Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper shows an interesting investigation of the Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis. Generally, this paper is well organized and can be published after a minor revision.

(1) Shortening the abstract in the revised version.

(2) Line 51-52, more than 3 references are not suggested to be cited in one sentence.

(3) The objective of this study is not clear enough, clearing it at the end of the introduction section.

(4) Adding the source for the Fig. 1,6-10.

Comments on the Quality of English Language

 Moderate editing of English language required.

Author Response

Manuscript ID: sustainability-3251952

Article Title: Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis

 

17 October 2024

Re: Revised submission

 

Dear Editor,

In keeping with our previous correspondence regarding the major revision, we are resubmitting our revised version of the manuscript ID: sustainability-3251952, entitled, Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis, for the Sustainability journal.

We thank the reviewers for constructive critiques on our manuscript. Valuable time and details provided by each reviewer and by you are greatly appreciated. The reviewers’ concerns and feedback have been thoroughly considered and addressed. Suggested changes have been incorporated in the revised manuscript. They include additional experimental. In addition, we have also ensured that the novelty of the paper and the methods are clearly and completely described. These changes are highlighted in green, while those highlighted in yellow are editorial/language/ and writing errors corrections.

Please kindly find in our re-submission package, the “sustainability-3251952_Revised” and containing point-by-point reviewers; specific concerns and our responses/revisions.

 

Your Sincerely,

Supattra Puttinaovarat

Corresponding author

 

 

 

Reviewer1

This paper shows an interesting investigation of the Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis. Generally, this paper is well organized and can be published after a minor revision.

(1) Shortening the abstract in the revised version.

 

Author Resonse: We thank the reviewers for constructive critiques on our manuscript. Valuable time and details provided by each reviewer and by you are greatly appreciated.  

Revised abstract (Line 10-24)

 

(2) Line 51-52, more than 3 references are not suggested to be cited in one sentence.

 

Author Resonse: Revised in Introduction Section (Line 28-108)

 

(3) The objective of this study is not clear enough, clearing it at the end of the introduction section.

Author Resonse: Revised in the end of introduction Section (Line 97-108)

 

(4) Adding the source for the Fig. 1,6-10.

Author Resonse:  Added the source for Fig. 1, 6-10

 

Moderate editing of English language required.

 

Author Resonse:  Highlighted in yellow are language and writing errors corrections.

 

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript calculated the carbon footprint associated with printing and photocopying, and classified green areas using satellite image processing and digital imaging techniques. This research is very interesting and provides a significant contribution to the knowledge of this field. Therefore, in my opinion, it can be accepted for publication after some revision. Several comments are given below:

(1) The abstract should be revised. There are too much content on the research background. The authors should present the important research work conducted and the research results obtained from this study, academic conclusion besides emphasize the important research work conducted in this study. The novelty of this study was not pointed out.

(2) The second section of Related works is suggested to be combined into the section of Introduction.

(3) In the section of Related works, after the literature review, the current problems should be proposed. Then, at the end of this section, the research work conducted in this study should be proposed.

(4) The title of the section 3. Materials and Methods should be changed. What materials did the authors use?

(5) The hypothesis and application condition of the carbon footprint calculation and visualization should be presented.

(6) How about the basement of the green space classification discussed in this study?

(7) The unit of the data in Table 2 should be added, which may be %.

(8) In the structure of this paper, Section 3 is on results and discussion. However, after this section, there is another section on discussion, which is 4. Discussion. The last section of discussion is suggested to be combined into the section results and discussion.

(9) The section of Conclusion should be simplified, which is often presented in several items.

Comments on the Quality of English Language

Minor editing of English language is required.

Author Response

Manuscript ID: sustainability-3251952

Article Title: Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis

 

17 October 2024

Re: Revised submission

 

Dear Editor,

In keeping with our previous correspondence regarding the major revision, we are resubmitting our revised version of the manuscript ID: sustainability-3251952, entitled, Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis, for the Sustainability journal.

We thank the reviewers for constructive critiques on our manuscript. Valuable time and details provided by each reviewer and by you are greatly appreciated. The reviewers’ concerns and feedback have been thoroughly considered and addressed. Suggested changes have been incorporated in the revised manuscript. They include additional experimental. In addition, we have also ensured that the novelty of the paper and the methods are clearly and completely described. These changes are highlighted in green, while those highlighted in yellow are editorial/language/ and writing errors corrections.

Please kindly find in our re-submission package, the “sustainability-3251952_Revised” and containing point-by-point reviewers; specific concerns and our responses/revisions.

 

Your Sincerely,

Supattra Puttinaovarat

Corresponding author

 

 

Reviewer2

This manuscript calculated the carbon footprint associated with printing and photocopying, and classified green areas using satellite image processing and digital imaging techniques. This research is very interesting and provides a significant contribution to the knowledge of this field. Therefore, in my opinion, it can be accepted for publication after some revision. Several comments are given below:

 

(1) The abstract should be revised. There are too much content on the research background. The authors should present the important research work conducted and the research results obtained from this study, academic conclusion besides emphasize the important research work conducted in this study. The novelty of this study was not pointed out.

 

Author Resonse: We thank the reviewers for constructive critiques on our manuscript. Valuable time and details provided by each reviewer and by you are greatly appreciated.  

Revised abstract (Line 10-24)

 

 

(2) The second section of Related works is suggested to be combined into the section of Introduction.

Author Resonse: Revised by combining the introduction with related works as recommended. (Line 28-108)

 

(3) In the section of Related works, after the literature review, the current problems should be proposed. Then, at the end of this section, the research work conducted in this study should be proposed.

Author Resonse: Revised according to the recommendations at the end of the introduction. (Line 91-108)

 

 

(4) The title of the section 3. Materials and Methods should be changed. What materials did the authors use?

 

Author Resonse: Changed the word Materials and Methods to Research Methodology. (Line 109)

 

 

(5) The hypothesis and application condition of the carbon footprint calculation and visualization should be presented.

Author Resonse: The hypothesis was added at the end of the introduction section (Line 105-108), and the application condition was explained in Section 2.3. (Line 184-188)

 

 

(6) How about the basement of the green space classification discussed in this study?

Author Resonse: Revised in topic 2.4 (Line 222-251)

 

 

 

(7) The unit of the data in Table 2 should be added, which may be %.

Author Resonse: Revised Table2

 

 

(8) In the structure of this paper, Section 3 is on results and discussion. However, after this section, there is another section on discussion, which is 4. Discussion. The last section of discussion is suggested to be combined into the section results and discussion.

Author Resonse: Combined into the section results and discussion (Page 17)

 

(9) The section of Conclusion should be simplified, which is often presented in several items.

Author Resonse: Revised Conclusions (Line 529-572)

 

Minor editing of English language is require

Author Resonse:  Highlighted in yellow are language and writing errors corrections.

 

Reviewer 3 Report

Comments and Suggestions for Authors

This research presents an innovative eco-friendly office platform that leverages machine learning and GIS for carbon footprint management and green space analysis. The study addresses significant gaps in real-time data capture and management for green office initiatives. The developed application demonstrates high accuracy in green space classification and provides comprehensive tools for carbon footprint tracking. While technically sound and practically relevant, the study could benefit from deeper exploration of the platform's long-term impacts on organizational sustainability goals.

comments:

1. The introduction provides a comprehensive background, but could benefit from a clearer statement of the specific research questions or hypotheses being addressed. How does this study aim to fill the identified gaps in existing green office management systems?
2. The authors mention using the Support Vector Machine (SVM) algorithm for green space classification. Could you elaborate on why SVM was chosen over other machine learning algorithms, and discuss any potential limitations of this approach?
3. The case study focuses on a single faculty at Prince of Songkla University. How generalizable are the results to other types of organizations or different geographical contexts? What factors might influence the applicability of this platform in diverse settings?
4. On page 7, you state: "More than 95 percent of office printing and photocopying is conducted in black and white, with most printers configured for black-and-white output." Could you provide a citation for this statistic or clarify if this is specific to your study context?
5. The application's ability to set and track carbon footprint targets is a valuable feature. How does the system account for potential variations in workload or seasonal changes that might affect printing and energy usage patterns?
6. The green space classification model shows high accuracy, but how robust is it to different types of vegetation or varying seasonal conditions? Have you tested its performance across different times of the year?
7. The study integrates carbon footprint calculation from printing/photocopying with green space analysis. How do you envision organizations using these two datasets in conjunction to make more informed sustainability decisions?
8. The manuscript discusses the limitations of Landsat 8 imagery resolution for detailed green space monitoring. Could you expand on how the integration of ground-truthing with smartphone photography addresses this limitation, and any potential challenges in this approach?
9. The application's user interface and functionalities are well-described. Have you conducted any usability testing or gathered feedback from potential end-users? How might this inform future iterations of the platform?
10. The conclusion suggests extending the platform to support carbon footprint calculations in additional domains, such as energy consumption. How feasible is this extension, and what challenges do you anticipate in integrating diverse data sources?
11. While the study focuses on the technical aspects of the platform, it would be interesting to explore the potential behavioral changes this system might encourage among users. Have you considered incorporating features that might promote more sustainable practices beyond just tracking?

 

 

Author Response

Manuscript ID: sustainability-3251952

Article Title: Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis

 

17 October 2024

Re: Revised submission

 

Dear Editor,

In keeping with our previous correspondence regarding the major revision, we are resubmitting our revised version of the manuscript ID: sustainability-3251952, entitled, Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis, for the Sustainability journal.

We thank the reviewers for constructive critiques on our manuscript. Valuable time and details provided by each reviewer and by you are greatly appreciated. The reviewers’ concerns and feedback have been thoroughly considered and addressed. Suggested changes have been incorporated in the revised manuscript. They include additional experimental. In addition, we have also ensured that the novelty of the paper and the methods are clearly and completely described. These changes are highlighted in green, while those highlighted in yellow are editorial/language/ and writing errors corrections.

Please kindly find in our re-submission package, the “sustainability-3251952_Revised” and containing point-by-point reviewers; specific concerns and our responses/revisions.

 

Your Sincerely,

Supattra Puttinaovarat

Corresponding author

 

 

Reviewer3

This research presents an innovative eco-friendly office platform that leverages machine learning and GIS for carbon footprint management and green space analysis. The study addresses significant gaps in real-time data capture and management for green office initiatives. The developed application demonstrates high accuracy in green space classification and provides comprehensive tools for carbon footprint tracking. While technically sound and practically relevant, the study could benefit from deeper exploration of the platform's long-term impacts on organizational sustainability goals.

comments:

1) The introduction provides a comprehensive background, but could benefit from a clearer statement of the specific research questions or hypotheses being addressed. How does this study aim to fill the identified gaps in existing green office management systems?

 

Author Resonse: We thank the reviewers for constructive critiques on our manuscript. Valuable time and details provided by each reviewer and by you are greatly appreciated.  

Revised in introduction section (Line 28-108)

 

 

2) The authors mention using the Support Vector Machine (SVM) algorithm for green space classification. Could you elaborate on why SVM was chosen over other machine learning algorithms, and discuss any potential limitations of this approach?

 

Author Resonse: Revised in topic 2.4 (Line 232-251)

 

 

 

3) The case study focuses on a single faculty at Prince of Songkla University. How generalizable are the results to other types of organizations or different geographical contexts? What factors might influence the applicability of this platform in diverse

 

Author Resonse: Revised contents in discussion section (Line 483-516)

 

 

4) On page 7, you state: "More than 95 percent of office printing and photocopying is conducted in black and white, with most printers configured for black-and-white output." Could you provide a citation for this statistic or clarify if this is specific to your study context?

 

Author Resonse: Revised Sentence in topic 2.3 (Line 210-213)

 

 

5) The application's ability to set and track carbon footprint targets is a valuable feature. How does the system account for potential variations in workload or seasonal changes that might affect printing and energy usage patterns?

 

Author Resonse: Revised in topic 2.3 (Line 189-197)

 

 

6) The green space classification model shows high accuracy, but how robust is it to different types of vegetation or varying seasonal conditions? Have you tested its performance across different times of the year?

 

Author Resonse: Revised in discussion topic 2.Geographical and Environmental Differences (Line 494-503)

 

 

7) The study integrates carbon footprint calculation from printing/photocopying with green space analysis. How do you envision organizations using these two datasets in conjunction to make more informed sustainability decisions?

 

Author Resonse: Revised in discussion (Line 517-526)

 

 

8) The manuscript discusses the limitations of Landsat 8 imagery resolution for detailed green space monitoring. Could you expand on how the integration of ground-truthing with smartphone photography addresses this limitation, and any potential challenges in this approach?

 

Author Resonse: Revised in results section (Line 419-439)

 

 

9) The application's user interface and functionalities are well-described. Have you conducted any usability testing or gathered feedback from potential end-users? How might this inform future iterations of the platform?

 

Author Resonse: Revised by adding usability test evaluation method and evaluation results in section 2.6 (Line 264-275)  and results and discussion (Line 447-465).

 

 

 

10) The conclusion suggests extending the platform to support carbon footprint calculations in additional domains, such as energy consumption. How feasible is this extension, and what challenges do you anticipate in integrating diverse data sources?

 

Author Resonse: Revised in Conclusion (Line 548-560)

 

 

11) While the study focuses on the technical aspects of the platform, it would be interesting to explore the potential behavioral changes this system might encourage among users. Have you considered incorporating features that might promote more sustainable practices beyond just tracking?

 

Author Resonse: Revised in Conclusion  (Line 561-571)

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript can be accepted for publication since all the comments have been addressed.

Reviewer 3 Report

Comments and Suggestions for Authors

Well revised. Can be processed for the next stage of publication.