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Does Green Finance Promote the Green Transformation of China’s Manufacturing Industry?

Sustainability 2023, 15(8), 6614; https://doi.org/10.3390/su15086614

by Ming Chen

, Lina Song^†, Xiaobo Zhu^*, Yanshuo Zhu^† and Chuanhao Liu

Reviewer 1: Anonymous

Reviewer 2:

Yi-Chang Chen

Sustainability 2023, 15(8), 6614; https://doi.org/10.3390/su15086614

Submission received: 2 February 2023 / Revised: 11 April 2023 / Accepted: 11 April 2023 / Published: 13 April 2023

(This article belongs to the Special Issue Green Innovation: The Power Source of Enterprise Sustainable Development)

Round 1

Reviewer 1 Report

The paper entitled as "Does Green Finance promote the green transformation of China's manufacturing Industry?" with authors words, focuses on discussing the impact mechanisms of the implementation of green finance on the green transformation of Chinese manufacturing industry from three aspects: fund formation and orientation, credit catalysis, integration & decentralization, and uses DID to conduct a quasi-natural experiment. Accordingly, they note the following findings: (1) The implementation of green finance significantly promotes the green transformation of China's manufacturing industry and has good sustainability. The mechanisms of fund formation and orientation, credit catalysis, integration and decentralization are the primary mechanism of green finance to promote the green transformation of the manufacturing industry, and the implementation effect of green finance has apparent heterogeneity; (2)The promoting effect of green finance on the green transformation of the manufacturing industry is only significant in state-owned industries but not significant in non-state-owned industries; (3) The influence of green finance on the green transformation efficiency of manufacturing industry with a better information environment is more significant than manufacturing industry with a worse information environment; (4) Faced with the pressure of investing in green industries, the coping strategies adopted by enterprises in different industries are quite different. The promoting effect of green finance on the green transformation of the manufacturing industry is significant in low-competition industries but insignificant in high-competition industries. Finally, authors noted that This study has enriched the research on the effect of green finance policies, explored solutions based on quasi-nature, and provided policy references for the green transformation of the manufacturing industry.

Following should be addressed and corrected.

1. The abstract should be shortened to be more focused in certain places. But most importantly, the statements are extrapolating. The sample data should be stated and the findings and policy suggestions should be related to the analyzed country and data.

2. Method is not stated in abstract. Similar to country and data. Please update.

3. If data section is read, what is the sample size in the model? Not clear. Time range is stated. Company types are stated. How many? What are their descriptive statistics?

4. I have suspicions if the dependent variable represents Efficiency of green transformation. Factors are listed by the authors for this variable are:

Total assets per dollar/yuan, Operating cost/yuan, Number of R&D personnel/person, R&D investment amount/yuan.

These factors are firm performance criteria, how are they related to green transformation or green efficiency? Only green factor among 8 factors listed for green efficiency is 1 out of 8, green patents. The rest are financial indicators.

5. Explanatory variable green finance policy also leads to doubts regarding its representative power for the variable it should represent.

Other than these, the control variables have no problems, which are common and logical financial and economic ndicators.

6. The descriptive table gives overall results for both samples. The results could alter and also give important insights regarding the differences of the experiment group.

7. Given the concerns regarding the representative power of explanatory variable and dependent variable of what they should represent, the findings seriously raise concerns for the empirics.

8. Lastly, the similarity is asked from the reviewers. I checked it and it led to more than 30%. This is a very large score for a research paper to be considered. It should be reduced. I did not check sources however first three led to 3% similarity totaling to 8%. There are 1% similarities to some papers or sources but when checked, I see no reference is given. Example is, almost the whole parahraph under green finance (post) heading.

Author Response

Dear Editor and Reviewer:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Does Green Finance Promote the Green Transformation of China's Manufacturing Industry?” ( sustainability-2227997). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing:

Responds to the Reviewer 1 comments:

Comment 1:

The abstract should be shortened to be more focused in certain places. But most importantly, the statements are extrapolating. The sample data should be stated and the findings and policy suggestions should be related to the analyzed country and data.

Response1: Thank you very much for your comment. We have simplified the abstract and added the time span, countries, and methods description of the study.

Comment 2:

Method is not stated in abstract. Similar to country and data. Please update.

Response2: Thank you very much for your comment. We have simplified the abstract and added the time span, countries, and methods description of the study. The revised summary is as follows:

Abstract: The green transformation of the manufacturing industry is related to the low-carbon and green development of the economy. The study explored the impact mechanism of the implementation of green finance policy on the green transformation of China's manufacturing industry from 2013 to 2021 from three aspects of capital formation and incentive, credit catalysis, integration and decentralization, and conducted a quasi-natural experiment using DID model.Research finds that: (1) The implementation of green finance significantly promotes the green transformation of China's manufacturing industry and has good sustainability. The mechanisms of fund formation and orientation, credit catalysis, integration and decentralization are the primary mechanism of green finance to promote the green transformation of the manufacturing industry, and the implementation effect of green finance has apparent heterogeneity; (2)The promoting effect of green finance on the green transformation of the manufacturing industry is only significant in state-owned industries but not significant in non-state-owned industries; (3)The influence of green finance on the green transformation efficiency of manufacturing industry with a better information environment is more significant than manufacturing industry with a worse information environment; (4) Faced with the pressure of investing in green industries, the coping strategies adopted by enterprises in different industries are quite different. The promoting effect of green finance on the green transformation of the manufacturing industry is significant in low-competition industries but insignificant in high-competition industries. This study has enriched the research on the effect of green finance policies, explored solutions based on quasi-nature, and provided policy references for the green transformation of the manufacturing industry.

Comment 3:

If data section is read, what is the sample size in the model? Not clear. Time range is stated. Company types are stated. How many? What are their descriptive statistics?

Response3: Thank you very much for your comment. We selected Chinese manufacturing listed companies with A-shares from 2013 to 2021 as the research object, and finally obtained 9994 sample observations from 1174 enterprises, including 3078 observations from 290 enterprises in the experimental group and 6916 observations from 884 enterprises in the control group. We have made correction according to the Reviewer’s comments in the manuscript.

Comment 4:

I have suspicions if the dependent variable represents Efficiency of green transformation. Factors are listed by the authors for this variable are:

Total assets per dollar/yuan, Operating cost/yuan, Number of R&D personnel/person, R&D investment amount/yuan.

Response4: Thank you very much for your advice. We are very sorry for the lack of consideration of the integrity of green indicators in the original indicator selection. In the revised manuscript, we re-selected the input and output indicators of green total factor productivity according to the requirements of the reviewers. In addition, expected output and unexpected output are also considered when calculating green total factor productivity.

Table 1. Variable table.

Variable properties	Variable name	Variable meaning	computingmethod
Input	Labor	Labor input	the number of on-the-job employees of each listed enterprise in each year
	Capital	Capital input	The net value of enterprise fixed assets (net value of enterprise fixed assets = original value of enterprise fixed assets - accumulated depreciation of enterprise fixed assets) is used as capital input
	Energy	Energy input	Weighted by the power consumption of manufacturing listed companies in the city where the listed company is located. As a proxy variable of energy after weighted processing
	GTI	Green technology innovation input	The ratio of the number of green patents applied by listed companies to the total number of patent applications（Ratio Green Pat) characterizes green technological innovation of enterprises.
Output	EP	Expected output	Total annual operating income of each enterprise
Output	UEP	Unexpected output	The weighted processing of carbon dioxide data of urban emissions and enterprise emissions is based on the correlation coefficient
Explained variable	GTFP	Green total factor productivity	Through the SBM model of non radial and non angle in DEA model and combined with ML index to calculate


Explanatory variables	Treat	Dummy variable	1 for the treatment group and 0 for the control group
	Post	Dummy variable	0 before 2017, otherwise 1
	Treat*Post	Net effect of policy	Treat*Post
Control variable	Size	Enterprise size	Logarithm of total assets of the enterprise
	Time	Business life	[current year - (Listing year +1)] take logarithm
	Cash	Corporate cash holdings	Monetary cash to assets ratio
	Cap	Asset-liability ratio	Ratio of total assets to total liabilities
	Jcap	Return on equity	Ratio of net profit to total assets
	Rev	Revenue growth rate	The ratio of the increase in the current year's operating Revenue to the total operating revenue of the previous year
	Tq	Tobin's Q value	The ratio of a firm's market price (share price) to its replacement cost

Variable setting

(1) Dependent variable

The dependent variable of this study is the green total factor productivity (GTFP) of Chinese manufacturing listed companies as the agent variable of green transformation. On the basis of traditional total factor productivity (TFP), green total factor productivity (GTFP) considers energy consumption and pollution emissions, and can objectively reflect the green development level of listed companies in China's manufacturing industry[49].

The SBM (Slack-Based Measure) model[50] allows the existence of inefficiency decision making units, and the unexpected output is also considered. The Malmquist-Luenberger index[51] can measure the dynamic change of green total factor productivity in the presence of unexpected output. Therefore, the research adopts the non-radial SBM model in the DEA model and combines the ML index to measure the GTFP of manufacturing listed companies.

;

(1)

denotes the i-th input quantity for the f--th manufacturing listed companies, denotes the i-th desired output for the f-th manufacturing listed companies and denotes the i-th undesired output for the f-th manufacturing listed companies. ,, ; ; ; ; . , and denote inputs, desired outputs and non-desired outputs respectively, represents the weight of the decision unit, when , = = = 0, it means that the decision unit inputs and outputs are fully efficient, when , it represents the loss of efficiency of the decision unit. The GTFP index of the decision unit from period t to the next period t+1 is the ML index, which can also be decomposed into the Technology Change and Efficiency Change indices, which are decomposed as follows.

(2)

denotes the GTFP index of the decision unit from period t to the next period t+1, denotes the input of the manufacturing listed companies i in period t, denotes the desired output of the manufacturing listed companies i in period t, denotes the non-desired output of the manufacturing listed companies i in period t, denotes the manufacturing listed companies i in period t, denotes the technological progress index, i.e. the degree of movement of the firm from period t to the technological frontier in period t+1, and denotes the technical efficiency index, i.e. the extent to which the firm moves closer to the production possibility frontier from period t to period t+1.

This paper selected Chinese manufacturing listed companies in A shares from 2013 to 2021 as a sample. After a series of treatments, a total of 1,174 were treated. The input-output indicators were obtained from the annual reports of both the Shanghai and Shenzhen stock exchange, the official websites of the listed enterprises and the CSMAR database. The specific input-output indicators are as follows.

The specific input-output indicators are as follows.

① Labor input. Labor input uses the number of employees of listed enterprises in various companies in various years[52].

② Capital input. Capital input selects the net asset value of the enterprise at the corporate level (the net value of the enterprise fixed asset = the original value of the enterprise fixed asset-the cumulative depreciation of the enterprise fixed assets) as the alternative indicator of capital investment[53].

③ Energy input. Considering the availability of micro-level data, the power consumption of manufacturing enterprises in the city where the listed company is located is used to approximate the energy input. After weighting, it is used as the proxy variable of energy[54].

④ Green technology innovation input. The ratio of the number of green patents applied by listed companies to the total number of patent applications（Ratio Green Pat) characterizes green technological innovation of enterprises[55].

⑤ Expected output. The expected output is expressed by the gross operating income of the manufacturing listed companies for each year[56].

⑥ Unexpected output. Since NO₂, SO₂, smoke, and CO₂ are the main pollutants for manufacturing listed companies, this paper selected industrial NO₂, SO₂, smoke and dust emissions, and CO₂ as non-expected output measures. The weighted processing of carbon dioxide data of urban emissions and enterprise emissions is based on the correlation coefficient[57].

(2) Key explanatory variables

The double difference model is used to investigate how the green financial policy set up in China in 2017 affects the green transformation of listed manufacturing companies. The model can use the double difference to mitigate the interference of other factors besides the policy on the estimated results. If the registered address of the manufacturing listed company is located in the green finance reform and innovation pilot zone, the value of this variable is 1, otherwise the value is 0[58-59]. Treat=1, otherwise Treat=0. At the same time, the policy time variable "post" is set according to the time node of the establishment of the green financial reform and innovation pilot zone[60]. When the sample observation value is in 2017 and after, the value of this variable is 1, otherwise the value is 0.

(3) Control variables

Referring to relevant studies [61-63], this paper selected firm size (Size), firm years (Time), cash holdings (Cash), asset-liability ratio (Cap), return on equity (Jcap), revenue growth rate (Rev) and Tobin's Q value as control variables. Refer to Table 1 for specific variable definitions.

Comment 5:

Explanatory variable green finance policy also leads to doubts regarding its representative power for the variable it should represent.

Other than these, the control variables have no problems, which are common and logical financial and economic ndicators.

Response5: Thank you very much for your comment. We re-explained and optimized the Key explanatory variable(Green Financial Policy), and revised it in the manuscript. The revised contents are as follows:

Key explanatory variables

In 2016, seven ministries and commissions including the People's Bank of China issued the Guidelines on Building a Green Finance System, which defined green finance as economic activities that support environmental improvement, climate change response, and efficient use of resources. It provides financial services for project investment and financing, project operation, and risk management in environmental protection, energy conservation, clean energy, green transportation, and green building. The green finance policy supports the development of green economy and serves the green transformation of enterprises. However, due to China's lack of experience in developing green finance, it is necessary to select some regions to carry out reform experiments. In 2017, the Chinese government issued the Overall Plan for the Construction of Green Finance Reform and Innovation Pilot Zone, which is a new attempt by the Chinese government to promote green finance by building green finance reform and innovation pilot zones in five provinces (Zhejiang, Jiangxi, Guangdong, Guizhou and Xinjiang) and providing support for the development of green finance through monetary and financial policies, fiscal and tax policies[58-59].

The double difference model is used to investigate how the green financial policy set up in China in 2017 affects the green transformation of listed manufacturing companies. The model can use the double difference to mitigate the interference of other factors besides the policy on the estimated results. Set the green finance policy grouping variable tree. If the registered address of the manufacturing listed company is located in the green finance reform and innovation pilot zone, the value of this variable is 1, otherwise the value is 0. Treat=1, otherwise Treat=0. At the same time, the policy time variable "post" is set according to the time node of the establishment of the green financial reform and innovation pilot zone[60]. When the sample observation value is in 2017 and after, the value of this variable is 1, otherwise the value is 0.

Comment 6:

The descriptive table gives overall results for both samples. The results could alter and also give important insights regarding the differences of the experiment group.

Response: Thank you very much for your comment. We have reanalyzed and carefully proofread the descriptive statistics. Table2 provides descriptive statistical analysis of the main variables. The observation value of the experimental group is 3078, and that of the control group is 6916, a total of 9994.

Table 2. Descriptive statistics of major variables.

Variable	Observed value	Average value	Standard deviation	Minimum value	Median	Maximum value
Gre	9994	0.36	0.21	0.05	0.32	1.00
Treat	9994	0.26	0.44	0.00	0.00	1.00
Post	9994	0.69	0.46	0.00	1.00	1.00
Size	9994	22.19	1.16	19.96	22.07	25.74
Cash	9994	0.17	0.11	0.02	0.14	0.57
Cap	9994	0.41	0.19	0.06	0.40	0.91
Jcap	9994	0.04	0.19	-1.24	0.06	0.32
Time	9994	10.91	6.98	0.00	9.00	27.00
Rev	9994	0.25	0.59	-0.68	0.13	3.79
Tq	9994	2.13	1.33	0.87	1.70	8.59

Comment 7:

Given the concerns regarding the representative power of explanatory variable and dependent variable of what they should represent, the findings seriously raise concerns for the empirics.

Response: Thank you very much for your advice. According to the suggestions of the reviewers, we supplemented and optimized the input and output indicators of the explained variable (green total factor productivity), redefined the explanatory variable, and made the research more scientific and rigorous. Due to the changes in the input and output indicators of the explained variables, we conducted a new empirical analysis on the original manuscript to test the effectiveness of the research results. Thank you again for your very valuable comments.

Comment 8:

Lastly, the similarity is asked from the reviewers. I checked it and it led to more than 30%. This is a very large score for a research paper to be considered. It should be reduced. I did not check sources however first three led to 3% similarity totaling to 8%. There are 1% similarities to some papers or sources but when checked, I see no reference is given. Example is, almost the whole parahraph under green finance (post) heading.

Response: Thank you very much for your advice. We corrected the original manuscript and reduced the repetition rate to below 15%, hoping to meet the requirements of publication.

52.Li, C.F.; Song, T.; Wang, W.F.; Gu, X.Y.; Li, Z.; Lai, Y.Z. Analysis and Measurement of Barriers to Green Transformation Behavior of Resource Industries. International Journal of Environmental Research and Public Health 2022, 19, 13821.

53.Deng, Q.Z.; Zhou, S.Z.; Peng, F. Measuring Green Innovation Efficiency for China’s High-Tech Manufacturing Industry: A Network DEA Approach. Mathematical Problems In Engineering 2020, 2020, 8902416.

54.Zhu, J.H.; Wang, S.S. Evaluation and Influencing Factor Analysis of Sustainable Green Transformation Efficiency of Resource-Based Cities in Western China in the Post-COVID-19 Era. Frontiers In Public Health 2022, 10, 832904.

55.Hu, D.X.; Jiao, J.L.; Chen, C.X.; Xiao, R.Q.; Tang, Y.S. Does global value China embeddedness matter for the green innovation value chain? Frontiers In Environmental Science 2022, 10, 779617.

56.Yang, Q.; Sun, Z.G.; Zhang, H.B.A. Assessment of Urban Green Development Efficiency Based on Three-Stage DEA: A Case Study from China’s Yangtze River Delta. Sustainability 2022, 14, 12076.

57.Zhou, Z.; Ma, Z.C.; Lin, X.W. Carbon emissions trading policy and green transformation of China’s manufacturing industry: Mechanism assessment and policy implications. Frontiers In Environment Science 2022, 10, 984612.

58.Dong, Z.; Xu, H.D.; Zhang, Z.F.; Lyu, Y.P.; Lu, Y.Q.; Duan, H.Y. Weather Green Finance Improves Green Innovation of Listed Companies Evidence from China. International Journal of Environmental Research and Pubic Health 2022, 19, 10882.

59.Huang, H.F.; Zhang, J. Research on the Environmental Effect of Green Finance Policy Based on the Analysis of Pilot Zones for Green Finance Reform and Innovations. Sustainability 2021, 13, 3754.

60.Lu, N.; Wu, J.H.; Liu, Z.M. How Does Green Finance Reform Affect Enterprises Green Technology Innovation? Evidence from China. Sustainability 2022, 14, 9865.

61.Gong, M.Q.; You, Z.; Wang, L.T.; Cheng, J.H. Environmental Regulation, Trade Comparative Advantage, and the Manufacturing Industry’s Green Transformation and Upgrading. International Journal of Environmental Research and Public Health 2020, 17, 2823.

62.Shen, L.; Fan, R.J.; Wang, Y.Y.; Yu, Z.Q.; Tang, R.Y. Impacts of Environmental Regulation on the Green Transformation and Upgrading Manufacturing Enterprises. International Journal of Environmental Research and Public Health 2020, 17, 7680.

63.Zhai, X.Q.; An, Y.F. Analyzing Influencing Factors of Green Transformation in China’s Manufacturing Industry under Environmental Regulation: A Structural Equation Model. Journal of Cleaner Production 2020, 251, 119760.

We appreciate for Editor/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Sincerely

Dr. Ming Chen (Associate Professor)

E-mail: [email protected]

Qingdao University of Science and Technology, China

Corresponding author:

Dr. Xiaobo Zhu (Lecturer)

E-mail: [email protected];

Author Response File: Author Response.pdf

Reviewer 2 Report

General comments:

The article treats a good research question, where the authors study the green finance policy effect on the green transformation for the Chinese manufacturing industries using the Max-DEA model and DID model. However, I have some concerns that should be addressed.

1. My first concern is whether the dummy variable “Green Finance (post)” is a reasonable measure of green transformation.

1) the authors mention that green finance provides financial services for project investment and financing, and so on. However, it is not clear in the article which companies are directly or indirectly affected by the policy. I think a clear and definite variable related to the policy is a more appropriate dummy variable setting rather than a single event day.

2. Is the significant relationship between “Green Transition Efficiency (Gre)” and green patents that the Max-DEA is constructed based on the policy?

1) As far as I know, the use of input and output variables is of great significance to the use of the DEA model. I found that among the input and output variables designed by the author, the only variable related to green conversion is the number of green patents. Obviously, the authors think that the number of green patents plays a role in the efficiency of green transformation. I have doubts about it. There are two reasons. First, the author did not provide the impact of changes in the number of patents under this policy background. Second, whether there is a clear definition and source of the number of green patents? In the preface, the author mentions many topics about neutralization, but they are not used as a variable in DEA, such as carbon emissions, but the number of green patents. In addition, there is no effective connection with carbon neutrality. The authors can also consider more composite measures, such as carbon credits.

2) Also, the workings of the Max-DEA model or the mathematical equations are not shown. It is used extensively in the two papers cited by the author but is not strongly related to this paper.

3. In the abstract, the authors mention that The mechanisms of fund formation and orientation, credit catalysis, integration and decentralization are the primary mechanism of green finance to promote the green transformation. How can the authors draw conclusions due to a lack of empirical findings in the context?

1) The first occurrence of an abbreviation in the abstract, such as "DID", should use the full name.

4. The author mentions a very extensive and detailed report on policy content in the text, which is not a good reading experience for readers. The author should focus on the content mentioned in the title, rigorously define the key sub-topics, and cite representative articles as evidence.

5. The estimation of this paper is based on cross-sectional data of the Chinese industries using the Max-DEA model. Have the authors considered a DEA estimation covering wider variables of both green finance and green transformation? This should provide more representative and general results.

6. check the presence of “Note: The Z-values in brackets are robust” is correct. (T-values?)

7. I don't think DID is necessary for the test of policy effect, it only needs to be used as a dummy variable in the panel regression. Focusing on the setting and definition of DEA is enough to contribute if the authors think that the efficiency of green transformation is the key variable.

sincerely,

Author Response

尊敬的编辑和审稿人：

感谢您的来信和审稿人对我们题为“绿色金融是否促进中国制造业绿色转型？”的稿件的评论。（可持续性-2227997）。这些意见都很有价值，对修改和完善我们的论文很有帮助，对我们的研究也有重要的指导意义。我们仔细研究了意见并进行了更正，希望得到批准。修改部分在论文中用红色标记。论文中的主要更正和对审稿人意见的回复如下：

回复审稿人2条评论：

评论 1：

我首先关心的是虚拟变量“绿色金融（后）”是否是衡量绿色转型的合理指标。

Response: Thank you very much for your advice. Green finance can provide financial services such as investment and financing for green projects, promote environmental protection and governance, and guide resources from enterprises with high pollution and energy consumption to enterprises with advanced technology. However, due to China's lack of experience in developing green finance, it is necessary to select some regions for reform experiments. In 2017, the Chinese government issued the Overall Plan for the Construction of Green Finance Reform and Innovation Pilot Zone, which is a new attempt by the Chinese government to build green finance reform and innovation pilot zones in five provinces (Zhejiang, Jiangxi, Guangdong, Guizhou and Xinjiang), and provide support for the development of green finance through monetary and financial policies, fiscal and tax policies, and promote green finance.

Therefore, we use the double-difference model to study how the green financial policies formulated by China affect the green transformation of listed manufacturing companies. Green financial policy is the explanatory variable, and green transformation is the explanatory variable. The model can use double differences to mitigate the interference of other factors other than policies on the estimated results.

The double difference method (DID) is a measurement model that can be used to evaluate the effect of policies. The model assumes that the policy will only affect some subjects in the market, but not other subjects. Therefore, it can be regarded as a natural experiment to compare and analyze the policy effects with the differences presented by the two types of subjects (Li Nan and Qiao Zhen, 2010). The principle is as follows: suppose Y is the external representation of different subjects; D is the policy dummy variable, D=0 represents the experimental group, D=1 represents the control group; T is a time variable, T=0 is before the implementation of the policy, and T=1 is the effective period of the policy.

If the registered address of the manufacturing listed company is located in the green finance reform and innovation pilot zone, the value of this variable is 1, but the value is 0 [58-59]. Treat=1, otherwise Treat=0. At the same time, the policy time variable "post" is set according to the time node of the establishment of the green financial reform and innovation pilot zone [60]. When the sample observation value is in 2017 and after, the value of this variable is 1, otherwise the value is 0.

Comment 2:

Is the significant relationship between “Green Transition Efficiency (Gre)” and green patents that the Max-DEA is constructed based on the policy?

2) Also, the workings of the Max-DEA model or the mathematical equations are not shown. It is used extensively in the two papers cited by the author but is not strongly related to this paper.

Response: Thank you very much for your comment. We are very sorry for the lack of consideration of the integrity of green indicators in the original indicator selection. In the revised manuscript, we re-selected the input and output indicators of green total factor productivity according to the requirements of the reviewers. In addition, expected output and unexpected output are also considered when calculating green total factor productivity.

The specific input-output indicators are as follows.

① Labor input. Labor input uses the number of employees of listed enterprises in various companies in various years[52].

⑤ Expected output. The expected output is expressed by the gross operating income of the manufacturing listed companies for each year[56].

Since we have improved the input and output indicators of green total factor productivity and added unexpected output variables, the original DEA model has been replaced, and the SBM-DEA model has been adopted and combined with the ML index to calculate green total factor productivity. The following are the contents we have supplemented and improved. We have added them to the manuscript.

;

(1)

denotes the i-th input quantity for the f--th manufacturing listed companies, denotes the i-th desired output for the f-th manufacturing listed companies and denotes the i-th undesired output for the f-th manufacturing listed companies.,;;;;., and denote the slack variables for inputs, desired outputs and non-desired outputs respectively,represents the weight of the decision unit, when , === 0, it means that the decision unit inputs and outputs are fully efficient, when , it represents the loss of efficiency of the decision unit. The GTFP index of the decision unit from period t to the next period t+1 is the ML index, which can also be decomposed into the Technology Change and Efficiency Change indices, which are decomposed as follows.

(2)

Comment 3:

In the abstract, the authors mention that The mechanisms of fund formation and orientation, credit catalysis, integration and decentralization are the primary mechanism of green finance to promote the green transformation. How can the authors draw conclusions due to a lack of empirical findings in the context?

1) The first occurrence of an abbreviation in the abstract, such as "DID", should use the full name.

Response: Thank you very much for your comment. "DID" is used by the full name in the abstract, we have modified it. In addition, the question you are interested in is an important discovery in our research. Through the mechanism analysis, we found that the main mechanisms of green finance to promote the green transformation of manufacturing industry are capital formation and guidance, credit catalysis, integration and decentralization.

（1）Based on the heterogeneity test of the nature of enterprise ownership, we find that green finance can effectively promote the green transformation of state-owned manufacturing industries through the capital formation and guidance mechanism.

Table 7. Heterogeneity analysis based on the nature of enterprise ownership.

Variable	Efficiency of green transformation (Gre)
Variable	State-owned industries	Non-state-owned industries
Treat×Post	0.0349***	0.0076
	（5.4）	(1.01)
Constant	1.7047***	1.6471***
	（18.82）	(16.56)
Control variable	Control	Control
Firm fixed effect	Control	Control
Year fixed effect	Control	Control
N	2828	7166
R²	0.6534	0.6676

The implementation of green finance policy requires the government to advocate and correct the capital orientation and make joint efforts with financial institutions. From the perspective of capital supply, finance is the source of capital for various industries and helps industrial development and transformation. In terms of demand, the state encourages and supports loans to energy conservation, environmental protection and technological innovation industries, and strictly restricts credit lines to industries with high pollution and energy consumption, thus affecting the capital demand of the manufacturing industry. The promoting effect of green finance on the green transformation of manufacturing industry is only significant in the sample of state-owned industries but not in the sample of non-state-owned industries. Compared with non-state-owned industries, state-owned industries are more affected by government policies. Under the green finance policy, state-owned industries will actively respond to the government's environmental governance demands and increase investment in green industries. Green finance can guide the formation of funds by providing funds for green projects or reducing the interest rate of loans. It can effectively reduce the financing cost of green innovation projects of manufacturing enterprises and provide the internal impetus for the green transformation of the manufacturing industry. At the same time, the development of green finance policy will convey the information of developing green economy and the signal of green transformation policy to the whole society, attract the public's attention to green products, guide the direction of funds and encourage manufacturing enterprises to develop new green products, transform the existing production lines, realize intensive and efficient production, and improve the market competitiveness of their products. However, non-state-owned industries are less willing to promote green investment due to the need for the above incentive mechanism. In addition, due to the existence of externalities, the market fails. Currently, the government needs to intervene in the credit behavior of financial institutions. The government advocates implementing green financial policies by financial institutions to guide the flow of funds to green industries, promote the green transformation of the manufacturing industry and correct the market mechanism. Green finance encourages manufacturing enterprises to carry out the green transformation, realize the gradual transformation of industrial structure from labor-intensive to capital, technology and knowledge-intensive, guide the formation and orientation of manufacturing capital to continuously increase the added value of products and improve economic benefits, and finally promote the green transformation of the manufacturing industry.

（2）Based on the heterogeneity test of enterprise information environment, we find that green finance can effectively promote the green transformation of manufacturing industry through a better information environment through the credit catalytic mechanism.

Table 8. Heterogeneity analysis based on manufacturing enterprise information environment.

Variable	Efficiency of green transformation(Gre)
Variable	(1)	(2)
Treat×Post	-0.0135	0.0817***
Treat×Post	(-1.22)	(5.70)
Constant	4.0819***	-0.6983
Constant	23.69	(-0.70)
Control variable	Control	Control
Firm fixed effect	Control	Control
Year fixed effect	Control	Control
N	4424	2245
R²	0.9182	0.7916

The credit catalytic mechanism of green finance invests capital not limited to the projects and industries with obvious high benefits but usually selects the projects and industries with forward-looking and good diffusivity as the starting point. That is, the green industry catalyzed the green transformation of the manufacturing industry to realize the sustainable development of the economy and society. The difference of enterprise information environment can represent its credit rating. A good information environment increases the sensitivity of enterprises to green finance policy. The influence of green finance on the green transformation efficiency of manufacturing industry with a better information environment is more significant than manufacturing enterprises with a poor information environment. Analysts' accurate earnings forecast of green finance provides a more accurate and valuable reference for manufacturing enterprises to make decisions. Meanwhile, through the double-layer credit catalytic mechanism of a good information environment of manufacturing enterprises and green finance policy, capital investment is not limited to projects and industries with obvious high benefits. Give full play to the initiative of green finance capital to drive the green transformation of the manufacturing industry. That is, green finance catalyzes the green transformation of the manufacturing industry to achieve the sustainable development of the economy and society. At the same time, the positive impact of the green transformation of the manufacturing industry on the information quantity and quality of manufacturing enterprises reduces the deviation of analysts' information acquisition, improves the information disclosure behavior of enterprises, and is conducive to the implementation of green finance.

（3）Based on the heterogeneity test of the degree of industry competition, we found that the green financial policy can effectively promote the green transformation of low competitive manufacturing enterprises through the integration and decentralization mechanism.

Table 9. Heterogeneity analysis based on industry competition degree.

Variable	Efficiency of green transformation (Gre)
Variable	High-competition industries	Low-competition industries
Treat×Post	0.0051	0.0907***
Treat×Post	(0.50)	(6.27)
Constant	0.8299	2.0439***
Constant	(1.58)	(4.69)
Control variable	Control	Control
Firm fixed effect	Control	Control
Year fixed effect	Control	Control
N	3954	3341
R²	0.6559	0.6474

Faced with the pressure of investment in the green industry, enterprises in different industries adopt different coping strategies. By calculating the Herfindahl-Hirschman index of each industry, we divided the sample enterprises into high-competition and low-competition industries for comparative analysis. The promoting effect of green finance policy on the green transformation of the manufacturing industry is significant in the samples of low-competition industries but insignificant in the samples of high-competition industries. The green transformation of the manufacturing industry requires a large amount of capital input, and implementing a green finance policy provides the necessary funds for developing manufacturing enterprises with low-competition industries. Although manufacturing enterprises in low-competition industries may gain super profits and have a monopoly in a specific field, they are, to some extent, promoted to enter green production. However, it will also promote integration and merger among enterprises. After a manufacturing enterprise in a low-competition industries obtains capital, the technology and capital invested in green production can produce greener and more popular commodities by consumers to gain greater market competitiveness, change the corporate governance structure of the manufacturing enterprise, and reduce the manufacturing cost through integration and economies of scale between enterprises. Changing its industrial structure, mergers and acquisitions between enterprises makes inter-industries competition moderate and promote green transformation. However, in order to survive in the fiercely competitive market, some manufacturing enterprises with high-competition industries can only take short-term interests as the biggest driving force for their development due to their small scale, excessively dispersed industries, and low market concentration, thus ignoring environmental protection and green transformation to a certain extent. So, the exogenous policy impact on the green transformation of such enterprises is not significant.

Comment 4:

The author mentions a very extensive and detailed report on policy content in the text, which is not a good reading experience for readers. The author should focus on the content mentioned in the title, rigorously define the key sub-topics, and cite representative articles as evidence.

Response: Thank you very much for your advice. According to your suggestions, we have done our best to improve our manuscript and let readers know the positive impact of green financial policy on the green transformation of China's manufacturing industry as much as possible. We hope that this policy can be promoted in other countries to promote the green development of local manufacturing industry, and we hope to make a contribution to the harmonious development of human economy and environment. We have supplemented the relevant topics of green financial policy and further enriched the references. The references we added include:

[49]Jin, W.;Gao, S.; Pan, S. Research on the impact mechanism of environmental regulation on green total factor productivity from the perspective of innovative human capital. Environmental Science And Pollution Research 2022, 8.

[50]Tonk,K. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 2001, 120, 498-509.

[51]Chung,Y.;Fare,R. Productivity and undesirable outputs: A directional distance function approach. Microeconomics 1997, 51, 229-240.

[52]Li, C.F.; Song, T.; Wang, W.F.; Gu, X.Y.; Li, Z.; Lai, Y.Z. Analysis and Measurement of Barriers to Green Transformation Behavior of Resource Industries. International Journal of Environmental Research and Public Health 2022, 19, 13821.

[53]Deng, Q.Z.; Zhou, S.Z.; Peng, F. Measuring Green Innovation Efficiency for China’s High-Tech Manufacturing Industry: A Network DEA Approach. Mathematical Problems In Engineering 2020, 2020, 8902416.

[54]Zhu, J.H.; Wang, S.S. Evaluation and Influencing Factor Analysis of Sustainable Green Transformation Efficiency of Resource-Based Cities in Western China in the Post-COVID-19 Era. Frontiers In Public Health 2022, 10, 832904.

[55]Hu, D.X.; Jiao, J.L.; Chen, C.X.; Xiao, R.Q.; Tang, Y.S. Does global value China embeddedness matter for the green innovation value chain? Frontiers In Environmental Science 2022, 10, 779617.

[56]Yang, Q.; Sun, Z.G.; Zhang, H.B.A. Assessment of Urban Green Development Efficiency Based on Three-Stage DEA: A Case Study from China’s Yangtze River Delta. Sustainability 2022, 14, 12076.

[57]Zhou, Z.; Ma, Z.C.; Lin, X.W. Carbon emissions trading policy and green transformation of China’s manufacturing industry: Mechanism assessment and policy implications. Frontiers In Environment Science 2022, 10, 984612.

[58]Dong, Z.; Xu, H.D.; Zhang, Z.F.; Lyu, Y.P.; Lu, Y.Q.; Duan, H.Y. Weather Green Finance Improves Green Innovation of Listed Companies Evidence from China. International Journal of Environmental Research and Pubic Health 2022, 19, 10882.

[59]Huang, H.F.; Zhang, J. Research on the Environmental Effect of Green Finance Policy Based on the Analysis of Pilot Zones for Green Finance Reform and Innovations. Sustainability 2021, 13, 3754.

[60]Lu, N.; Wu, J.H.; Liu, Z.M. How Does Green Finance Reform Affect Enterprises Green Technology Innovation? Evidence from China. Sustainability 2022, 14, 9865.

Comment 5:

The estimation of this paper is based on cross-sectional data of the Chinese industries using the Max-DEA model. Have the authors considered a DEA estimation covering wider variables of both green finance and green transformation? This should provide more representative and general results.

Response: Thank you very much for your valuable suggestions on our research. In the revised version of the manuscript, we have selected more extensive and comprehensive indicators for the variables of green transformation, and interpreted the green financial policy in more detail. We have answered the questions about green transformation indicators in Comment 2, and we have answered the questions about green financial policies in Comment 1, and have made detailed supplements in the manuscript. In addition, since we have supplemented and improved the input and output variables of the green transformation, we have conducted another empirical analysis (including benchmark regression, dynamic effect analysis, placebo test, robust test, heterogeneity analysis, and mechanism analysis) to ensure the scientific nature of the research results.

Comment 6:

check the presence of “Note: The Z-values in brackets are robust” is correct. (T-values?)

Response: Thank you very much for your comment. We carefully proofread the research results. The values in brackets are Z values.

Comment 7:

I don't think DID is necessary for the test of policy effect, it only needs to be used as a dummy variable in the panel regression. Focusing on the setting and definition of DEA is enough to contribute if the authors think that the efficiency of green transformation is the key variable.

Response: Thank you very much for your recognition of the efficiency of green transformation in this study. In fact, DID's test of policy effectiveness is one of the issues we are interested in: green finance can significantly promote the green transformation of manufacturing industry. In addition, we also found other meaningful research results through the heterogeneity test and the mechanism research: (1) The formation and orientation of funds, credit catalysis, integration and decentralization mechanism are the main mechanisms of green finance to promote the green transformation of manufacturing industry, and the implementation effect of green finance has obvious heterogeneity; (2) The role of green finance in promoting the green transformation of manufacturing industry is only significant in state-owned industries, but not in non-state industries; (3) The impact of green finance on the green transformation efficiency of manufacturing industry with good information environment is more significant than that of manufacturing industry with poor information environment; (4) Facing the pressure of investing in green industries, enterprises in different industries have adopted different strategies. The role of green finance in promoting the green transformation of manufacturing industry is significant in low competitive industries, but not in high competitive industries.

我们真诚地感谢编辑/审稿人的热情工作，并希望更正能够获得批准。

再次非常感谢您的意见和建议。

真挚地

陈明博士（副教授）

邮箱：[email protected]

中国青岛科技大学

通讯作者：

朱晓波博士（讲师）

邮箱：[email protected]；[email protected]

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have corrected the paper. The majority of the commends are addressed. Responses are sound. My decision is accept.

Author Response

Dear Reviewer,
Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Does Green Finance Promote the Green Transformation of China's Manufacturing Industry?” ( sustainability-2227997). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches.Thank you very much for your recognition of our research.

Sincerely
Dr. Ming Chen (Associate Professor)
E-mail: [email protected]
Qingdao University of Science and Technology, China

Corresponding author:
Dr. Xiaobo Zhu (Lecturer)
E-mail: [email protected]; [email protected]

Reviewer 2 Report

Thank you for your thorough response to my inquiries and for the significant revisions you have made. I have a few additional comments:

1. The author should revise the "Mechanism diagram" in Figure 1. Some of the processes shown are not mentioned or consistent with the text. Please modify the diagram to reflect the relevant mechanisms and processes related to the theme, and consider including an evaluation model including the DEA and DID. If this mechanism diagram is referenced from a government organization or other publication, please provide the source.

2. The author has proposed four research hypotheses, but the descriptions are too broad, which could result in cognitive biases in interpreting the empirical results. I suggest that the author establish the main research hypotheses based on relevant literature and variables used. This will help ensure that the results are intuitive and aligned with the hypotheses.

2.1 Additionally, the author could include more input and output variables in the efficiency model to enhance the verifiability of the results. Have you considered using efficiency results as a subtopic of the hypotheses?

3. I suggest that the author present a brief efficiency distribution table in the appendix or add an additional panel in Table 2 for efficiency values in the descriptive statistics.

4. I noticed that there are some discrepancies between the previous and revised versions of the empirical results section, and some of the explanations have not been amended. The author should confirm and address these differences.

Sincerely,

Author Response

Dear Reviewer,

Responds to the Reviewer 2 comments:

Comment 1: The author should revise the "Mechanism diagram" in Figure 1. Some of the processes shown are not mentioned or consistent with the text. Please modify the diagram to reflect the relevant mechanisms and processes related to the theme, and consider including an evaluation model including the DEA and DID. If this mechanism diagram is referenced from a government organization or other publication, please provide the source.

Response: Thank you very much for your advice. We have improved the mechanism diagram according to the requirements of the reviewers.

Figure 1. Mechanism diagram.

Comment 2: The author has proposed four research hypotheses, but the descriptions are too broad, which could result in cognitive biases in interpreting the empirical results. I suggest that the author establish the main research hypotheses based on relevant literature and variables used. This will help ensure that the results are intuitive and aligned with the hypotheses.

Additionally, the author could include more input and output variables in the efficiency model to enhance the verifiability of the results. Have you considered using efficiency results as a subtopic of the hypotheses?

Response: Thank you very much for your comment. We have obtained many meaningful research conclusions from our research. You are right to say that we should establish the main research driving based on the use of variables. In fact, we established the main research hypotheses based on variables, and verified the research hypotheses and conclusions one-to-one.

In addition, the efficiency of green transformation is only a variable (explanatory variable) in our research. Compared with other assumptions, it may not fully reflect the impact of green finance on the green transformation of manufacturing industry. Therefore, it may not be considered as a sub-theme, and we hope that our views can be recognized and understood by you. Thank you again for your suggestions.

Comment 3:

I suggest that the author present a brief efficiency distribution table in the appendix or add an additional panel in Table 2 for efficiency values in the descriptive statistics.

Response: Thank you very much for your comment. According to the comments of reviewers, we added the distribution of green total factor productivity in Table 2-2.

Table 2-2. Distribution table of green total factor productivity

Efficiency interval	0-0.1	0.1-0.2	0.2-0.3	0.3-0.4	0.4-0.5	0.5-0.6	0.6-0.7	0.7-0.8	0.8-0.9	0.9-1
Frequency	893	943	2876	2135	1144	726	492	326	181	278
Account for percentage	8.94%	9.44%	28.78%	21.36%	11.45%	7.26%	4.92%	3.26%	1.81%	2.78%

Comment 4: I noticed that there are some discrepancies between the previous and revised versions of the empirical results section, and some of the explanations have not been amended. The author should confirm and address these differences.

Response: Thank you very much for your advice. According to your suggestions, we have done our best to improve our manuscript and let readers know the positive impact of green financial policy on the green transformation of China's manufacturing industry as much as possible. Yes, you are right. Even if we change the selection of input and output indicators of the explained variables, it does not change our conclusions, which further proves the accuracy of our research conclusions. Thank you for your recognition and valuable comments on our research, which makes our manuscript research more rigorous.

We appreciate for Editor/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Sincerely

Dr. Ming Chen (Associate Professor)

E-mail: [email protected]

Qingdao University of Science and Technology, China

Corresponding author:

Dr. Xiaobo Zhu (Lecturer)

E-mail: [email protected]; [email protected]

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Thank you for the author's careful revisions to the article. The quality of this article has significantly improved. However, there are still some statements that need to be revised, especially regarding the research hypotheses.

For example, with limited research support, the author believes that there can be a very "significant" (H1) or "efficient" (H3) improvement. This shows a subjective bias that can lead to cognitive distortions in research hypotheses. It is recommended to use appropriate language based on the strength of the literature support.

Furthermore, the hypotheses are still too broad and should be more specific and consider variables or efficiencies that make a contribution. If the author can improve on these points, it will be beneficial for the article to be considered for acceptance.

Author Response

Dear Reviewer,

Responds to the Reviewer 2 comments:

Comment 1: Thank you for the author's careful revisions to the article. The quality of this article has significantly improved. However, there are still some statements that need to be revised, especially regarding the research hypotheses.

Response: Thank you very much for your advice. We modified some words that were not accurately expressed in the research hypothesis.

Hypothesis 1: Green finance can promote the green transformation of the manufacturing industry.

In addition, we have added specific descriptions to both Hypothesis 2, Hypothesis 3 and Hypothesis 4, making the expression of the hypothesis more specific and clearer. The following are modifications to Hypothesis 2 , Hypothesis 3 and Hypothesis 4.

Hypothesis 2: Compared to non-state owned manufacturing enterprises, green finance has a greater role in promoting the green transformation of state-owned enterprises.

Hypothesis 3: Green finance has a greater impact on the green transformation of manufacturing enterprises with good information environments than those with poor information environments.

Hypothesis 4: Compared to highly competitive manufacturing enterprises, green finance has a greater impact on the green transformation of low competitive manufacturing enterprises.

We appreciate for Editor/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Sincerely

Dr. Ming Chen (Associate Professor)

E-mail: [email protected]

Qingdao University of Science and Technology, China

Corresponding author:

Dr. Xiaobo Zhu (Lecturer)

E-mail: [email protected]; [email protected]

Author Response File: Author Response.pdf

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