analysis of data from a sample of 12 cities in China.

Upon reviewing the existing literature, it is evident that there is a wealth of research on measuring green economy efficiency and analyzing the micro-mechanisms of environmental taxes on green economy development. This body of work has laid a strong foundation for our study. However, there are certain limitations in the existing research that need to be addressed. Firstly, most studies primarily focus on analyzing the impact of environmental regulations or policies on green economy efficiency, with fewer studies examining the influence of environmental taxes on green economy development. Furthermore, there is a scarcity of research exploring the differential effects of various environmental taxes on green economy development across different regions. Secondly, in terms of research methodology, many studies rely on general panel models to empirically analyze the relationship between environmental taxes and the green economy. However, it is crucial to consider the spillover effects of environmental taxes on green economy development, especially considering the strong geographical and economic correlations observed in local environmental tax policies and the green economy (C. Cindy Fan, 2004).

Taking into account these shortcomings in existing research, our study assumes that environmental taxes have a spatial spillover effect on green economic efficiency, and aims to contribute in the following ways. Firstly, in terms of methodology, we employed a spatial econometric model to empirically analyze the impact of environmental taxes on green economic efficiency. This approach allowed us to consider the spatial relationships and potential spillover effects in our analysis. Secondly, in terms of content, we comprehensively analyzed the impact of environmental taxes as a policy tool on green economic development. Moreover, we explicitly examined the diverse effects of different environmental taxes on green economic development across different regions. This provides valuable insights for formulating region-specific environmental tax policies that are conducive to promoting sustainable and green economic growth. ”

 

 

5. 5. Method and Findings: 

- Please, add theoritical cited authors related to this model used in your study

Response and revision:

Thank you for your suggestions on the revision of this paper.In view of the lack of references and references in the spatial econometric model.We have added the source of the model in the "3.1 Model Setting" section, and the specific modifications are as follows. 

”This model builds upon the spatial lag model proposed by Shao Yanfei (2022) and represents an improvement in our study. We selected this model due to its simplicity and accuracy, which align with the structure of our paper. The model construction is as follows.”

 

- Tone (2003) 170 and Cheng-Gang (2014) : Please, indicate they principal theortical and empirical findings. And why you have specially chosed this model from others?

Response and revision:

Thank you for your suggestion. In view of the theoretical findings and selection reasons of tone (2003) and Cheng Gang (2014), we made the following changes.”Many studies currently measure green economy efficiency using DEA within the input-output framework, conducted at various spatial scales. However, the traditional DEA model often overlooks non-desired outputs, leading to imprecise efficiency assessments. To address this limitation, we adopted the SBM model, which accounts for non-desired outputs, to measure green economic efficiency.

In our research, we referred to the book “Data Envelopment Analysis Method and MaxDEA Software” by Cheng-Gang (2014), which provides a detailed derivation of the super-efficient SBM model. Building upon the works of scholars like Tone (2003), we further summarized and expanded upon the existing research to obtain a more concise formula for the SBM model. Given that our measurement software and calculation procedures were based on Cheng-Gang’s (2014) book, we made appropriate modifications to the super efficiency SBM formula presented by Cheng-Gang (2014) to suit our specific needs”

 

- Control Variables : It will be more clair to indicate all these variables in TABLE.

Response and revision:

Thank you for your advice. According to your suggestion,To clearly distinguish the control variables, we have added a horizontal line between the control variables and other variables in Table 2.The amended content is as follows.

	Variable	Average	Standard deviation	Minimum	Maximum
Dependent variable	Green economy efficiency	0.470	0.250	0.150	1.300
Explanatory variables	Environmental taxes	583.360	423.140	61.230	3159.500
Control variables	Economic development level	4.628	2.805	0.612	16.489
	Industry structure	44.880	8.740	15.800	61.500
	Pollution control efforts	20.420	19.630	0.050	141.600
	Openness to the outside world	157.883	291.873	1.998	2744.956
	Population density	2839.240	1207.910	597.840	6307.380

 

- Table 2. Descriptive statistics of variables : Add source at the end of the table with the statistical software used.

Response and revision:

Thank you for your suggestions on the revision of the paper. We have added a source note specifying the statistical software used at the end of Table 2. "Note: Stata17 software was used for the descriptive statistics of variables."

 

- Empirical Results and Analysis : We did not find Hypotheses (to be supported/ rejected) in this study?

Response and revision:

Thank you for your suggestion. We have followed your suggestions in the article "2 The conclusion of the Literature Review adds the assumptions of the article, as follows.” our study assumes that environmental taxes have a spatial spillover effect on green economic efficiency.”

 

 

* Table 4. Global Moran's I of green economy efficiency and environmental taxes:  ( Add Source: Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 302 10%, respectively;)

Response and revision:

Thank you for your suggestions on the revision of the paper. According to your suggestion,we have added the following notes at the end of Tables 4.”Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 10%, respectively; the statistics are in parentheses.”

 

* (Figure 1 and Figure 2) : More discription and make comparision test between the two figures.

Response and revision:

Thank you for your advice. According to your suggestion,we addmore discription and make comparision test between Figure 1 and Figure 2,the contents added in the paper are as follows.”Upon further examination of Figures 1 and 2, it is evident that the scatter points representing green economic efficiency and environmental tax in both 2006 and 2020 are mainly concentrated in the first and third quadrants. However, there are differences in the level of dispersion between the two time periods. In 2020, the dispersion of green economic efficiency is lower compared to 2006, and it has expanded to include the second quadrant (H-L agglomeration). Despite this expansion, the overall concentration still remains primarily in the first and third quadrants.

By contrast, the concentration of environmental taxes in the first and third quadrants is higher in 2020 compared to 2006. These characteristics align with the numerical changes observed in the Moran’s I index for the years 2006 and 2020.”

 

* Analysis of Tax Heterogeneity : Table 6. Results of the impacts of environmental taxes on green economic efficiency ( Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 302 10%, respectively; the statistics are in parentheses.)

Response and revision:

Thank you for these suggestion. According to your suggestion,We have added the following notes at the end of Tables 6.”Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 10%, respectively; the statistics are in parentheses.”

 

* Table 7. Spatial heterogeneity analysis of the effect of environmental taxes (  ( Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 302 10%, respectively; the statistics are in parentheses.)

Response and revision:

Thank you for these suggestion. According to your suggestion,We have added the following notes at the end of Tables 7.”Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 10%, respectively; the statistics are in parentheses.”

 

* Table 9. Spatial heterogeneity analysis of the effect of environmental taxes on green economic efficiency for the economic geography matrix ( to add : ***, **, * indicate that the variables are significant at the level of 1%, 5%, and10%, respectively; the statistics are in parentheses.)

Response and revision:

Thank you for your advice. According to your suggestion, We have added the following notes at the end of Tables 9.”Note: ***, **, * indicate that the variables are significant at the level of 1%, 5%, and 10%, respectively; the statistics are in parentheses.”

 

 

 

6. 6. Research Findings and Policy Recommendations : ( Add limitation of this study)

Response and revision:

Thank you for these suggestions. Based on your suggestion, we have added the limitations of this study to the conclusion section of this article. The specific content is as follows.”This study has several limitations that should be considered. Firstly, the analysis primarily focuses on data from 30 provinces and autonomous regions in China, which may limit the generalizability of the findings to other countries. Economic development levels, population sizes, and national policies vary across countries, and therefore, the findings may not be directly applicable elsewhere. To address this limitation, future studies could expand the sample size to include a broader range of countries, specifically examining the impact of environmental tax policies on green economy development in five East Asian countries or 16 East and Southeast Asian countries. By conducting such analyses and exploring the heterogeneity among countries at different stages of development, more comprehensive and general conclusions can be drawn.”

 

 

7. 7. Summary CONCLUSION: missed !!

Response and revision:

We greatly appreciate your insights. Taking your suggestions into account, we have streamlined and improved the content of the conclusion, and made the following changes. Firstly, we have bifurcated the concluding section into two distinct parts: “5.1 Research Conclusion”and”5.2 Recommendations”.

Secondly, we have incorporated a discourse on the limitations of the study and potential future research avenues,the content can be found in "5.1 Research Conclusion”.The specific content is as follows.”This study has several limitations that should be considered. Firstly, the analysis primarily focuses on data from 30 provinces and autonomous regions in China, which may limit the generalizability of the findings to other countries. Economic development levels, population sizes, and national policies vary across countries, and therefore, the findings may not be directly applicable elsewhere. To address this limitation, future studies could expand the sample size to include a broader range of countries, specifically examining the impact of environmental tax policies on green economy development in five East Asian countries or 16 East and Southeast Asian countries. By conducting such analyses and exploring the heterogeneity among countries at different stages of development, more comprehensive and general conclusions can be drawn.”           

Finally, in the suggestions section, we propose suggestions based on the conclusions of this article and further enrich the content of the suggestions.

 

8. 8. Reference : all new articles you will add in the introduction or in the liturature review MUST be added in this section according the model of reference of Sustainability Journal (with the DOI certainly).

Thank you for these suggestions. Based on your suggestion,We have supplemented and revised the references in accordance with the model of reference of Sustainability Journal. 

 

9.If you need more related articles to improve the literature review we can suggested some.

Thank you very much. According to your suggestions, we have added relevant literature review content.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear authors,

this is the revision report for your manuscript, entitled:"Spatial and Heterogeneity Analysis of the Effect of Environmental Taxes on China's Green Economy Development from a Sustainable Development Perspective"

The authors within this manuscript, aim to investigate through a spatial lag model, the green economic efficiency of Chinese regions, and obtain important results. Furthermore, they empirically examine the effects of environmental fiscal policies on the promotion of economic development. As far as I am concerned, the article needs to undergo more revision before possible publication in a sustainability journal. 

Some comments:

1. In my opinion, the introduction should be changed and 1/2 paragraphs (6 lines) should be inserted.

2. The literature review, is not properly constructed, but more importantly, is not properly referenced, more recent references are needed, there are many articles in the literature that discuss the topic you have addressed.

3. The spatial model, must be explained in detail (149-157)

4. Table 1, it might be interesting to insert a new column where you specify the source of the data.

5. Table 2, insert a space to distinguish (Dependent Variable, Explanatory variable), currently everything is very confusing and not understandable. I refer to lines 246-247

6. I think the paper could be improved by inserting an introductory section in this section: "4.1 Spatial panel model testing and analysis" Rows 248-250.

7. The results obtained from the estimates, should be commented on more, using the existing scientific literature on the subject as support.

8. I think a section discussing limitations and future research perspectives should be added.

9. The results and conclusions, should also be developed by discussing policy implications.

10. English language is fine.

 

Best Regards

Author Response

1. In my opinion, the introduction should be changed and 1/2 paragraphs (6 lines) should be inserted.

Response and revision:

Thank you for your suggestion.We have made comprehensive modifications to the introduction section of this article, and the results of the changes are shown below.

”Since the beginning of the industrial civilization era, mankind, while driving rapid global economic growth, has also accelerated the seizure of natural resources, disrupting the balance of the Earth’s ecosystem and increasingly revealing deep-seated conflicts between man and nature. According to the 2019 Global Resource Outlook report by the United Nations Environment Programme, the exploitation of natural resources has increased from 27 to 92 billion tons over the last 50 years. This has led to 90% of biodiversity loss and water scarcity and is responsible for about half of the effects of climate change. The 2022 Global Air Quality Report shows that 97.3% of the world’s population now lives in areas where air pollution exceeds health standards. Given the increase in resource and environmental problems, “developing a green economy” has become a necessary requirement for governments to break the resource and environmental constraints, accelerate the transformation of economic development, and achieve sustainable socio-economic development (Mc Afee, Kathleen, 2016).

A green economy is an economic development model that seeks to ensure both the natural environment and human well-being can coexist without causing ecological crises or social divisions resulting from the relentless pursuit of economic growth; it aims to avoid unsustainable socio-economic growth caused by the depletion of natural resources (Pierce, 1989). Achieving green economic development hinges on finding solutions to energy use inefficiency and environmental pollution that often accompany socio-economic development (Daly & Cobb, 1989). In this regard, economist BiGu pioneered the concept of “government regulation of environmental pollution through macro taxation.” This approach involves taxing emitters based on the difference between private and social costs of emissions, thereby internalizing the negative externalities of pollution. This theoretical foundation supports government intervention and management of environmental problems. Environmental taxes, as a crucial tool for the government to protect the environment and conserve resources, have multiple benefits. In the short term, they directly restrict polluters’ emission behavior and encourage rational use of environmentally friendly production materials. In the long terms, they incentivize technological innovation, leading to improved production efficiency and enhanced market competitiveness (Zhan Lei, 2022).

A well-defined environmental taxation system can effectively curb environmental destruction and excessive resource consumption, thus promoting the development of a green economy. The Chinese government has been gradually establishing a comprehensive environmental taxation system while promoting green economic transformation. This system encompasses taxes related to environmental protection, resources, urban construction and maintenance, vehicles, vehicle purchases, urban land use, and arable land occupation.

Considering the competitive behaviors among local governments and regional economic development disparities, it becomes important to examine the spatial correlation between environmental taxes and the level of green economic development in each region. It is essential to determine whether the current environmental tax system in China effectively fosters the development of a green economy and whether there is heterogeneity in the impact of various environmental taxes on green economic development across regions. Clarifying these questions holds great theoretical and practical significance for the government in reforming and improving the environmental tax system while promoting the development of a green economy.”

 

2. The literature review, is not properly constructed, but more importantly, is not properly referenced, more recent references are needed, there are many articles in the literature that discuss the topic you have addressed.

Response and revision:

Thank you for your valuable input. In light of your recommendations, we have made amendments to the Literature Review section of our article and have incorporated recent scholarly articles with relevant theoretical citations pertaining to our research objectives. The amended content is as follows.

“Green economic efficiency serves as a significant indicator for measuring the progress of green economic development. It addresses the limitations of traditional socio-economic development, which focuses solely on increasing factor inputs without considering environmental costs. Evaluating high-quality socio-economic development now includes the consideration of green economy efficiency, as it has become a consensus for sustainable development worldwide.

Many studies have examined green economic efficiency at various spatial scales using data envelopment analysis (DEA) within the input-output framework. For example, Zhao Jinkai et al. (2021) utilized a four-stage disaggregated DEA approach, excluding the impact of external environmental variables and employed a Bootstrap-DEA model to account for random shocks. Their study focused on measuring the green development efficiency of Chinese provinces and regions. Similarly, Qianqian Geng (2023) assessed industrial green total factor productivity in China from 2004 to 2020 using the slacks-based measure (SBM) approach. Zhao PJ (2020) and Shen Y (2022) employed a similar approach to measure green economic efficiency in 30 Chinese provinces. Additionally, Fangmei Liu (2023) evaluated provincial green economic efficiency in China by employing the stochastic non-smooth data envelope (StoNED) model. Furthermore, Liu’s study examined the gradient differences in green economic efficiency among the eastern, central, and western regions of China.

The analysis of the impact mechanism of environmental tax on green economic development primarily focuses on the micro-individual level. Firstly, environmental taxes influence the production and operational behavior of enterprises, thereby promoting energy conservation and emission reduction. According to Muhammad (2021), environmental taxes encourage cleaner production in industrial enterprises, leading to sustainable economic and environmental development. Guangqiang Liu (2022) empirically examined the effects of environmental taxes on corporate environmental investment using data from Chinese listed companies between 2015 and 2019, revealing a significant increase in environmental investment due to the implementation of environmental taxes. According to Akio Yamazaki (2022), rather than diverting resources from production, environmental taxes contribute to a net increase in firm productivity by encouraging investments in environmental protection. Xu He (2022) analyzed data from a sample of listed companies in China from 2015 to 2020 and discovered that environmental tax reforms have a positive impact on corporate profitability while curbing corporate pollution behaviors. Zastempowski (2023) analyzed business survey data collected from 13 EU member states in 2014 and discovered that the implementation of environmental taxes motivated companies to replace fossil fuel with renewable energy sources.

Secondly, environmental taxes can drive firms to innovate green technologies and enhance resource efficiency. Greaker (2018) explored how environmental policies can guide firms toward technological innovation, achieving both environmental and economic benefits. Vitenu-Sackey (2021) argued that a significant increase in environmental taxes can incentivize firms to adopt green technologies, mitigating environmental pollution and improving their total factor productivity. Zhangsheng Jiang (2023) and Xiaomin Zhao (2023) demonstrated that the implementation of environmental taxes stimulated firms to engage in green innovation, consequently driving business performance. Min Fan (2022) discovered that environmental taxes indirectly contribute to regional green total factor productivity by fostering higher levels of green technological innovation among firms. Johan Albrecht (2023) confirmed that environmental taxes largely determine the adoption of energy efficiency-related technological innovations by small and medium sized-enterprises (SMEs).

Empirical studies on the impact of environmental taxes on green economic development have primarily focused on the influence of environmental regulations on green economy development. For example, Shuai S, Fan Z (2020) performed an empirical analysis using panel data from China’s regions spanning 2007 to 2018. They measured China’s green economy efficiency using a super-efficient DEA model and found a nonlinear relationship between environmental regulation and green economy efficiency. Shang Y, et al. (2022) investigated the effect of environmental regulation on circular economy performance and discovered a linear contribution of environmental regulation to it. Shen Y, Zhang X. (2022) utilized provincial-level panel data from China spanning 2004 to 2020 and employed a two-way fixed effects model to analyze the impact of environmental taxes on industrial green transformation. They found that broad environmental taxes, such as taxes on vehicles and boats, resources, and urban land use, had a significant positive impact on industrial green transformation. Syed Abdul (2022) identified a significant positive relationship between environmental policies and green total factor productivity through empirical analysis of data from a sample of 12 cities in China.

Upon reviewing the existing literature, it is evident that there is a wealth of research on measuring green economy efficiency and analyzing the micro-mechanisms of environmental taxes on green economy development. This body of work has laid a strong foundation for our study. However, there are certain limitations in the existing research that need to be addressed. Firstly, most studies primarily focus on analyzing the impact of environmental regulations or policies on green economy efficiency, with fewer studies examining the influence of environmental taxes on green economy development. Furthermore, there is a scarcity of research exploring the differential effects of various environmental taxes on green economy development across different regions. Secondly, in terms of research methodology, many studies rely on general panel models to empirically analyze the relationship between environmental taxes and the green economy. However, it is crucial to consider the spillover effects of environmental taxes on green economy development, especially considering the strong geographical and economic correlations observed in local environmental tax policies and the green economy (C. Cindy Fan, 2004).

Taking into account these shortcomings in existing research, our study aims to contribute in the following ways. Firstly, in terms of methodology, we employed a spatial econometric model to empirically analyze the impact of environmental taxes on green economic efficiency. This approach allowed us to consider the spatial relationships and potential spillover effects in our analysis. Secondly, in terms of content, we comprehensively analyzed the impact of environmental taxes as a policy tool on green economic development. Moreover, we explicitly examined the diverse effects of different environmental taxes on green economic development across different regions. This provides valuable insights for formulating region-specific environmental tax policies that are conducive to promoting sustainable and green economic growth.”

 

3. The spatial model, must be explained in detail (149-157)

Response and revision:

Thank you for pointing this out.According to your suggestion, we added the description of spatial econometric model in the "3.2 model setting" part of the paper. The specific content is”Spatial econometrics is specialized field that incorporates the concepts of spatial autocorrelation and spatial heterogeneity (Anselin, 1988). One of its key features is the explicit consideration of spatial interdependence and variability among different units of analysis. Unlike standard econometric approaches that primarily focus on testing heterogeneity, spatial econometrics places significant emphasis on detecting and analyzing spatial dependence. Moreover, spatial econometric models often employ the maximum likelihood estimation (MLE) method, which is known for its high precision and reliability.”

4. Table 1, it might be interesting to insert a new column where you specify the source of the data.

Response and revision:

Thank you for your suggestion.According to the advice,we have made the following improvements.In Table 1, we re insert a column to describe the source of relevant data to make the table more clear.The amended content is as follows.

Indicators	Variables	Variable description	Data sources
Input indicators	Energy input	Total energy consumption	China Energy Statistical Yearbook for the years 2007 to 2021
	Labor input	Employed population by region	China Statistical Yearbook for the years 2007 to 2021
	Capital input	Capital stock	China Fixed Assets Statistical Yearbook for the years 2007 to 2021
Desired output indicators	Economic benefits output	Regional GDP	China Statistical Yearbook for the years 2007 to 2021
Non-desired output indicators	Wastewater emissions	Industrial wastewater emissions	China Environmental Statistical Yearbook for the years 2007 to 2021
	Exhaust emissions	Industrial waste gas emissions	China Environmental Statistical Yearbook for the years 2007 to 2021

 

 

5. Table 2, insert a space to distinguish (Dependent Variable, Explanatory variable), currently everything is very confusing and not understandable. I refer to lines 246-247

Response and revision:

Thank you for your suggestions on the revision of this paper.We inserted several underscores in Table 2 to distinguish Dependent variable, Explanatory variables and Control variables.The amended content is as follows.

	Variable	Average	Standard deviation	Minimum	Maximum
Dependent variable	Green economy efficiency	0.470	0.250	0.150	1.300
Explanatory variables	Environmental taxes	583.360	423.140	61.230	3159.500
Control variables	Economic development level	4.628	2.805	0.612	16.489
	Industry structure	44.880	8.740	15.800	61.500
	Pollution control efforts	20.420	19.630	0.050	141.600
	Openness to the outside world	157.883	291.873	1.998	2744.956
	Population density	2839.240	1207.910	597.840	6307.380

 

 

6. I think the paper could be improved by inserting an introductory section in this section: "4.1 Spatial panel model testing and analysis" Rows 248-250.

Response and revision:

Thank you for your suggestions on the revision of this paper.In the "4.1 spatial panel model testing and analysis" section, we added a brief introduction and Analysis on the spatial autocorrelation test. The details are as follows. ”In the application of spatial econometric methods, it is essential to assess the presence of spatial dependence in the data. This step is crucial as it serves as a prerequisite for employing spatial econometric methods. One commonly used approach for evaluating spatial dependence is Moran’s I test. Moran’s I value ranges between -1 and 1, with positive values greater than 0 indicating high-high (H-H) and low-low (L-L) agglomeration, while negative values less than 0 indicate high-low (H-L) agglomeration. Positive correlation is generally more prevalent. A value approaching 0 suggests a random spatial distribution, indicating the absence of spatial autocorrelation among the variables. To ensure the applicability of spatial econometric methods in this study, spatial autocorrelation tests were conducted on both the dependent and independent variables.”

 

7. The results obtained from the estimates, should be commented on more, using the existing scientific literature on the subject as support.

Response and revision:

Thank you for your suggestion. Based on your suggestion, we have made appropriate modifications to the conclusion of "4.1.2 Spatial Effect Regression Result Analysis" in this article (mainly making modifications to the analysis in Tables 5 and 6).In the explanation of the regression results in Table 5 of "4.1.2 Analysis of Spatial Effect Regression Results", we have added support from relevant papers,the specific content is as follows.”The results of the model show that environmental taxes have a positive and significant impact on the green economy’s efficiency, according to all three weighting matrices. Additionally, economic development level, openness to the outside world, and population density were found to be significantly and positively associated with green economic efficiency across all three weighting matrices. This finding is in line with the results of Baek Jungho (2011) and Hashim Zameer (2020), who studied the relationship between environmental taxes and international trade. Hence, these factors are conducive to the development of the green economy. However, the proportion of GDP as secondary industry was negatively correlated with green economic efficiency at the 1% significance level, indicating that it hinders the improvement of green economic efficiency. This conclusion aligns with the result of Muhammad Zahid Rafifique (2021) and Bingnan Guo (2021), who found that environmental taxes can impact industrial structure. Notably, the correlation between pollution control efforts and green economy efficiency was not found to be significant in the present study, suggesting that local governments’ investments in environmental pollution control may not be efficient enough to promote economic development and achieve performance goals.”

In addition, in "4.1.3 Analysis of Tax Heterogeneity", support for the research results of this article has been added in the explanation of regression results in Table 6 from other scholars' papers.The changes are as follows.”According to Table 6, in terms of specific environmental taxes, the estimated coefficients of the model for all three matrices were positive and significant at the 1% or 5% level, indicating that the vehicle and vessel taxes levied at this stage effectively contributed to improving green economic efficiency. This conclusion, which is in line with Yang Shen (2022), is probably derived because vehicle and vessel taxes are levied on vehicles traveling on public roads and vessels navigating domestic rivers, lakes, and territorial sea ports; their taxation can reduce people’s use of motor vehicles and vessels and control the emission of pollutants. The results demonstrate that the environmental protection, arable land occupation, and urban land use taxes have all exhibited negative and statistically significant regression coefficients at the 1% level. This suggests that the introduction of these taxes has an inhibitory effect on the development of the green economy, possibly due to the current costs associated with paying these taxes being outweighed by the revenue gained from increasing pollution emissions. This, in turn, has resulted in enterprises failing to reduce their pollution emissions and giving little attention to technological innovation in enterprise emission reduction, which is detrimental to promoting the green transformation of enterprises. The regression coefficient of urban maintenance and construction tax for the spatial weighting matrix of economic geographic distance was found to be -0.024 and significant at the 10% level. This indicates that at the economic level, the urban maintenance tax has negative utility for improving green economic efficiency in the region. The urban maintenance and construction tax, as an additional tax, has a large impact on the main industries such as traditional manufacturing and energy processing industries, increasing the tax burden of enterprises and reducing their green innovation investment. Although the estimated coefficients of resource tax for the three weightings were negative, the results are not significant, indicating that the levy of resource tax does not have the expected effect on local green economic development. This result is supported by Parry (2005), who studied gasoline tax collection in Britain and the United States. A possible reason behind this conclusion is that the current scope of resource tax in China is relatively narrow, including only crude oil, natural gas, coal, and other non-metallic ores, which is insufficient to greatly influence enterprises’ resource utilization and pollution emission behaviors, resulting in a slow green transformation process. Additionally, all the standard deviation values in Table 6 are below 0.1, indicating that the estimated empirical results are highly representative, affirming the accuracy of the equation’s estimates.”

 

8. I think a section discussing limitations and future research perspectives should be added.

Response and revision:

Thank you for these suggestions. Based on your suggestion, in the "5 At the end of the '5.1 Research Conclusion' section in 'Research Findings and Policy Recommendations', additional content on limitations and prospects has been added. The modifications are as follows. ”This study has several limitations that should be considered. Firstly, the analysis primarily focuses on data from 30 provinces and autonomous regions in China, which may limit the generalizability of the findings to other countries. Economic development levels, population sizes, and national policies vary across countries, and therefore, the findings may not be directly applicable elsewhere. To address this limitation, future studies could expand the sample size to include a broader range of countries, specifically examining the impact of environmental tax policies on green economy development in five East Asian countries or 16 East and Southeast Asian countries. By conducting such analyses and exploring the heterogeneity among countries at different stages of development, more comprehensive and general conclusions can be drawn.”

 

9. The results and conclusions, should also be developed by discussing policy implications.

Response and revision:

We greatly appreciate your insights. Taking your suggestions into account, we have executed pertinent revisions to the conclusion of this article. Firstly, we have bifurcated the concluding section into two distinct parts: “5.1 Research Conclusion”and”5.2 Recommendations”.

Secondly, we have incorporated a discourse on the limitations of the study and potential future research avenues,the content can be found in "5.1 Research Conclusion”.The specific content is as follows.”This study has several limitations that should be considered. Firstly, the analysis primarily focuses on data from 30 provinces and autonomous regions in China, which may limit the generalizability of the findings to other countries. Economic development levels, population sizes, and national policies vary across countries, and therefore, the findings may not be directly applicable elsewhere. To address this limitation, future studies could expand the sample size to include a broader range of countries, specifically examining the impact of environmental tax policies on green economy development in five East Asian countries or 16 East and Southeast Asian countries. By conducting such analyses and exploring the heterogeneity among countries at different stages of development, more comprehensive and general conclusions can be drawn.”           

Finally, in the suggestions section, we propose suggestions based on the conclusions of this article and further enrich the content of the suggestions.Our revised content is as follows.

”Drawing from the aforementioned research results, we present the subsequent policy suggestions. First, based on the spatial characteristics of environmental taxation and green economic efficiency, coordination and cooperation among governments should be strengthened. The spatial spillover effect of environmental taxation could potentially provoke neighboring governments into either free-riding or blindly imitating such policies. Building a perfect information communication and interest coordination mechanism among governments can prompt local governments to target their policies, understand key points, and clarify targets, thus realizing inter-governmental cooperation in pollution control and accelerating the green transformation and development of enterprises. From a different perspective, it is clear that green economic efficiency exhibits positive autocorrelation; therefore, governments at all levels should understand the scientific basis of economic efficiency under current resource and environmental constraints, clarify the key industries in each region, form regional characteristics, and cooperate with neighboring regions to guide the development of industrial layout in the region to achieve green economic growth. Governments at all levels can play a crucial role by creating a platform for regional exchange and cooperation. This allows them to coordinate efforts with other regions and develop tailored environmental tax strategies that align with the specific attributes of each region. By doing so, governments can maximize the guiding potential and fiscal leverage of environmental taxes, leading to more effective and targeted outcomes in promoting sustainable development.Second, environmental tax policy should be improved and a comprehensive environmental tax system constructed. We found that the environmental tax presently imposed encourages environmentally sustainable economic growth. However, some pre-existing environmental tax measures have yet to yield noticeable results, or they may have even led to adverse effects. Local governments should follow the current status of economic development and resource endowment of each region to further develop and improve the content of environmental tax policies suitable for local areas. After careful analysis, it is recommended that the resource tax levy be broadened, tax burden be increased for arable land occupation and urban land use, and environmental protection tax levy standards be adjusted to reflect the actual pollution emissions and economic development level of the region. It is advisable to levy taxes on all forms of pollutants to provide effective policy support. Simultaneously, consideration can be given to reducing the tax burden of other taxes such as VAT and corporate income tax in the form of green innovation incentives, to reduce the cost-effect of environmental taxes and thus promote development of the green economy. In response to this scenario, it is essential for nations worldwide, particularly those that have recently implemented or reformed environmental tax policies, to conduct a comprehensive analysis of the effects of these taxes on various regions and industries. This analysis will enable them to identify any necessary adjustments that need to be made in a timely manner, ensuring that environmental tax strategies effectively achieve their intended outcomes. By closely monitoring and adapting these strategies, governments can maximize the positive impact of environmental taxes and ensure their continued effectiveness in promoting sustainable development.Thirdly, based on the results of the heterogeneity tests for different taxes, and considering the negative impact of the industrial structure on green economy efficiency, it is crucial to focus on upgrading the industrial structure and enhancing the efficiency of local governments in environmental pollution control. By prioritizing these areas, policymakers can effectively address the challenges and improve the overall performance of the green economy. Although the current industrial structure of most Chinese provinces has entered the “three-two-one” mode, because of high level of pollution emissions in China’s secondary industry, in order to promote economic development, there remains a need to prioritize the expansion of the tertiary sector while gradually diminishing the significance of the secondary sector. At the same time, in secondary industry, it is important to strengthen the capacity constraints of high-pollution and high-energy-consumption enterprises and use environmental tax policy to urge them to enhance their production technology and speed up transformation and upgradation. Regarding the issue of unremarkable pollution control effectiveness, in order to enhance the effectiveness of pollution control investment funds, local authorities must intensify their evaluations of investment projects, tailor investment plans to the unique characteristics of the region, and augment their oversight and monitoring of plan execution. Simultaneously, it is imperative to contemplate integrating the pollution mitigation level into the all-encompassing appraisal framework of economic and societal progress in every locality. This would ensure that the impact of pollution control investment is one of the pivotal assessment benchmarks for regional authorities. To prevent local officials from being trapped in the “GDP growth” mindset, it is essential to introduce measures that discourage industries with high pollution and energy consumption, even if they offer economic benefits. As mentioned earlier, countries can customize their environmental tax objectives and standards to suit their specific circumstances and implement these policies nationwide. A cohesive national environmental tax strategy can effectively deter businesses from exploiting regional tax and policy differences to relocate their polluting industries. Additionally, the central government plays a crucial role in coordinating efforts and managing potential competition among regional governments. By adopting these approaches, countries can strike a balance between economic development and environmental protection, avoiding the negative impacts that unsustainable industries may have on the green economy.”

 

Author Response File: Author Response.pdf

Reviewer 3 Report

he following should be considered:

- A deeper vision should be offered, which addresses, reviews and analyzes the causes and effects of what it raises.

- The bibliography should be updated because there is little updated bibliography and it should be increased.

 Regarding the objective of the work, it should be highlighted, as well as the working hypothesis. On the other hand, emphasis should be given to the innovation of the work and its specific contribution, the discussion and conclusions should be separated and improved.

- Figures and tables should be improved and in an adequate and legible format. In this regard, it does not explain in depth the meaning of the standard deviation, variance, and the three-hold of its data, likewise in Figure 1, for example, the meaning of the dispersion of points and its significance are not explained.

 Minor editing of English language required

Author Response

- A deeper vision should be offered, which addresses, reviews and analyzes the causes and effects of what it raises.

Response and revision:

We greatly appreciate your insights. Taking your suggestions into account, we have executed pertinent revisions to the conclusion of this article. Firstly, we further enrich the Analysis content of Empirical Results in the part of "4. Empirical Results and Analysis", and cite scholars' opinions for evidence.

Secondly, we have incorporated a discourse on the limitations of the study and potential future research avenues,the content can be found in"5.1 Research Conclusion”.The specific content is as follows.”This study has several limitations that should be considered. Firstly, the analysis primarily focuses on data from 30 provinces and autonomous regions in China, which may limit the generalizability of the findings to other countries. Economic development levels, population sizes, and national policies vary across countries, and therefore, the findings may not be directly applicable elsewhere. To address this limitation, future studies could expand the sample size to include a broader range of countries, specifically examining the impact of environmental tax policies on green economy development in five East Asian countries or 16 East and Southeast Asian countries. By conducting such analyses and exploring the heterogeneity among countries at different stages of development, more comprehensive and general conclusions can be drawn.” Thirdly, in the suggestions section, we propose suggestions based on the conclusions of this article and further enrich the content of the suggestions.

 

- The bibliography should be updated because there is little updated bibliography and it should be increased.

Response and revision:

Thank you for these suggestions. Based on your suggestion,We  supplemented the latest bibliography, which content is mainly in the literature review section of our article.

 

-Regarding the objective of the work, it should be highlighted, as well as the working hypothesis. On the other hand, emphasis should be given to the innovation of the work and its specific contribution, the discussion and conclusions should be separated and improved.

Response and revision:

Thank you for your suggestion. We have added research hypotheses based on existing research achievements and emphasize the contributions In the section of "2. Literature Review". The specific contents are as follows.

“Taking into account these shortcomings in existing research, our study assumes that environmental taxes have a spatial spillover effect on green economic efficiency, and aims to contribute in the following ways. Firstly, in terms of methodology, we employed a spatial econometric model to empirically analyze the impact of environmental taxes on green economic efficiency. This approach allowed us to consider the spatial relationships and potential spillover effects in our analysis. Secondly, in terms of content, we comprehensively analyzed the impact of environmental taxes as a policy tool on green economic development. Moreover, we explicitly examined the diverse effects of different environmental taxes on green economic development across different regions. This provides valuable insights for formulating region-specific environmental tax policies that are conducive to promoting sustainable and green economic growth.”

In addition, we have bifurcated the concluding section into two distinct parts: “5.1 Research Conclusion”and”5.2 Recommendations”, also proposed suggestions based on the conclusions of this article and further enrich the content of the suggestions.

 

- Figures and tables should be improved and in an adequate and legible format. In this regard, it does not explain in depth the meaning of the standard deviation, variance, and the three-hold of its data, likewise in Figure 1, for example, the meaning of the dispersion of points and its significance are not explained.

Response and revision:

Thank you for your suggestions on the revision of this paper.In response to your comments, we have made the following series of improvements.We have added the following comparative analysis on Figure 1 and Figure 2 in the "4.1.1 Spatial Autocorrelation Test" section.The results are as follows. “Upon further examination of Figures 1 and 2, it is evident that the scatter points representing green economic efficiency and environmental tax in both 2006 and 2020 are mainly concentrated in the first and third quadrants. However, there are differences in the level of dispersion between the two time periods. In 2020, the dispersion of green economic efficiency is lower compared to 2006, and it has expanded to include the second quadrant (H-L agglomeration). Despite this expansion, the overall concentration still remains primarily in the first and third quadrants. By contrast, the concentration of environmental taxes in the first and third quadrants is higher in 2020 compared to 2006. These characteristics align with the numerical changes observed in the Moran’s I index for the years 2006 and 2020.”

In addition, explanations for the standard deviation in the regression results have been added in lines 1-10 of the "4.1.2 Analysis of Spatial Effect Regression Results" section,and the results are as follows. ”The standard deviation is a measure that quantifies the dispersion or variability of a set of values. In the context of regression results, the standard deviation of the estimated values reflects the spread or deviation of individual data points from the average estimated value. A smaller standard deviation indicates that the estimates are tightly clustered around the mean, indicating higher accuracy and consistency in the regression analysis. Considering the nature of standard deviation, we first examined the values within brackets in Table 5. We observed that none of the values exceeded 0.1. This observation suggests that the standard deviations associated with the regression results are very small, indicating the precision and reliability of the estimated regression coefficients.”

The second analysis on standard deviation is in the "4.1.3 Analysis of Tax Heterogeneity" section, with the specific content as follows .”Additionally, all the standard deviation values in Table 6 are below 0.1, indicating that the estimated empirical results are highly representative, affirming the accuracy of the equation’s estimates.”

 Finally, in lines 5-10 of the "4.1.4 Regional Heterogeneity Analysis" section, we analyzed the standard deviation of the regression results in Tables 7, 8, and 9, and the results are as follows. ”To ensure the accuracy of the estimated results, we first analyzed the standard deviations of the regression results presented in Tables 7, 8, and 9. A closer examination revealed that the standard deviation values were consistently small, suggesting that the estimated results of the spatial heterogeneity tests conducted using the three types of spatial weight matrices were highly precise, Hence, we moved on to further analysis. “

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear Author,

Please this table needs more correcction form to be well present before publication. try to revise it.

 

Minor revision in grammar.

Take consideration please.

Reviewer 2 Report

Modifications requested, performed correctly!