Study on Rural Residents’ Satisfaction with the Clean Energy Heating Program in Northern China—A Case Study of Shandong Province
Round 1
Reviewer 1 Report
The introduction of the government’s policy and the rationale for the study is well introduced in Section 1. The literature published is clearly categorized. The tables need to be reorganized to make it more clear for the reader. No specific comments are provided for the tables as yet, but at the moment it contains more information than needed. Also, there is a significant effort needed in improving the grammar in the paper.
Page 1, Line 3: ‘implementation lies is rural area’ should be ‘implementation lies is rural areas’ (Grammar).
Page 1, Line 4: ‘Peasant households who directly benefit’, could you please check if ‘Peasant’ is appropriate, it is not very common to come across this word in scientific literature. It could be appropriate as well, but please check.
Page 1, Lines 18 to 20: ‘It is a worldwide challenge to fulfil increasing energy needs caused by improved human living standards while protect environment and maintain sustainability at the same time.’ This sentence is not grammatically correct. Please revise.
Page 3, Line 110: The sentence should read the ‘square’ in meter square should be in super-scripts.
Page 3, Lines 121 to 125: References may be cited for these figures of disposable income.
Page 15 to 18: The section on conclusion needs to be shortened, may be to 1.5 pages at the maximum. It also will be useful to summarize the policy recommendations as bullet points, rather than elaborate. Please consider shifting any leftover discussion after itemizing to previous sections (if necessary).
Author Response
Comment 1: The tables need to be reorganized to make it more clear for the reader. No specific comments are provided for the tables as yet, but at the moment it contains more information than needed.
Answer: Thanks for your suggestions. We have re-organized all tables to make them easy to read.
Comment 2: there is a significant effort needed in improving the grammar in the paper.
Answer: Thanks for your advice. A professional translator has been hired to improve the grammar in the paper.
Comment 3: Page 1, Line 3: ‘implementation lies is the rural area’ should be ‘implementation lies is rural areas’ (Grammar).
Answer: This sentence has been revised according to the reviewer’s response.
Comment 4: Page 1, Line 4: ‘Peasant households who directly benefit’, could you please check if ‘Peasant’ is appropriate, it is not very common to come across this word in scientific literature. It could be appropriate as well, but please check.
Answer: Thank you for your suggestion. We have checked the word ‘peasant’ and realized that it is more about the profession that a person conduct. Hence, it is not precise to use it to refer to residents in this study. It has been changed into rural residents in the manuscript.
Comment 5: Page 1, Lines 18 to 20: ‘It is a worldwide challenge to fulfill increasing energy needs caused by improved human living standards while protecting the environment and maintain sustainability at the same time.’ This sentence is not grammatically correct. Please revise.
Answer: Thank you for pointing out the grammatical error. This sentence has been revised into “It is a worldwide challenge to fulfill increasing energy needs caused by improved human living standards while protecting the environment and maintaining sustainability at the same time.”
Comment 6: Page 3, Line 110: The sentence should read the ‘square’ in meter square should be in super-scripts.
Answer: Thank you for this. The “square” has been changed into super-scripts.
Comment 7: Page 3, Lines 121 to 125: References may be cited for these figures of disposable income.
Answer: Thank you for this. The reference has been cited and added to the reference list.
Comment 8: Page 15 to 18: The section on the conclusion needs to be shortened, maybe to 1.5 pages at the maximum. It also will be useful to summarize the policy recommendations as bullet points, rather than elaborate. Please consider shifting any leftover discussion after itemizing to previous sections (if necessary).
Answer: Thank you for your suggestion. We have revised and deleted redundant discussion, the revised version has been shortened to 1.5 pages as suggested. The given suggestions have been summarized as listed points as suggested.
Reviewer 2 Report
This paper studies the satisfaction level of rural residents in Northern China regarding the Clean Energy Heating Program (CEHP), which replaces coal with cleaner sources of energy for residential heating. The authors surveyed 341 rural households who have adopted such cleaner technology in three cities, asking them to rate 13 dimensions of the program’s implementation. They then applied a TOPSIS method with entropy weights to rank the cities’ approaches and identify major obstacles in implementation.
I find the topic of this paper to be quite important, as switching to cleaner energy sources for residential use is necessary for China to achieve its carbon abatement ambition. The survey evidence in this paper also has the potential to provide new insight into the demand side. However, I have some serious concerns regarding the method. The exposition in this paper also needs major revision. My comments are detailed below.
Major comments:
- The main idea of the entropy weights-as I understand it-is to attach more weight to indicators with less concentrated answers. I am having trouble seeing why a dimension that people disagree on would be considered more important than, say, a dimension on which people unanimously vote unfavorably. I suggest the authors provide more rationale for this scheme.
- In addition, the authors calculate a single entropy value for each indicator by pooling together responses across cities. This implicitly assumes that the relative importance of the indicators is the same across cities, which is questionable. Moreover, if the cities have very different program rules in a certain dimension, then the response would diverge across cities, which would mechanically give it a higher entropy weight. This makes the interpretation of the obstacles difficult – if an indicator has a high obstacle degree, how do I know if the reason is its inherent importance or simply some large differences across cities in this dimension? To test the robustness of these results, I suggest the authors calculate city-specific entropy values and weights to check if they consistently identify the same set of major obstacles.
- As the survey contains only adopters, there is the question of who selects into adopting the technology. Are they wealthier than average? Are their homes newer? Are they closer to urban centers? These questions are important for understanding whether the survey responses represent the preference of non-adopters and thus inform on further expansion of the program. To gauge the extent of this selection, I suggest the authors provide statistics on the adoption rate in these cities and compare the demographics collected in the survey to general rural households in these places. If they are different, then this selection issue should be discussed as a major caveat for interpreting the results. This also means that the authors should be careful when making inferences about “rural residents”. For example, “rural residents are generally satisfied with CEHP” (line 418) should really be “rural participants in CEHP are generally satisfied”.
- In the Introduction, the authors should discuss this paper’s contribution relative to Xu and Ge (2020) and Gong et al (2020). These seem to be the most relevant studies on the same subject but were not mentioned until much later on a technical point.
- There are many instances of grammatical mistakes and unnatural wording. For example, in the abstract: “The key difficult for…” (line 2) should be “difficulty”, “integrally involved” (line 5), “satisfied indicators” (line 7), “transferred into questionnaire”, “four important factors affect peasant households’ satisfaction” should be “affecting”. This issue goes far beyond the abstract, and I would strongly recommend getting help with copyediting.
- The mathematical notation in Section 3.1 is very sloppy and needs extensive correction and proofreading. These instances include but are not limited to:
- fkj in equation (3) should be frj.
- In line 180, U is missing “…” and uj should be uk.
- Equation (5) contains several mistakes: Y is a n-by-k matrix, U is a k-by-1 vector, then their product should be a n-by-1 vector. I am guessing U should be a diagonal matrix here. The subscripts inside the matrix are also wrong: vij should be vnk. The order of the subscripts are also different from standard matrix notation.
- Equations (6) and (7) are missing some structure and they don’t make sense in the current form.
- Equation (10) would not result in Ti ranging from 0 to 1.
Minor comments:
- Please double-check the columns in Table 7 starting from “entropy weight” as I tried but cannot figure out how the numbers come about. As mentioned above, I might have some misunderstanding about the formulae. In that case, please clarify.
- In equation (1), it seems Xmin is just 1, and Xmax is just 5. I suggest writing it out directly. In fact, I doubt you need normalization here at all since all variables are on the same scale and everything will be converted to a relative scale in Equation (2).
- Table 5 is unreadable. There are less serious but similar formatting issues with other tables as well.
Author Response
Comment 1: The main idea of the entropy weights-as I understand it-is to attach more weight to indicators with less concentrated answers. I am having trouble seeing why a dimension that people disagree on would be considered more important than, say, a dimension on which people unanimously vote unfavorably. I suggest the authors provide more rationale for this scheme.
Answer: Many thanks for your question. And, yes, according to the definition of entropy weight, more weight is attached to indicators with less concentrated answers. However, the entropy weight of one indicator doesn’t mean its importance degree to the studied question but refers to the relative competitive intensity of each index after its value is determined. When the values of the evaluated objects differ greatly and the entropy weight is large, the index provides more useful information to decision-makers than other indexes and deserves more attention from the researcher.
In this research, if rural residents’ evaluation of one particular factor differs greatly, it means that rural residents’ levels of satisfaction with one particular factor differ greatly. Hence this particular factor should be given more weight and deserves more investigation. Therefore, the identified key indicators and obstacle factors have a greater impact on rural residents’ evaluation of CEHP’s implementation and deserve more investigation and discussion for further development. The rationale has been improved in lines 168-173 on page 5.
Comment 2: In addition, the authors calculate a single entropy value for each indicator by pooling together responses across cities. This implicitly assumes that the relative importance of the indicators is the same across cities, which is questionable. Moreover, if the cities have very different program rules in a certain dimension, then the response would diverge across cities, which would mechanically give it a higher entropy weight. This makes the interpretation of the obstacles difficult – if an indicator has a high obstacle degree, how do I know if the reason is its inherent importance or simply some large differences across cities in this dimension? To test the robustness of these results, I suggest the authors calculate city-specific entropy values and weights to check if they consistently identify the same set of major obstacles.
Answer: Many thanks for your comments. We are convinced that different programs in the three selected cities would result in residents’ s diverse evaluation. Hence, indicators’ entropy weight in each city should be calculated separately. We reconducted calculations for each city and have updated the analysis process and results in tables 7, 8, 9, and the following discussion section. Please see the updated results on pages 12-13.
Comment 3: As the survey contains only adopters, there is the question of who selects into adopting the technology. Are they wealthier than average? Are their homes newer? Are they closer to urban centers? These questions are important for understanding whether the survey responses represent the preference of non-adopters and thus inform on further expansion of the program. To gauge the extent of this selection, I suggest the authors provide statistics on the adoption rate in these cities and compare the demographics collected in the survey to general rural households in these places. If they are different, then this selection issue should be discussed as a major caveat for interpreting the results. This also means that the authors should be careful when making inferences about “rural residents”. For example, “rural residents are generally satisfied with CEHP” (line 418) should really be “rural participants in CEHP are generally satisfied”.
Answer: Thanks for these comments. As we have demonstrated on page 9 and Table 5, the selected rural residents include both males and females and cover different age periods. The disposable annual income per household of selected residents also covers different levels (see Table 5). The selected villages are distributed evenly across the three cities and have been marked as red points in Figure 1. The number of rural residents in Jinan in 2020 is 2,442,425, that is 1,210,500 in Zibo and 4,334,114 in Heze. The percentage of selected participants in total rural residents in three cities is low, considering the difficulties of conducting a survey during the COVID-19 situation. Hence, we accept the reviewer’s suggestion to be careful of using ‘rural residents’ and have revised accordingly. The above information has been added into the line 283-294 in page 9.
Comment 4: In the Introduction, the authors should discuss this paper’s contribution relative to Xu and Ge (2020) and Gong et al (2020). These seem to be the most relevant studies on the same subject but were not mentioned until much later on a technical point.
Answer: Thanks for your suggestion. Yes, these two papers have been reviewed in the introduction section in lines 95-100 on page 3.
Comment 5: There are many instances of grammatical mistakes and unnatural wording. For example, in the abstract: “The key difficult for…” (line 2) should be “difficulty”, “integrally involved” (line 5), “satisfied indicators” (line 7), “transferred into questionnaire”, “four important factors affect peasant households’ satisfaction” should be “affecting”. This issue goes far beyond the abstract, and I would strongly recommend getting help with copyediting.
Answer: Many thanks for identifying these grammatical mistakes. We have hired a professional translator to revise grammatical errors.
Comment 6:
The mathematical notation in Section 3.1 is very sloppy and needs extensive correction and proofreading. These instances include but are not limited to:
fkj in equation (3) should be frj.
In line 180, U is missing “…” and uj should be uk.
Equation (5) contains several mistakes: Y is a n-by-k matrix, U is a k-by-1 vector, then their product should be a n-by-1 vector. I am guessing U should be a diagonal matrix here. The subscripts inside the matrix are also wrong: vij should be vnk. The order of the subscripts are also different from standard matrix notation.
Equations (6) and (7) are missing some structure and they don’t make sense in the current form.
Equation (10) would not result in Ti ranging from 0 to 1.
Answer: Thanks for pointing out these errors, we have double-checked and revised all equations. Please see the revised equations on pages 6 and 7.
Comment 7: Please double-check the columns in Table 7 starting from “entropy weight” as I tried but cannot figure out how the numbers come about. As mentioned above, I might have some misunderstandings about the formulae. In that case, please clarify.
Answer: Thanks for this. We double-checked all equations and have updated tables 7, 8.
Comment 8: In equation (1), it seems Xmin is just 1, and Xmax is just 5. I suggest writing it out directly. In fact, I doubt you need normalization here at all since all variables are on the same scale and everything will be converted to a relative scale in Equation (2).
Answer: Thank you for your kind suggestion. The Xmin and Xmax are kept as we would like to provide an equation that is not limited to this research context.
Comment 9: Table 5 is unreadable. There are less serious but similar formatting issues with other tables as well.
Answer: Many thanks for your suggestion. We have readjusted all tables.
Round 2
Reviewer 1 Report
None
Reviewer 2 Report
The revision has greatly improved the paper, and I appreciate your responses to my comments. The dimension of matrix multiplication in equation (5) still seems off to me. Please double-check.
