Does the Land Market Have an Impact on Green Total Factor Productivity? A Case Study on China

Round 1

Reviewer 1 Report

Referee Report on “Does land market have an impact on green total factor productivity? A case study on China”_land-1199601

 

This paper analyzes the impact of land market on urban GTFP and explores the regulatory effect of the innovation investment level and the infrastructure investment level on land market which based on the panel data of 271 cities in China from 2004 to 2016  Following the analysis, the authors argued that: First, land market restrains the improvement of urban GTFP, whether analyzed from the dimension of land transfer price or land transfer scale, and the influence degree varies in different dimensions. Second, there is regional heterogeneity in the inhibition effect of the land market on urban GTFP. Third, the level of innovation investment and the level of infra-structure investment have significantly different regulatory effects on the impact of land market on urban GTFP; the level of innovation investment aggravates the inhibition effect of urban GTFP by the land market, while the infrastructure investment level weakens this inhibition effect.  It is an interesting paper. However, I have the following specific concerns.

 

Major Concerns and Comments:

1. This study uses panel data, so panel regression should be used. However, the author stated that the main measurement method is OLS (line 271). I am not sure whether the regression method used by the author is panel OLS. The author should explain clearly.

 

2. Before 2.2 Methods, it is recommended that the author should add a section of data source and description, and use the table to display it. The authors should explain briefly.

 

3. In Ling 291, the author pointed out that i is an individual fixed effect, and it seems that the estimation method in this article is the fixed effect model of Panel OLS. The author should explain why the random effect model is not used. I think it should be that the estimated coefficient or explanatory power of the fixed effect model is better, but the author should still explain.

 

4. In 2.4 Measure of GTFP, the author uses SBM-DEA (GML index) as the GTFP index (line 343 to 347). At the same time, use lnGTFP as the interpreted (dependent) variable (Table 6, page 10). GTFP descriptive statistics processed with GML index should be displayed. At the same time, whether the GTFP processed by GML index can be estimated with Panel OLS, the author may have to think in detail. Is it appropriate to use Panel Probit? Or use both at the same time and compare the estimated results. It can increase the contribution of this article.

 

5. The author uses the data from 2004-2016. As China’s inflation is relatively serious, whether price deflation should be used, the author should consider.

 

 

Minor Comments:

 

1. Some typos should be corrected. For example, in page 3, line 134, “level, However,…” should be “level. However,…”; line 135, “(regions of China, It is …” should be “regions of China. It is …”. The authors should carefully check the symbols and spelling.

 

Evaluation:

For the above reasons, I believe that the current situation in this article is not suitable for publication in this journal. I encourage authors to make submissions after making appropriate corrections.

 

Comments for author File: Comments.pdf

Author Response

Dear Reviewer 1:

Thank you for your letter and for the your comments concerning our manuscript entitled “Does land market have an impact on green total factor productivity? A case study on China”. 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 corrections which we hope meet with approval. The revised portion is marked in red in this box. The main corrections in the paper and the response to the your comments are as following:

 

 

 

Point 1: This study uses panel data, so panel regression should be used. However, the author stated that the main measurement method is OLS (line 271). I am not sure whether the regression method used by the author is panel OLS. The author should explain clearly.

 

Response 1: According to Reviewer1’s comments, this paper already added the explanation of Panel OLS regression method. The explanation is in P.6, line 287 to line 291.

 

Point 2: Before 2.2 Methods, it is recommended that the author should add a section of data source and description, and use the table to display it. The authors should explain briefly

 

Response 2: As Reviewer1’s suggested, we already added a section of data source and description. The section is in P.6 line 267 to line 277.

 

Point 3: In Ling 291, the author pointed out that i is an individual fixed effect, and it seems that the estimation method in this article is the fixed effect model of Panel OLS. The author should explain why the random effect model is not used. I think it should be that the estimated coefficient or explanatory power of the fixed effect model is better, but the author should still explain.

 

Response 3: According to Reviewer1’s comments, this paper already used the Hausman test to refuse the random effect model. The estimated coefficient or explanatory power of the fixed effect model is better. The explanation is in P.6, line 287 to line 291.

 

Point 4: In 2.4 Measure of GTFP, the author uses SBM-DEA (GML index) as the GTFP index (line 343 to 347). At the same time, use lnGTFP as the interpreted (dependent) variable (Table 6, page 10). GTFP descriptive statistics processed with GML index should be displayed. At the same time, whether the GTFP processed by GML index can be estimated with Panel OLS, the author may have to think in detail. Is it appropriate to use Panel Probit? Or use both at the same time and compare the estimated results. It can increase the contribution of this article.

 

Response 4: Firstly, as Reviewer1’s suggested, we already added the GML index processing GTFP descriptive statistics in P.10 line 391 to line 393.

Secondly GML index has time comparability. The difference between GML index and ML index was mentioned in the Oh’s (2010). GTFP calculated by GML index is an explanatory variable that can be compared year by year, and GTFP calculated by ring comparison can be regressed by Panel OLS, the paper analysed the impact of land market on green total factor productivity. At the same time, the GTFP calculated by GML index is used for panel regression in most literatures. For example, Liu and Li (2019) used the GTFP calculated by GML index for regression discontinuity, and Li et al (2020) mentioned that GML index is comparable, and the GTFP calculated by GML index is time-varying, we can control the time effect by adding time dummy variable to carry out Panel OLS model regression and so on. Therefore, it is reasonable to think that GTFP after GML index processing can be estimated by using Panel OLS.

Thirdly, this paper is not suitable to use Panel Probit model. The explained variable GTFP in this paper is not a binary variable, but a continuous variable. Therefore, the Panel Probit model is not suitable in this paper. This paper makes two different dimensions of empirical analysis, mechanism analysis and robustness test, which can fully show that the conclusion of this paper is significant.

1. Oh, D., 2010. A global Malmquist–Luenberger productivity and index. J. Prod. Anal. 34(3), 183–197.

2. Z. Liu and L. Xin, “Has China’s belt and road initiative promoted its green total factor productivity? —evidence from primary provinces along the route,” Energy Policy, vol. 129,360–369, 2019.
3. Li, Z., Huang, Z., & Li, T. Does Economic Growth Driving Force Convert? Evidence from China. Mathematical Problems in Engineering, 2020.

 

Point 5: The author uses the data from 2004-2016. As China’s inflation is relatively serious, whether price deflation should be used, the author should consider.

 

Response 5: According to Reviewer1’s comments, this paper gave two reasons to answer why the data does not do price index adjustment.

Firstly, there are many types of price deflators in China, some of which may have large errors in the process of data collection and calculation. Therefore, there may be large errors in the data of price index adjustment. This paper does not suggest that the variable data should be treated as price index adjustment.

Secondly, the key explanatory variable of this paper is the ratio of land grant fee to GDP, which does not need to exclude the influence of price. The same is true for LAS. China's inflation rate averaged 2% - 3% from 2004 to 2016, which is a moderate inflation. Therefore, under the premise of moderate inflation, this paper assumes that there is a common trend among variables, and the data in this paper are not processed by any price index.

 

Point 6: Some typos should be corrected. For example, in page 3, line 134, “level,

However,…” should be “level. However,…”; line 135, “(regions of China, It

is …” should be “regions of China. It is …”. The authors should carefully check

the symbols and spelling.

Response 6: As Reviewer1’s suggested, we already corrected the symbols and spelling.

The attachment is the article.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper investigates the relationship between land market and green total factor productivity (GTFP) in China. Four hypotheses are verified in the process of land marketization: 1) the relationship between land transfer fee/land transfer scale and urban GTFP; 2) the regional heterogeneity of the impact of land marketization on urban GTFP; 3) the impact of innovation investment on the relationship between land market and urban GTFP; 4) The impact of infrastructure investment on the relationship between land market and urban GTFP.

OLS regression method is used. In particular, 2 models as benchmark models are carried out to test whether the impact of land marketization on urban GTFP is consistent with hypothesis 1. Furthermore, 4 models are carried out in order to investigate the regulatory effect of innovation investment and infrastructure investment on land marketization, so as to verify hypotheses 2 and 3. The SBM-DEA method is used to calculate urban GTFP (explained variable).

The analyses highlighted that the development of land market in terms of land market price and scale inhibits the increase of GTFP, considering that a stronger impact is caused by land transfer scale compared to land transfer fee. Furthermore, the inhibition effect of land market on GTFP varies among different regions. Finally, the level of innovation and infrastructure investments have significant regulatory effects on the land market.

The topic is interesting and the methodology appears correctly applied in line with the aim of the study. Thus, in my opinion, just the two following improvements are suggested:

• a better explanation of GTFP variable should be reported in the introduction section;
• in table 1, negative minimum values of the considered variables should be explained.

Author Response

Dear Reviewer 2:

Thank you for your letter and for the your comments concerning our manuscript entitled “Does land market have an impact on green total factor productivity? A case study on China”. 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 corrections which we hope meet with approval. The revised portion is marked in red in this box. The main corrections in the paper and the response to the your comments are as following:

 

 

 

Point 1: A better explanation of GTFP variable should be reported in the introduction section.

 

Response 1: As Reviewer 2’s suggested, we already added a better explanation of GTFP in P.2 line 51 to line 56.

 

Point 2: In table 1, negative minimum values of the considered variables should be explained.

 

Response 2: According to Reviewer 2’s comments, we already added an explanation of negative minimum values of the considered variables in P.7 line 345 to line 348.

Author Response File: Author Response.docx

Reviewer 3 Report

The article explores factors that affect green total factor productivity including land marking and land transfer, among others.

I found the article very interesting and well designed. My only suggestion is to extend the political implication of the results. Perhaps redicing technical discussion of the model (or putting this material in an appendix) and extending te debate of the implications of the results.

Author Response

Dear Reviewer 3:

Thank you for your letter and for the your comments concerning our manuscript entitled “Does land market have an impact on green total factor productivity? A case study on China”. 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 corrections which we hope meet with approval. The revised portion is marked in red in this box. The main corrections in the paper and the response to the your comments are as following:

 

 

 

Point 1: My only suggestion is to extend the political implication of the results. Perhaps redicing technical discussion of the model (or putting this material in an appendix) and extending the debate of the implications of the results.




Response 1: Firstly, according to Reviewer 3’s suggestion, we already added the political implication of the results, specifically added in P. 16 line 593 to P.17 line 609.

Secondly, the technical discussion of this model refers to the writing style of some scholars. This format helps readers understand the reasons behind the empirical results, so we hope to keep the original format, but we are willing to put this part in the appendix.

Thirdly, as Reviewer 3’s suggested, we already added a discussion part as a further analysis of the empirical results, specifically added in P.16 line 563 to P.17 line 609.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Referee Report on “Does land market have an impact on green total factor productivity? A case study on China”_land-1199601

Thanks to the authors for their efforts to revise. However, there are still the following correction suggestions.

Major Concerns and Comments:
1. Table 1 should contain all sources of variables. Please contains all the variables from Line 323 to Line 342.

2. A large amount of literature shows that when using DEA data as the explained variable, the Tobit model is mostly used for estimation. Is it appropriate to use Panel OLS? Or use both at the same time and compare the estimated results. It can increase the contribution of this article. Please refer, for example, Yang and Fang (2020) and Kuang et al. (2020).

Reference
Yang, Z and Fang, H., 2020, Research on Green Productivity of Chinese Real Estate Companies—Based on SBM-DEA and TOBIT Models, Sustainability, 12, 3122.
Kuang, B., Lu, X., Zhou, M. and Chen, D., 2020, Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered, Technological Forecasting & Social Change, 151, 119874.

Evaluation:
For the above reasons, I believe that this article is publishable in this journal after appropriate revised.

Author Response

Dear Reviewer 1:

Thanks for your letter concerning our manuscript entitled “Does land market have an impact on green total factor productivity? A case study on China”. These comments are all precious and very useful for revising and improving our paper, as well as the significant guiding important to our researches. We have studied suggestions carefully and have made corrections which we hope meet with approval. The revised portion is marked in red in the box. The main corrections in the paper and the response to your comments are as following:

 

 

 

Point 1: Table 1 should contain all sources of variables. Please contains all the variables from Line 323 to Line 342.

 

Response 1: Thanks for your suggestions. According to Reviewer1’s comments, this paper already added the all sources of variables in Table 1, P.6, line 278.

 

Point 2: A large amount of literature shows that when using DEA data as the explained variable, the Tobit model is mostly used for estimation. Is it appropriate to use Panel OLS? Or use both at the same time and compare the estimated results. It can increase the contribution of this article. Please refer, for example, Yang and Fang (2020) and Kuang et al. (2020).

 

Reference
Yang, Z and Fang, H., 2020, Research on Green Productivity of Chinese Real Estate Companies—Based on SBM-DEA and TOBIT Models, Sustainability, 12, 3122.
Kuang, B., Lu, X., Zhou, M. and Chen, D., 2020, Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered, Technological Forecasting & Social Change, 151, 119874.

 

Response 2: Thank you for your comments and references. Firstly, the explained variable of this paper GTFP measured by GML and DEA approaches is efficiency value which meets the applicable scope of Tobit and OLS model, and we study the GTFP by using panel OLS method since the panel regression method have been utilized to study the influencing factors of GTFP by many scholars. Secondly, referring to the references mentioned by Reviewers1, new chapter 3.3.2 is added to comparing the estimation results of Panel Tobit and Panel OLS, which increased our contributions. It is in P.16 line 565 to P.17 line 581.

Author Response File: Author Response.docx