Environmental Regulation and Employment Changes in Chinese Manufacturing Enterprises: Micro Evidence from the Top 10,000 Energy-Consuming Enterprises Program
Round 1
Reviewer 1 Report
1. The abstract lacks more information on the background on the study needed before start mentioning results.
2. The study intends to investigate the efficacy of the "Top 10,000 Energy Consuming Enterprise Program", yet there is no paragraph briefly describing this program. Please add that and assume the reader has no knowledge about it.
3. Hypothesis 1 and 2 are not written as testable hypothesis. They are presented more in terms of discussion statements.
4. Detail of the all models estimated in the empirical analysis is missing (equations, estimation strategy etc.).
Kindly revise the statements and grammar across the whole document.
In particular, use more appropriate verbs and adverbs.
Author Response
Thank you for carefully reading and reviewing our article in your busy schedule. We appreciate for your warm work earnestly. According to your suggestions, we have tried our best to improve the manuscript and made some changes in the manuscript. We sincerely hope that the correction will meet with approval.
Comment 1: The abstract lacks more information on the background on the study needed before start mentioning results.
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, more information on the background is added in the abstract, referring to Line 9-27 in the revised manuscript.
Comment 2: The study intends to investigate the efficacy of the "Top 10,000 Energy Consuming Enterprise Program", yet there is no paragraph briefly describing this program. Please add that and assume the reader has no knowledge about it.
Response 2: Thank you for pointing this out. We agree with this comment. Therefore, Part 3 of “Policy background” is added to introduce the background of this program, referring to Line 225-276 in the revised manuscript.
Comment 3：Hypothesis 1 and 2 are not written as testable hypothesis. They are presented more in terms of discussion statements.
Response 3: Thank you for pointing this out. Although Hypothesis 1 and 2 are presented in terms of discussion statements, but they can be verified in the empirical analysis which is shown in Part 5 and Part 6, referring to Line 453-771. And Hypothesis 3 is also added in this part, which states that “The impact of the carbon reduction policy on enterprise employment varies heterogeneously for different types of enterprises and is influenced by factors such as institutional environment, ownership type and industry pollution characteristics.” More specifically, Hypothesis 1 and 2 can be verified through analysis of empirical results shown in Figure1-2, and Hypothesis 3 can be verified through analysis of empirical results shown in Figure3-6.
Figure 1 presents the GPSM results for the average employment scale of enterprises over 3 years, using the base year of 2010 as the reference, representing the average dose-response function. Figure 1 shows a positive causal relationship between the intensity of environmental regulations and enterprise employment scale, indicating that the carbon reduction policy positively affects expanding enterprise employment. Moreover, the expansion of the enterprise employment scale exhibits an inverted U-shape as the intensity of environmental regulations changes. Based on the theoretical analysis mentioned earlier, the carbon reduction policy’s output or substitution effect on enterprise employment may promote or restrict the increase. Therefore, the dose-response function graph reflects the superimposition of potential promotion and restriction effects; however, we cannot currently distinguish whether the promotion or restriction effect comes from the output or substitution effect. Under the implementation of the carbon reduction policy, the overall trend of employment in the manufacturing enterprises affected by the policy shows an increase. The policy’s positive impact outweighs the negative impact, resulting in a positive treatment effect of the intensity of the carbon reduction policy.
Furthermore, by comparing the results in Figure 2, it can be seen that the carbon reduction policy affects both the channels of employment creation and employment destruction and impacts overall employment levels. Combining the GPSM results indicates that regulatory policy has a positive effect on enterprise employment; however, the promoting effect tends to weaken after a certain intensity of regulatory policy.
Furthermore, this study also considers the potential influence of different base periods on the conclusions above. In this test, the GPSM matching of key variables is conducted using 2011 as the base year to examine the impact of the carbon reduction policy’s intensity on enterprises’ employment scale. The average employment scale of enterprises in 2012 and 2013 is taken as the dependent variable following the specific steps mentioned earlier, and the empirical results remain consistent, as shown in Figure 3. The primary conclusion of the dose-response function presenting a U-shaped remains robust regardless of the choice of the base period. In conclusion, the promoting effect of the policy on employment is not affected by either considering the lag effect after policy implementation or the choice of the base period.
Figure 4 shows that both samples exhibit a characteristic of inverted U-shape and are generally greater than 0, consistent with the conclusions from the previous analysis, indicating that the carbon reduction policy positively promotes enterprise employment. By comparing the graphic features of the two samples, it can be found that there are significant differences between them. Compared to the sample with an institutional environment index greater than the mean, the dose-response function of the sample with an index less than the mean is smoother. We believe that enterprises have a more flexible response to the carbon reduction policy in regions with a better institutional environment.
Figure 5 shows that the dose-response curves of all three types of enterprise samples generally exhibit an inverted U-shape, indicating that the carbon reduction policy positively affects enterprise employment to varying degrees in all three samples. The dose-response curve of the state-owned enterprise sample shows the highest value, followed by the foreign-funded enterprise sample; the private enterprise sample shows the lowest value. These results suggest that the impact of the carbon reduction policy on state-owned enterprises is the largest, followed by foreign-funded enterprises, while private enterprises are affected to a lesser extent.
Figure 6 presents that both the high pollution-inducing and clean industry samples exhibit dose-response curves that roughly resemble an inverted U-shape, indicating that the promotion effect of the carbon reduction policy on enterprise employment still holds in both types of samples. Comparing the graphic features of the two samples shows little difference in the dose-response curves of the high pollution-inducing industry and the clean industry samples.
Comment 4：Detail of the all models estimated in the empirical analysis is missing (equations, estimation strategy etc.).
Response 4: Thank you for pointing this out. We think that Part 4.1 gives enough information about the equations including equation(1)-(4). For the specific implementation of the three steps of GPSM test process and the corresponding balance test requirements, please refer to our previous published paper
- Liu, X.; Kang, Z. Environmental Policy and Exports in China: An Analysis Based on the Top 10,000 Energy Consuming Enterprises Program[J]. Sustainability, 2022,14 (21): 14157.
And the estimation strategy is shown like this:
This study takes the T10000P implemented by the Chinese government in 2011 as a quasi-natural experiment and conducts empirical research using the ‘counterfactual’ method. Specifically, we construct treatment and control groups based on whether the sampled enterprises are included in the list of the top 10,000 Energy Consuming enterprises. Considering that policy intensity may impact enterprise employment, a ‘counterfactual’ model is required to deal with policy intensity. The traditional propensity score matching (PSM) model for handling variables is limited to binary variables, i.e. whether impacted by the carbon reduction policy and cannot address employment differences caused by varying environmental regulation intensity. Therefore, this study intends to use the GPSM model as the primary econometric method. As an extension of the PSM method, GPSM effectively handles continuous variables.
GPSM eliminates all biases associated with covariate differences between the treatment and control groups, allowing us to determine whether the intensity of environmental regulation policy has a causal effect on employment in manufacturing enterprises. If the difference between the expected employment levels is significantly positive (or negative), we can assume that when environmental regulatory intensity transitions from one level to another, the expected employment will also increase (or decrease). Because the GPSM model controls for covariate differences, changes in enterprise employment can be understood as the causal impact of different environmental regulatory intensities on enterprise employment: Here, Y(g) represents the outcome value when the treatment variable G takes the value g; this value corresponds to the enterprise employment level when the environmental regulation intensity faced by enterprises is g. This condition implies that after controlling for the factors in the covariate X, we can eliminate the selective bias of treatment intensity and resulting endogeneity issues. The selection requirement for multivariate covariates X affects the treatment variable G and the outcome value Y.
We still carefully checked this part and did some modification, referring to Line 278 in the revised manuscript.
Comments on the Quality of English Language: Kindly revise the statements and grammar across the whole document. In particular, use more appropriate verbs and adverbs.
Response to Comments on the Quality of English Language: We have revised the statements and grammar across the whole document, trying to use more appropriate verbs and adverbs.
Additional clarifications
All revisions in the manuscript are highlighted, including fixing typos and correcting error numbers. Other revision is shown as follows.
Line 2: The title of this article is slightly modified.
Line 9: Abstract is rewritten.
Line 118: The last paragraph of Part 1 is updated.
Line 225: New part 3 about policy background is added.
Line 334: Hypothesis 3 is added.
Line 771: Discussion about policy implication is added.
Line 811: Conclusions is separated from policy implication.
Line 851: Reference list is updated and all literature is reorganized.
Once again, thank you very much for your comments and suggestions!
Author Response File: 
Author Response.docx
Reviewer 2 Report
This study investigates the impact of environmental regulation policy 10 and policy intensity on the employment of Chinese manufacturing enterprises using the GPSM method. I found the paper informative. I have some suggestions to improve the quality of the paper. Please find them below:
1. The organization of the paper should be presented at the end of the introduction.
2. Literature section should be categorized so that readers can easily follow.
3. Coefficients such as 8.72e-11 should be reorganized. These are not reader-friendly.
4. Decimal numbers should be separated using ,
5. Extensive discussions are required regarding policy implications. In addition, policy implications should be a separate part presented just before the conclusion.
Must be read throughout to fix typos.
Author Response
Thank you for carefully reading and reviewing our article in your busy schedule. We appreciate for your warm work earnestly. According to your suggestions, we have tried our best to improve the manuscript and made some changes in the manuscript. We sincerely hope that the correction will meet with approval.
Comment 1: The organization of the paper should be presented at the end of the introduction.
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, the organization of the paper is added at the end of the introduction, referring to Line 118-123 in the revised manuscript, which is shown as follows.
The rest of this paper is structured as follows. Part 2 reviews the relevant literature. Part 3 introduces the policy background of the Top 10,000 Energy Consuming Enterprises Program. Part 4 presents empirical model, variables and data. Part 5 conducts an empirical analysis. Part 6 provides a further expansion analysis. Part 7 discusses policy recommendations. Part 8 summarizes the entire article.
Comment 2: Literature section should be categorized so that readers can easily follow.
Response 2: Thank you for pointing this out. Literature is actually categorized but isn’t pointed out definitely. The first paragraph in Part 2 (Line 125-161) discusses international studies about environmental regulations and employment, while the second paragraph in Paragraph 2(Line 162-187) discusses Chinese studies about environmental regulations and employment, and the third paragraph in Part 2(Line 188-213) is discussion about the main difficulties. The last paragraph in Part 2(Line 214-224) provides some ideas for solving difficult problems in this study.
Comment 3：Coefficients such as 8.72e-11 should be reorganized. These are not reader-friendly.
Response 3: Thank you for pointing this out. We agree with this comment. We re-express this kind of number like 8.72*10-11. Specific modifications are made in table 2(Line 473) and Table 3(Line 484).
Comment 4：Decimal numbers should be separated using ,
Response 4: Thank you for pointing this out. We agree with this comment. We have updated decimal numbers using , in Table 1-4, referring to line 427,473,484,628. And the incorrect numbers have also been modified.
Comment 5：Extensive discussions are required regarding policy implications. In addition, policy implications should be a separate part presented just before the conclusion.
Response 5: Thank you for pointing this out. We agree with this comment. Extensive discussions regarding policy implications is added, referring to Part 7 from line 771 to 810. Conclusion now is separated from policy implications, and is presented after that, referring to Part 8 from line 811 to 828. The extensive discussions regarding policy implications is shown as follows.
The findings of this study carry significant policy implications. Firstly, when formulating environmental regulation policies, the Chinese government can further raise standards. Environmental regulation policies, including the carbon reduction policy, may not necessarily reduce the overall employment of manufacturing enterprises. The key lies in striking a balance in policy intensity to achieve the dual goals of economic development and environmental optimization. How to determine the intensity of environmental policies is crucial for successful policy implementation. In the context of the rise of big data and artificial intelligence, policy-making departments need to conduct in-depth research and evaluation of enterprises, and develop differentiated policies based on the specific characteristics of different industries and regions. The approach of "trial and error" and "dynamic adjustment" should be utilized to continuously optimize environmental regulation policies. Secondly, enterprises should have a certain level of flexibility in response to the pressures of environmental regulation policies, but they also require a certain amount of time to respond and adjust. Policy-makers should maintain the continuity and coherence of policies, avoiding unnecessary high costs caused by frequent policy adjustments. At the same time, a "one-size-fits-all" environmental policy may be a drastic measure for the government to address environmental degradation. However, from the perspective of achieving a "win-win" situation of economic development and environmental improvement, this may not be the best choice. It is necessary to fully consider the requirements of environmental protection in different regions and the capacity of enterprises in different industries, and formulate flexible and effective environmental protection policies and enforcement intensity. Thirdly, the key to helping enterprises overcome the "pain" of environmental policies lies in effectively stimulating and encouraging green investments and the development of energy-saving and clean production technologies, in order to avoid the negative impact of environmental regulations on employment. The corresponding supporting policies during the implementation of the carbon reduction policy should revolve around effectively promoting technological innovation in enterprises. In other words, the implementation path of balancing environmental improvement goals and economic development goals should organically combine "blocking" and "guiding". By formulating comprehensive environmental regulation policies, the "leaks" in environmental aspects of enterprise development can be addressed. Coupled with corresponding financial and tax policies, active "guidance" should be provided to encourage green investments and the development of energy-saving and clean production technologies, helping enterprises effectively transform "environmental pressures" into "innovation drivers". This represents a crucial focal point for government environmental policy implementation.
Comments on the Quality of English Language: Must be read throughout to fix typos.
Response to Comments on the Quality of English Language: We have read the whole document and fixed typos.
Additional clarifications
All revisions in the manuscript are highlighted, including fixing typos and correcting error numbers. Other revision is shown as follows.
Line 2: The title of this article is slightly modified.
Line 9: Abstract is rewritten.
Line 118: The last paragraph of Part 1 is updated.
Line 225: New part 3 about policy background is added.
Line 334: Hypothesis 3 is added.
Line 771: Discussion about policy implication is added.
Line 811: Conclusions is separated from policy implication.
Line 851: Reference list is updated and all literature is reorganized.
Once again, thank you very much for your comments and suggestions!
Author Response File: Author Response.docx
Reviewer 3 Report
Research subject and problem
In general, the content of the article is subordinate to the purpose. The title also corresponds to the purpose and methodology of the research performed. The research problem has been clearly defined. It is significant and up-to-date.
Objectives and tasks
The objectives of the article are stated in the introduction. They harmonize with the hypotheses set in the methodological part. The tasks are described in detail in the methodological part. It is advisable to list them synthetically in the introduction.
Research gaps
The article includes a critical review of the literature. It is not broad, but intensive and sufficient to achieve the objectives of the research. It highlights the deficits of the existing research. However, it is necessary to clearly enumerate the existing research gaps (theoretical, methodological, and empirical), which became the premise of the research undertaken and the formulation of the research problem.
Questions and hypotheses
The questions are implicit in the content of the article. Their emanation is the two research hypotheses posed. It is advisable to make changes in their number (increase to three) and content (modification of the first hypothesis). Indeed, there should be a correspondence between the number and content of the hypotheses (lines 263-268) to the three main research conclusions (lines 697-702). Proposal:
H1 - The carbon reduction policy has a positive promoting effect and a negative constraining effect on the employment scale of manufacturing enterprises; its comprehensive effects must be interpreted through empirical analysis; It is possible to identify a critical point of the intensity of the impact of the carbon reduction policy
H2 - no change
H3 - the impact of the carbon reduction policy on enterprise employment varies heterogeneously for different types of enterprises and is influenced by factors
Methodology (selection of methods and tools)
The methods and tools used are those already known to date (GPSM). As for direction, the study follows the path widely described in the literature. However, in the first place, the value of the study is that it was conducted in a sizeable developing country (this was lightly mentioned in the literature review but nevertheless needs to be more strongly emphasized). Secondly, the research was done on a broad research sample (as one can only guess) and correlated with the implementation of a widespread government program.
Data and research test
The data has not been sufficiently characterized. It is necessary to present the number and structure of the companies surveyed by the Top 10,000 participants and non-participants and by characteristics, including those that are variables in the model (e.g., object of activity, intensity of environmental regulation, size of employment, total production.... etc., institutional environment, type of ownership, degree of pollution).
In a synthetic way (a multi-cross-sectional table), this characterization should be found in Section 3.2.1, and as detailed in the appendix.
Without the addition of this characterization, it is not possible to assess the value/strength of the research done and the reliability of its results.
Interpretation of the results
The results obtained are presented in detail in the article (sections 4 and 5). The scope and quality of the findings are not in doubt.
The interpretation of the results led to three main conclusions. These should be related to the proven hypotheses - a comment made earlier.
Discussion and conclusions
The discussion is not presented in the article. The discussion is a polemic against the results of other studies. Instead, implications for conducting further economic policies are briefly presented. The conclusion (point 6) should be expanded to indicate the limitations of the research undertaken and to point the way for new research, open with the current one.
Structure and composition
In general, the structure of the article is correct. A certain shortcoming is the absence of a discussion. On the other hand, the characterization (quantitative-structural) of the studied enterprises requires necessary extension - comments previously made.
Formal requirements (language, edition)
In general, the language and writing technique are correct. The bibliography list could be expanded but is sufficient as a basis for a critical review of the literature and research methodology.
General opinion
The article has a demonstrable positive scientific value, although its methodology is not novel. There are some infirmities in the article that need to be removed to enhance its value.
The language of the article is generally correct and, above all, understandable. Overly elaborate linguistic constructions and multiple subordinated sentences are not created, which facilitates the perception of the content.
Author Response
Thank you for carefully reading and reviewing our article in your busy schedule. We appreciate for your warm work earnestly. According to your suggestions, we have tried our best to improve the manuscript and made some changes in the manuscript. We sincerely hope that the correction will meet with approval.
Comment 1: Research subject and problem
In general, the content of the article is subordinate to the purpose. The title also corresponds to the purpose and methodology of the research performed. The research problem has been clearly defined. It is significant and up-to-date.
Response 1: Thank you very much for carefully reading and reviewing our article in your busy schedule, and we are very appreciated for your positive comments!
Comment 2: Objectives and tasks
The objectives of the article are stated in the introduction. They harmonize with the hypotheses set in the methodological part. The tasks are described in detail in the methodological part. It is advisable to list them synthetically in the introduction.
Response 2: Thank you for pointing this out. The objectives and tasks may be already listed in the end of introduction, referring to line 100 to 118, which is shown as follows.
This paper enriches and expands existing research in several aspects. The first aspect concerns the choice of environmental regulation policies; this study regards the carbon reduction policy implemented by the Chinese government as a policy shock, avoiding potential debates on the effectiveness of environmental policy implementation. Moreover, this policy directly affects manufacturing enterprises; therefore, it can more effectively prevent the problem of aggregation bias when studying micro-enterprise behaviour using traditional macro-policy, ensuring the reliability of research conclusions to the greatest extent. The second aspect concerns the choice of research methods. This study adopts the GPSM method within a quasi-natural experiment framework to identify the impact of environmental regulations on employment changes in Chinese manufacturing enterprises and address endogeneity issues that previous studies may have faced. This approach can also identify whether the intensity of the carbon reduction policy has differential effects on enterprise employment. The third aspect is the selection of research perspectives. Unlike previous studies, this study examines the impact of environmental regulations on the total employment of enterprises and the effect of environmental regulations on changes in the employment structure of enterprises. In other words, we examine the impact of environmental regulations on employment creation and destruction by enterprises, allowing for a preliminary exploration of how environmental regulations affect enterprise employment.
Comment 3：Research gaps
The article includes a critical review of the literature. It is not broad, but intensive and sufficient to achieve the objectives of the research. It highlights the deficits of the existing research. However, it is necessary to clearly enumerate the existing research gaps (theoretical, methodological, and empirical), which became the premise of the research undertaken and the formulation of the research problem.
Response 3: Thank you for pointing this out. The present study addresses the aforementioned issue comprehensively. While international research has yielded inconclusive results regarding whether environmental regulatory policies increase or decrease employment in enterprises, studies specific to China are scarce and lack micro-level evidence from enterprises. Moreover, the objectivity of determining environmental regulatory variables may be compromised due to potential government intervention. The value of this study lies in its examination of a sample of manufacturing enterprises nationwide in China, taking into account the comprehensive impact of the carbon reduction policy, which imposes constraints on enterprises' energy use rights. It provides a supplementary contribution to the existing literature by considering the effects of environmental regulatory policies on both overall employment levels and employment structure.
Comment 4：Questions and hypotheses
The questions are implicit in the content of the article. Their emanation is the two research hypotheses posed. It is advisable to make changes in their number (increase to three) and content (modification of the first hypothesis). Indeed, there should be a correspondence between the number and content of the hypotheses (lines 263-268) to the three main research conclusions (lines 697-702). Proposal:
H1 - The carbon reduction policy has a positive promoting effect and a negative constraining effect on the employment scale of manufacturing enterprises; its comprehensive effects must be interpreted through empirical analysis; It is possible to identify a critical point of the intensity of the impact of the carbon reduction policy
H2 - no change
H3 - the impact of the carbon reduction policy on enterprise employment varies heterogeneously for different types of enterprises and is influenced by factors
Response 4: Thank you for pointing this out. We agree with this comment. The three hypotheses are restated in the revised manuscript, referring to Line 328-337, which is shown as follows.
Hypothesis 1(H1). The carbon reduction policy has a positive promoting effect and a negative constraining effect on the employment scale of manufacturing enterprises; its comprehensive effects must be interpreted through empirical analysis.
Hypothesis 2 (H2). The impact of the carbon reduction policy on the employment scale of manufacturing enterprises operates simultaneously through two channels of improving employment creation and reducing employment destruction.
Hypothesis 3 (H3). The impact of the carbon reduction policy on enterprise employment varies heterogeneously for different types of enterprises and is influenced by factors such as institutional environment, ownership type and industry pollution characteristics.
We didn’t add the statement “It is possible to identify a critical point of the intensity of the impact of the carbon reduction policy” in H1 because this point is not the most important finding, and we already conducted in-depth research on similar issues in the previous published paper using different method, which is referred to reference list in Line 911.
[37]Liu, X.; Kang, Z. Environmental Policy and Exports in China: An Analysis Based on the Top 10,000 Energy Consuming Enterprises Program[J]. Sustainability, 2022,14 (21): 14157.
Comment 5：Methodology (selection of methods and tools)
The methods and tools used are those already known to date (GPSM). As for direction, the study follows the path widely described in the literature. However, in the first place, the value of the study is that it was conducted in a sizeable developing country (this was lightly mentioned in the literature review but nevertheless needs to be more strongly emphasized). Secondly, the research was done on a broad research sample (as one can only guess) and correlated with the implementation of a widespread government program.
Response 5: Thank you for pointing this out. In the first place, the value of the study is that it was conducted in a sizeable developing country, which is emphasized in both introduction (Line 37-79) and literature review(Line 162-213). More specifically, the second paragraph in Paragraph 2(Line 162-187) discusses Chinese studies about environmental regulations and employment, and the third paragraph in Part 2(Line 188-213) is discussion about the main difficulties.
The sample data used in this study consists of the China Industrial Enterprises database from 2010 to 2013 and the list of enterprises and energy-saving targets published by the National Development and Reform Commission for T10000P. This study referred to the data matching and cleaning performed by Liu and Kang[37] to match the two databases, resulting in a successful match of 7,880 top 10,000 Energy Consuming enterprises.
Comment 6：Data and research test
The data has not been sufficiently characterized. It is necessary to present the number and structure of the companies surveyed by the Top 10,000 participants and non-participants and by characteristics, including those that are variables in the model (e.g., object of activity, intensity of environmental regulation, size of employment, total production.... etc., institutional environment, type of ownership, degree of pollution).
In a synthetic way (a multi-cross-sectional table), this characterization should be found in Section 3.2.1, and as detailed in the appendix.
Without the addition of this characterization, it is not possible to assess the value/strength of the research done and the reliability of its results.
Response 6: Thank you for pointing this out. Table 1 presents the employment scale, employment creation and employment destruction of the 10,000 enterprises and non 10,000 enterprises by characteristics, including those that are variables in the model (e.g., object of activity, intensity of environmental regulation, size of employment, total production.... etc., institutional environment, type of ownership, degree of pollution).
The addition of this characterization is already presented in our previous published paper [37]37.Liu, X.; Kang, Z. Environmental Policy and Exports in China: An Analysis Based on the Top 10,000 Energy Consuming Enterprises Program[J]. Sustainability, 2022,14 (21): 14157.
If we include this section in the appendix, it will overlap with this article. So I prefer no addition in the appendix.
Appendix 1: Identification of breakpoints
According to the Program’s guidelines, an enterprise’s inclusion in the 10,000 enterprises list is determined by its carbon use scale in the base period (2010). Enterprises or institutions that use more than 10,000 tons of standard coal is a major condition. The list of the 10,000 enterprises was meant to be dynamic, and enterprises were to be added every year after 2011 according to the criterion of the scale of carbon used exceeding 10,000 tons. However, in light of the changes in this list, the increase has been relatively small. If and when new enterprises enter the list, they come under the government’s radar, with clear carbon reduction targets for the entire 12th Five-Year Plan period. In other words, “10,000 tons of standard coal” is a clear discontinuity point to assess if the Program seriously affected the enterprises. This also enables the testing of robustness using a regression discontinuity design (RDD) in the empirical study. The energy consumption scale data in the base period are the premise of the RDD method. So far, no national data on the carbon consumption scale of industrial enterprises have been released. Fortunately, due to the implementation of the program, Sichuan Province reported carbon scale data of some enterprises and institutions during the base period, enabling our study to use a clear RDD test (SRDD) using enterprise data from Sichuan Province.
We select the carbon consumption scale of the enterprise in 2010 uniformly reduced by 10,000 tons as the horizontal axis variable, where the breakpoint 0 means the carbon consumption scale is exactly 10,000 tons. We then select whether it is in the 10,000 enterprises list as the vertical axis variable. If yes, the value is 1, and if not, the value is 0. Based on the sample data of 10,000 enterprises in Sichuan Province, the clear breakpoint mechanism described is shown in Figure 1. It can be seen that there is a clear discontinuity point at 10,000 tons of standard coal, which indicates that the enterprise data from Sichuan Province are valid for testing. Under the clear discontinuity point mechanism, the influence of covariates can be erased. Furthermore, the empirical analysis can add covariates in SRD to ensure the robustness of the conclusion.
Figure 1. The 10,000 enterprises program and the carbon scale of enterprises in the base period.
Appendix 2: Continuity Test of Co-Variables near Discontinuity Points
To assess regression discontinuity, we need to judge whether it is reasonable to use the discontinuity point mechanism to identify the treatment effect of the carbon reduction policy on the scale of exports. First, it is necessary to assess whether there is a jump in the main covariates near the discontinuity point. If there exists a jump, the jump in the result variables near the discontinuity point cannot be considered as the result of policy variables, but the result of co-variables. Table 1 presents the empirical tests in detail. At the level of 5%, the original assumption that the co-variables jump near the discontinuity point can be rejected completely.
Table 1. Continuity test of co-variables near discontinuity points.
|
Co-Variables |
Wald Coefficient |
Standard Deviation |
p Value |
Result |
|
Total output value |
0.008 |
1.966 |
0.996 |
No jump |
|
Capital per capita |
0.019 |
1.163 |
0.987 |
No jump |
|
Total factor productivity (TFP) |
2.323 |
2.748 |
0.398 |
No jump |
|
Financial standing |
0.410 |
0.719 |
0.568 |
No jump |
|
Age of enterprise |
7.062 |
8.752 |
0.420 |
No jump |
|
Export scale |
0.833 |
1.690 |
0.622 |
No jump |
|
Subsidies |
5.423 |
3.267 |
0.097 |
Almost no jump |
|
R&D |
3.281 |
2.079 |
0.115 |
No jump |
Note: All the reports in the table are the results of continuity test of co-variables near discontinuity points under optimal bandwidth. The results under 1/2 and 2 times optimal bandwidth turned out unchanged; these have not been reported in order to meet article length limits.
Appendix 3:Continuity Test of the Density Function of the Reference Variable at the Discontinuity Point
For validity of RDD results, we need to test whether individuals can manipulate their reference variables. Enterprises cannot control whether they enter the state of energy saving and carbon reduction by adjusting the scale of carbon consumption in the base period optionally. In fact, the Program’s implementation plan, as formulated by the NDRC, can provide some evidence for the fact that enterprises are unable to manipulate the reference variables. As we know, the Program was officially released at the end of 2011. However, the selection of the 10,000 enterprises was based on the scale of carbon consumption for enterprises in 2010. Therefore, even if enterprises had foreknowledge of the Program, they could not have adjusted the scale of carbon consumption in the previous year to produce self-selection for policy implementation. Whether enterprises can manipulate reference variables depends on specific tests as well. McCrary [47] provides a specific idea for such a test. Whether the density function of the distribution of reference variables is continuous at the discontinuity point is used to check whether an individual can manipulate the reference variables. If there is a clear jump, it means that the individual can manipulate the reference variables to achieve self-selection for policy implementation. Otherwise, it means that the individual cannot manipulate the reference variables, ensuring that the policy is exogenous. We choose the carbon consumption scale of enterprises in 2010 as the horizontal axis variable, and its density function distribution as the vertical axis variable. Based on the data of carbon consumption scale of enterprises in 2010, the results of the density function distribution test are shown in Figure 2. As expected, the density function near the discontinuity point shows very good continuity. This implies that enterprises cannot choose whether to adopt the Program by manipulating the scale of carbon use in the base period.
Figure 2. Continuity of the density function of the reference variable at the discontinuity point.
Comment 7：Interpretation of the results
The results obtained are presented in detail in the article (sections 4 and 5). The scope and quality of the findings are not in doubt.
The interpretation of the results led to three main conclusions. These should be related to the proven hypotheses - a comment made earlier.
Response 7: Thank you for pointing this out. Hypothesis 1 and 2 can be verified through analysis of empirical results shown in Figure1-2, and Hypothesis 3 can be verified through analysis of empirical results shown in Figure3-6.
Figure 1 presents the GPSM results for the average employment scale of enterprises over 3 years, using the base year of 2010 as the reference, representing the average dose-response function. Figure 1 shows a positive causal relationship between the intensity of environmental regulations and enterprise employment scale, indicating that the carbon reduction policy positively affects expanding enterprise employment. Moreover, the expansion of the enterprise employment scale exhibits an inverted U-shape as the intensity of environmental regulations changes. Based on the theoretical analysis mentioned earlier, the carbon reduction policy’s output or substitution effect on enterprise employment may promote or restrict the increase. Therefore, the dose-response function graph reflects the superimposition of potential promotion and restriction effects; however, we cannot currently distinguish whether the promotion or restriction effect comes from the output or substitution effect. Under the implementation of the carbon reduction policy, the overall trend of employment in the manufacturing enterprises affected by the policy shows an increase. The policy’s positive impact outweighs the negative impact, resulting in a positive treatment effect of the intensity of the carbon reduction policy.
Furthermore, by comparing the results in Figure 2, it can be seen that the carbon reduction policy affects both the channels of employment creation and employment destruction and impacts overall employment levels. Combining the GPSM results indicates that regulatory policy has a positive effect on enterprise employment; however, the promoting effect tends to weaken after a certain intensity of regulatory policy.
Furthermore, this study also considers the potential influence of different base periods on the conclusions above. In this test, the GPSM matching of key variables is conducted using 2011 as the base year to examine the impact of the carbon reduction policy’s intensity on enterprises’ employment scale. The average employment scale of enterprises in 2012 and 2013 is taken as the dependent variable following the specific steps mentioned earlier, and the empirical results remain consistent, as shown in Figure 3. The primary conclusion of the dose-response function presenting a U-shaped remains robust regardless of the choice of the base period. In conclusion, the promoting effect of the policy on employment is not affected by either considering the lag effect after policy implementation or the choice of the base period.
Figure 4 shows that both samples exhibit a characteristic of inverted U-shape and are generally greater than 0, consistent with the conclusions from the previous analysis, indicating that the carbon reduction policy positively promotes enterprise employment. By comparing the graphic features of the two samples, it can be found that there are significant differences between them. Compared to the sample with an institutional environment index greater than the mean, the dose-response function of the sample with an index less than the mean is smoother. We believe that enterprises have a more flexible response to the carbon reduction policy in regions with a better institutional environment.
Figure 5 shows that the dose-response curves of all three types of enterprise samples generally exhibit an inverted U-shape, indicating that the carbon reduction policy positively affects enterprise employment to varying degrees in all three samples. The dose-response curve of the state-owned enterprise sample shows the highest value, followed by the foreign-funded enterprise sample; the private enterprise sample shows the lowest value. These results suggest that the impact of the carbon reduction policy on state-owned enterprises is the largest, followed by foreign-funded enterprises, while private enterprises are affected to a lesser extent.
Figure 6 presents that both the high pollution-inducing and clean industry samples exhibit dose-response curves that roughly resemble an inverted U-shape, indicating that the promotion effect of the carbon reduction policy on enterprise employment still holds in both types of samples. Comparing the graphic features of the two samples shows little difference in the dose-response curves of the high pollution-inducing industry and the clean industry samples.
Comment 8：Discussion and conclusions
The discussion is not presented in the article. The discussion is a polemic against the results of other studies. Instead, implications for conducting further economic policies are briefly presented. The conclusion (point 6) should be expanded to indicate the limitations of the research undertaken and to point the way for new research, open with the current one.
Response 8: Thank you for pointing this out. We agree with this comment. The conclusion is already expanded to indicate the limitations of the research and also point out the further research direction, referring to Part 8 from Line 811 to 828.
Comment 9：Structure and composition
In general, the structure of the article is correct. A certain shortcoming is the absence of a discussion. On the other hand, the characterization (quantitative-structural) of the studied enterprises requires necessary extension - comments previously made.
Response 9: Thank you for pointing this out. We agree with this comment. Discussion about policy implication and conclusion is added in Part 7 and Part 8, referring to Line 771-828. The characterization (quantitative-structural) of the studied enterprises is shown in Table 1(Line 427) and other points are already in Response 6.
Comment 10：Formal requirements (language, edition)
In general, the language and writing technique are correct. The bibliography list could be expanded but is sufficient as a basis for a critical review of the literature and research methodology.
Response 10: Thank you for pointing this out. We agree with this comment. All revision in the manuscript are highlighted, including fixing typos and correcting error numbers. Reference list is updated and all literature is reorganized, referring to Line851.
Comment 11：General opinion
The article has a demonstrable positive scientific value, although its methodology is not novel. There are some infirmities in the article that need to be removed to enhance its value.
Response 11: Thank you for carefully reading and reviewing our article in your busy schedule. We appreciate for your warm work earnestly. According to your suggestions, we have tried our best to improve the manuscript and made some changes in the manuscript. We sincerely hope that the correction will meet with approval!
Comments on the Quality of English Language: The language of the article is generally correct and, above all, understandable. Overly elaborate linguistic constructions and multiple subordinated sentences are not created, which facilitates the perception of the content.
Response to Comments on the Quality of English Language: Thank you very much for carefully reading and reviewing our article in your busy schedule, and we are very appreciated for your positive comments!
Additional clarifications
All revisions in the manuscript are highlighted, including fixing typos and correcting error numbers. Other revision is shown as follows.
Line 2: The title of this article is slightly modified.
Line 9: Abstract is rewritten.
Line 118: The last paragraph of Part 1 is updated.
Line 225: New part 3 about policy background is added.
Line 334: Hypothesis 3 is added.
Line 771: Discussion about policy implication is added.
Line 811: Conclusions is separated from policy implication.
Line 851: Reference list is updated and all literature is reorganized.
Once again, thank you very much for your comments and suggestions!
Author Response File: Author Response.docx
Reviewer 4 Report
RE: Environmental Regulation and Employment Changes in Chinese Manufacturing Enterprises: An Empirical Study Based on the Top 10,000 Energy-Consuming Enterprises Program
The paper aims to investigate the impact of environmental regulations—particularly China's Top 10,000 Energy-Consuming Enterprises Program—on employment within the country's manufacturing sector. The study employs the Generalized Propensity Score Matching (GPSM) method and RDD for its analysis and presents several conclusions related to policy implications. The paper attempts to offer constructive policy recommendations for balancing environmental and employment objectives. Overall, the topic is timely and important, especially given the global focus on sustainable development and the regulatory landscape. However, this paper should deal with several issues before considering for publication.
1. The paper is not clearly articulated. Phrases are cumbersome, and terms like 'policy intensity' and 'employment creation and destruction channels' are not explicitly defined. In addition, what the background of the Top 10,000 Energy-Consuming Enterprises Program. There is no specific introduction.
2. Methodology issue:
(a) The paper fails to demonstrate the parallel trends assumption, which is crucial for causal inference. Without this, the robustness of the GPSM results may be questioned.
(b) The paper appears to employ a Regression Discontinuity Design (RDD) in some capacity, but the typical RDD figures are not shown, and there are issues with the design that need to be addressed for the results to be reliable.
(c) How many observations in the analysis for each column result?
3. The paper claims that the impact of the regulation varies across different types of enterprises but does not delve into how these effects differ based on various characteristics, missing an opportunity for a more nuanced analysis.
4. The paper concludes that environmental regulations positively affect employment but fails to convincingly explain the mechanisms through which this occurs, making the findings less credible.
5. The hypothesis is not well developed.
6. Some references cited in the paper are not easily found online, which raises questions about the paper's academic rigor.
7. The title is too long.
8. While the study focuses on China, it lacks a discussion on the generalizability of its findings to other contexts or countries.
The paper is not clearly articulated. Phrases are cumbersome, and terms like 'policy intensity' and 'employment creation and destruction channels' are not explicitly defined. In addition, what the background of the Top 10,000 Energy-Consuming Enterprises Program. There is no specific introduction.
Author Response
Thank you for carefully reading and reviewing our article in your busy schedule. We appreciate for your warm work earnestly. According to your suggestions, we have tried our best to improve the manuscript and made some changes in the manuscript. We sincerely hope that the correction will meet with approval.
Comment 1: The paper is not clearly articulated. Phrases are cumbersome, and terms like 'policy intensity' and 'employment creation and destruction channels' are not explicitly defined. In addition, what the background of the Top 10,000 Energy Consuming Enterprises Program. There is no specific introduction.
Response 1: Thank you for pointing this out. The term 'policy intensity' is defined in the 1st paragraph in section 4.2.1 of the revised manuscript from Line 371 to 379, which is stated as follows. The term 'employment creation and destruction’ is defined in the 2nd paragraph in section 4.2.1 of the revised manuscript from Line 388 to 394, which is stated as follows. Part 3 of “Policy background” is added to introduce the background of the Top 10,000 Energy Consuming Enterprises Program, referring to Line 225-276.
The first variable is the treatment variable, i.e. environmental regulation intensity. This study measures the carbon reduction policy intensity by dividing the target scale of the carbon reduction policy for the top 10,000 Energy Consuming enterprises during the ‘Twelfth Five-Year Plan’ period by the production scale of enterprises in the base period. The policy intensity values fall within the range of [0, 1], with only 36 samples of the top 10,000 Energy Consuming enterprises having a treatment intensity greater than 1. These 36 samples are winsorized to ensure that the variable values meet the requirements of the fractional logit model.
Second, following the research framework of Davis and Haltiwanger[33] and considering the characteristics of the study sample, total employment is decomposed into employment creation and employment destruction. Specifically, drawing on the methods used by Groizard[34] and Mao and Xu[35], employment creation (create job: c_job) is defined as c_jobit = max(ΔJit, 0), where ΔJit = lnJit-lnJit-1 and employment destruction (destroy job: d_job) is defined as d_jobit = max(-ΔJit, 0).
- Policy Background
Within the framework of the 12th Five-Year Plan's Comprehensive Work Program for Energy Conservation and Emission Reduction, the Chinese government established the Top 10,000 Energy Consuming Enterprises Program (T10000P). The primary objective of this initiative is to identify and prioritize key energy consumers, particularly industrial enterprises, with comprehensive energy consumption exceeding 10,000 tons of standard coal in 2010 and an annual consumption of over 5,000 tons of standard coal. The program is overseen by multiple relevant departments, including the State Council, the Development and Reform Commission, the Ministry of Education, the Ministry of Industry and Information Technology, the Ministry of Finance, the Ministry of Housing and Urban Rural Development, the Ministry of Transport, the Ministry of Commerce, the SASAC of the State Council, the AQSIQ, the National Bureau of Statistics, the CBRC, and the National Energy Administration. During the 12th Five-Year Plan period (2011-2015), approximately 17,000 enterprises were selected to participate in T10000P, accounting for more than 60% of China's total energy consumption. These companies are required to establish individual energy-saving targets within the plan period in order to achieve their overall energy-saving goals. Local energy-saving authorities are responsible for decomposing the energy-saving targets of the 10,000 enterprises and reporting them to the National Development and Reform Commission for record-keeping and assessment purposes. To ensure the sustainability of T10000P, it has been established that any significant changes to the list of participating enterprises will not take place during the Twelfth Five-Year Plan period (2011-2015). The policy’s implementation cycle spans this entire period, providing a stable framework for evaluating the various impacts of the program scientifically.
The State Council has mandated the integration of energy-saving objectives and the implementation of energy-saving measures into the provincial government's assessment system for energy-saving goals. The evaluation results of energy-saving targets in different regions must be compiled and published annually, with copies sent to relevant departments such as the state-owned assets supervision and administration commission (SASAC), the China banking regulatory commission (CBRC), and others. Furthermore, the performance appraisal of central enterprises should include their energy-saving goals, and a robust accountability system should be established as part of the comprehensive evaluation of leading groups and leaders' performance assessment.In order to promote sustainable energy consumption, the CBRC encourages banks and financial institutions to provide increased credit support for energy-saving projects under the Program. However, they must also adhere to risk control and business sustainability principles. The achievement of energy-saving goals should be taken into full consideration in enterprise credit ratings, credit access, and exit management. Banking and financial institutions are required to strictly regulate lending to companies that fail to meet energy-saving standards or demonstrate ineffective rectification efforts. Energy-saving supervisory institutions at all levels are obligated to take specific actions to fulfill the objectives of the Program. Firstly, they need to strengthen energy-saving supervision. Secondly, they should conduct specialized supervision of the implementation of energy conservation management systems among the selected 10,000 enterprises in accordance with the law. Thirdly, they should evaluate and review energy savings in fixed assets investment projects. Fourthly, they should enforce energy consumption quota standards. Lastly, they should eliminate outdated equipment and implement energy-saving plans. Ultimately, the Program will enforce strict compliance with energy-using regulations by investigating and penalizing any illegal activities. The institutional framework of this initiative seeks to implement stringent guidelines for corporate energy consumption decisions, thereby promoting a culture of accountability and responsibility towards sustainable development practices.
Comment 2: Methodology issue:
(a) The paper fails to demonstrate the parallel trends assumption, which is crucial for causal inference. Without this, the robustness of the GPSM results may be questioned.
(b) The paper appears to employ a Regression Discontinuity Design (RDD) in some capacity, but the typical RDD figures are not shown, and there are issues with the design that need to be addressed for the results to be reliable.
(c) How many observations in the analysis for each column result?
Response 2:Thank you for pointing this out.
- Please refer to Table 3 of this article for a test of the balance conditions in Generalized Propensity Score Matching (GPSM), which is from Line 484.
- RDD is already presented in our previous published paper [37]37.Liu, X.; Kang, Z. Environmental Policy and Exports in China: An Analysis Based on the Top 10,000 Energy ConsumingEnterprises Program[J]. Sustainability, 2022,14 (21): 14157. More detail is shown in Appendix 1-3.
- Numbers in Table 1 is obtained from database, numbers in Table 2 and Table 3 shows results from empirical tests.
Appendix 1: Identification of breakpoints
According to the Program’s guidelines, an enterprise’s inclusion in the 10,000 enterprises list is determined by its carbon use scale in the base period (2010). Enterprises or institutions that use more than 10,000 tons of standard coal is a major condition. The list of the 10,000 enterprises was meant to be dynamic, and enterprises were to be added every year after 2011 according to the criterion of the scale of carbon used exceeding 10,000 tons. However, in light of the changes in this list, the increase has been relatively small. If and when new enterprises enter the list, they come under the government’s radar, with clear carbon reduction targets for the entire 12th Five-Year Plan period. In other words, “10,000 tons of standard coal” is a clear discontinuity point to assess if the Program seriously affected the enterprises. This also enables the testing of robustness using a regression discontinuity design (RDD) in the empirical study. The energy consumption scale data in the base period are the premise of the RDD method. So far, no national data on the carbon consumption scale of industrial enterprises have been released. Fortunately, due to the implementation of the program, Sichuan Province reported carbon scale data of some enterprises and institutions during the base period, enabling our study to use a clear RDD test (SRDD) using enterprise data from Sichuan Province.
We select the carbon consumption scale of the enterprise in 2010 uniformly reduced by 10,000 tons as the horizontal axis variable, where the breakpoint 0 means the carbon consumption scale is exactly 10,000 tons. We then select whether it is in the 10,000 enterprises list as the vertical axis variable. If yes, the value is 1, and if not, the value is 0. Based on the sample data of 10,000 enterprises in Sichuan Province, the clear breakpoint mechanism described is shown in Figure 1. It can be seen that there is a clear discontinuity point at 10,000 tons of standard coal, which indicates that the enterprise data from Sichuan Province are valid for testing. Under the clear discontinuity point mechanism, the influence of covariates can be erased. Furthermore, the empirical analysis can add covariates in SRD to ensure the robustness of the conclusion.
Figure 1. The 10,000 enterprises program and the carbon scale of enterprises in the base period.
Appendix 2: Continuity Test of Co-Variables near Discontinuity Points
To assess regression discontinuity, we need to judge whether it is reasonable to use the discontinuity point mechanism to identify the treatment effect of the carbon reduction policy on the scale of exports. First, it is necessary to assess whether there is a jump in the main covariates near the discontinuity point. If there exists a jump, the jump in the result variables near the discontinuity point cannot be considered as the result of policy variables, but the result of co-variables. Table 1 presents the empirical tests in detail. At the level of 5%, the original assumption that the co-variables jump near the discontinuity point can be rejected completely.
Table 1. Continuity test of co-variables near discontinuity points.
|
Co-Variables |
Wald Coefficient |
Standard Deviation |
p Value |
Result |
|
Total output value |
0.008 |
1.966 |
0.996 |
No jump |
|
Capital per capita |
0.019 |
1.163 |
0.987 |
No jump |
|
Total factor productivity (TFP) |
2.323 |
2.748 |
0.398 |
No jump |
|
Financial standing |
0.410 |
0.719 |
0.568 |
No jump |
|
Age of enterprise |
7.062 |
8.752 |
0.420 |
No jump |
|
Export scale |
0.833 |
1.690 |
0.622 |
No jump |
|
Subsidies |
5.423 |
3.267 |
0.097 |
Almost no jump |
|
R&D |
3.281 |
2.079 |
0.115 |
No jump |
Note: All the reports in the table are the results of continuity test of co-variables near discontinuity points under optimal bandwidth. The results under 1/2 and 2 times optimal bandwidth turned out unchanged; these have not been reported in order to meet article length limits.
Appendix 3:Continuity Test of the Density Function of the Reference Variable at the Discontinuity Point
For validity of RDD results, we need to test whether individuals can manipulate their reference variables. Enterprises cannot control whether they enter the state of energy saving and carbon reduction by adjusting the scale of carbon consumption in the base period optionally. In fact, the Program’s implementation plan, as formulated by the NDRC, can provide some evidence for the fact that enterprises are unable to manipulate the reference variables. As we know, the Program was officially released at the end of 2011. However, the selection of the 10,000 enterprises was based on the scale of carbon consumption for enterprises in 2010. Therefore, even if enterprises had foreknowledge of the Program, they could not have adjusted the scale of carbon consumption in the previous year to produce self-selection for policy implementation. Whether enterprises can manipulate reference variables depends on specific tests as well. McCrary [47] provides a specific idea for such a test. Whether the density function of the distribution of reference variables is continuous at the discontinuity point is used to check whether an individual can manipulate the reference variables. If there is a clear jump, it means that the individual can manipulate the reference variables to achieve self-selection for policy implementation. Otherwise, it means that the individual cannot manipulate the reference variables, ensuring that the policy is exogenous. We choose the carbon consumption scale of enterprises in 2010 as the horizontal axis variable, and its density function distribution as the vertical axis variable. Based on the data of carbon consumption scale of enterprises in 2010, the results of the density function distribution test are shown in Figure 2. As expected, the density function near the discontinuity point shows very good continuity. This implies that enterprises cannot choose whether to adopt the Program by manipulating the scale of carbon use in the base period.
Figure 2. Continuity of the density function of the reference variable at the discontinuity point.
Comment 3: The paper claims that the impact of the regulation varies across different types of enterprises but does not delve into how these effects differ based on various characteristics, missing an opportunity for a more nuanced analysis.
Response 3: Thank you for pointing this out. This point is written in Hypothesis 3(Line 334), which can be verified through analysis of empirical results shown in Figure3-6.
Furthermore, this study also considers the potential influence of different base periods on the conclusions above. In this test, the GPSM matching of key variables is conducted using 2011 as the base year to examine the impact of the carbon reduction policy’s intensity on enterprises’ employment scale. The average employment scale of enterprises in 2012 and 2013 is taken as the dependent variable following the specific steps mentioned earlier, and the empirical results remain consistent, as shown in Figure 3. The primary conclusion of the dose-response function presenting a U-shaped remains robust regardless of the choice of the base period. In conclusion, the promoting effect of the policy on employment is not affected by either considering the lag effect after policy implementation or the choice of the base period.
Figure 4 shows that both samples exhibit a characteristic of inverted U-shape and are generally greater than 0, consistent with the conclusions from the previous analysis, indicating that the carbon reduction policy positively promotes enterprise employment. By comparing the graphic features of the two samples, it can be found that there are significant differences between them. Compared to the sample with an institutional environment index greater than the mean, the dose-response function of the sample with an index less than the mean is smoother. We believe that enterprises have a more flexible response to the carbon reduction policy in regions with a better institutional environment.
Figure 5 shows that the dose-response curves of all three types of enterprise samples generally exhibit an inverted U-shape, indicating that the carbon reduction policy positively affects enterprise employment to varying degrees in all three samples. The dose-response curve of the state-owned enterprise sample shows the highest value, followed by the foreign-funded enterprise sample; the private enterprise sample shows the lowest value. These results suggest that the impact of the carbon reduction policy on state-owned enterprises is the largest, followed by foreign-funded enterprises, while private enterprises are affected to a lesser extent.
Figure 6 presents that both the high pollution-inducing and clean industry samples exhibit dose-response curves that roughly resemble an inverted U-shape, indicating that the promotion effect of the carbon reduction policy on enterprise employment still holds in both types of samples. Comparing the graphic features of the two samples shows little difference in the dose-response curves of the high pollution-inducing industry and the clean industry samples.
Comment 4: The paper concludes that environmental regulations positively affect employment but fails to convincingly explain the mechanisms through which this occurs, making the findings less credible.
Response 4: Thank you for pointing this out. This article explores the use of sample splitting in research, rather than subjective assumptions. The differences in regional systems are taken into account, as suggested by Grossman and Helpman[38] and Nathan[39], which may affect the quality of contract execution, implementation, and efficiency. This can further impact the implementation of economic policies and ultimately lead to economic development. Differences in ownership are also considered, as different types of enterprises with varying degrees of ownership may have varying responses to environmental policies. For instance, private enterprises play a vital role in China's labor market but are also subject to discriminatory policies. The classification of sectors as high-polluting or clean is based on the assumption that similar policies and policy intensity may have a more significant impact on high-polluting enterprises. This can be referred to the 1st paragraph of section 6.2.1 from Line 673.
Comment 5: The hypothesis is not well developed.
Response 5: Thank you for pointing this out. We agree with this comment. The three hypotheses are restated in the revised manuscript, referring to Line 328-337, which is shown as follows.
Hypothesis 1(H1). The carbon reduction policy has a positive promoting effect and a negative constraining effect on the employment scale of manufacturing enterprises; its comprehensive effects must be interpreted through empirical analysis.
Hypothesis 2 (H2). The impact of the carbon reduction policy on the employment scale of manufacturing enterprises operates simultaneously through two channels of improving employment creation and reducing employment destruction.
Hypothesis 3 (H3). The impact of the carbon reduction policy on enterprise employment varies heterogeneously for different types of enterprises and is influenced by factors such as institutional environment, ownership type and industry pollution characteristics.
Comment 6: Some references cited in the paper are not easily found online, which raises questions about the paper's academic rigor.
Response 6: Thank you for pointing this out. We agree with this comment. We apologize for the misunderstanding of the reference list due to the incorrect citation format. We have eliminated some low-citation rate literature and cited some high-citation rate literature. Now Reference list (Line 851) is updated and all literature is reorganized.
Comment 7: The title is too long.
Response 7: Thank you for pointing this out. We agree with this comment. The title of an article is indeed very important as it should include the main research object. We have tried our best to try to shorten the title and make specific modifications as follows. “Environmental Regulation and Employment Changes in Chinese Manufacturing Enterprises: Micro evidence from the Top 10,000 Energy Consuming Enterprises Program”, which is shown in Line 2-4 of the revised manuscript.
Comment 8: While the study focuses on China, it lacks a discussion on the generalizability of its findings to other contexts or countries.
Response 8: Thank you for pointing this out. While international research has yielded inconclusive results regarding whether environmental regulatory policies increase or decrease employment in enterprises, studies specific to China are scarce and lack micro-level evidence from enterprises. Moreover, the objectivity of determining environmental regulatory variables may be compromised due to potential government intervention. The value of this study lies in its examination of a sample of manufacturing enterprises nationwide in China, taking into account the comprehensive impact of the carbon reduction policy, which imposes constraints on enterprises' energy use rights. It provides a supplementary contribution to the existing literature by considering the effects of environmental regulatory policies on both overall employment levels and employment structure.
Comments on the Quality of English Language: The paper is not clearly articulated. Phrases are cumbersome, and terms like 'policy intensity' and 'employment creation and destruction channels' are not explicitly defined. In addition, what the background of the Top 10,000 Energy Consuming Enterprises Program. There is no specific introduction.
Response to Comments on the Quality of English Language: Thank you for pointing this out. All these problems have been fixed in the previous responses.
Additional clarifications
All revisions in the manuscript are highlighted, including fixing typos and correcting error numbers. Other revision is shown as follows.
Line 2: The title of this article is slightly modified.
Line 9: Abstract is rewritten.
Line 118: The last paragraph of Part 1 is updated.
Line 225: New part 3 about policy background is added.
Line 334: Hypothesis 3 is added.
Line 771: Discussion about policy implication is added.
Line 811: Conclusions is separated from policy implication.
Line 851: Reference list is updated and all literature is reorganized.
Once again, thank you very much for your comments and suggestions!
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
I am happy with the revised version.
Reviewer 2 Report
Well done
