ELECTRIC VEHICLE CHARGING UTILIZATION IN DISADVANTAGED AND NON-DISADVANTAGED COMMUNITIES
Bibliographic record
Abstract
The increasing affordability of electric vehicles (EVs) and the introduction of zero emission regulations have increased EV purchases. Consequently, the EV infrastructure must be developed to meet this rapidly rising demand. This need is particularly crucial in disadvantaged communities (DACs), as projections indicate that by 2030, over one-quarter of new EV owners will come from these communities. However, studies have revealed an unequal distribution of charging stations between DACs and non-disadvantaged communities (non-DACs), with a higher concentration in non-DACs. While governments have made efforts to expand charging stations through funding programs, a universal approach may not effectively address this issue across different communities. Instead, a more effective strategy involves analyzing charging behaviors among charging infrastructure users to develop tailored approaches for installing charging stations strategically. This study aims to identify differences and similarities within and between DACs and non-DACs based on utilization patterns, and race/ethnicity to provide detailed insights into the communities' characteristics. The analysis employed the Gaussian Mixture Model (GMM) to cluster 19 markets within each DAC and non-DAC category across the U.S., considering various EV infrastructure utilization patterns. These patterns were then categorized to provide a framework for stakeholders, including policymakers and EV infrastructure providers. The goal is to enable them to classify communities based on their charging station utilization patterns and demographic characteristics, thereby making informed decisions regarding the placement of EV infrastructure. The study concludes that communities should not be treated as homogenous entities. Instead, tailored approaches that address the unique needs of different communities must be developed to expand EV infrastructure and effectively promote EV adoption. Achieving this objective requires adapting current policy implications. The study offers several suggestions to adapt policies as a base for effective decision-making concerning EV infrastructure, ultimately reducing disparities in charging station distribution between DACs and non-DACs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".