Strategic Infrastructure Sequencing for Freight Transport Decarbonization Under Declining Demand Using Data from Latvia

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1、The paper adopts a method combining scenario tree analysis and S-curve technology to model, and evaluates the optimal infrastructure sequencing strategies for hydrogen fuel cell vehicles and electric vehicles (BEV). It provided a solid theoretical foundation for the progress of society.

2、After reading this article, I felt that the viewpoints and solutions presented were quite innovative. It specifically analyzed the development directions of fuel cell vehicles and electric vehicle-related infrastructure. If there were more data to support the arguments presented in the article, it would make the content more concrete.

Author Response

Comment 1:
The paper adopts a method combining scenario tree analysis and S-curve technology to model, and evaluates the optimal infrastructure sequencing strategies for hydrogen fuel cell vehicles and electric vehicles (BEV). It provided a solid theoretical foundation for the progress of society.

Response:
We sincerely thank the reviewer for recognizing the theoretical contribution and methodological approach of our study. We appreciate the positive evaluation of the integration of scenario tree analysis and S-curve modeling as a foundation for advancing sustainable transport infrastructure planning.

Comment 2:
After reading this article, I felt that the viewpoints and solutions presented were quite innovative. It specifically analyzed the development directions of fuel cell vehicles and electric vehicle-related infrastructure. If there were more data to support the arguments presented in the article, it would make the content more concrete.

Response:

We thank the reviewer for the positive feedback and constructive suggestion. All data used in this study are publicly available through the Open Science Framework platform, ensuring transparency and reproducibility. Sensitive data is available upon request. We provided all necessary information for the correct request as well. Additionally, we have included direct links to the CSDD (Road Traffic Safety Directorate) data sources to facilitate access to national vehicle registration and infrastructure statistics. We hope that these additions address the reviewer’s concern regarding data availability.

Reviewer 2 Report

Comments and Suggestions for Authors

Numerous punctuation errors have been identified throughout the manuscript. It is recommended that the entire text be carefully reviewed and corrected for proper punctuation usage.

It is suggested that the abstract provides more emphasis on the study’s findings to give readers a clearer understanding of the research outcomes.

The format between lines 96 and 106 appears to deviate from the general formatting style used in the rest of the manuscript. Please revise this section to ensure consistency with the article’s standard format.

It would be more logical to present the research questions and hypotheses immediately after the Literature Review section. This would improve the flow of the paper and provide a clearer transition to the research design.

It is recommended that the figures be revised and reformatted. The current presentation style reduces the scientific readability and visual clarity of the study.

Although the Data and Methods section is generally well written, some parts include excessive technical detail. These should be condensed or removed for conciseness.
Example:
“Statistical analysis was performed using the R programming language with packages readxl, dplyr, forecast, purrr, ggplot2, patchwork, gridExtra, and knitr, etc., with Claude AI (Anthropic) providing technical coding assistance based on author-specified instructions.” This level of detail may be unnecessary and can be simplified.

For Reproducibility and Ethical Considerations, these aspects should ideally be discussed at the end of the manuscript, rather than within the main body of the paper, to maintain a smooth logical flow.

All abbreviations should be defined when first introduced in the text.
Example:
“The seasonal and trend decomposition using locally estimated scatterplot smoothing (LOESS) methodology (STL) applied to the 144-month cold-chain transport dataset reveals a complex variance structure where irregular components dominate systematic patterns.”  Here, abbreviations like LOESS and STL should be clearly defined at their first occurrence.

Please clarify the meanings of abbreviations and symbols used in Figure 2 within the text, either in the figure caption or in the main body of the paper.

The Results section is overly long and contains too many figures and details. It would be preferable to focus only on the key findings. If a large number of results are essential, the most significant ones should also be highlighted in the Abstract.

Author Response

We appreciate the reviewer for all valuable suggestions!

Comment 1: Numerous punctuation errors have been identified throughout the manuscript. It is recommended that the entire text be carefully reviewed and corrected for proper punctuation usage.

Response: we have engaged MDPI’s professional Author Services for comprehensive proofreading. This process has ensured that punctuation, grammar, abbreviation definitions, figure clarity, and overall manuscript formatting meet high publication standards.

Comment 2: It is suggested that the abstract provides more emphasis on the study’s findings to give readers a clearer understanding of the research outcomes.

Response: to provide greater emphasis on the study’s key findings, we added a sentence emphasizing key results, ensuring that readers gain a clearer understanding of the research contributions and practical implications.

Comment 3: The format between lines 96 and 106 appears to deviate from the general formatting style used in the rest of the manuscript. Please revise this section to ensure consistency with the article’s standard format.

Response: we have engaged MDPI’s professional Author Services for comprehensive proofreading. This process has ensured that punctuation, grammar, abbreviation definitions, figure clarity, and overall manuscript formatting meet high publication standards.

Comment 4: It would be more logical to present the research questions and hypotheses immediately after the Literature Review section. This would improve the flow of the paper and provide a clearer transition to the research design.

Response: at the end of the literature review section, we have clarified how the literature review informed the development of our hypotheses, providing a clearer logical transition to the research design and improving the overall flow of the paper.

Comment 5: It is recommended that the figures be revised and reformatted. The current presentation style reduces the scientific readability and visual clarity of the study.

Response: we have engaged MDPI’s professional Author Services for comprehensive proofreading as well as regenerated all plots according to editor comments. Additionally we provide all figures in high resolution on OSF platform.

Comment 6: Although the Data and Methods section is generally well written, some parts include excessive technical detail. These should be condensed or removed for conciseness.
Example:
“Statistical analysis was performed using the R programming language with packages readxl, dplyr, forecast, purrr, ggplot2, patchwork, gridExtra, and knitr, etc., with Claude AI (Anthropic) providing technical coding assistance based on author-specified instructions.” This level of detail may be unnecessary and can be simplified.

For Reproducibility and Ethical Considerations, these aspects should ideally be discussed at the end of the manuscript, rather than within the main body of the paper, to maintain a smooth logical flow.

Response: We would like to clarify that the level of technical detail provided reflects the reproducibility standards of our affiliation. Each element of the methodology, including software, packages, and coding assistance, is essential to allow other researchers to fully reproduce our results. Therefore, we respectfully request that no content in this section be removed to maintain full reproducibility.

Comment 7: All abbreviations should be defined when first introduced in the text.
Example:
“The seasonal and trend decomposition using locally estimated scatterplot smoothing (LOESS) methodology (STL) applied to the 144-month cold-chain transport dataset reveals a complex variance structure where irregular components dominate systematic patterns.”  Here, abbreviations like LOESS and STL should be clearly defined at their first occurrence.

Please clarify the meanings of abbreviations and symbols used in Figure 2 within the text, either in the figure caption or in the main body of the paper.

Response: We have carefully reviewed the manuscript and ensured that all abbreviations, including LOESS and STL, are clearly defined at their first occurrence. Subsequent references use the abbreviations consistently, improving clarity and readability throughout the text. We clarified abbreviations used in Figure 2. 

Comment 8: The Results section is overly long and contains too many figures and details. It would be preferable to focus only on the key findings. If a large number of results are essential, the most significant ones should also be highlighted in the Abstract.

Response: We carefully reviewed the Results section in consultation with our scientific group and concluded that all presented results are essential for the integrity and reproducibility of the study. To improve readability and allow readers to focus on key findings, we have added an overview at the beginning of the Results section, summarizing the main outcomes. This provides guidance for readers to navigate the detailed results and focus on sections most relevant to their interests. Additionally, the most significant findings are now highlighted in the Abstract to ensure key contributions are clearly communicated.

We sincerely thank the reviewer for all their efforts and constructive suggestions, and we agree that, with these improvements, the manuscript has become more comprehensive, clearer, and more accessible to readers!!!

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript introduces a multi-factor S-curve model to forecast technology adoption, integrating market signals (via TCO competitiveness), fleet renewal cycles, and regulatory pressures. It surpasses traditional single-factor methods by offering enhanced forecasting accuracy, calibrated with empirical data and industry validation. Crucially, the framework addresses declining freight volumes by optimizing infrastructure for efficiency rather than expansion. This “right-sizing” approach achieves decarbonization goals while cutting public investment by 70% compared to deploying technologies in parallel. Considering the significance of this work, I would recommend acceptance provided that the authors can address the following points satisfactorily:

 

1. How would the results change if you incorporated a dynamic feedback loop where infrastructure deployment in one period directly influences the Total Cost of Ownership (TCO) and thus the adoption rate in the subsequent period?
2. Given the small sample size, to what extent can the qualitative barriers (e.g., universal low ratings for BEV/HFCV integration potential) be considered representative of the entire Latvian cold chain sector?
3. Could you explicitly model the risk of stranded assets for the hydrogen refuelling stations, given their high cost and the projected adoption of <0.5% in the long-haul segment by 2030?

Author Response

We thank the reviewer for these three important observations, all of which have been carefully addressed in the revised manuscript., as follows:

Comment 1: How would the results change if you incorporated a dynamic feedback loop where infrastructure deployment in one period directly influences the Total Cost of Ownership (TCO) and thus the adoption rate in the subsequent period?

Response: We highly appreciate the comment as we are currently working on extending our framework to include such feedback loops in future research, and this comment has been particularly helpful in guiding that development. We have amended the Discussion section to address this important topic, highlighting the potential role of dynamic feedback loops.

Comment 2: Given the small sample size, to what extent can the qualitative barriers (e.g., universal low ratings for BEV/HFCV integration potential) be considered representative of the entire Latvian cold chain sector?

Response: We have added a discussion in the manuscript clarifying that, given the small qualitative sample, the identified barriers (e.g., low ratings for BEV/HFCV integration potential) should be interpreted as indicative trends rather than fully representative of the entire Latvian cold chain sector. We also wish to notify, that the respondents were carefully selected to capture diverse perspectives, providing meaningful insights to support the modeling and interpretation of technology adoption patterns.

Comment 3. Could you explicitly model the risk of stranded assets for the hydrogen refuelling stations, given their high cost and the projected adoption of <0.5% in the long-haul segment by 2030?

Response: We acknowledge the potential risk of stranded hydrogen refueling assets given their high cost and low projected adoption!  While the current study does not explicitly model this risk, we have added a discussion in the manuscript and will incorporate explicit stranded asset modeling in our future research to better inform investment strategies and policy decisions.

We sincerely thank the reviewer for their careful reading of our manuscript and for providing these thoughtful and constructive comments. Their suggestions have greatly helped us improve the clarity, rigor, and relevance of our work, and we believe that the revisions have made the manuscript substantially stronger and more comprehensive!

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have satisfactorily addressed and incorporated all revisions requested in my earlier review report. As a result, the manuscript entitled “Strategic Infrastructure Sequencing for Freight Transport Decarbonization Under Declining Demand Using Data from Latvia” has been substantially improved and is considered appropriate for publication in its current form.