Heterogeneous Dynamic Correlation Research among Industrial Structure Distortion, Two-Way FDI and Carbon Emission Intensity in China
Round 1
Reviewer 1 Report
The paper analyzes industrial structure distortion, two-way FDI and carbon emission intensity in China. The topic is relevant nad interesting. The methodology and data are clearly explained. There are some sugesstions regarding the presentation of reserach results.
The research results are prestented in the chapter 5, but also in chapter 6 - Intermediary Effect Test. The first sentence of this chapter is not gramatically correct. It is not clear why these test results are not presented in the chaper 5 (5.5)?
The paper lacks discussion in terms of comparing the results and conclusions with other relevant studies.
Author Response
Dear Reviewer,
Manuscript ID sustainability-1781394
Title: "Heterogeneous Dynamic Correlation Research Among Industrial Structure Distortion, Two-way FDI, and Carbon emission intensity"
Thank you for your letter and comments from referees on our above-named paper. We have carefully studied the comments and criticisms offered by the referees and made a thorough revision on the original paper. The revised paper is forwarded herein. The following just details what we have done to the original paper, with a 1-1 point responses with respect to referees' comments.
We feel that the new version of the paper is much improved thanks to the comments and suggestions of the referees. We hope that the revised paper has now met the high standard requirements of your journal and would be pleased to hear from you again.
Sincerely yours
Point by point responses to referees’ comments
Response to Reviewer 1 Comments
We are grateful to reviewer 1 for his/her effort reviewing our paper and his/her positive feedback. The summary of our work as written by this reviewer is precise. Here below we address the questions and suggestions raised by the reviewer 1.
We have carefully addressed all the reviewer's concerns. Please see below our replies. We hope he/she is satisfied with our answers and the new data we provided. Changes highlighted in red have been made accordingly, in the revised manuscript and in the revised supplementary information.
Point 1: The paper analyzes industrial structure distortion, two-way FDI and carbon emission intensity in China. The topic is relevant and interesting. The methodology and data are clearly explained. There are some suggestions regarding the presentation of research results.
Response 1: Thank you so much for your comments.
Point 2:The research results are presented in the chapter 5, but also in chapter 6 - Intermediary Effect Test. The first sentence of this chapter is not grammatically correct. It is not clear why these test results are not presented in the chapter 5 (5.5)?
Response 2: The reviewer's comments are useful and relevant. According to the reviewer's reminder, we transferred the chapter 6 (Intermediary Effect Test) to chapter 5.5. On the grammatically question, we have also proof reading and modified it.
Point 3:The paper lacks discussion in terms of comparing the results and conclusions with other relevant studies.
Response 3: Your suggestion is very correct. The discussion chapter of this paper compares the results and conclusions with other relevant studies. See below for more information.
For a long time, Industrial structure change is considered as an important reason to promote economic growth (Zhang et al.,2014). Rogerson (2008) concluded that under the current global warming environment, the change of industrial structure is also of great significance for controlling the total energy consumption and reducing carbon emissions. Meanwhile, the impact of foreign trade on domestic environment mainly includes the hypothesis of "pollution heaven hypothesis" and "pollution halo hypothesis" (Kisswani & Zaitouni, 2021). However, at present, there are relatively few studies on the overall analysis of carbon emission intensity by integrating industrial structure distortion with foreign trade (Yang et al.,2019). This paper focuses on the spatial correlation among the above three variables, and explores the effect of industrial structure distortion and two-way FDI on carbon emissions with the apply of spatial econometric model. In fact, this study found that China's carbon emissions have significant spatial spillover effects among provinces, which is consistent with the current research on carbon emissions from a spatial perspective (Han F, Xie R.,2017). Through the data results, we can find that the distortion of industrial structure is not conducive to reducing carbon emissions. At the same time, both foreign direct investment and foreign investment can be explained by the theory of "pollution halo hypothesis", which also confirms the conclusion that the expansion of foreign trade will promote domestic technological progress and achieve carbon emission reduction. Similar to previous studies, industrial structure upgrading can significantly inhibit carbon emissions (Dong et al.,2020). However, after adding the variable of industrial structure distortion in this paper, the research data show that industrial structure distortion can also reduce carbon emissions through the intermediary mechanism of two-way FDI, which indicates that the most critical driving factor in the process of carbon emission reduction lies in the technical effect, and its effect has exceeded the structure and scale effect (Wang et al.,2019).
Dong B, Ma X, Zhang Z, et al. Carbon emissions, the industrial structure and economic growth: Evidence from heterogeneous industries in China[J]. Environmental Pollution, 2020, 262: 114322.
Han F, Xie R. Does the agglomeration of producer services reduce carbon emissions[J]. The Journal of Quantitative & Technical Economics, 2017, 3: 40.
Kisswani, K. M., & Zaitouni, M. (2021). Does FDI affect environmental degradation? Examining pollution haven and pollution halo hypotheses using ARDL modelling. Journal of the Asia Pacific Economy, 1-27.
Richard Rogerson. Structural Transformation and the Deterioration of European Labor Market Outcomes[J]. Journal of Political Economy, 2008, 116(2) : 235-259.
Wang Y, Liao M, Wang Y, et al. Carbon emission effects of the coordinated development of two-way foreign direct investment in China[J]. Sustainability, 2019, 11(8): 2428.
Yang Y, Zhou Y, Poon J, et al. China's carbon dioxide emission and driving factors: A spatial analysis[J]. Journal of Cleaner Production, 2019, 211: 640-651.
Zhang, Y. J., Liu, Z., Zhang, H., & Tan, T. D. (2014). The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China. Natural hazards, 73(2), 579-595.
Author Response File: 
Author Response.docx
Reviewer 2 Report
1) This paper focuses on the nexus of FDI and carbon emission in China. I would add something like "in China" or "within regions of China," etc. in the title.
2) In the abstract, when you refer to "west", "middle", "east," etc., add something like "of China" or "western region of China" etc.
3) Neequaye and Oladi (2015) also focuses on the nexus of FDI and growth and carbon emission, albeit within a cross-country panel framework. Explain how your approach is different (apart from its focus on China).
Ref: Neequaye, N.A., and R. Oladi (2015) "Environment, growth, and FDI revisited," International Review of Economics and Finance 39: 47-56.
Author Response
Dear Reviewer，
Manuscript ID sustainability-1781394
Title: "Heterogeneous Dynamic Correlation Research Among Industrial Structure Distortion, Two-way FDI, and Carbon emission intensity"
Thank you for your letter and comments from referees on our above-named paper. We have carefully studied the comments and criticisms offered by the referees and made a thorough revision on the original paper. The revised paper is forwarded herein. The following just details what we have done to the original paper, with a 1-1 point responses with respect to referees' comments.
We feel that the new version of the paper is much improved thanks to the comments and suggestions of the referees. We hope that the revised paper has now met the high standard requirements of your journal and would be pleased to hear from you again.
Sincerely yours
Point by point responses to referees’ comments
Response to Reviewer 2 Comments
We appreciate reviewer 2 for his/her effort to review our manuscript, and his/her positive feedback. The reviewer gives an accurate summary of our work and brings forward constructive questions. We have addressed them below.
Point 1: 1) This paper focuses on the nexus of FDI and carbon emission in China. I would add something like "in China" or "within regions of China," etc. in the title.
Response 1: The reviewer's comments are useful and relevant. We add in China to the title. See below for more information.
“Heterogeneous Dynamic Correlation Research Among Industrial Structure Distortion, Two-way FDI, and Carbon emission intensity in China”
Point 2: In the abstract, when you refer to "west", "middle", "east," etc., add something like "of China" or "western region of China" etc.
Response 2:
These suggestions provided by reviewer is very useful. When I refer to "west", "middle", "east," etc., I added China. See below for detail.
The increase of carbon emissions year by year poses a severe challenge to the high-quality development and sustainability of China's economy. How to reduce the intensity of carbon emissions has become a prominent issue to promote green growth. Based on the provincial panel data from 2011 to 2020, this paper uses Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test to analyze. The following results are drawn. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west of China, followed by the middle of China, and the lowest in the east of China. Secondly, The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. Thirdly, the results of intermediary effect analysis show that industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI. This paper has a profound practical significance for promoting the process of industrial upgrading by insisting on developing foreign trade to achieve carbon emission reduction. The main innovation of this paper is to put forward the concept of industrial structure distortion, and bring it into a unified research framework with two-way FDI and carbon emission intensity.
Point 3:Neequaye and Oladi (2015) also focuses on the nexus of FDI and growth and carbon emission, albeit within a cross-country panel framework. Explain how your approach is different (apart from its focus on China).
Ref: Neequaye, N.A., and R. Oladi (2015) "Environment, growth, and FDI revisited," International Review of Economics and Finance 39: 47-56
Response 3:
Thank you for your encouragement. According to the suggestions of the reviewers, we added the following parts for reviewers and readers to understand.
The differences are mainly shown in the following aspects. Firstly, in terms of variables, Neequaye, N.A., and R. Oladi (2015) only examined the impact of FDI on the host country's environment. On this basis, this paper added the variable OFDI, which made the research more comprehensive. In addition, Neequaye, N.A., and R. Oladi (2015) selected economic growth as the intermediate mechanism, and this paper selected industrial structure distortion as the intermediate mechanism.
Secondly, in terms of methods, Neequaye, N.A., and R. Oladi (2015) analyzed it by constructing Environmental Kuznets Curve and mathematical model of pollution haven hypothesis. In this paper, the spatial econometric method is used to analyze the impact of two-way FDI on carbon emission intensity from a spatial perspective.
Thirdly, in terms of theory, Neequaye, N.A., and R. Oladi (2015) mainly carried out empirical analysis based on Environmental Kuznets Curve and pollution haven hypothesis, while this paper mainly carried out this research based on pollution halo hypothesis theory.
The similarities between this paper and Neequaye, N.A., and R. Oladi (2015) are that Neequaye, N.A., and R. Oladi (2015) and this paper both focus on the environmental impact of foreign trade on own countries.
Neequaye, N.A., and R. Oladi (2015) "Environment, growth, and FDI revisited," International Review of Economics and Finance 39: 47-56
Reviewer 3 Report
Dear Authors,
Please address the below comments to enhance the quality of the paper.
Comments:
1. The abstract is too long and highly confusing. Moreover, practical significance, novelty, and JEL codes are missing in the abstract.
2. Each sentence maximum consists of one and half lines.
3. This paper is grammatically very poor. Please proof read it cautiously.
4. The introduction should be concise and to the point.
5. Which theory is supporting your study? Please add theory and theorization in the literature section.
6. Please discuss your methodology separately.
7. Apply unit-root test to check data stationarity?
8. Your econometric model consists of macroeconomic variables and there are more chances that macroeconomic variables are more correlated with error terms and due to this the problem of endogeneity exists? Apply WALD to check the existence of endogeneity problems.
9. The reason of choosing Chinese economy and data duration (2011-2020) is missing.
10. Please add footnotes on the bottom of the tables.
11. Add some references in the results discussion to support your argument.
12. Some paragraphs are too lengthy. Please arrange them accordingly.
Author Response
Dear Reviewer，
Manuscript ID sustainability-1781394
Title: "Heterogeneous Dynamic Correlation Research Among Industrial Structure Distortion, Two-way FDI, and Carbon emission intensity"
Thank you for your letter and comments from referees on our above-named paper. We have carefully studied the comments and criticisms offered by the referees and made a thorough revision on the original paper. The revised paper is forwarded herein. The following just details what we have done to the original paper, with a 1-1 point responses with respect to referees' comments.
We feel that the new version of the paper is much improved thanks to the comments and suggestions of the referees. We hope that the revised paper has now met the high standard requirements of your journal and would be pleased to hear from you again.
Sincerely yours
Point by point responses to referees’ comments
Response to Reviewer 3 Comments
We appreciate reviewer 3 for his/her effort to review our manuscript, and his/her positive feedback. The reviewer gives an accurate summary of our work and brings forward constructive questions. We have addressed them below.
Point 1: The abstract is too long and highly confusing. Moreover, practical significance, novelty, and JEL codes are missing in the abstract
Response 1: Thank you so much for your comments. At the end of the abstract, I added practical significance and novelty.
This is the JEL code of this article. JEL classification: F18; F21; O12. See below for more information.
The increase of carbon emissions year by year poses a severe challenge to the high-quality development and sustainability of China's economy. How to reduce the intensity of carbon emissions has become a prominent issue to promote green growth. Based on the provincial panel data from 2011 to 2020, this paper uses Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test to analyze. The following results are drawn. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west of China, followed by the middle of China, and the lowest in the east of China. Secondly, The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. Thirdly, the results of intermediary effect analysis show that industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI. This paper has a profound practical significance for promoting the process of industrial upgrading by insisting on developing foreign trade to achieve carbon emission reduction. The main innovation of this paper is to put forward the concept of industrial structure distortion, and bring it into a unified research framework with two-way FDI and carbon emission intensity.
Point 2: Each sentence maximum consists of one and half lines
Response 2:
The sentence pattern and grammar of the whole article have been revised again. Thank the reviewers for pointing out the mistakes.
Point 3: This paper is grammatically very poor. Please proof read it cautiously.
Response 3:
On the grammatically question, we have also proof reading and modified it.
Point 4: The introduction should be concise and to the point.
Response 4:
We have re-deleted and re-narrated the introduction. See below for more information.
As the largest energy consuming country in the world, China's long-term implementation of the inclined development policy with economic growth as the priority and rapid industrial system construction has accelerated the pace of China's modernization to a certain extent, but it has also caused great damage to the ecological environment [1]. With the rapid development of industry, China's energy consumption has always maintained a strong growth demand, which not only affects its industrial development and energy supply but also profoundly affects the global carbon emission pattern [2].
In view of the current grim situation of carbon emission reduction, Chinese leaders made an important commitment at the Paris Summit that China will reach the peak of carbon in 2030 and be carbon neutral by 2060. [3]. As to how to achieve carbon emission reduction, academia generally believe that industrial structure adjustment, energy consumption structure transformation and technological progress are the three major ways to promote energy conservation and emission reduction, among which industrial structure adjustment is the most important supporting point to achieve carbon emission reduction. However, at present, the economic development and industrial structure of different provinces, municipalities, and autonomous regions in China are highly out of balance, which leads to significant differences in carbon emission levels in different regions [4]. Therefore, it is of great significance for each province to implement feasible industrial development policies according to local conditions to achieve the goal of "double-carbon".
Foreign trade is also considered as an important means to achieve energy conservation and emission reduction. However, due to the unbalanced economic development among different regions in China, the lack of infrastructure construction, the deviation of resource allocation caused by the distortion of factor markets, and the excessive drive of economic development by energy factors, the promotion effect of two-way FDI on economic development has been diluted. In addition, the excessive and inefficient energy input caused by the distortion of industrial structure also increases the carbon emission intensity. So, does the distortion of industrial structure lead to the increase in carbon emission intensity? In the current international environment, can actively "going out" and "bringing in" reduce carbon emission intensity? Does the distortion of the industrial structure have a conduction effect between two-way FDI and carbon emission intensity? The effective answers to the above questions are of great practical significance for realizing "carbon peak" and "carbon neutralization", promoting the rationalization of industrial structure and accelerating the reform of the ecological civilization system.
The main contributions of this study are as follows. Firstly,most of the previous studies only paid attention to the positive effect of industrial structure, but rarely mentioned the negative effect. This paper innovatively put forward a new concept of industrial structure distortion, and discusses the impact of two-way FDI on carbon emission intensity as a breakthrough point. Secondly, in the past, the impact of OFDI and IFDI on carbon emissions was considered as an isolated single impact. This paper studies the relationship between two-way FDI and carbon emissions. In this paper, IFDI and OFDI are brought into the same research framework, and their impacts on China's carbon emission intensity are systematically analyzed, making the conclusion more scientific. Thirdly, regarding the influence of two-way FDI and carbon emission intensity, most scholars use the threshold model and intermediary effect model to explore the mechanism, but inevitably ignore the spatial law of research samples. In short, this paper breaks through the traditional practice, investigates its evolution law from the spatial perspective, and expands the existing research.
In view of the above analysis, this paper brings the distortion of industrial structure, two-way FDI, and carbon emission intensity into the unified research framework. Firstly, based on the national panel data from 2011 to 2020, this paper calculates the carbon emission intensity of each province. Secondly, this paper combines the exploratory spatial data analysis (ESDA) and a spatial econometric model to analyze the spatial evolution characteristics of industrial structure distortion, two-way FDI and carbon emission intensity. Thirdly, to clarify the mechanism of industrial structure distortion on carbon emissions, this study also sets two-way FDI as an intermediary variable for empirical test. Finally, this paper determines the key factors affecting carbon emission intensity and expects to provide targeted suggestions for China's carbon emission reduction from the perspective of regional coordination with the help of the spatial measurement method.
This paper adopts the following structural arrangements: the second part combs the literature review and theoretical hypotheses; The third part introduces the research methods; The fourth part makes an empirical test; The fifth part is the conclusion and enlightenment.
Point 5: Which theory is supporting your study? Please add theory and theorization in the literature section.
Response 5:
Your suggestion is very correct. According to your comment. I integrated the results part to make the structure of the article clearer. The following are the first class subject and the second class subject of the result part. See below for more information.
This paper is mainly based on the theory of pollution halo hypothesis theory, which holds that international trade promotes the technological progress and management concept of the host country (Mert & Caglar, 2020). At the same time, it also enhances the understanding of international environmental protection standards, which can promote the host country to improve its own production methods and reduce environmental pollution. Because China has implemented a strong environmental protection system for a long time, the theory of pollution halo hypothesis is more suitable for this study (Repkine& Min, 2020).
Mert, M., & Caglar, A. E. (2020). Testing pollution haven and pollution halo hypotheses for Turkey: a new perspective. Environmental Science and Pollution Research, 27(26), 32933-32943.
Repkine, A., & Min, D. (2020). Foreign-funded enterprises and pollution halo hypothesis: a spatial econometric analysis of thirty Chinese regions. Sustainability, 12(12), 5048.
Point 6: Please discuss your methodology separately.
Response 6: The comments made by the reviewer was very pertinent.
This problem is described in the first paragraph of Chapter 5.4.1 (5.4.1. Model Selection)of this paper.
Considering that the factors affecting carbon emission intensity are complex, the traditional OLS model, spatial autoregressive model, spatial autocorrelation model, and spatial multi-objective model are constructed for spatial econometric regression. Based on ignoring the spatial correlation, the statistical results of the houseman rejected the original hypothesis of the random effect model at a 1% significance level. Considering the individual heterogeneity of provinces and cities in the sample, the AC-FE, SAR-FE, and SDM-FE models are tested based on the time-space dual fixed effect regression model. In Table 6, LR is significant at the statistical level of 1%, rejecting the original assumption that the coefficients of the spatial lag explanatory variable are equal to 0, that is, the SDM model can‘t be simplified to the SAR model. According to the further test of AIC, BIC, and log-likelihood values, the SDM model has smaller values and is a better fit than the sac model. Therefore, this paper finally selects the estimation results of the SDM model to illustrate the impact of various factors on carbon emission intensity.
Point 7: Apply unit-root test to check data stationarity?
Response 7 :
We have done the unit root test before the variable regression in this paper, which was not shown before due to space limitation. Now we present the test results.
In order to avoid spurious regression, before analyzing the time series data, the unit root test should be carried out on the data related to China's industrial structure distortion, two-way FDI and carbon emission intensity. On this basis, it is also necessary to introduce the difference method to stabilize the non-stationary data after the unit-root test. Therefore, we choose Augmented Dickey-Fuller (ADF) method to test China's industrial structure distortion, two-way FDI and carbon emission intensity, as follows. Before the ADF unit root test, the variables in this paper are logarithmicized.
Table 10. Augmented Dickey-Fuller(ADF) Unit-root test results
|
variables |
Level test results |
First order difference test results |
||
|
ADF Value |
P Value |
ADF Value |
P Value |
|
|
lnCI |
-0.6348 |
0.319 |
-4.4282 |
0.000 |
|
lnD |
-0.3761 |
0.218 |
-3.6554 |
0.002 |
|
lnIFDI |
-2.4218 |
0.943 |
-3.4847 |
0.005 |
|
lnOFDI |
-1.5378 |
0.437 |
-5.5497 |
0.013 |
|
lnENER |
-0.8137 |
0.349 |
-4.3482 |
0.000 |
|
lnER |
-0.5484 |
0.417 |
-7.9259 |
0.006 |
|
lnPGRP |
-1.7786 |
0.664 |
-6.1387 |
0.024 |
|
lnR＆D |
-0.9372 |
0.573 |
-4.3761 |
0.011 |
|
lnURBAN |
-2.6347 |
0.617 |
-5.7461 |
0.007 |
The results of ADF unit root test show that ADF values of all variables are greater than the critical value at 10% significance level, so the original hypothesis of unit root cannot be rejected. Next, the variables were processed by first-order difference, and the results showed that the ADF values of all variables passed the significance test at the 5% level. Therefore, the original hypothesis with unit-root was rejected, and all variables met the preconditions for further empirical analysis.
Point 8: Your econometric model consists of macroeconomic variables and there are more chances that macroeconomic variables are more correlated with error terms and due to this the problem of endogeneity exists? Apply WALD to check the existence of endogeneity problems.
Response 8：
We added the chapter of robustness test to test the robustness and endogeneity of the regression results in this paper. In this paper, the spatial measurement method is used to calculate, so the alternative spatial weight matrix is selected for robustness test. For the endogeneity problem, we do not apply WALD test, but apply instrumental variable method and GMM estimation method which are more suitable for this paper to test its endogeneity.
5.7 Robustness test
5.7.1 Replacement weight matrix
Due to the unbalanced industrial development among provinces in China, the carbon emission intensity also shows differences. In order to test the rationality of the spatial spillover effect of various influencing factors on carbon emission intensity under different weight matrices, this paper replaces the 0-1 matrix (W1) in SDM model with the economic distance matrix (W2) and the geographical distance weight matrix (W3). The regression results are shown in Table 11. The regression coefficient of spatial lag term is significantly positive in different spatial matrices, except that the regression coefficient of some control variables has small fluctuations, and its mechanism is basically similar to that in the previous part of this paper, which proves that the above conclusions are more robust.
Table 11. Regression results of spatial Dubin model under different spatial weight matrices
|
Influence factor |
W1 |
W2 |
W3 |
|
lnD |
0.284*** |
0.293*** |
0.274*** |
|
lnIFDI |
-0.045** |
-0.037** |
-0.048** |
|
lnOFDI |
-0.036*** |
-0.042*** |
-0.027*** |
|
lnENER |
0.134** |
0.168* |
0.211** |
|
lnER |
1.613 |
0.834* |
1.436 |
|
lnPGRP |
-0.645** |
-0.613** |
0.265* |
|
lnR＆D |
-0.136** |
0.301* |
-0.242** |
|
lnURBAN |
-0.442*** |
-0.409*** |
0.139 |
|
lnIFDI×lnOFDI |
-0.154** |
0.064 |
-0.238* |
|
lnD·W |
0.045 |
0.037 |
0.051 |
|
lnIFDI·W |
-0.036** |
-0.031* |
-0.049* |
|
·lnOFDI·W |
0.047 |
0.056 |
0.028 |
|
Spatialρ |
0.165*** |
0.159*** |
0.155* |
|
Log-likelihood |
488.3451 |
491.5738 |
486.3147 |
|
R2 |
0.591 |
0.617 |
0.606 |
|
Individual effect |
control |
control |
control |
|
time effect |
control |
control |
control |
|
observations |
300 |
300 |
300 |
5.7.2 Instrumental variable method and GMM Estimation
Considering the possible endogenous problems between industrial structure distortion and carbon emission intensity, and avoiding missing variables and possible reverse causal problems, this paper constructs appropriate instrumental variables for the core explanatory variables. This paper selects the coefficients of capital mismatch as a instrumental variable to identify the net effect of industrial structure distortion on carbon emission intensity.
The instrumental variable of capital mismatch coefficient is selected for the following two reasons. On the one hand, from the perspective of China's economic development, capital mismatch is one of the reasons for the low efficiency of energy utilization. Capital mismatch increases carbon emission intensity, and industrial distortion causes high carbon emission areas, which may also be areas with high capital mismatch. At the same time, the rational allocation of capital is also the main driving force to reduce carbon emissions. Therefore, this paper chooses capital mismatch as instrumental variable, which meets the requirement of instrumental variable correlation. On the other hand, compared with the distortion of industrial structure, it mainly indicates the imbalance of input and output in the industrial sector, while capital mismatch reflects the low efficiency of capital and labor flow in the market. Therefore, after controlling other variables, introducing capital mismatch as a instrumental variable in this paper meets the exclusive requirements.
Table 12 reports the empirical results based on instrumental variable method. Column (1) shows that capital mismatch is positively correlated with carbon emission intensity, and the F-statistic is 19.65. At the same time, the number of instrumental variables selected is equal to the number of explanatory variables in this paper, which avoids the problems of weak instrumental variables and over-recognition.
Table 12. Instrumental variable method and GMM estimation results
|
Variables |
D |
CI |
CI |
CI |
CI |
|
2SLS First stage |
2SLS Second stage |
LIMI Estimation |
Optimal GMM |
Iterative GMM |
|
|
D |
|
0.634 |
0.827 |
0.61 |
0.767 |
|
Iv |
0.416** |
|
|
|
|
|
F |
19.650 |
21.280 |
37.970 |
43.170 |
40.380 |
|
Control variable |
Yes |
||||
|
Fixed effect |
Yes |
||||
Point 9: The reason of choosing Chinese economy and data duration (2011-2020) is missing.
Response 9：The reason why this paper chooses 2011-2020 as the research sample period is that China put forward the goal of carbon neutrality and peak carbon dioxide emissions's "double carbon" in 2020, which is closely related to the research topic of this paper. Selecting the sample data from 2011-2020 can provide effective decision-making suggestions for carbon emission reduction in the future. Secondly, this paper chooses 2011 as the starting year for research, because in 2011, the Chinese government put forward the policy of cultivating and developing new industries, driving by innovation and development, and enhancing the core competitiveness of China's economy, which is in line with the perspective of industrial structure distortion in this paper. Finally, due to the availability of data, the data from 2011 to 2020 are relatively complete, which can provide a good research foundation for this paper.
Point 10: Please add footnotes on the bottom of the tables.
Response 10：We added footnotes at the bottom of the form, thanks to the reviewers for reminding us.
Note: *, **, *** represent significance at the levels of 0.1, 0.05, 0.01, respectively, and the estimated robust standard deviations are in parentheses.
Point 11: Add some references in the results discussion to support your argument.
We added some references to the results discussion to support the argument of this paper.
Response 11：
For a long time, Industrial structure change is considered as an important reason to promote economic growth (Zhang et al.,2014). Rogerson (2008) concluded that under the current global warming environment, the change of industrial structure is also of great significance for controlling the total energy consumption and reducing carbon emissions. Meanwhile, the impact of foreign trade on domestic environment mainly includes the hypothesis of "pollution heaven hypothesis" and "pollution halo hypothesis" (Kisswani & Zaitouni, 2021). However, at present, there are relatively few studies on the overall analysis of carbon emission intensity by integrating industrial structure distortion with foreign trade (Yang et al.,2019). This paper focuses on the spatial correlation among the above three variables, and explores the effect of industrial structure distortion and two-way FDI on carbon emissions with the apply of spatial econometric model. In fact, this study found that China's carbon emissions have significant spatial spillover effects among provinces, which is consistent with the current research on carbon emissions from a spatial perspective (Han F, Xie R.,2017). Through the data results, we can find that the distortion of industrial structure is not conducive to reducing carbon emissions. At the same time, both foreign direct investment and foreign investment can be explained by the theory of "pollution halo hypothesis", which also confirms the conclusion that the expansion of foreign trade will promote domestic technological progress and achieve carbon emission reduction. Similar to previous studies, industrial structure upgrading can significantly inhibit carbon emissions (Dong et al.,2020). However, after adding the variable of industrial structure distortion in this paper, the research data show that industrial structure distortion can also reduce carbon emissions through the intermediary mechanism of two-way FDI, which indicates that the most critical driving factor in the process of carbon emission reduction lies in the technical effect, and its effect has exceeded the structure and scale effect (Wang et al.,2019).
Zhang, Y. J., Liu, Z., Zhang, H., & Tan, T. D. (2014). The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China. Natural hazards, 73(2), 579-595.
Richard Rogerson. Structural Transformation and the Deterioration of European Labor Market Outcomes[J]. Journal of Political Economy, 2008, 116(2) : 235-259.
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Point 12: Some paragraphs are too lengthy. Please arrange them accordingly.
Response 12：Thank you for reminding the reviewers, some paragraphs are too long, and we have deleted and recounted them accordingly.
Round 2
Reviewer 3 Report
The authors addressed my comments. Regards
