Trend Forecasting of Public Concern about Low Carbon Based on Comprehensive Baidu Index and Its Relationship with CO2 Emissions: The Case of China
Round 1
Reviewer 1 Report
minor corrections are required.. please do as suggested and send back to editor
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Author Response
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Reviewer 2 Report
Climate warming is harmful to ecosystems and public health, so the concern about climate warming has been aroused worldwide. This research explores the relationship between the public concern about low carbon and ??2 emissions of China, as well as the respective trends of each. Based on the daily data of Baidu-related keyword searches and ??2 emission, this research proposes GMM-CEEMD-SGIA-LSTM hybrid model for constructing comprehensive Baidu Index (CBI) to reflect public concern about low carbon, analyzing the relationship between CBI and ??2 emissions, and forecasting CBI. This is also a well-written manuscript, The content is concise, the citations are reasonable, the study design, problems and methods are clearly stated, the results are obvious, and the conclusions support the findings of the article. So it is recommended for publication in sustainability. The paper still needs improvement before acceptance for publication.
My detailed comments are as follows:
1. A total of 86 low-carbon-related keywords were collects as daily Baidu Index data sources. While some very important low-carbon-related words are missing, e.g. peak carbon dioxide emissions, carbon tax, and other policy related words. Please provide more detailed description about the criteria why these 86 words are chosen.
2. The CBI construction process should be more specific and detailed.
3. Deep learning model LSTM, do not have a derivation process for the model, nor a verification process for a clear model.
4. In order to improve the accuracy of the analysis, it is appropriate to add two machine learning methods to compare with the models of this study.
5. As the proportion of people using Baidu as major searching engine are declining, several more social media can be selected to focus on public access to low-carbon information and expand data.
6. Uncertainty analysis should be provided.
7. Some of the pictures in the manuscript were simply made. These figures should be modified appropriately to be clearer.
No comments.
Author Response
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Reviewer 3 Report
This research presents a study on the trend of public concern about low carbon and its relationship with emissions. Based on the daily search data of baidu keywords and daily emission data, this research proposes GMM-CEEMD-SGIA-LSTM hybrid model for constructing comprehensive baidu index to reflect public concern about low carbon, analyzing the relationship between comprehensive baidu index and emissions, and forecasting comprehensive baidu index. The forecasting results help the government to better implement low carbon policies and effectively respond to the challenges of climate warming. This study is significant in guiding the formulation of low-carbon development policies. With continued public concern about low carbon, policymakers can better understand public needs and attitudes, adopt low-carbon policies that are more in line with the public's expectations, and promote the participation and cooperation of the whole society to accelerate the reduction of carbon emissions. Overall, it is an interesting study. The analysis procedures are detailed and reliable.
The manuscript can be accepted with the following revisions:
Major revision:
1. Lines 91-103, this paragraph should emphasize the significance of the study of the public concern about low carbon in China, but it is not clear enough, so this paragraph should be revised.
2. Lines 104-108, this paragraph should summarize the shortcomings of other scholars' studies as well as the content and significance of this research, but it is not sufficiently expressed, so this paragraph should be revised.
3. There are grammatical errors in some of the statements, which should be checked and revised. For example, Line 98, “on” is inappropriate.
4. This paper mainly uses the GMM-CEEMD-SGIA-LSTM model, why the model is used, what is the relationship between the theoretical logic of the model and the mechanism to be tested, and whether the limitations of the model will affect the reliability of the test.
Minor revision:
1. Line 31, the last ”;” should be deleted.
2. Line 96, “channel” should be “way”.
3. Line 102, ’examine’ should be revised.
4. Line 296, “direct summation method” should be consistent with the 'search data summation' in Figure 4.
5. The reference should use “;” among authors.
6. Unify table format on font, thickness, and table size, etc.Some forms are obviously translated into Chinese.
Moderate editing of English language required
Author Response
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Reviewer 4 Report
I have reviewed the manuscript entitled “Trend Forecasting of Public Concern about Low Carbon Based on Comprehensive Baidu Index and Its Relationship with ??? Emissions: The Case of China”. This study discussed very important environmental issues. However, many comments need to be considered to improve the study to be suitable for publication in this journal.
- In line 11, this is not the right place for this sentence. Please shift it to the end of the manuscript in the contribution section.
- In the abstract, the aim and background are clear; however, the study's method, results, and significance are not clear.
- The introduction needs to improve. Authors should write the problem statement and gap. Moreover, the last paragraph should contend with the study's objective and the manuscript's structure.
Literature review
- In line (58), Many scholars explored the public perceptions and concerns about climate 58 warming. For example, while in line (74) the authors wrote, “Some scholars explore the public concern about low carbon”. I think this paragraph needs proofreading.
- In line (80), Currently, scholars usually study the status of public concern through questionnaire 80 surveys. Please cite these studies.
- Who said this? “The questionnaire survey is a traditional method for public opinion survey. 81 However, the data obtained from questionnaire surveys are vulnerable to the influence of 82 collection scope and respondents' subjective emotions, which may lead to inaccurate results.”
- Please expend this abbreviation (PM2.5)
- We can not write currently for this reference Lu et al. [21] because it was published in 2018.
- Do you think this is a convincing reason to study public concern about low carbon?” 2) China's 93 internet penetration rate increased significantly from 34.3% in 2010 to 75.6% in 2022, and 94 the internet users reached 1.067 billion in 2022 [24], which indicates that the internet has 95 become an essential channel for the Chinese public to obtain information.” The reason should be about your problem, which is the carbon emission. You can write about the internet in the method section as justification why you are going to use this method to collect data. As well as, reason three is not appropriate.
- “Most current studies on public concern about low carbon focus on assessing the cur-104 rent status without actual data or in-depth studies on its trends and changes” Where are these studies? Please cite them.
- The study's contribution is usually written in the introduction or under the conclusion section. At the end of the literature review section, the author should come out with the gap and the difference in their study.
-In the method section, authors should write more about the tradition of the study. What kind of study? What kind of data?
-In the results, can you tell me where the correlation test? As long as there is the word of the relationship in the title, should be a correlation test in the results.
-Discussion section should be supported by previous studies
-The author should write the practical and theoretical contributions of this study in the conclusion.
-Language needs to improve in the whole manuscript.
Author Response
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Round 2
Reviewer 4 Report
Actually, authors have taken my comments seriously, and the manuscript has improved accordingly. Best regards.
