Electric Vehicles Are Coming: Are Charging Stations in North Carolina a Harbinger of this Change?
Bibliographic record
Abstract
Electric Vehicles Are ComingAre Charging Stations in North Carolina a Harbinger of this Change? Gregory J. Carlton (bio) Electric Vehicles (EVs) are no longer solely a coffee table conversation topic in affluent households, they are becoming relevant throughout society and could foreshadow a transitional change into a new American lifestyle in the next decade. With increasing investment in EVs from well-known automobile manufacturers including Tesla, Ford, BMW, and Mercedes, and from emerging companies (e.g., Byton, Lordstown), the average price for an EV is dropping and sales are rising. In North Carolina, there were 12,566 registered EVs in June 2020, and that number increased to 19,752 by June 2021; this represents a 36 percent year-over-year increase (NCDOT 2021). While Wake County (home to Raleigh) has the highest number of registered EVs in North Carolina, Guilford County (Greensboro) ranks 5th with 813 registered EVs. With this increase in EV sales, NC is preparing to establish multiple vehicle-charging corridors and more charging infrastructure over the next decade. This may be an important step towards electrification, as evidence suggests that consumer interest in EVs increases as they are exposed to charging stations at multiple locations in their community (Bailey et al. 2015) and through having social connections to people who own EVs (Fleming 2018). Glowing a neon-green hue, the EV charging station at Greensboro's South Side Walmart (cover photo) beckons customers to recharge their vehicles while they shop at their leisure. Since it can take a significant amount of time to charge an EV, especially using Level I or II chargers that have slow currents between 16 and 32 amps (Savari et al. 2020), placing charging facilities near activities such as either shopping or dining is a common practice (Huang et al. 2016). What is uncommon about this particular station is both its size and its location. Most charging stations in the Piedmont region of North Carolina are proximate to "higher-end" establishments such as golf courses, lifestyle malls, downtown shopping districts, and upscale retailers (Figure 1). These charging stations also tend to be small, offering two or three charge points, while the Walmart charging station offers sixteen. Charging stations of this large size are comparable to a typical gas station in terms of refueling ability, and its location at a discount supermarket in a working-class area of the city indicates its possible appeal to a wider subset of society. Just as there are many gas station and convenience store brands in the United States, there are also many operators of charging stations. The charging station at the South [End Page 1] Click for larger view View full resolution Figure 1. (From Top Left to Bottom Right) Examples of typical charging stations in the Piedmont Area of North Carolina. Charging stations tend to be small and are often located proximate to high-end amenities. Photo A is a ChargePoint charging station located at a food cooperative near the CBD of Greensboro. Photo B is a charging station associated with an upscale industrial conversion project in downtown Winston-Salem. Photo C shows an EV being charged near the historic city center of Apex. Photo D is of a ChargePoint station placed on a golf course in High Point. This station was sited as part of a partnership with the state. Greensboro Walmart is operated by Electrify America, a subsidiary of Volkswagen, which leases spaces from retailers to build its stations (Leung and Peace 2020). The Electrify America charging stations are unique as they are part of a legal settlement made with federal regulators to supply $2 billion USD worth of charging to consumers following an emissions cheating scandal (Reck 2020). Other industry leaders include ChargePoint, which has the largest market share of charging stations in the United States (Brown et al. 2020), and Tesla's Supercharger and Destination Charging Networks, which are geared towards Tesla owners. [End Page 2] Some might see the charging station at Greensboro's Walmart as unremarkable, but this development portends a remarkable change that may soon upend life in the American South. While current EV adoption and marketing trends favor society's wealthy "kinetic elites" (Henderson 2020), a...
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".