Evaluation of Fiscal and Non-Fiscal Policies for Electric Vehicles—A Multi-Criterion Sorting Approach

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Although the author has revised and improved the manuscript, unfortunately, it still does not meet the journal's requirements. Specific comments are as follows.

1.      The author should highlight the gap by reviewing the existing literature and presenting the paper's theoretical and practical contributions to the current research. 

2.      In section "3.2. Definition of the criteria and input data", the data source is not clear. What are experts and literature, respectively? How do the authors conduct the interview? Why do the authors choose these countries and policies? 

3.      The quality of the figures needs to be improved. 

4.      The result only describes the figures, and the author can discuss it with the actual situation.

Author Response

Reviewer #1:

Although the author has revised and improved the manuscript, unfortunately, it still does not meet the journal's requirements. Specific comments are as follows.

1. The author should highlight the gap by reviewing the existing literature and presenting the paper's theoretical and practical contributions to the current research.

We have revised, updated and enriched the literature review for comprehensiveness. We have reinforced the methodological and practical (for policy design and analysis) contributions. The mains contributions encompass the development of a comprehensive MCDA evaluation framework devoted to the sorting problem using the ELECTRI TRI method to derive meaningful insights regarding the assessment of fiscal and non-fiscal policies for promoting electric vehicles. This framework is supplied with preference information parameters to be elicited from decision makers, thus enabling to accommodate different aims and scope of the study, to shape policy recommendations.

2. In section "3.2. Definition of the criteria and input data", the data source is not clear. What are experts and literature, respectively? How do the authors conduct the interview? Why do the authors choose these countries and policies?

The data for the evaluation model (score matrix in Table 1) was obtained from several sources in the references. The data concerning the ELECTRE TRI parameters including reference profiles, thresholds and weights were elicited from a convenience sample of higher education professors and students to whom a questionnaire was distributed. For instance, for the criterion “Consumer purchase incentives” (the higher the best), the questions to define the reference actions (profiles delimiting the categories) were of the type: “if you would decide based on this criterion only, what would be the minimum value of purchase incentives to place a country in the middle category?”, and the answer was “It should not be less that 1500€ because in this case it should be in the worst category” thus establishing g(b¹). For setting the thresholds, the questions were of the type: “would you think that up to a difference of x€ two entities with similar values would be considered as being equivalent?”, and the answer was “Yes, I think that a difference not exceeding x=200€ is not significant enough to say one would entity be better than the other”, thus setting the indifference threshold q1. Similar questions were included in the questionnaire for all the reference actins delimiting the categories and the thresholds in all criteria. Concerning the weights, the participants were asked to allocate “100 points” among the multiple criteria according to their perspectives and expertise (management, finance, economics, engineering, etc.). The responses were then normalized to generate the weights used to yield our illustrative results. These parameter elicitation schemes can be implemented in different decision contexts.

3. The quality of the figures needs to be improved.

The figures result directly from the output (screen copies) of the IRIS package implementing the ELCTRE TRI method. That is, the figures are copies of what is displayed to users (decision makers and possibly an analyst/advisor with technical expertise on the method to facilitate communication) according to the technical parameters conveying the decision makers’ preferences. We have done our best to improve the quality of figures but we want to present as faithfully as possible what if offered to users to support the decision-making process. 

4. The result only describes the figures, and the author can discuss it with the actual situation.

Thank you for this comment.  In the analysis of the illustrative results, we have reinforced the discussion on the insights that can be derived from such a study.

 

Author Response File: Author Response.docx

Reviewer 2 Report (New Reviewer)

It is estimated that the transportation sector is responsible for about 14% of yearly... The reference cited is very old. Please update.

Reference 2: link is not working.

Close to one 30 half of these emissions come from private cars.: please cite.

for the years 2025 and 2030, respectively please cite

line 114: multiple criteria.. such as??

line 141: remove one ]

line 143: what is "m"

line 162: an alternative ai outranks the reference profile bh.. Does that mean if ai is equal to the upper limit bh? Similarly line 163: does that mean the lower limit bh-1. 

In that sense, ai will always outrank the category if it is equal to it or indifferent. 

In table 1: the authors assigned 0s in g1 and g4. Yet, they did not assign any 4. What is the justification for that since the sample of courtiers is only 7. i.e., Germany and Italy have the lowest scores in tax benefits hence they were assigned 0. Yet Norway has the best in the sample and it was assigned 3. 

How were the parameters in table 2 selected? What are the upper and lower limits of the profiles? 

What the veto vi has not been considered? How considering it would affect the results? 

line 256-258: The weights for all criteria.. How were they selected? please elaborate

This is a numerical analysis based article. Yet, many details about the data analysis were not shown in the manuscript. For instance: How are tables 1 and 2 and corelated in order to reach the results and yet the conclusion. 

Author Response

Reviewer #2:

It is estimated that the transportation sector is responsible for about 14% of yearly... The reference cited is very old. Please update.

Those figures were the most recent ones when we began our study. We have done a thorough search in order to update those figures to the most recent ones, namely using data from authoritative sources including the Global Electric Vehicle Outlook 2022 published by the International Energy Agency.

Reference 2: link is not working.

Thank you for pointing out this issue. The link was corrected.

Close to one 30 half of these emissions come from private cars.: please cite.

This part of the Introduction section has been reformulated, and new references have been added.

for the years 2025 and 2030, respectively please cite

This part of the Introduction section has been reformulated, and new references have been added.

line 114: multiple criteria.. such as??

“Multiple criteria” herein refers, in general, to the multiple evaluation criteria used in MCDA sorting models in which alternatives are assigned to predefined ordered categories of merit that are defined by reference profiles. The multiple evaluation criteria used in our model are detailed below in the text.

line 141: remove one ]

Thank you for pointing out this issue – corrected.

line 143: what is "m"

“m” is the number of alternatives being evaluated. This is clarified in the revised version.

line 162: an alternative ai outranks the reference profile bh.. Does that mean if ai is equal to the upper limit bh? Similarly line 163: does that mean the lower limit bh-1.

In that sense, ai will always outrank the category if it is equal to it or indifferent.

The outranking relationship is not established between the alternatives (actions, entities) under evaluation bur rather between the alternatives and the profiles (reference actions) that define the frontiers of each category. That is, alternative a outranks alternative b (denoted a S b) if a is at least as good as b considering their performances in each criterion. The verification of this relationship between each alternative under evaluation a_i and the reference actions b^h (h=1,…,k) determines the category an alternative under evaluation is sorted into. Please note that if a_i is not worse than b^h in every criterion, then a_i S b^h. Even if there are some criteria for which a_i is worse than b^h then a_i may still outrank b^h, depending on the relative importance of those criteria and the differences in the evaluations in face of the indifference, preference and veto thresholds.

In table 1: the authors assigned 0s in g1 and g4. Yet, they did not assign any 4. What is the justification for that since the sample of courtiers is only 7. i.e., Germany and Italy have the lowest scores in tax benefits hence they were assigned 0. Yet Norway has the best in the sample and it was assigned 3.

Traffic regulation incentives (g₁) and Tax benefits (g₄) are criteria expressed in a qualitative scale of 4 levels [0, 1, 2, 3], in which the values really have the meaning of a label (e.g., poor, fair, good, very good). The values used in our illustrative example resulted from the feedback obtained from our convenience sample representing possible decision makers. The fact that countries with very good scores in these criteria did not achieve the best category in the classification result from the interplay of the weights (herein understood as the “voting power” of each criterion to validate the outranking relationship), the definition of the reference actions delimiting the categories, and the thresholds.

How were the parameters in table 2 selected? What are the upper and lower limits of the profiles?

The preference expression parameters, which are presented in table 2, have been elicited from The data for the evaluation model (score matrix in Table 1) was obtained from several sources in the references. The data concerning the ELECTRE TRI parameters including reference profiles, thresholds and weights were elicited from a convenience sample of higher education professors and students to whom a questionnaire was distributed. The process of elicitation of this input information is described in the reply to Comment 2 of Reviewer #1.

The lower and upper limits of the profiles are the reference actions delimiting the categories, which were elicited as described above. For instance, these limits mean that if only the Consumer purchase incentives criterion (g₃) was considered a country offering less than 1500€ would be classified in the lowest category, a country offering between 1500€ and 4000€ would be classified in the middle category, and a country offering more than 4000€ would be classified in the best category. All the information is then combined according to the methodological principles of the ELECTRE TRI method (as described in section 2) to generate a sorting of the alternatives under evaluation.

What the veto vi has not been considered? How considering it would affect the results?

The veto thresholds have been duly considered in the description of the methodological approach but we decide not to include them in the illustrative results for the sake of illustration of the essential aspects. The veto threshold may or may not affect the results depending on the criteria for which they are considered and its magnitude. Please note that not considering the veto thresholds is in itself the expression of stating “no matter how much an alternative is worst than another alternative in any criterion, this may not be detrimental for its evaluation if in the other criterion the former alternative is superior to the latter one”. What is really important is that the evaluation model is versatile to accommodate all these types of preference expression.

line 256-258: The weights for all criteria.. How were they selected? please elaborate

In the framework of the ELECTRE methods based on the construction and exploration of an outranking relationship, the weights have the meaning of the “voting power” of each criterion to assess the (crips or valued) outranking. This meaning and operational use of weights is different from the multiple attribute value (or utility) theory, in which the weights are used to build a value (utility) to be assigned to each alternative under evaluation. Specific methods for eliciting weights exist under both perspectives outlined above. In our study, the weights were elicited from a convenience sample of higher education professors and students to whom a questionnaire was distributed asking them to distribute “100 points” among the multiple criteria according to their perspectives and expertise (management, finance, economics, engineering, etc.). The responses were then normalized to generate the weights used to yield our illustrative results.

This is a numerical analysis based article. Yet, many details about the data analysis were not shown in the manuscript. For instance: How are tables 1 and 2 and corelated in order to reach the results and yet the conclusion.

The main value-added of our study is the proposal of a model and a methodological approach to assess countries regarding fiscal and extra-fiscal policies for promoting electric vehicles in the framework of a multi-criterion sorting approach. The results are illustrative of the insights for decision support that can be derived from this approach, which is totally flexible to consider other weight coefficients, thresholds, reference alternatives bounding the categories, etc.

 

Author Response File: Author Response.docx

Reviewer 3 Report (New Reviewer)

Title: Evaluation of fiscal and extra-fiscal policies for electric vehicles – a multi-criterion sorting approach  

Manuscript Number: sustainability-2187596 

Journal: Sustainability 

 

It is my pleasure to review the manuscript for the esteemed journal. In this manuscript, the authors

Developed a multi-criterion approach to evaluate the performance of countries regarding fiscal and non-fiscal policies for promoting electric vehicles. The work presented is relevant to the Journal's field. The manuscript has got some potential. I would like to congratulate the author for a considerable amount of work that they have done. Especially, the authors reported that the proposed model considers fiscal (vehicle registration, annual registration, subsidies) and non-fiscal (traffic regulations, charging infrastructure) potential policies to define a comprehensive set of evaluation criteria. However, the manuscript needs further improved before to be accepted for publication. The reviewer has listed some specific comments that might be helpful of the author to further enhance the quality of the manuscript. Please consider the particular comments listed below.

 

Comment 1: Abstract. The abstract is well-written. However, it should underscore the scientific value added of your paper in your abstract, rather than others. 

 

Comment 3: sections of Introduction. The novelty of this paper should be further justified by highlighting main contributions to the existing Introduction and Literature Review. This could be clearly presented in the Literature review related work. Please consider please consider citing following papers: entitled “Heterogeneous effects of energy efficiency, oil price, environmental pressure, R&D investment, and policy on renewable energy--evidence from the G20 countries”, and entitled “Do environmental regulation and urbanization help decouple economic growth from water consumption at national and subnational scales in China? It should be better elaborate the contribution of the work to the existing literature.

 

Comment 3: sections of The ELECTRE TRI method, The MCDA model. Please read carefully the recent papers published in Sustainability. These two parts are usually combined into one section, such as named method and data. In addition, it would be better to further highlight your improvement of the method and your innovation in methods.

 

Comment 4: sections of results and discussion. The section is well-structured. However, it would be better to discuss what your findings are different from the past works.

 

Comment 5: section of Conclusion. Please make sure your conclusions' section underscore the scientific value added of your paper, and/or the applicability of your findings/results, as indicated previously. Basically, you should enhance your contributions, limitations, underscore the scientific value added of your paper, and/or the applicability of your findings/results and future study in this session.

 

Comment 6: There are still some occasional grammar errors through the revised manuscript especially the article ''the'', ''a'' and ''an'' is missing in many places, please make a spellchecking in addition to these minor issues. In addition, some sentences are too long to be easy to read. It is recommended to change to short sentences, which are easier to read.

 

Comment 7: References. Please check the references in the text and the list; You should update the reference.

 

Good luck!

Author Response

Reviewer #3:

It is my pleasure to review the manuscript for the esteemed journal. In this manuscript, the authors

Developed a multi-criterion approach to evaluate the performance of countries regarding fiscal and non-fiscal policies for promoting electric vehicles. The work presented is relevant to the Journal's field. The manuscript has got some potential. I would like to congratulate the author for a considerable amount of work that they have done. Especially, the authors reported that the proposed model considers fiscal (vehicle registration, annual registration, subsidies) and non-fiscal (traffic regulations, charging infrastructure) potential policies to define a comprehensive set of evaluation criteria. However, the manuscript needs further improved before to be accepted for publication. The reviewer has listed some specific comments that might be helpful of the author to further enhance the quality of the manuscript. Please consider the particular comments listed below.

Comment 1: Abstract. The abstract is well-written. However, it should underscore the scientific value added of your paper in your abstract, rather than others.

The abstract has been rewritten to clarify the main value added of our study and findings.

Comment 3: sections of Introduction. The novelty of this paper should be further justified by highlighting main contributions to the existing Introduction and Literature Review. This could be clearly presented in the Literature review related work. Please consider please consider citing following papers: entitled “Heterogeneous effects of energy efficiency, oil price, environmental pressure, R&D investment, and policy on renewable energy--evidence from the G20 countries”, and entitled “Do environmental regulation and urbanization help decouple economic growth from water consumption at national and subnational scales in China? It should be better elaborate the contribution of the work to the existing literature.

The main contribution of this paper is the development of a comprehensive multi-criteria decision analysis (MCDA) evaluation framework devoted to the sorting problem (i.e., assigning the alternatives under evaluation to pre-defined ordered categories of merit) using the ELECTRI TRI method to derive meaningful results regarding the assessment of fiscal and non-fiscal policies for promoting electric vehicles. The main contributions are stated at the end of the Introduction section in the revised version.

Thank you for suggestion these papers to enrich the literature review and positioning our contributions in face of the existing literature.

Comment 3: sections of The ELECTRE TRI method, The MCDA model. Please read carefully the recent papers published in Sustainability. These two parts are usually combined into one section, such as named method and data. In addition, it would be better to further highlight your improvement of the method and your innovation in methods.

Thank you for this suggestion. After establishing the motivation for the study and our main contribution in section 1, in section 2 we make a short yet comprehensive description of the ELECTRE TRI method devoted to the sorting problem in face of multiple evaluation criteria. Then we present in section 3 what is the main contribution of our study, which is a MCDA model for the assessment of fiscal and non-fiscal policies for electric vehicles, comprising the identification of the alternatives under evaluation, the definition of the criteria, and the parameterization of the model with all the technical preference expression parameters required by the method, including category profiles (reference actions), weights, thresholds, cutting level for assessing the outranking relationship and additional constraints on preferences. Section 4 offers illustrative results of an instantiation of the model and a discussion on the results obtained, evidencing the flexibility of the MCDA model to capture different decision-making contexts and requisites. In this way, we think it is easier for the reader to capture the flow of ideas of the components of the study (problem, method, model, results and possible policy insights that can be derived).

Comment 4: sections of results and discussion. The section is well-structured. However, it would be better to discuss what your findings are different from the past works.

We have revised this section to enhance the novel features of our methodological proposal in face of the existing literature.

Comment 5: section of Conclusion. Please make sure your conclusions' section underscore the scientific value added of your paper, and/or the applicability of your findings/results, as indicated previously. Basically, you should enhance your contributions, limitations, underscore the scientific value added of your paper, and/or the applicability of your findings/results and future study in this session.

We have revised the Conclusions section to emphasize the main novelty and significance of our methodological proposal, namely regarding its versatility to accommodate different preference expression parameters according to the real-world decision context. The limitations refer mainly to the technical nature of the preference parameters required, which in practice may require that the communication between the software implementing the methodology and the decision makers is mediated by a facilitator with some technical expertise who may “translate” the meaning of the technical parameters in the context of the problem.

Comment 6: There are still some occasional grammar errors through the revised manuscript especially the article ''the'', ''a'' and ''an'' is missing in many places, please make a spellchecking in addition to these minor issues. In addition, some sentences are too long to be easy to read. It is recommended to change to short sentences, which are easier to read.

Thank you for pointing out those issues. The paper has been thoroughly revised for spelling and grammar by an English-native speaker.

Comment 7: References. Please check the references in the text and the list; You should update the reference.

We have revised, updated and enriched the references for completeness.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report (Previous Reviewer 1)

The revised version has no substantive improvements.

Author Response

We have tried to emphasize the main contributions of our paper, which is the development of a comprehensive multi-criterion decision analysis (MCDA) evaluation framework to derive meaningful insights regarding the assessment of countries according to fiscal and non-fiscal policies for promoting electric vehicles. As far as we know, this is the first time an MCDA approach is used in this context, which has the potential to provide insights for the definition of adequate policy measures.

Reviewer 2 Report (New Reviewer)

Looks much better than previous version. 

Author Response

Thank you for your positive comment.

Reviewer 3 Report (New Reviewer)

I would recommend the paper to be accepted for publication.

Author Response

Thank you for your positive comment.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

This paper evaluates fiscal and extra-fiscal policies for promoting electric vehicles by the ELECTRE TRI method. In general, this paper did not draw valuable findings, and did not meet the publication requirements.

1. The abstract sector lacks primary findings and conclusions.
2. Compared with the existing research, what is the contribution of this paper?
3. Method description is not clear. The authors need to supplement the estimation process of key parameters and detailed data sources and introduce why evaluation indicators such as vehicle registration are selected.
4. ELECTRE TRI Method is a multi-criteria evaluation method. The title of Sector 2 conflicts with that of Sector 3.
5. Please be consistent in the expression of extra-fiscal and non-fiscal.
6. Figures are not standard.

Reviewer 2 Report

Please see attached file.

Comments for author File: Comments.pdf