PONE-D-23-42641Prediction of Carbon Emissions in Guizhou Province-Based on Different Neural Network ModelsPLOS ONE

Dear Dr. Yang

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“This research was supported by Concealed Ore Deposit Exploration and Innovation Team of Guizhou Colleges and Universities (Guizhou Education and Cooperation Talent Team [2015]56), Provincial Key Discipline of Geological Resources and Geological Engineering of Guizhou Province (ZDXK[2018]001), Huang Danian Resources of National colleges and universities Teachers' Team of Exploration Engineering (Teacher Letter [2018] No. 1), Geological Resources and Geological Engineering Talent Base of Guizhou Province (RCJD2018-3), Key Laboratory of Karst Engineering Geology and Hidden Mineral Resources of Guizhou Province (Qianjiaohe KY [2018] No. 486Guizhou Institute of Technology Rural Revitalization Soft Science Project(2022xczx10), Education and Teaching Reform Research Project of Guizhou Institute of Technology (JGZD202107,2022TDFJG01).”

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Additional Editor Comments (if provided):

Reviewer 1#:

It is interesting that prediction of carbon emissions in guizhou province-based on different neural network models is carried out, because global warming caused by greenhouse gas emissions has become a major challenge facingpeople all over the world. The study of regional human activities and their impacts on carbon emis-sions is necessary which can help to achieve the ambitious goal of carbon neutrality and sustainable eco-nomic development.

Suggest reading these literature to expand research ideas, which may include (but are not limited to) the following,https://doi.org/10.1016/j.istruc.2020.12.089. https://doi.org/10.1016/j.jobe.2022.104459.

https://doi.org/10.1007/s12205-023-0677-9.https://doi.org/10.1016/j.istruc.2023.02.048.

https://doi.org/10.1007/s11069-019-03709-x. https://doi.org/10.1016/j.engstruct.2019.109500.

https://doi.org/10.1016/j.engstruct.2020.110434.https://doi.org/10.2749/101686616X1311.

It is noted that your manuscript needs careful editing by someone with expertise in technical English editing paying particular attention to English grammar, spelling, and sentence structure so that the goals and results of the study are clear to the reader.

Reviewer 2#:

Global warming caused by greenhouse gas emissions has become a major challenge facing people all over the world. The study of regional human activities and their impacts on carbon emissions is of great significance to achieve the ambitious goal of carbon neutrality and sustainable economic development. Guizhou Province is a typical karst area in China, and its energy consumption is mainly based on fossil fuels. Therefore, it is necessary to predict and analyze its carbon emissions. In this paper, BP neural network and extreme learning machine (ELM) model, which have the advantage of nonlinear processing, will be used to predict the carbon emissions of Guizhou Province from 2020 to 2040. Based on the energy consumption data of Guizhou Province, the carbon emissions of Guizhou Province are calculated by using the conversion method and the inventory compilation method. Overall, I think this manuscript is suitable for this journal’s scope.

However, there are some issues that may need to be improved.

1 Abstract. The review part should be more concise, and the findings part should be more accurate and detailedly. The summary can be accurately summarized in several points

2 Tons should be tonnes

3 Introduction: The novelty of this paper should be further justified by highlighting main contributions to the existing introduction and literature review. For example, what are the other researches on the prediction model? There are many articles, e.g., one about the ARIMA method entitled “Ecological footprint simulation and prediction by ARIMA model-A case study in Henan Province of China. https://doi.org/ 10.1016/j.ecolind.2009.06.007”.

4 Yuan should be CHY

5 The formula part of the method of the full text, as detailed and specific as possible, at present, the formula is few and unclear

6 There are still unclear in the graphics

7 Grammar problems. Need to check the polish carefully

8 There should be a conclusion. The conclusion can also be clearly and concisely divided into several points.

9 The conclusion can be preceded by a discussion section. The section should enhance the relevant discussion. For example, the comparison of the results of this paper with those of its predecessor could be strengthened with regard to the results of carbon emissions in China or World or GUIZHOU. One China’s article entitled “China's CO2 emissions: A systematical decomposition concurrently from multi-sectors and multi-stages since 1980 by an extended logarithmic mean divisia index. https://doi.org/10.1016/j.esr.2023.101141”. Next world one entitled “Contribution of Renewable Energy Consumption to CO2 Emission Mitigation: A Comparative Analysis from a Global Geographic Perspective, https://doi.org/ 10.3390/su13073853”.

Reviewer 3#:

This manuscript proposes an improved neural network model to predict the future carbon emissions in Guizhou Province. Currently, limited research uses machine learning methods to predict carbon emissions, and most studies use relatively simple neural networks. Directly training neural networks with backpropagation algorithms can lead to local optima, but this manuscript combines neural networks with various optimization strategies to enhance generalization capabilities. The training dataset consists of carbon emissions in Guizhou Province from 2000 to 2020, calculated using the inventory method. Twelve relevant descriptors were initially selected, with the five most correlated ones chosen as inputs to improve prediction accuracy. Several improved neural network models were designed, including WOA-BP and WOA-ELM models, to provide a more reasonable initial weight for the neural network to avoid overfitting. Compared to the naive BP neural network, the improved WOA-ELM model significantly enhances carbon emission prediction, validating its effectiveness. To comprehensively analyze carbon emissions in Guizhou Province, three carbon emission scenarios were constructed, and using the best-performing WOA-ELM model, the paper predicted the years of reaching carbon emission peaks under different scenarios. Based on the prediction results, the authors also provided reasonable suggestions for reducing Guizhou's carbon emissions.

The results of this work are substantial and innovative in the field, and the paper is recommended for acceptance after addressing the following points:

(1) There are instances of colloquial English expressions and language issues. Please review carefully. For example, the phrase "collects the relevant data" in line 372 and "Setting of model input layer and output layer" in line 397, and more.

(2) The motivation for using ELM and WOA to enhance the neural network must be established. It would be beneficial to elaborate on why these optimization strategies were chosen and whether they have been used in the field.

(3) It is necessary to provide the training curves and convergence of the machine learning models to ensure they do not suffer overfitting on such a limited dataset.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: I Don't Know

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: It is interesting that prediction of carbon emissions in guizhou province-based on different neural network models is carried out, because global warming caused by greenhouse gas emissions has become a major challenge facingpeople all over the world. The study of regional human activities and their impacts on carbon emis-sions is necessary which can help to achieve the ambitious goal of carbon neutrality and sustainable eco-nomic development.

Suggest reading these literature to expand research ideas, which may include (but are not limited to) the following,https://doi.org/10.1016/j.istruc.2020.12.089. https://doi.org/10.1016/j.jobe.2022.104459.

https://doi.org/10.1007/s12205-023-0677-9.https://doi.org/10.1016/j.istruc.2023.02.048.

https://doi.org/10.1007/s11069-019-03709-x. https://doi.org/10.1016/j.engstruct.2019.109500.

https://doi.org/10.1016/j.engstruct.2020.110434.https://doi.org/10.2749/101686616X1311.

It is noted that your manuscript needs careful editing by someone with expertise in technical English editing paying particular attention to English grammar, spelling, and sentence structure so that the goals and results of the study are clear to the reader.

Reviewer #2: Global warming caused by greenhouse gas emissions has become a major challenge facing people all over the world. The study of regional human activities and their impacts on carbon emissions is of great significance to achieve the ambitious goal of carbon neutrality and sustainable economic development. Guizhou Province is a typical karst area in China, and its energy consumption is mainly based on fossil fuels. Therefore, it is necessary to predict and analyze its carbon emissions. In this paper, BP neural network and extreme learning machine (ELM) model, which have the advantage of nonlinear processing, will be used to predict the carbon emissions of Guizhou Province from 2020 to 2040. Based on the energy consumption data of Guizhou Province, the carbon emissions of Guizhou Province are calculated by using the conversion method and the inventory compilation method. Overall, I think this manuscript is suitable for this journal’s scope.

However, there are some issues that may need to be improved.

1 Abstract. The review part should be more concise, and the findings part should be more accurate and detailedly. The summary can be accurately summarized in several points

2 Tons should be tonnes

3 Introduction: The novelty of this paper should be further justified by highlighting main contributions to the existing introduction and literature review. For example, what are the other researches on the prediction model? There are many articles, e.g., one about the ARIMA method entitled “Ecological footprint simulation and prediction by ARIMA model-A case study in Henan Province of China. https://doi.org/ 10.1016/j.ecolind.2009.06.007”.

4 Yuan should be CHY

5 The formula part of the method of the full text, as detailed and specific as possible, at present, the formula is few and unclear

6 There are still unclear in the graphics

7 Grammar problems. Need to check the polish carefully

8 There should be a conclusion. The conclusion can also be clearly and concisely divided into several points.

9 The conclusion can be preceded by a discussion section. The section should enhance the relevant discussion. For example, the comparison of the results of this paper with those of its predecessor could be strengthened with regard to the results of carbon emissions in China or World or GUIZHOU. One China’s article entitled “China's CO2 emissions: A systematical decomposition concurrently from multi-sectors and multi-stages since 1980 by an extended logarithmic mean divisia index. https://doi.org/10.1016/j.esr.2023.101141”. Next world one entitled “Contribution of Renewable Energy Consumption to CO2 Emission Mitigation: A Comparative Analysis from a Global Geographic Perspective, https://doi.org/ 10.3390/su13073853”.

Reviewer #3: This manuscript proposes an improved neural network model to predict the future carbon emissions in Guizhou Province. Currently, limited research uses machine learning methods to predict carbon emissions, and most studies use relatively simple neural networks. Directly training neural networks with backpropagation algorithms can lead to local optima, but this manuscript combines neural networks with various optimization strategies to enhance generalization capabilities. The training dataset consists of carbon emissions in Guizhou Province from 2000 to 2020, calculated using the inventory method. Twelve relevant descriptors were initially selected, with the five most correlated ones chosen as inputs to improve prediction accuracy. Several improved neural network models were designed, including WOA-BP and WOA-ELM models, to provide a more reasonable initial weight for the neural network to avoid overfitting. Compared to the naive BP neural network, the improved WOA-ELM model significantly enhances carbon emission prediction, validating its effectiveness. To comprehensively analyze carbon emissions in Guizhou Province, three carbon emission scenarios were constructed, and using the best-performing WOA-ELM model, the paper predicted the years of reaching carbon emission peaks under different scenarios. Based on the prediction results, the authors also provided reasonable suggestions for reducing Guizhou's carbon emissions.

The results of this work are substantial and innovative in the field, and the paper is recommended for acceptance after addressing the following points:

(1) There are instances of colloquial English expressions and language issues. Please review carefully. For example, the phrase "collects the relevant data" in line 372 and "Setting of model input layer and output layer" in line 397, and more.

(2) The motivation for using ELM and WOA to enhance the neural network must be established. It would be beneficial to elaborate on why these optimization strategies were chosen and whether they have been used in the field.

(3) It is necessary to provide the training curves and convergence of the machine learning models to ensure they do not suffer overfitting on such a limited dataset.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Jia-Ji Zhu

**********

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Carbon peaking prediction scenarios based on different neural network models: A case study of Guizhou Province

PONE-D-23-42641R1

Dear Dr. Yang

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Salim Heddam

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewer 1#:

After revisions, the introduction in this manuscript provide sufficient background and include all relevant references,and the research design is appropriate.What's more, the methods are adequately described and the results are clearly presented, so the conclusions are supported by the results.The analyses and the results reported in this manuscript are interesting and useful for future researches and preliminary design.

In my opinion,it can be accepted in present form.

Reviewer 2#: The author should revise and polish the paper according to the format of the journal.

Reviewer 3#:

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: After revisions, the introduction in this manuscript provide sufficient background and include all relevant references,and the research design is appropriate.What's more, the methods are adequately described and the results are clearly presented, so the conclusions are supported by the results.The analyses and the results reported in this manuscript are interesting and useful for future researches and preliminary design.

In my opinion,it can be accepted in present form.

Reviewer #2: The author should revise and polish the paper according to the format of the journal.

The author should revise and polish the paper according to the format of the journal.

The author should revise and polish the paper according to the format of the journal.

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********