The paper titled "Drive on a Greener Way: A Case Study on Navigating Cross-regional Traffic Networks in South China" by Yuqi Zhang , Yingying Zhou, et al. This article propose a carbon emission evaluation model based on Life Cycle Assessment (LCA) and a dynamic route planning algorithm that focuses on near real-time traffic states. Firstly, the author develop an evaluation model for carbon emission from both the road and driver perspectives using a method of carbon footprint measurement. This model calculates the carbon emissions caused by all parties involved, providing a comprehensive assessment of the total carbon emissions generated by the traffic. Then,propose a route planning method with both static calculation and near real-time adjustment to minimize carbon emissions. Finally, select three cases with different characters in South China to verify the effectiveness of our model and algorithm. I suggest that this paper can be accepted after major revision. However, there are still some issues that should be clarified and complemented before publication.

1. Abstract should be brief and concise.The innovation and main research results of this work should be fully displayed.

2. The title should be concise and clear，which can reflect the core points of this article, such as sustainable and low-carbon transportation concepts

3. Can we comprehensively consider factors such as vehicles, roads, maintenance, and weather, and assign weights to each factor through the proposed LCA model to obtain a reference formula for carbon emissions?

4. The article proposes a greenest route planning algorithm that combines static planning with dynamic adjustment. Design through static+dynamic real-time adjustment, what are the triggering factors and adjustment methods for dynamic adjustment。

5. The article proposes three real traffic cases in South China, and how is the applicability of LCA method in it？

6. Due to the significant increase in carbon emissions caused by vehicles compared to road emissions, will the proportion of electric vehicles today greatly affect the original assessment in this article?.

7. Figures 3-9 are not very clear, and it is recommended to rearrange some tables and images.

8. Major problem with this paper is that it requires substantial editing in English usage. Some words are missing or sentences are not complete or difficult to understand.

Major problem with this paper is that it requires substantial editing in English usage. Some words are missing or sentences are not complete or difficult to understand.

Author Response

Dear Reviewer,

First and foremost, I would like to express my sincere gratitude for taking the time to review my paper. Your valuable feedback and suggestions are of great importance to me as they will help me further refine and improve my research work.

Regarding the issues and suggestions you raised, I have carefully considered them and made revisions accordingly. Allow me to elaborate on my understanding of these issues and the solutions I have implemented:

Abstract should be brief and concise.The innovation and main research results of this work should be fully displayed.

I have added the illustration of innovation (lines 20-24) and main research results (lines 24-30).

There are two innovations. One is the evaluation model calculates the carbon emissions caused by all parties involved, providing a comprehensive assessment of the total carbon emissions generated by the traffic. The other one is the near real-time route planning approach addresses the problem of traditional route planning, which often fails to account for the influence of variable traffic conditions on the greenest route.

There are three main research results. Firstly, we verify the effectiveness of the greenest navigation algorithm and near real-time green navigation and apply them in cases Secondly, the greenest route was compared with other common navigation results from different dimensions. Finally, compare carbon emissions from vehicles and roads from electricity vehicles and gasoline vehicles. If the electricity vehicle is powered by wind power, the proportion of vehicle carbon emissions will be very small. If other vehicle types, vehicle carbon emissions will be more than 1,000 times that of road emissions

The title should be concise and clear，which can reflect the core points of this article, such as sustainable and low-carbon transportation concepts

This title reflects sustainable and low-carbon transportation concepts. Greener is the keyword of the title. It shows the goal of the navigation algorithm is finding the greenest route that uses low carbon as an indicator. In addition, Greener also has carbon emission allocation from the road’s perspective that is over the low-carbon concept. Low-carbon is a part of sustainable development, so greener also reflects the sustainable concept. So Greener can reflect the corn points of this article.

Can we comprehensively consider factors such as vehicles, roads, maintenance, and weather, and assign weights to each factor through the proposed LCA model to obtain a reference formula for carbon emissions?

Yes, I can.

In page 6 line 290, “3.2. Road model estimation”, I have indicated an abstract formula for the carbon emissions of the road, and in line 308, “3.3. Route model estimation”, I have indicated an abstract formula for the carbon emissions of the route. Since there is no parameter value in the case in Chapter 3, only abstract formulas can be written.

In page 15 line 547, “5.6 Parameters”, I wrote the specific formula for calculating the carbon emissions of the road section in this case. This formula is obtained from the parameter assignment of the abstract formula. Note that this formula is limited to the current case, and the formula will change if you change the parameter setting. For example, changes in materials and standards required for road maintenance.

The article proposes a greenest route planning algorithm that combines static planning with dynamic adjustment. Design through static+dynamic real-time adjustment, what are the triggering factors and adjustment methods for dynamic adjustment

The triggering factors are shows as follow.

Triggering factors	Details
The weight of one road in the shortest route is increased	Congestion is increased on non-shortest routes
The weight of one road in the shortest route is increased	More rainfall on the shortest routes
The weight of one road that is not on the shortest route is reduced	Congestion reduction on non-shortest routes
	Less rainfall on the non-shortest routes

The adjustment method for dynamic adjustment

We use the A* algorithm to determine the optimal route component and the nodes being compared at the current location and recalculate the step based on the new information. If the optimal road changes as a result of this process, the calculation will continue until an updated optimal route is determined. If the same road remains chosen, the original optimal route will remain unchanged.

This part is in page 10 line 396 and lines 415-421. In Chapter “4.4. Dynamic adjustment trigger”, “4.5. Dynamic adjustment algorithm”.

The article proposes three real traffic cases in South China, and how is the applicability of LCA method in it？

The LCA method applies to the calculation of carbon footprint in road traffic. Firstly, LCA provides a systematic approach that integrates all parties involved in road traffic to calculate the total carbon emissions. Secondly, LCA takes a comprehensive perspective, considering various dimensions of time and carbon-emitting events to ensure accurate calculation results. Furthermore, carbon emissions can be measured, and there is a lot of data available for transportation, making it feasible to use LCA for calculating carbon emissions using mechanistic models. Finally, LCA has reliable and quantifiable results, which can be used to further analyze.

The three scenarios mentioned in the article all involve road traffic situations where LCA can be used to calculate carbon footprints. LCA can comprehensively consider the carbon emissions generated by various parties, including road maintenance and driving, etc. The data available for the three scenarios are complete, allowing for objective and quantifiable calculations of carbon emissions.

I have added this part to the article in page 12 lines 483-496, “5.4 Applicability of LCA method”.

Due to the significant increase in carbon emissions caused by vehicles compared to road emissions, will the proportion of electric vehicles today greatly affect the original assessment in this article?.

No, the proportion of electric vehicles today greatly will not affect the result in this article. The article discusses individual vehicle's green route navigation and does not address the issue of the entire traffic flow. Considering the case of electric vehicles, I have added relevant experiments on electric cars in the case analysis section of the article. If the electricity source is clean energy, the carbon emissions of the vehicle can be considered as zero. However, if the electricity source is from sources such as coal-fired power plants, the vehicle's carbon emissions will be attributed to the emissions from electricity generation. This article refers to a standard document that sets the carbon dioxide emissions from generating one kilowatt-hour of electricity at 0.272 kg.

I have added this part to the article in page 13 line 512, “5.6 Parameters”.

Figures 3-9 are not very clear, and it is recommended to rearrange some tables and images.

Figure 3-9 represents the travel OD (Origin-Destination) or travel trajectories on a map. I have made significant efforts to make the following modifications: 1. Replaced the image with a higher resolution of 5472*3416 pixels to ensure clarity when viewed in Word. 2. Enhanced the visibility of the trajectory by changing the color and thickness of the road network in the image. After the revisions were made to the article, the corresponding figure number for Figure 3-9 is now Figure 4-6 and Figure 7-11.

This part is in page 11,12,17,18 and 19, “5.2 Route planning cases” and “5.7 Results”.

Major problem with this paper is that it requires substantial editing in English usage. Some words are missing or sentences are not complete or difficult to understand.

I have made significant revisions to the English text, focusing on word choice, grammar, sentence structure, and overall coherence.If you still feel that the English requires further improvement, I will use the English editing service offered by MDPI to enhance the clarity and expression.

Thank you for your valuable comments and suggestions. Please do not hesitate to contact us if you have any further questions or concerns. We look forward to receiving your approval of our manuscript.

Reviewer 2 Report

Overview and general recommendation:

This article discusses the challenges of green navigation and sustainable mobility, proposing a carbon emission evaluation model and a dynamic route planning algorithm. The model considers carbon emissions from both roads and drivers, while the algorithm adjusts routes in near real-time based on traffic conditions. The effectiveness of the model and algorithm is verified through case studies in South China. The article also reviews related works on sustainable mobility evaluation and planning.

The article validates the effectiveness of the model and algorithm by conducting case studies in three different scenarios in southern China. These case studies provide actual data and results that enhance the credibility and usefulness of the article.

The models and algorithms proposed in this article are practical and can be applied to actual traffic planning and management.

Major comments:

1. The number of citations in the article is low and does not provide sufficient bibliographic support. In order to enhance the credibility of the article, more citations need to be added to support the validity and practicality of the proposed models and algorithms.

2. There are few data and case studies in the article, and not enough details and data are provided to support the validity of the models and algorithms. In order to verify the feasibility of the model and algorithm, more empirical research and data analysis are needed, including more case studies and the collection and analysis of actual data.

3. The structure and organization of the article could be clearer. The flow of information sometimes feels not coherent and clear. In order to improve the structure and organization, the readability and comprehension of the article can be improved through better paragraph division and logical connection.

4. The conclusions and discussions of the article are relatively brief, and the significance and impact of the research findings are not explored in depth.

Minor comments:

1. The study does not consider other factors that may affect route planning, such as safety, traffic flow, or road quality.

2. The study does not provide a detailed analysis of the economic or social implications of the proposed model and algorithm.

3. The study does not address the potential limitations or challenges of implementing the proposed model and algorithm in real-world scenarios.

4. The study assumes that the carbon emissions from road maintenance are proportional to the amount of carbon-containing materials used, which may not always be the case. Future research could explore more accurate and detailed methods for estimating the carbon emissions from road maintenance.

5. The study does not address the issue of equity and fairness in green route planning, which may be important considerations in urban planning and transportation policy. Future research could explore ways to incorporate equity and fairness considerations into the proposed model and algorithm.

Minor editing of English language required

Author Response

Dear Reviewer,

Major comments:

The number of citations in the article is low and does not provide sufficient bibliographic support. In order to enhance the credibility of the article, more citations need to be added to support the validity and practicality of the proposed models and algorithms.

I have added multiple references to support the related work on the research problem. Additionally, I have added a section about related work on sustainable mobility route planning in the article at lines 152-191, “2.2 Route planning for sustainable mobility”.

There are few data and case studies in the article, and not enough details and data are provided to support the validity of the models and algorithms. In order to verify the feasibility of the model and algorithm, more empirical research and data analysis are needed, including more case studies and the collection and analysis of actual data.

About data: The data of cases is confidential and cannot be publicly disclosed. However, I can present the key information of the data in the form of data analysis. This can include proportions related to road length, road service life, rainfall, and other relevant aspects.

About the case studies: We have added two types of experiments.

(1) I have proposed an analysis of electric vehicles. If the electricity used by electric vehicles comes from clean energy sources, they will not generate carbon emissions. However, if the electricity comes from sources such as thermal power generation, the carbon emissions produced by electric vehicles will be set according to the emissions from electricity generation. The article refers to a standard document that sets the carbon dioxide emissions from generating one kilowatt-hour of electricity at 0.272 kg.

(2) I have added the presentation of results for dynamic navigation. It shows the optimal route changes in response to continuous changes in road conditions.

I have added this part to the article in line 512, No.2 and No.3, “5.6 Parameters”. The results are at “5.7 Results”.

The structure and organization of the article could be clearer. The flow of information sometimes feels not coherent and clear. In order to improve the structure and organization, the readability and comprehension of the article can be improved through better paragraph division and logical connection.

I have modified the article structure to make the article clearer. Chapters 1 and 2 are “Introduction” and “Related work”. Chapter 3 illustrates the carbon emission evaluation model that can be divided into two levels, routes and roads. Chapter 4 illustrates the route planning method that consists of state route planning and dynamic adjustment method. The weight of route planning should be obtained from the result of Chapter 3. Chapter 5 is a case study. The detailed modification are as follow:

(1) Adding 2.1 and 2.2 subtitles to make the illustration of related words clearer.

(2) Dividing “5.4 Parameters” into “Vehicles”, “Road maintenance”, “Traffic network”, “Patrol maintenance” and “Data description”.

The conclusions and discussions of the article are relatively brief, and the significance and impact of the research findings are not explored in depth.

In the discussion part, I added two sets of experiments corresponding to two kinds of electric vehicles respectively. I added a multi-dimensional comparison between the two sets of greenest routes and the shortest fastest routes. And I compared the vehicle carbon emissions and road carbon emissions respectively from the overall and the case itself and gave management suggestions from a deeper level.

In the conclusion part, I improved the conclusion part and added the analysis of the generality of the model and algorithm. At the end of the article, I add limitations to my work.

Minor comments:

The study does not consider other factors that may affect route planning, such as safety, traffic flow, or road quality.

I have considered a little about other factor of road carbon emissions. In page 7 line 289, I set ”Carbon emissions from additional road maintenance and road inspections”. In addition to periodic road maintenance, other factors can cause additional maintenance needs for roads. These include accidents, road damage caused by natural disasters or human activities, and changes in weather conditions that can lead to increased wear and tear on the road surface. But the detail functions are not in this research problem.

The study does not provide a detailed analysis of the economic or social implications of the proposed model and algorithm.

Yes. Thanks for your suggestions. The article discusses individual vehicle's green route navigation and does not address the issue of the entire traffic flow. the economic or social implications are the concepts of the macro perspective. I will continue my research about traffic flow with different regional characteristics such as economic or social implications.

The study does not address the potential limitations or challenges of implementing the proposed model and algorithm in real-world scenarios.

I agree with you. I have added the analysis of generalizability in conclusion (Lines 715-722). The carbon emission evaluation model can be used in many different regions unless some other characteristics have a great influence on carbon emissions such as steep roads. The navigation algorithm can be used in other traffic networks. The results of comparing vehicle carbon emission and road carbon emission are also generalizability unless there are some characteristics of road construction.

The study assumes that the carbon emissions from road maintenance are proportional to the amount of carbon-containing materials used, which may not always be the case. Future research could explore more accurate and detailed methods for estimating the carbon emissions from road maintenance.

Thanks for your suggestion. I have added this part into the limitation of my work in Conclusion. (Line 723)

The study does not address the issue of equity and fairness in green route planning, which may be important considerations in urban planning and transportation policy. Future research could explore ways to incorporate equity and fairness considerations into the proposed model and algorithm.

Thanks for your suggestion. I have added this part into the limitation of my work in Conclusion. I will continue this part in my next research. (Line 723)

Thank you for your valuable comments and suggestions. Please do not hesitate to contact us if you have any further questions or concerns. We look forward to receiving your approval of our manuscript.

Reviewer 3 Report

Dear authors,

Thank you for the interesting paper!

It is a very interesting idea to incorporate road wear into routing.

I though have objections against the paper. You say that your system adapts to changes in traffic conditions on-line. This is also given in a Figure. But the methods are not really described and there is no example for this. In this way, the paper seems incomplete.

I would propose to simply remove this part (the on-line adaptation to the current traffic state) and to concentrate on the plain routing only. This would focus the topic and the paper would be complete.

Some further notes:

- page 3, line 136: What does "between 0.02 and 0.03 carbon emissions." mean? What is the measure?

- Figure 2: there are four boxes with the same content ("The weight of one road in the shortest route is increased"). Why? What does it mean?

- Table 7: What is the measure of "Maintainance frequency"?

- Figures 7-9: You should change the colors. As-is, chosen route is hardly visible, disallowing to interprete the results.

- line 311: A* is directed, plain Dijkstra is not. But not even the plain Dijkstra algorithm has to visit all nodes.

You should try to find a native speaker to correct the language issues. Some things I found:

- page 1, line19: "generated by traffic", not "generated by the traffic"

- line 33: "driver [1]. Drivers" not "driver [1]. drivers"

- page 2, line 58: "and road structure. There", not "and road structure.There"

- line 64: "Secondly, the navigation", not "Secondly, The navigation"

- line 81: "Then, we propose", not "Then, We propose"

- page 5, line 187: "mentioned above. According", not "mentioned above.According"

- line 192: The sentence is doubled: "The main source of carbon emissions from traffic is vehicles and roads. The main sources of carbon emissions from traffic are vehicles and roads."

- line 206: "Rain is a negative", not "Rainy is a negative"

- line 212: "The vehicle maintenance process consumes", not "The vehicle maintenance process needs to consume"

- line 221: "CO2" is written in a different way as before

- line 226: "flow. Heavy", not "flow. heavy"

- line 303: "??,? is the carbon emission of", not "??,? means that the carbon emission of"

- line 306: "is at intersection", not "is in an intersection"

- line 432: "day.", not "day. ."

Author Response

Dear Reviewer,

Question 1: I thought have objections against the paper. You say that your system adapts to changes in traffic conditions on-line. This is also given in a Figure. But the methods are not really described and there is no example for this. In this way, the paper seems incomplete.

Answer: In this article, the focus is on near real-time adjustments based on traffic conditions. The optimal route is updated in real time as traffic conditions change. However, since the impact on travel is only considered when traffic conditions change more than the threshold, triggers have been set as the conditions for near real-time updates.

To demonstrate this point in the case study, I have created a new scenario:

Time 1: The vehicle departs, and the initial optimal route is outputted.

Time 2: When the vehicle reaches a point that is not the destination, a road of the subsequent optimal route has congestion. The updated optimal route is outputted.

Time 3: When the vehicle reaches a point that is not the destination, the congestion on the surrounding road of the subsequent optimal route eases. The updated optimal route is outputted.

The adjustments made at each step will be presented using the screenshot.

I have added this part to the article in page 18 lines 581-589, “5.7 Results – 2. Near real time adjustment”,

Question 2: page 3, line 136: What does "between 0.02 and 0.03 carbon emissions." mean? What is the measure?

Answer: This sentence says that the method can save 0.02 – 0.03 ton carbon emissions. I have corrected this sentence.

Question 3: Figure 2: there are four boxes with the same content ("The weight of one road in the shortest route is increased"). Why? What does it mean?

Answer: The four boxes were intended to show the four possible scenarios for traffic condition changes. Taking congestion as an example, the four potential variations would be: congestion on the optimal route, congestion relief on the optimal route, congestion near the optimal route, and congestion relief near the optimal route. The reason these four boxes with the same content is I filled in the wrong content. I have corrected it.

This part is in line 325, Figure 2, “4.1. Method overview”.

Question 4: Table 7: What is the measure of "Maintainance frequency"?

Answer: This column in the table represents the frequency of road maintenance. The data is divided into two categories: one category ranges from 0 to 1, indicating the frequency of maintenance, and the other category is measured in years, representing the number of maintenances per year. All of the values of frequency will be transferred to the number of days one year when I calculate carbon emission from road maintenance.

This part is in line 533, Table 8.

Question 5: Figures 7-9: You should change the colors. As-is, chosen route is hardly visible, disallowing to interprete the results.

Answer: Figure 7-9 represents the travel OD (Origin-Destination) or travel trajectories on a map. I have made significant efforts to enhance the visibility of the trajectory by changing the color and thickness of the road network in the image. After the revisions were made to the article, the corresponding figure number for Figure 7-9 is now Figure 8-10.

Question 6: line 311: A* is directed, plain Dijkstra is not. But not even the plain Dijkstra algorithm has to visit all

Answer: You are correct. I have removed the description that Dijkstra needs to traverse all nodes. The Dijkstra algorithm, when finding the shortest route, determines the shortest route by extending from the node closest to the starting point, without explicitly traversing all nodes.

Question 7: You should try to find a native speaker to correct the language issues. Some things I found:

- page 1, line19: "generated by traffic", not "generated by the traffic"

- line 33: "driver [1]. Drivers" not "driver [1]. drivers"

- page 2, line 58: "and road structure. There", not "and road structure.There"

- line 64: "Secondly, the navigation", not "Secondly, The navigation"

- line 81: "Then, we propose", not "Then, We propose"

- page 5, line 187: "mentioned above. According", not "mentioned above.According"

- line 192: The sentence is doubled: "The main source of carbon emissions from traffic is vehicles and roads. The main sources of carbon emissions from traffic are vehicles and roads."

- line 206: "Rain is a negative", not "Rainy is a negative"

- line 212: "The vehicle maintenance process consumes", not "The vehicle maintenance process needs to consume"

- line 221: "CO2" is written in a different way as before

- line 226: "flow. Heavy", not "flow. heavy"

- line 303: "??,? is the carbon emission of", not "??,? means that the carbon emission of"

- line 306: "is at intersection", not "is in an intersection"

- line 432: "day.", not "day. ."

Answer: Thank you very much. I have corrected these language issues and also other issues I found. As you said, I am not a native speaker but I have tried my best. If you still feel that the English requires further improvement, I will use the English editing service offered by MDPI to enhance the clarity and expression.

Thank you for your valuable comments and suggestions. Please do not hesitate to contact us if you have any further questions or concerns. We look forward to receiving your approval of our manuscript.

Reviewer 4 Report

This work proposes a carbon emission evaluation model based on Life Cycle Assessment (LCA) and a dynamic route planning algorithm for green navigation. The model considers both road and driver perspectives to comprehensively assess total carbon emissions generated by traffic. The dynamic route planning algorithm incorporates near real-time traffic states to minimize carbon emissions, addressing the limitations of traditional route planning. Three cases in South China are used to verify the model and algorithm, comparing the most sustainable route with the shortest route to analyze carbon emission reduction achieved through our approach. Overall, the manuscript addresses an important and relevant topic in sustainable mobility and green navigation. The proposed carbon emission evaluation model based on LCA and the dynamic route planning algorithm show potential for optimizing sustainable route planning. So, the manuscript could be considered for publication with minor revision. However, some improvements can be made to enhance the clarity and coherence of the paper.

1. The introduction provides a good overview of the problem, but it could benefit from a clearer statement of the research objectives and the contribution of the proposed model and algorithm. Adding specific research questions that the study aims to answer would help readers understand the significance of the work.

2. The manuscript mentions three cases in South China to verify the effectiveness of the model and algorithm. While these case studies are valuable, it would be helpful to provide more details on the selection criteria for these specific cases. Additionally, consider discussing the generalizability of the results and whether the findings can be applied to other regions or traffic scenarios.

3. In the methodology section, provide more information about the data sources used for the LCA calculation and the dynamic traffic states.

4. While the manuscript discusses the proposed algorithm for route planning, it lacks a clear comparison with existing route planning methods. To establish the novelty and advantages of the proposed approach, include a comparative analysis with other state-of-the-art green navigation algorithms, highlighting the strengths of your method.

The quality of English language is acceptable.

Author Response

Dear Reviewer,

The introduction provides a good overview of the problem, but it could benefit from a clearer statement of the research objectives and the contribution of the proposed model and algorithm. Adding specific research questions that the study aims to answer would help readers understand the significance of the work.

I have corrected the structure and expression of Introduction to make the research problem clearer. The structure of Introduction is as follow.

Background – Paragraph 1 and 2

Research objective – Paragraph 3

Research question – Paragraph 4

Over solution – Paragraph 5-7

Contribution – Paragraph 8

The structure of article – Paragraph 9

The manuscript mentions three cases in South China to verify the effectiveness of the model and algorithm. While these case studies are valuable, it would be helpful to provide more details on the selection criteria for these specific cases. Additionally, consider discussing the generalizability of the results and whether the findings can be applied to other regions or traffic scenarios.

The selection criteria for these specific cases are explained in the case introduction. I have highlighted the characteristics of each case in “5.2 Route planning cases”.

Case 1 focuses on roads with different service times, ranging from the 1970s to the 2010s. It is observed that the maintenance of older roads results in higher carbon emissions. (Lines 445-449)

Case 2 focuses on routes with various types, particularly highlighting bridges that require different volumes of maintenance materials. (Lines 459-463)

Case 3 emphasizes the impact of variable weather, specifically noting that rainfall increases the volume of material usage. (Lines 470-472)

In different regions, navigation results may be different due to differences in the structure of the traffic network. However, the analysis results of these cases remain consistent, assuming other regions employ the same maintenance technology. If there are changes in the carbon emission from vehicle driving, it will affect the percentage of vehicles' carbon emissions. To account for this, I have added two additional experiences to calculate the relationship between vehicles and road maintenance. (“5.7 Results”)

I have added this part to the article in lines 695-701.

In the methodology section, provide more information about the data sources used for the LCA calculation and the dynamic traffic states.

The data of cases is confidential and cannot be publicly disclosed. However, I can present the key information of the data in the form of data analysis. This can include proportions related to road length, road service life, rainfall, and other relevant aspects.

I have added this part to the article in lines 497-504, “5.5 Data description”.

While the manuscript discusses the proposed algorithm for route planning, it lacks a clear comparison with existing route planning methods. To establish the novelty and advantages of the proposed approach, include a comparative analysis with other state-of-the-art green navigation algorithms, highlighting the strengths of your method.

I have added Figure 12-14 to compare the algorithm this article proposed with other related algorithm such as the shortest route and the fastest route. (Lines 602-608) Regarding green routing algorithms, there is currently no research available on such results due to the involvement of multiple parties and the complexity of the steps involved.

Thank you for your valuable comments and suggestions. Please do not hesitate to contact us if you have any further questions or concerns. We look forward to receiving your approval of our manuscript.

Round 2

Reviewer 1 Report

The authors have made careful revisions. However, the current picture quality needs to be improved, and the English language also needs further proofreading. I suggest that the manuscript be published with minor revisions.

Author Response

Dear reviewer,

I would like to express my sincere gratitude for your invaluable feedback provided by regarding my manuscript. I have carefully considered your comments and made revisions to our work.

(1) The current picture quality needs to be improved.

There are two types of pictures in my paper. One is maps, the other one is pictures generated by Python "matplotlib" or "plot3D".

For Maps, I have used a picture with high resolution that is 5472*3416. It becomes clearer for the maps, and it can satisfy the need. The size of the Word file of my manuscript is 84,674 kb.
For pictures generated by Python, I have changed the PNG file to an SVG file which is a kind of vector graphic file. I can make sure the picture is clear when readers modify the scale.

(2) The English language also needs further proofreading.

I have finished revising English grammar and vocabulary. There are over 50 mistakes have been corrected that have been highlighted in the manuscript. The following part records some examples for correcting mistakes.

Article:
(1) Line 325, change "the processes of method" to "the processes of the method".
(2) Line 352, change "the greater probability of" to "the greater the probability of"
(3) Line 606, change "the result of electricity vehicle" to "the result of an electricity"

Singular and plural
(1) Line 26, change "emission" to "emissions"
(2) Line 220, change "relationship" to "relationships"
(3) Line 24, change"hinder" to "hinders".

Preposition
(1) Line 636, change "z1 is on the range of" to "z1 is in the range of"
(2) Line 521, change "in different kinds of roads" to "for different kinds of roads"

Word error
(1) Line 596, change "some else routes" to "some other routes"
(2) Line 640, change "chear power generation" to "clean power generation"
(3) Line 456, change "So0uth" to "South"

I greatly appreciate your patience and dedication in this process, and I hope that the revised manuscript will now meet your standards.

Reviewer 2 Report

Thank you to the author for the updates and feedback on each review comment. The review comments can answer my doubts.

If possible, double-check the vocabulary and language used in the article for coherence.

Wish ya'll good luck.

Author Response

Dear reviewer,

I would like to express my sincere gratitude for your feedback provided by regarding my manuscript. I have carefully considered your comments and made revisions to our work.

(1) Double-check the vocabulary and language used in the article for coherence.

Singular and plural
(1) Line 26, change "emission" to "emissions"
(2) Line 220, change "relationship" to "relationships"
(3) Line 24, change "hinder" to "hinders".

Preposition
(1) Line 636, change "z1 is on the range of" to "z1 is in the range of"
(2) Line 521, change "in different kinds of roads" to "for different kinds of roads"

Word error
(1) Line 596, change "some else routes" to "some other routes"
(2) Line 640, change "chear power generation" to "clean power generation"
(3) Line 456, change "So0uth" to "South"

I greatly appreciate your patience and dedication in this process, and I hope that the revised manuscript will now meet your standards.

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Drive on a Greener Way: A Case Study on Navigating Cross-Regional Traffic Networks in South China

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