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Regional Corruption, Foreign Trade, and Environmental Pollution

Sustainability 2023, 15(1), 859; https://doi.org/10.3390/su15010859

by Suisui Chen¹, Xintian Liu¹, Shuhong Wang^2,* and Peng Wang³

Reviewer 1:

Li Li

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Reviewer 4:

Dervis Kirikkaleli

Sustainability 2023, 15(1), 859; https://doi.org/10.3390/su15010859

Submission received: 1 November 2022 / Revised: 26 December 2022 / Accepted: 30 December 2022 / Published: 3 January 2023

(This article belongs to the Special Issue Towards an Ecological Economics of Sustainability: From the Perspective of Environmental Regulation Policies and Pollutant Emissions Governance)

Round 1

Reviewer 1 Report

After reviewing the “Regional Corruption, Foreign Trade, and Environmental Pollution”, I think it can’t be accepted in the present version. And there are some comments in the following.

1. Abstract

(1) The authors could add the importance of the research in the abstract.

(2) The authors first mentioned “After excluding the effect of corruption on trade, foreign trade is conducive to the improvement of environmental pollution” , but then the authors also mentioned “Hence, the intensification of corruption on pollution will be weakened with increased openness”, which was confusing.

2. Introduction

(1) The first sentence had nothing in parentheses. Please check it.

(2) More deep analysis about the related environment pollution control policy should be added.

3. Literature review

The authors could add the difference of this study, the research methods and the importance of this study.

4. Model construction and indicator selection

(1) Why didn’t the author explain the β₂?

(2) Why did the authors choose the data from 2004 to 2017?

(3) “LnEP1, lnEP2 and lnEP3” were in the table1, but in the construction of model (1), the authors had no relevant explanation and explanation.

(4) There are many format problems in the form, please check and modify them.

(5) What is the “L.lnEP_i,t-1” ? The authors need to explain in it.

(6) What are the values in the table? The authors need to explain it in the text.

5. Empirical analysis

(1) The model should be consistent and reflected in the model construction.

(2) The authors should indicate the basis of regional division in the text.

6. Effects of corruption and trade-environmental pollution by region

(1)The title “Effects of corruption and trade-environmental pollution by region” was confusing.

(2) Why only per capita industrial wastewater discharge was selected as the explanatory variable. The authors needed to explain it in the text.

6. Conclusions and policy recommendations

(1) The conclusions and policies should be consistent.

(2)The authors say “The results of this study allow us”. What were the results or the conclusions?

7.At the end of the article, it is suggested to add the research limitations and future research directions, so that more readers can pay attention to your research.

8.There are many errors in the paper, please check them again. Such as: line 15, line 194, line 411 and so on.

Author Response

Response to Reviewers’ Comments on

“Regional Corruption, Foreign Trade, and Environmental Pollution”

for

Sustainability-2036994

We thank the reviewers for their insightful and constructive comments. We have addressed their concerns and believe that the quality of the manuscript has improved. Their comments, our responses, and all revisions made in the manuscript are outlined below. Please feel free to contact us if you require any information or further clarification.

Comments from the editors and reviewers:

Abstract

(1) The authors could add the importance of the research in the abstract.

Response and revision:

Thank you for your suggestion. Based on your suggestion, we have restated the importance for the study in the abstract section of the article. The changes are as follows.

As an effective means and an important guarantee for environmental pollution management in China, enhancing the level of foreign openness and reducing the level of regional corruption, respectively, the successful implementation of both are key steps in determining the future development of China's transformation of trade development and green transformation. This paper attempts to systematically examine the mechanisms of regional corruption and foreign trade on environmental pollution from both theoretical and empirical levels. Using the panel data of 30 provinces in China from 2004 to 2017, this study constructs a dynamic panel model with a one-stage pollution index. The system GMM is used to verify the relationship between corruption, trade and the environment. Empirical results show that corruption reduces investment in environmental governance, R&D, and the introduction of environmental technology, and it increases environmental pollution by reducing the implementation and control of environmental policies. After excluding the effect of corruption on trade, foreign trade is conducive to the improvement of environmental pollution. Hence, the intensification of corruption on pollution will be weakened with increased openness; that is, China’s expansion of foreign trade is beneficial to attenuating the pollution effect of corruption on the environment. These findings suggest that expanding trade will ultimately improve the environment and even mitigate the negative impact of corruption on the environment. Therefore, China should dredge the channel of factor flow, give full play to the vitality of market players, strictly investigate corruption, and encourage opening up.

Dear reviewer, thank you for your proposal.

In fact, in the course of the original empirical test，model (3) adds the corruption variable and its interaction with foreign trade to control the impact of foreign trade on environmental pollution depends on the degree of corruption. The results show a larger coefficient for lnt than in model (1), and model (2), and therefore conclude that "after excluding the effect of corruption on trade, foreign trade is beneficial to the improvement of environmental pollution". At the same time, the regression coefficient of the interaction variable is significantly negative, while the regression coefficient of the lnCO is positive. Therefore, "the intensification of corruption on pollution will be weakened with increased openness" is a conclusion derived from the sign of the interaction and corruption coefficients and is not relevant to the supporting evidence in the previous sentence. Therefore, I have followed your suggestion to replace "hence" with "meanwhile".

After excluding the effect of corruption on trade, foreign trade is conducive to the improvement of environmental pollution. Meanwhile, the intensification of corruption on pollution will be weakened with increased openness;

Introduction

(1) The first sentence had nothing in parentheses. Please check it.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have revised it.

More deep analysis about the related environment pollution control policy should be added.

Response and revision:

Dear Reviewers, thank you for your suggestion, we have added the following deep analysis about the related environment pollution control policy to this article.

Accordingly, the Congress proposed a series of environmental policies related to the green economy, such as ecological environmental protection, environmental pollution control, and protective development and economical use of resources. Among them, environmental regulation is considered as an important means to achieve sustainable development and solve the problems of resource shortage and environmental pollution (Ouyang et al., 2020). Multiple policy instruments possess different governance effects and costs (Blackman and Kildegaard, 2010). And due to the differences in institutional factors such as tax sharing system, performance assessment methods, natural conditions, industrial layout, and development status (Chen and Qian, 2020), the effects of environmental regulation tools implementation also vary significantly among different regions.

Literature review

The authors could add the difference of this study, the research methods and the importance of this study.

Response and revision:

Dear reviewers, your suggestions are very constructive. We have added relevant contents based on your suggestions.

It can be seen that some scholars have studied the role of corruption and trade on the environment, while very little direct reference has been made to the impact of corruption on the environmental pollution and its interaction effects of a country's foreign trade. Most studies have shown that environmental regulations were strengthened by the expansion of trade and weakened by the deepening of corruption (Damania and Fredriksson , 2003). Owing to the rising level of corruption, government policies ignore the consideration of public welfare and encourage the bribery of interest groups (Cole and Fredriksson 2006); then, the positive effects of trade liberalization will be weakened. Chang et al. (2020) also confirmed that corruption reduces the effect of trade on environmental conservation. Therefore, this study contributes to the following aspects. Firstly, we analyse the mechanism of corruption's effect on the environmental pollution and its interaction effect of foreign trade, empirically investigating how different levels of corruption affect the environmental pollution effect of regional foreign trade. Secondly, we consider the non-linear and dynamic connection of corruption to the environment. Thirdly, with regard to regional heterogeneity and differences over time, most studies have focused more on heterogeneity between countries, and few have compared regions within a country. We also examines changes in the quality of a country's environment following important environmental policies. This is important and necessary for the analysis of regional corruption and environmental governance.

Model construction and indicator selection

(1) Why didn’t the author explain the β2?

Response and revision:

Dear Reviewer, your constructive comments are greatly appreciated. We have added explanations.

Here, i and t represent province and time, respectively; the dependent variable is selected as EP as the level of environmental pollution; lnEP_i,t-1represents the lagged period of the environmental pollution. T is the scale of foreign trade in each province; CO is the level of corruption in each province; and X_it represents other control variables, including economic scale, industrial structure, technological progress, environmental regulations, and FDI. Further, β₁_,β₂and β_j are the regression coefficients of the variables, and α_itand u_it represent the intercept and random disturbance terms of the model, respectively.

（2）Why did the authors choose the data from 2004 to 2017?

Response and revision:

Dear reviewer, thank you for your suggestion on the paper and I have rethought the research window of the paper based on your suggestions.

In the context of China's reality, environmental pollution has always been a problem. The reason for selecting the period 2004-2017 for this paper is to maximize the time frame of the study and to consider the mechanisms of regional corruption and foreign trade on environmental pollution since 2004 in an integrated manner. Also, the time window ends in 2017 due to the lack of data for measuring pollution after this year.

“LnEP1, lnEP2 and lnEP3” were in the table1, but in the construction of model (1), the authors had no relevant explanation and explanation.

Response and revision:

Dear reviewer, in response to your suggestion, we have added the relevant explanation as follows.

Here, i and t represent province and time, respectively; the dependent variable is selected as EP as the level of environmental pollution, which can be divided into industrial wastewater discharge (lnEP₁), industrial waste gas emissions (lnEP₂) and industrial waste solid emission (lnEP₃) in per capita form ;

(4) There are many format problems in the form, please check and modify them.

Response and revision:

Dear Reviewers, Your valuable comments are greatly appreciated. We have revised it.

(5) What is the “L.lnEPi,t-1” ? The authors need to explain in it.

Response and revision:

Dear Reviewer, your constructive comments are greatly appreciated. We have added explanations.

What are the values in the table? The authors need to explain it in the text.

Response and revision:

Dear Reviewer, your suggestions are very constructive. We have added relevant contents based on your suggestions.

Before analyzing the empirical fitting model of environmental pollution, a descriptive statistical analysis was performed on the full sample data to further understand the characteristics of the research data (Table 1). Table 1 contains the extreme values, the mean, and the standard deviation of the the full sample data in logarithmic form. And the last column shows the predicted sign based on the previous theoretical analysis.

Empirical analysis

(1) The model should be consistent and reflected in the model construction.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have revised it.

(2) The authors should indicate the basis of regional division in the text.

Response and revision:

Dear reviewers, we appreciate your suggestion and we have added the basis for the division in the text.

The following section verifies the conclusion that corruption will reduce environmental pollution when the level of trade scale is higher than the critical value and explains the phenomenon that the signs of technological progress in Table 2 are contrary to expectations. Considering the obvious differences in location advantages and economic development levels, this section divides the sample data into three regions, East, West and Central, based on geographical location. Fig 1 shows the trend and comparison of the degree of corruption in the eastern, central, and western regions that have addressed using these methods.

Effects of corruption and trade-environmental pollution by region

(1)The title “Effects of corruption and trade-environmental pollution by region” was confusing.

Response and revision:

Dear reviewers, We have revised the title to Regional heterogeneity test.

(2) Why only per capita industrial wastewater discharge was selected as the explanatory variable. The authors needed to explain it in the text.

Dear reviewer, thank you very much for your advice. We have explain the reason for selecting this variable as follows.

Furthermore, the empirical results of regional heterogeneity for the other two indicators are unsatisfactory and not representative due to the varying implementation pressures of the policy environment in each region. Therefore, the explained variable is selected as the per capita industrial wastewater discharge.

7. Conclusions and policy recommendations

(1) The conclusions and policies should be consistent.

(2)The authors say “The results of this study allow us”. What were the results or the conclusions?

Response and revision:

Dear Reviewer, your constructive comments are greatly appreciated. We have re-summarized the conclusions of the paper as follows.

This paper examines the impact of corruption, foreign trade on environmental pollution in China and how the impact of corruption on environmental pollution depends on the level of openness. After combing through the relevant literature,this paper initially conjectured that corruption leads to environmental degradation and that the level of trade openness determines the outcome of the pollution effect of corruption. In a subsequent empirical test, a dynamic panel data model is constructed, and a T x CO interaction term was introduced to extract the fraction of the environmental utility of corruption acting on foreign trade.A significant negative sign for T and a significant positive sign for CO suggest that foreign trade and corruption have opposite effects on the environment. Corruption exacerbates environmental pollution, while foreign trade plays a good role. The empirical test also indicates that the quadratic term of corruption is significantly positive, indicating that the positive role of corruption in improving the effect of trade on the environment may not be absolute but stemporary. While the regression coefficient of the interactive variables of corruption and foreign trade is significantly negative, this shows that higher levels of corruption play the bad role of trade in improving environmental pollution. That is, the increase in the level of corruption has weakened the positive effect of foreign trade on environmental improvement through channels such as the negative impact on per capita income, the creation of high trade barriers to hinder trade, the distortion of public investment, the reduction of environmental governance and the efficiency of green innovation. On the other hand, the expansion of foreign trade can weaken the impact of corruption, which will provides good policy inspiration for improving the poor environmental impact of corruption.Also at the regional level, this paper analyses the current state of corruption in the East, Middle and West. It also finds that the relatively backward central and western regions have not enjoyed the benefits of opening up to the environment due to differences in the level of human capital, R&D investment funds, financing channels and the efficiency of digestion and absorption of imported equipment, technology and investment funds in each region.The results of this study allow us to provide policy recommendations on environmental governance from the perspective of trade openness and corruption.

8.At the end of the article, it is suggested to add the research limitations and future research directions, so that more readers can pay attention to your research.

Response and revision:

Thanks to the valuable suggestions put forward by the reviewers. Your suggestions have been very helpful for our future study. Following your suggestion, we have added the limitations and improvements on the article at the end of the text.

Limitations and improvements

The limitations of this paper are mainly in the following areas. Firstly, due to the hidden nature of regional corruption and the limitations of data availability, there are limited indicators to measure corruption that could be further refined. Future research could use and develop different indicators to provide a more comprehensive picture of the level of regional corruption. More data could be collected on cases of government corruption, efficiency in the use of government funds, and inactivity of civil servants. With the development of information technology, the use of big data methods to collect corruption data can be further explored. Meanwhile, this paper analyses the impact of corruption on environmental pollution mainly from the perspective of governance. And regional corruption is only one of the factors that affect regional environmental governance. Carbon governance not only depends on the quality of government systems, but is also influenced by the level of economic development and the level of technology. In addition, uncertainty in the external environment and major emergencies such as epidemics can also affect the strength and effectiveness of environmental governance. Therefore, in future research, a more comprehensive analysis can be conducted by considering the impact of institutional quality, government policies, economic factors and environmental factors on carbon governance.

8.There are many errors in the paper, please check them again. Such as: line 15, line 194, line 411 and so on.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have revised it.

Author Response File: Author Response.pdf

Reviewer 2 Report

1- Line 29: There is an empty parenthesis; please correct this mistake.

2- Line 31: Since you refer to the estimate of the World Bank, please cite the appropriate reference.

3- Line 53: Please explain the “Baidu index.”

4- Table 1: I think the term “expected” is better than “estimated. “

5- Tables 2 & 3: Please explain the AR(2) and Sargan tests. The description below the table is not illustrative.

6- You employed signs to show which variables are significant, but there are no test statistics for the coefficients. Please also provide the test statistics for testing the significance of the parameters.

7- Line 194, 368-372, 408, 411: Please revise the size/format of the fonts of the text.

8- Line 418: There is a Chinese symbol here. Please use the English of the term.

9- The findings of the paper should be compared with the literature.

Author Response

Response to Reviewers’ Comments on

“Regional Corruption, Foreign Trade, and Environmental Pollution”

for

Sustainability-2036994

Comments from the editors and reviewers:

1. Line 29: There is an empty parenthesis; please correct this mistake.

Response and revision:

Dear Reviewers, your comments are greatly appreciated. We have made the deletion.

2.Line 31: Since you refer to the estimate of the World Bank, please cite the appropriate reference.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have re-scheduled this section and added new literature.

Since its reform and opening up, China’s economy has developed rapidly, and people’s living standards have continuously improved. However, China’s environment has also been critically damaged[1]. Over the past 20 years, China's GDP has maintained at an average growth rate of 10% per year[2], emissions of some pollutants tripled from 1990 to 2005 [3].

[2]. Feng T, Chen H, Liu J.(2022) Air pollution-induced health impacts and health economic losses in China driven by US demand exports. J Environ Manage 324, 116355. https://doi.org/10.1016/j.jenvman.2022.116355

[3]. Su SS et al.(2011) Sulfur dioxide emissions from combustion in China: from 1990 to 2007. Environ. Sci. Technol 45:8403-8410.https://doi.org/10.1021/es201656f

3. Line 53: Please explain the “Baidu index.”

Response and revision:

Baidu is the largest Chinese search engine. Baidu Company launched Baidu Index function based on massive data, and has provided different keyword's internet daily search frequency data since 2006. The Baidu Index is a free massive data analysis service based on Baidu web search and Baidu news, which used to reflect the different keyword's ‘user awareness’ and ‘media attention’ during the past period. From the Baidu Index, one can find, share, and mine information to reflect social hot spots, users' interests and needs. In this paper, using "pm2.5" and "smog" as keywords in Baidu search engine, we can get the Baidu index of both.

4. Table 1: I think the term “expected” is better than “estimated.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have revised it.

5. Tables 2 & 3: Please explain the AR(2) and Sargan tests. The description below the table is not illustrative.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have revised it.

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively. AR(2) is used to judge whether the estimated residuals have a serial correlation. The Sargan test assesses whether the instrumental variables are effective overall.

6. You employed signs to show which variables are significant, but there are no test statistics for the coefficients. Please also provide the test statistics for testing the significance of the parameters.

Response and revision:

Dear Reviewers, your suggestion is very constructive and we have added test statistics for the coefficients to Table2 and Table3. Also, considering the EI variable only looks at sewerage fees and hence stronger for waste water definition of pollution - though the effect should be reverse. Larger the fee means more pollution or more pollution should imply more fees collected. And in the text we have replaced the EI proxy variables and explained them as follows. The empirical results are therefore subject to change.

3.7. Environmental regulation (EI)

Accurate measurement of environmental regulation intensity is still a challenge for current domestic and international research, and the indicators adopted by existing studies vary widely. Among them, the problem of using governance cost to measure EI is that larger the fee means more pollution or more pollution should imply more fees collected.Therefore, this study refers to Chen et al. [48] and uses the frequency of words related to environmental protection" in local government work reports as a proxy variable for environmental governance (ER), which is a good indicator of local governments' determination and concern to improve the environment. It is also staggered with the implementation of environmental management throughout the year, which alleviates endogenous problems, with a positive prognostic coefficient.

Table 2. System GMM regression results.

Per capita industrial wastewater discharge

Per capita Industrial waste gas emissions

Per capita industrial waste solid emissions

（1）

（2）

（3）

（1）

（2）

（3）

（1）

（2）

（3）

L.lnEP_i,t-1

0.910^***

（15.83）

0.902^***

（37.80）

0.638^***

（7.25）

0.913^***

（22.97）

0.881^***

（21.24）

0.991^***

（24.64）

0.663^***

（22.54）

0.651^***

（19.98）

0.341^***

（10.52）

lnVG

1.267

（1.16）

0.029

（-1.12）

1.246（0.69）

0.428

（0.71）

0.579

（0.98）

0.998^*

（1.65）

3.079^*

（1.92）

4.77^*

（1.83）

4.091

（1.62）

lnVG²

-0.058

（-1.10）

0.031

（2.43）

-0.056

（-0.67）

-0.014

（-0.48）

-0.022

（-0.81）

-0.043

（-1.53）

-0.106

（-1.37）

-0.189

（-1.48）

-0.154

（-1.28）

lnIS

0.120

（-0.47）

0.137^***

（2.43）

0.541^***

（2.54）

0.200

（2.67）

0.213^***

（2.77）

0.193^**

（2.19）

0.654^***

（4.72）

0.655^***

（4.48）

1.059^***

（-1.45）

lnTE

-0.054

（-0.48）

-0.024

（-0.60）

-0.344

（-1.59）

-0.043

（-0.75）

0.001

（0.02）

-0.094^*

（ -1.73）

-0.378^***

（-3.89）

-0.240^***

（-3.45）

-0.213

（-1.45）

lnEI

-0.026

（-0.77）

-0.022（-0.85）

-0.030

（0.56）

-0.015

（-0.42）

0.009

（-0.34）

-0.008

（-0.26）

0.039

（1.62）

0.026

（0.84）

0.052^**

（2.02）

lnT

-0.003

（-0.20）

-0.011

（-1.59）

-0.138

（-1.60）

-0.057^***

（-2.95）

-0.096^***

（-3.03）

-0.149^***

（-2.77）

-0.248^***

（-9.86）

-0.377^***

（-8.42）

-0.442^***

（-4.33）

lnCO

0.070^**

（0.92）

2.265^***

（2.91）

0.002

（0.05）

1.209^**

（2.30）

0.024

（0.80）

1.362^*

（1.70）

lnT× lnCO

-0.123^***

（-2.82）

-0.072^**

（-2.37）

-0.079^*

（-1.75）

lnF

0.035^***

（3.97）

0.037^***

（3.23）

-0.001

（-0.006）

0.023

（1.63）

0.021

（1.44）

0.032^**

（2.29）

0.031^***

（3.88）

0.031^***

（3.98）

0.031^***

（3.32）

cons

-5.940

（-1.17）

2.609

（1.05）

-4.756

（-0.49）

-2.381

（-0.74）

-3.108

（-1.00）

-3.395^*

（-1.15）

-17.027^**

（-2.01）

-25.926^*

（-1.89）

-20.828^*

（-1.55）

AR(2)-P value

0.690

0.676

0.954

0.336

0.347

0.381

0.384

0.381

0.471

Sargan-P value

0.395

0.391

0.141

0.172

0.237

0.286

0.744

0.775

0.647

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively. The values of the z-statistic are in parentheses.The AR(2) and Sargan test results are both P values. The null hypothesis is that no second-order serial correlation exists in the random error term of the first-order difference equation and the instrumental variables used are valid.

Table 3. Empirical results by region. (Explained variable: per capita industrial wastewater discharge)

Western Region

Central Region

Eastern Region

L.lnEP_{i, t-1}

0.836^***

（19.32）

0.833^***

（11.48）

0.733^***

（10.76）

lnVG

2.181^*

（1.67）

2.791^*

（1.79）

2.861^***

（3.27）

lnVG²

-0.137^**

（-2.07）

-0.137^*

（-1.78）

-0.116^***

（-2.95）

lnIS

0.304

（1.22）

0.146

（2.13）

0.276^**

（3.07）

lnTE

0.338^***

（3.22）

-0.098

（-1.62）

-0.161^**

（-2.21）

lnEI

0.105^**

（2.11）

-0.020

（-0.55）

-0.112^***

（-2.33）

lnT

0.079^*

（1.86）

0.026

（0.53）

-0.105^**

（-2.39）

lnCO

0.212^***

（2.69）

0.135^***

（2.43）

-0.024

（-0.32）

lnF

-0.003

（-0.15）

0.044^*

（1.78）

0.042^***

（3.23）

cons

-9.797

（-1.53）

-14.308^*

（-1.88）

-16.893^***

（-3.64）

AR(2)-P value

0.918

0.335

0.245

Sargan-P value

0.314

0.278

0.985

7. Line 194, 368-372, 408, 411: Please revise the size/format of the fonts of the text.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have revised it.

8. Line 418: There is a Chinese symbol here. Please use the English of the term.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have revised it.

9. The findings of the paper should be compared with the literature.

Response and revision:

Dear Reviewers, your valuable comments are greatly appreciated. We have reorganized the conclusions

Author Response File: Author Response.pdf

Reviewer 3 Report

Dear Author(s),

The topic developed is of interest and relevance. The research on the topic addressed in your manuscript –especially in developing countries– is a key issue in the sustainability context. The topic discussed in your document could provide an important contribution to this research area. However, in my opinion, your manuscript –in its present form– doesn’t support the sufficient quality for its publication in Sustainability. It is in no way your methodical approach, but alone the yet imperfect style of scientific writing. Publications in peer-reviewed journals –as Sustainability – are to disseminate knowledge. In this sense, there is still very significant room for improvement to be published. You can see, for this purpose, an interesting paper entitled, ‘How to write a paper for successful publication in an international peer-reviewed journal’ (Tress et al., 2014). You should review the “Guide for Authors” and/or consult with other colleagues to adjust your text to this type of paper. Perhaps, it may be possible its publication in other Journals. I am completely sure of your ability to make progress on this subject. I encourage you to carry on down the path you have chosen.

Author Response

Response to Reviewers’ Comments on

“Regional Corruption, Foreign Trade, and Environmental Pollution”

for

Sustainability-2036994

Comments from the editors and reviewers:

Dear Author(s),

The topic developed is of interest and relevance. The research on the topic addressed in your manuscript –especially in developing countries– is a key issue in the sustainability context. The topic discussed in your document could provide an important contribution to this research area. However, in my opinion, your manuscript –in its present form– doesn’t support the sufficient quality for its publication in Sustainability. It is in no way your methodical approach, but alone the yet imperfect style of scientific writing. Publications in peer-reviewed journals –as Sustainability – are to disseminate knowledge. In this sense, there is still very significant room for improvement to be published. You can see, for this purpose, an interesting paper entitled, ‘How to write a paper for successful publication in an international peer-reviewed journal’ (Tress et al., 2014). You should review the “Guide for Authors” and/or consult with other colleagues to adjust your text to this type of paper. Perhaps, it may be possible its publication in other Journals. I am completely sure of your ability to make progress on this subject. I encourage you to carry on down the path you have chosen.

Response and revision:

Dear Reviewer,

Thank you very much for your encouragement and support. There are many problems with the paper, including the format and writing style of the paper. We have also recently been revising and improving the paper, including the discussion of the model , the optimization of indicators and the analysis of the empirical results, and have also tried to add the research limitations and future research outlook of the paper. If your time allows, you can browse the revised manuscript and we hope you can see our progress.

Your encouragement will be my motivation for a long time, and I will continue to improve the paper and strive to write a better paper.

I wish you all the best in your work and happy life!

Author Response File: Author Response.pdf

Reviewer 4 Report

According to what I've read, the study needs revisions before it can be accepted. Please see below my suggestions for revising the authors' manuscript:

1. The authors used the panel data of 30 provinces in China from 2004 to 2017. The authors clearly report the reason for selecting 30 provinces in China.

2. In addition, the authors might extend the time range up to 2020 if the dataset exists.

3. The authors used the system GMM approach for the present study but there is no methodology section in the study. This is one of the weaknesses of the present work. The authors must explain why they used the system GMM approach. What is the main advantage of the system GMM approach?

4. The introduction provided helpful information about the intended topic and explained prior research contribution, limitation, but the novelty of this study to literature and practice is missing.

5. The readability of table 2 is not good. The authors should re-organize the table

6. The authors should use the numbering referencing style of Sustainability in the text.

7. There is an inconsistency in the font size of the text. The authors should fix this issue.

8. To strengthen the literature review section. the authors might use some recently published studies.

https://doi.org/10.1016/j.jup.2021.101244

https://doi.org/10.1016/j.jenvman.2022.116043

https://doi.org/10.3390/su141811391

https://doi.org/10.3390/economies9020062

https://doi.org/10.1016/j.jenvman.2021.113463

Author Response

Response to Reviewers’ Comments on

“Regional Corruption, Foreign Trade, and Environmental Pollution”

for

Sustainability-2036994

Comments from the editors and reviewers:

The authors used the panel data of 30 provinces in China from 2004 to 2017. The authors clearly report the reason for selecting 30 provinces in China.

Response and revision:

Dear Reviewer, thanks to the valuable suggestions, we report the reason for selecting 30 provinces as follows.

In order to obtain more convincing and covering results, data from 30 provinces (municipalities and autonomous regions) in China from 2004 to 2017 were selected for research and analysis. Tibet , Hong Kong, Macao and Taiwan were excluded in the analysis sample due to serious data deficiencies.

In addition, the authors might extend the time range up to 2020 if the dataset exists.

Response and revision:

Dear Reviewer, your constructive comments are greatly appreciated. The relevant database involving the explained variables of this paper whose data for 2018 and 2019 are missing, which constrains our data updating problem.

The authors used the system GMM approach for the present study but there is no methodology section in the study. This is one of the weaknesses of the present work. The authors must explain why they used the system GMM approach. What is the main advantage of the system GMM approach?

Response and revision:

Dear Reviewer, your suggestions are greatly appreciated. We have reset the model and added the advantages of the systematic GMM approach, as follows.

Based on the previous analysis, two core influencing factors, the scale of foreign trade and the level of corruption, are selected to estimate their effects on environmental pollution, and the empirical equations are set as follows.

(1) Static model setting

(2) Dynamic model setting

To avoid biased estimates due to the omission of other important explanatory variables, we extend Equation (1) to a dynamic model by introducing a lagged term for the level of environmental pollution emissions, thus being able to eliminate the endogeneity problem of the model through a dynamic panel approach; Simultaneously, considering that any economic activity itself has a certain inertia (Chen et al., 2022), there is likely to be a lagged effect of pollution emissions in each province. Therefore, the dynamic model is considered to be more consistent with the Chinese reality. The lagged one-period dynamic panel model constructed in this paper is as follows.

The introduction provided helpful information about the intended topic and explained prior research contribution, limitation, but the novelty of this study to literature and practice is missing.

Response and revision:

Thank you for your suggestion. Based on your suggestion, We added literature and practice at the beginning of the abstract. The changes are as follows.

The readability of table 2 is not good. The authors should re-organize the table

Response and revision:

Thank you for your suggestion. Based on your suggestion, we have reformatted the table and provided the test statistics for testing the significance of the parameters.

Table 2. System GMM regression results.

Per capita industrial wastewater discharge

Per capita Industrial waste gas emissions

Per capita industrial waste solid emissions

（1）

（2）

（3）

（1）

（2）

（3）

（1）

（2）

（3）

L.lnEP_i,t-1

0.910^***

（15.83）

0.902^***

（37.80）

0.638^***

（7.25）

0.913^***

（22.97）

0.881^***

（21.24）

0.991^***

（24.64）

0.663^***

（22.54）

0.651^***

（19.98）

0.341^***

（10.52）

lnVG

1.267

（1.16）

0.029

（-1.12）

1.246（0.69）

0.428

（0.71）

0.579

（0.98）

0.998^*

（1.65）

3.079^*

（1.92）

4.77^*

（1.83）

4.091

（1.62）

lnVG²

-0.058

（-1.10）

0.031

（2.43）

-0.056

（-0.67）

-0.014

（-0.48）

-0.022

（-0.81）

-0.043

（-1.53）

-0.106

（-1.37）

-0.189

（-1.48）

-0.154

（-1.28）

lnIS

0.120

（-0.47）

0.137^***

（2.43）

0.541^***

（2.54）

0.200

（2.67）

0.213^***

（2.77）

0.193^**

（2.19）

0.654^***

（4.72）

0.655^***

（4.48）

1.059^***

（-1.45）

lnTE

-0.054

（-0.48）

-0.024

（-0.60）

-0.344

（-1.59）

-0.043

（-0.75）

0.001

（0.02）

-0.094^*

（ -1.73）

-0.378^***

（-3.89）

-0.240^***

（-3.45）

-0.213

（-1.45）

lnEI

-0.026

（-0.77）

-0.022（-0.85）

-0.030

（0.56）

-0.015

（-0.42）

0.009

（-0.34）

-0.008

（-0.26）

0.039

（1.62）

0.026

（0.84）

0.052^**

（2.02）

lnT

-0.003

（-0.20）

-0.011

（-1.59）

-0.138

（-1.60）

-0.057^***

（-2.95）

-0.096^***

（-3.03）

-0.149^***

（-2.77）

-0.248^***

（-9.86）

-0.377^***

（-8.42）

-0.442^***

（-4.33）

lnCO

0.070^**

（0.92）

2.265^***

（2.91）

0.002

（0.05）

1.209^**

（2.30）

0.024

（0.80）

1.362^*

（1.70）

lnT× lnCO

-0.123^***

（-2.82）

-0.072^**

（-2.37）

-0.079^*

（-1.75）

lnF

0.035^***

（3.97）

0.037^***

（3.23）

-0.001

（-0.006）

0.023

（1.63）

0.021

（1.44）

0.032^**

（2.29）

0.031^***

（3.88）

0.031^***

（3.98）

0.031^***

（3.32）

cons

-5.940

（-1.17）

2.609

（1.05）

-4.756

（-0.49）

-2.381

（-0.74）

-3.108

（-1.00）

-3.395^*

（-1.15）

-17.027^**

（-2.01）

-25.926^*

（-1.89）

-20.828^*

（-1.55）

AR(2)-P value

0.690

0.676

0.954

0.336

0.347

0.381

0.384

0.381

0.471

Sargan-P value

0.395

0.391

0.141

0.172

0.237

0.286

0.744

0.775

0.647

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively. The values of the z-statistic are in parentheses. AR(2) is used to judge whether the estimated residuals have a serial correlation. The Sargan test assesses whether the instrumental variables are effective overall.

The authors should use the numbering referencing style of Sustainability in the text.

Response and revision:

Dear Reviewer, your suggestions are greatly appreciated. We have numerically sorted the literature according to the Sustainability journal style.

There is an inconsistency in the font size of the text. The authors should fix this issue.

Response and revision:

Dear Reviewer, your suggestions are greatly appreciated. We have made adjustments to the paper.

To strengthen the literature review section. the authors might use some recently published studies.

Response and revision:

Dear Reviewer, your suggested literature is much appreciated. We have read these articles carefully, and in the process we have also identified the shortcomings of this paper and added Limitations and improvements section. The newly added literature has been highlighted.

Limitations and improvements

The limitations of this paper are mainly in the following areas. Firstly, due to the hidden nature of regional corruption and the limitations of data availability, there are limited indicators to measure corruption that could be further refined. Future research could use and develop different indicators to provide a more comprehensive picture of the level of regional corruption. More data could be collected on cases of government corruption, efficiency in the use of government funds, and inactivity of civil servants. With the development of information technology, the use of big data methods to collect corruption data can be further explored. Meanwhile, this paper analyses the impact of corruption on environmental pollution mainly from the perspective of governance. And regional corruption is only one of the factors that affect regional environmental governance. Carbon governance not only depends on the quality of government systems, but is also influenced by the level of economic development and the level of technology. In addition, uncertainty in the external environment and major emergencies such as epidemics can also affect the strength and effectiveness of environmental governance. Scholars Su[51] and Kartal[52] have also clarified the important role of better political environment in their studies. Therefore, in future research, a more comprehensive analysis can be conducted by considering the impact of institutional quality, government policies, economic factors and environmental factors on carbon governance.

(1)https://doi.org/10.1016/j.jup.2021.101244

https://doi.org/10.1016/j.jenvman.2022.116043

https://doi.org/10.3390/su141811391

https://doi.org/10.3390/economies9020062

https://doi.org/10.1016/j.jenvman.2021.113463

Round 2

Reviewer 1 Report

The authors have revised the comments. And the paper can be accepted.

Author Response

thanks a lot

Reviewer 3 Report

Dear Author(s),

Thank you very much for diligently considering the given remarks in your revision. The wording, in my opinion, has improved. However, in my view, your manuscript, although the manuscript does provide an interesting case study, in its present form doesn’t support the sufficient quality for its publication in Sustainability. It is in no way your methodical approach, but alone the yet imperfect style of scientific writing. Publications in peer-reviewer journals –as Sustainability – are to disseminate knowledge. In this sense, there is still very significant room for improvement in order to be published. You can see, for this purpose, an interesting paper entitled, ‘How to write a paper for successful publication in an international peer-reviewed journal’ (Tress et al., 2014). You should also review the “Guide for Authors”. I am completely sure of your ability to make progress on this subject. I encourage you to carry on down the path you have chosen.

Author Response

Thanks for your suggestions. We have improved the quality of the paper, please re-consider the probability for publication. Thanks a lot

Reviewer 4 Report

The revised study can be accepted

Author Response

Thanks a lot

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