Understanding the Correlation of Demographic Features with BEV Uptake at the Local Level in the United States
Round 1
Reviewer 1 Report
This is a nice and interesting study, especially while we are moving to a new era of transportation. The manuscript is well written and described, but authors should address the attached concerns:
Comments for author File: 
Comments.pdf
Author Response
Comments and Suggestions for Authors
This is a nice and interesting study, especially while we are moving to a new era of transportation. The manuscript is well written and described, but authors should address the attached concerns:
- Maintaining power quality is another factor to place public charging infrastructure and that has influence on EV market. You can also check the following studies on Quebec, Canada https://ieeexplore.ieee.org/abstract/document/9090360 https://ieeexplore.ieee.org/abstract/document/8909746 https://ieeexplore.ieee.org/abstract/document/9512522
Response
These studies have been added to line 130.
- This type of work is not very new, as examples https://www.sciencedirect.com/science/article/pii/S0959652618338071?casa_token=leYcnugd SS8AAAAA:q RYhSm bCECt3 M nfs-
yEBABRG1TiCSWyDdq_ Y8BIhjh9pQq3SiH7NYHim72FLv12fo3 M DJmNkF7g https://ieeexplore.ieee.org/abstract/document/9582980
This would be great to add a literature review section separately to compile this type of studies.
Response
The first reference is already included Ref [18], on line 86.
The second study has been added to line 117.
- Since you study is based on 2019-2020, there was an opportunity to study the impact of pandemic. Or you at least can consider this for a future
Response
The potential impact of Covid-19 has been addressed in line 198. While this paper deliberately excludes post Covid data in order to focus on purely demographic effects, future longitudinal studies would definitely consider the impact of Covid. The following text was added:
“The data timeframe is chosen in part to exclude the impact of COVID-19, the effects of which can be examined by following a similar framework in future longitudinal studies.”
- Considering fuel price and electricity price of different areas should have some significant
Response
Reference 30 has been added to line 127, which considers fuel price. While this study focuses solely on the quantification of demographic impacts, the following text has been added to line 135 in the introduction:
“While this study isolates quantifiable demographic factors, comparison of results across states or ZIP codes with disparate non-demographic factors, such as EV policies or fuel prices, can yield information about the net impact of non-demographic factors as well”
In addition, discussion of non-demographic factors has also been added to the new section 4.4, on lines 537.
- The conclusion is not well presented. It is better to add a discussion section and then conclude the paper in a concise
Response
Much of the conclusion section has been modified and moved to a discussion section at the end of the results (Section 4.4). The final conclusions have been made more concise.
Author Response File: Author Response.pdf
Reviewer 2 Report
- First and foremost, the paper must give thorough evidence why it makes sense to only focus on the demographic features to analyze the BEV uptake when there is a large evidence that supports the EV policy influencing the BEV update. For instance, the results for California are understandably very different from other states due to the zero-emission goals. The authors must consider the effect of EV policies that influence the behavior of EV uptake, otherwise, the correlation analysis is significantly biased.
- The interpretation of the correlation analysis is missing. In addition to describing the results, the authors must explain why such results were expected and shine a light on important issues that policymakers or infrastructure planners must consider.
- Please show the sample size for each state in each result. If the sample size is very different from state to state, are the correlation analysis and comparison results justified?
- The phrase 'at a granular level at a larger scale' must be polished to be used for a title
- Line 48 claims there is a lack of extensive empirical studies. This is a very strong and risky claim and the author must give explicit and thorough examples of the current literature and sharply point out what they lack to give this remark.
- Line 142, 147, and 149 explain that the framework will be applicable to other vehicles and that this is the contribution of the research. Correlation analysis with t-statistics is not proposed by the authors and this paper cannot claim that it is a contribution. The third and fourth bullet points of contribution must be discarded or reconsidered.
- Line 170: which month of 2020?
- Line 214: the phrase 'mobility of the residents' is misleading as mobility often means the access/ability for people to use transportation means
- Line 266: describe what 'travel time' means here - average travel time per day? average commuting time?
- Algorithm 1: most steps do not need to be described - it's a standard t-test procedure
- Algorithm 1: step 2: why n=15?
- Figure 4: normal distribution seems a poor fit - another distribution may give more useful information. Please label the x-axis.
- Figure 5: the figure must be updated for better resolution
Author Response
Comments and Suggestions for Authors
- First and foremost, the paper must give thorough evidence why it makes sense to only focus on the demographic features to analyze the BEV uptake when there is a large evidence that supports the EV policy influencing the BEV update. For instance, the results for California are understandably very different from other states due to the zero-emission goals. The authors must consider the effect of EV policies that influence the behavior of EV uptake, otherwise, the correlation analysis is significantly biased.
Response
The introduction has been modified in lines 135 to further emphasize that demographic factors are one of many types that may correlate with BEV uptake, and the purpose of the present work is to quantify this relationship:
“While this study isolates quantifiable demographic factors, comparison of results across states or ZIP codes with disparate non-demographic factors, such as EV policies or fuel prices, can yield information about the net impact of non-demographic factors as well”
Discussion of the implications of varying results across states has been added in lines 558 in section 4.4:
“In addition, certain important non-demographic factors, including charging infrastructure, EV incentives, and fuel and electricity prices can vary significantly between states. These factors not only affect BEV uptake directly but can change the relationship between demographic factors and BEV uptake.”
2. The interpretation of the correlation analysis is missing. In addition to describing the results, the authors must explain why such results were expected and shine a light on important issues that policymakers or infrastructure planners must consider.
Response
Further discussion of correlation results has been added to a new discussion section 4.4, in lines 537.
3.Please show the sample size for each state in each result. If the sample size is very different from state to state, are the correlation analysis and comparison results justified?
Response
The sample size for each state (number of ZIP codes) is given in Table 1. While some states have more ZIP codes than others, this study includes all ZIP codes (or a complete sample) in each state.
4.The phrase 'at a granular level at a larger scale' must be polished to be used for a title
Response
The title has been shortened for clarity.
5. Line 48 claims there is a lack of extensive empirical studies. This is a very strong and risky claim and the author must give explicit and thorough examples of the current literature and sharply point out what they lack to give this remark.
Response
This claim has been greatly softened in line 50, shifting the focus to a need for further research in this field:
“It is important to empirically study the numerous socio-demographic factors that impact actual local BEV uptake, particularly in the US.”
6. Line 142, 147, and 149 explain that the framework will be applicable to other vehicles and that this is the contribution of the research. Correlation analysis with t-statistics is not proposed by the authors and this paper cannot claim that it is a contribution. The third and fourth bullet points of contribution must be discarded or reconsidered.
Response
Discussion of the scope of this research, and the potential implications to other vehicle technologies has been moved to follow the list of contributions, to emphasize that the current paper’s contributions focus on demographic factor impact on BEV uptake. The 4th contribution has been edited to reflect that the quantification of the relationship between demographic factors and BEV uptake is what is being contributed - not the methodology of correlation analysis itself. The updated contribution has been edited as follows:
“4. Quantifying the relationship between the demographic features and BEV uptake at different geographic locations.”
7. Line 170: which month of 2020?
Response
Line 198 has been updated to specify February of 2020.
8. Line 214: the phrase 'mobility of the residents' is misleading as mobility often means the access/ability for people to use transportation means
Response
The term “mobility” of the residents has been replaced with “migration” of the residents for clarity, with explanation on line 246:
“Category 4- Migration of the residents: Growth of the ZIP code in terms of residents moving out of the area or coming in.”
9.Line 266: describe what 'travel time' means here - average travel time per day? average commuting time?
Response
In line 244, the explanation for Category 3 now includes the average daily commute time. “travel time” has been updated throughout the paper where appropriate.
“Category 3- Traveling characteristics: Characterizes the traveling nature of the residents of the place, including means of transportation and average daily commute time.”
10. Algorithm 1: most steps do not need to be described - it's a standard t-test procedure
Response
Algorithm 1 has been described explicitly to clarify how the features have been applied in the standard t-test procedure, including the notation used.
11. Algorithm 1: step 2: why n=15?
Response
Step 2 is modified to address this. n=15 groups as described in Table 2.
12. Figure 4: normal distribution seems a poor fit - another distribution may give more useful information. Please label the x-axis.
Response
X-axis is labeled. The figure caption has been updated to emphasize that the purpose of the figure is to show the non-normal distribution of a typical demographic feature.
13. Figure 5: the figure must be updated for better resolution
Response
Figure 5 has been updated with larger text.
Author Response File: Author Response.pdf
Reviewer 3 Report
The proposal is appealing and interesting, and the method deserves some consideration. Moreover, the paper is almost well written and well organized.
- The text, in general, reads well, but a grammatical revision could improve it further.
- The paper adequately puts the progress it reports in the context of previous work, representative referencing and first discussion.
- The authors could highlight better the new scientific contribution, for instance, analyzing several recent literature works.
Author Response
Comments and Suggestions for Authors
The proposal is appealing and interesting, and the method deserves some consideration. Moreover, the paper is almost well written and well organized.
Response
The authors appreciate the reviewer’s positive feedback.
- The text, in general, reads well, but a grammatical revision could improve it further.
Response
The paper has been further edited for grammar and clarity.
- The paper adequately puts the progress it reports in the context of previous work, representative referencing and first discussion.
Response
The authors appreciate the feedback.
- The authors could highlight better the new scientific contribution, for instance, analyzing several recent literature works.
Response
More recent literature has been added to the introduction, beginning in line 117.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Most of the comments are fairly addressed. Thank you. However, a have a few minor comments (mainly editorial).
1) Add a reference for figure 2
2) Add a space after table 2
3) The usage of different colors in figure 3 might not be very meaningful
4) Please follow the same pattern for table 4 (like other tables of the manuscript)
Author Response
1) Add a reference for figure 2
Response: Reference has been added to Figure 2.
2) Add a space after table 2
Response: Space has been added after Table 2.
3) The usage of different colors in figure 3 might not be very meaningful
Response: All the different colors have been replaced with black ink.
4) Please follow the same pattern for table 4 (like other tables of the manuscript)
Response: Table 4 has been changed to the same font as the rest of the manuscript.
Author Response File: Author Response.pdf
Reviewer 2 Report
The revision acknolwedges the comments from the previous review but does not improve the paper accordingly. What is the key finding of this paper? The key finding seems to be that 'higher BEV adoption in a state result in a stronger correlation between demographic factors and BEV uptake. Features related to the number of individuals in a ZIP code with an annual income greater than 75k are strongly correlated with BEV uptake, followed by the number of owner-occupied housing units, individuals driving alone and working from home.' But this is a widely known fact that a group with higher income (hence more owner-occupied housing units) tends to have a larger BEV uptake. This is not a new finding.
The authors argue that 'In-depth knowledge of local BEV uptake is important for applications related to the accommodation of BEVs and understanding what causes differences in local uptake can allow for both the prediction of future growth and stimulation of it.' but what in-depth knowledge does this paper provide that was unknown in the literature before? Do authors identify a means to offset the differences in local uptake?
Author Response
The revision acknolwedges the comments from the previous review but does not improve the paper accordingly. What is the key finding of this paper? The key finding seems to be that 'higher BEV adoption in a state result in a stronger correlation between demographic factors and BEV uptake. Features related to the number of individuals in a ZIP code with an annual income greater than 75k are strongly correlated with BEV uptake, followed by the number of owner-occupied housing units, individuals driving alone and working from home.' But this is a widely known fact that a group with higher income (hence more owner-occupied housing units) tends to have a larger BEV uptake. This is not a new finding.
The authors argue that 'In-depth knowledge of local BEV uptake is important for applications related to the accommodation of BEVs and understanding what causes differences in local uptake can allow for both the prediction of future growth and stimulation of it.' but what in-depth knowledge does this paper provide that was unknown in the literature before? Do authors identify a means to offset the differences in local uptake?
Response:
The authors agree that many of the well-correlated features at the ZIP code level are consistent with past studies into individual behavior, and such intuitions and past results informed the starting hypotheses for feature engineering. As discussed in Section 4.4, lines 538-544, the demographic factors are descriptors of a ZIP code, not of BEV purchasers, and the authors are unaware of other literature quantifying the degree of impact of a large number of demographic features across many ZIP codes and states. That many of the feature descriptions are qualitatively similar to past results in surveys or localized studies is seen as a positive, but the findings in this paper deal with the quantitative impact of such factors at the aggregate level, where a multitude of known and unknown factors can affect any target metric such as BEV uptake.
The conclusions have been slightly expanded, and some of the claims modified, based on this feedback. While the authors believe such quantitative information will be beneficial for localized EV policy decisions, explicit recommendations regarding the stimulation of BEV uptake are beyond the scope of this analysis and have been de-emphasized.
Author Response File: Author Response.pdf
Round 3
Reviewer 2 Report
I believe the manuscript has been modified sufficiently to the previous comments.
Author Response
We thank the reviewer for helping us to improve our paper.
