Counting the Energy Transition

The shift from internal combustion to electric vehicles is one of the most significant infrastructure transformations in a century. But how far along is it really? Headlines alternate between breathless EV optimism and skeptical pushback, and both sides cherry-pick data to support their narrative.

The most objective measure of progress is infrastructure. You can debate EV sales projections and battery technology timelines endlessly, but charging stations either exist or they do not. Gas stations either close or they do not. Location data tells the unvarnished truth about where the energy transition stands and where it is heading.

Web scraping location data from charging networks, gas station directories, and government databases provides the raw material for this analysis.

The Current Count: Where Things Stand

As of mid-2025, the United States has approximately 75,000 public EV charging locations housing over 190,000 individual charging ports. This includes Level 2 chargers (the slower, more common variety found at shopping centers, workplaces, and hotels) and DC fast chargers (the high-speed units that can add 100+ miles of range in 20-30 minutes).

By comparison, the US has roughly 145,000 gas stations — a number that has been slowly declining for decades, down from over 200,000 in the 1990s. On a pure location count basis, EV charging has reached roughly 50% of the gas station count. But this comparison is misleading in important ways.

A gas station serves a vehicle in 5 minutes. A fast charger takes 20-40 minutes. A Level 2 charger takes 4-8 hours. The throughput per station is fundamentally different, which means EV charging needs significantly more locations to serve the same number of vehicles. The raw count comparison understates the infrastructure gap for fast charging and overstates it for Level 2 charging (which primarily serves parked vehicles that do not need quick turnaround).

Scraping data from major charging networks — Tesla Supercharger, ChargePoint, EVgo, Electrify America, and others — provides the detailed location-by-location data needed to assess real coverage versus simple counts.

Geographic Distribution: Two Different Maps

Plotting EV charger locations and gas station locations on a map reveals the energy transition's geographic unevenness. Gas stations distribute relatively evenly across the country, with density roughly proportional to population and road traffic. Every interstate exit, every small town, every rural crossroad has fueling infrastructure.

EV charger distribution is dramatically different. Scraping location data and mapping it reveals extreme coastal concentration. California alone accounts for roughly 25% of all US public chargers. The Northeast corridor from Washington DC to Boston has dense coverage. The Pacific Northwest, Colorado Front Range, and Texas metros show strong and growing networks.

But vast stretches of the interior — the Great Plains, rural Appalachia, the Mountain West — remain charging deserts. Drive from Denver to Kansas City and your EV charging options are sparse. The same route has gas stations every few miles.

This geographic gap is the most significant infrastructure challenge for EV adoption. Scraping both charger and gas station location data simultaneously and calculating coverage density by county or zip code reveals exactly where infrastructure investment is most needed.

Growth Rate: The Trend Lines Tell the Story

The static comparison between current EV charger and gas station counts misses the most important part of the story: trajectory. Gas station counts are declining at roughly 1-2% per year. EV charger installations are growing at 25-35% per year.

Scraping Department of Energy data, state utility commission filings, and charging network press releases over time shows the acceleration. In 2020, the US added roughly 10,000 new public charging ports. In 2023, that number exceeded 30,000. Federal infrastructure spending — including the $7.5 billion NEVI (National Electric Vehicle Infrastructure) program — is pushing installations higher still.

At current growth rates, the US will have more public EV charging locations than gas stations by 2028-2030. Whether this timeline proves accurate depends on permitting bottlenecks, grid capacity constraints, and the pace of EV adoption itself. But the directional trend is unmistakable.

Tracking this growth through regular scraping of charging network station locators provides a real-time view of deployment progress that government statistics, published quarterly with months of lag, cannot match.

Infrastructure Gaps: Where Data Reveals Opportunity

The most commercially valuable application of this location data analysis is identifying infrastructure gaps — places where EV charging demand exists or will exist but supply has not yet followed.

Several data signals help identify these gaps. High EV registration counts combined with low charger density indicate underserved markets. Scraping state DMV registration data (where publicly available) alongside charger location data reveals these mismatches. A county with 5,000 registered EVs and only 3 public charging locations has obvious unmet demand.

Highway corridors with long gaps between fast chargers represent strategic investment opportunities. Scraping charger locations along major interstate routes and calculating inter-station distances identifies the "charging deserts" where drivers experience range anxiety. These gaps are exactly where NEVI funding is targeted, and early movers who install stations in these locations will capture federally subsidized demand.

Commercial real estate locations near highway exits with high traffic counts but no existing chargers represent another opportunity. Scraping commercial real estate listings, traffic count data, and charger location data together identifies sites where a charging installation would attract high utilization.

Network Competition: Who Is Winning the Buildout

The EV charging market is contested by multiple networks, each with different strategies and geographic focus. Scraping station locator data from each network reveals competitive dynamics.

Tesla's Supercharger network remains the largest fast-charging network in the US with over 2,500 locations. Their recent decision to open the network to non-Tesla vehicles through the NACS connector standard fundamentally changes the competitive landscape. Scraping Tesla's station locator shows continued aggressive expansion, particularly along highway corridors.

ChargePoint operates the largest overall network by location count, but their model is predominantly Level 2 chargers installed at commercial properties. Electrify America, funded by the Volkswagen diesel emissions settlement, has focused on highway fast-charging corridors. EVgo targets urban fast-charging for apartment dwellers and rideshare drivers.

Tracking each network's growth through periodic scraping reveals market share shifts. Tesla's Supercharger opening creates competitive pressure on every other network. Smaller networks that cannot match Tesla's reliability and coverage may consolidate or exit.

What This Data Means for Different Stakeholders

For energy companies and utilities, location data analysis reveals where grid infrastructure investment is needed to support charging demand. For real estate developers, it identifies properties where charging amenities add value. For fleet operators, it maps viable routes for electric commercial vehicles. For investors, the growth trajectory and competitive dynamics inform investment theses across the EV value chain.

The common thread is that location data — scraped, structured, and analyzed — provides ground truth about the energy transition that no amount of forecasting or modeling can replace. The chargers are either there or they are not, and the data does not lie.

If you need structured location data for EV charging infrastructure, gas station networks, or energy transition analysis, connect with ScrapeAny. We build data pipelines that turn publicly available location data into the intelligence your business decisions require.