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EECA data via eolas

The Energy Efficiency and Conservation Authority (EECA) runs New Zealand's energy-efficiency programmes — funding EV charging infrastructure, modelling industrial heat demand, and publishing the Energy End-Use Database (EEUD). eolas serves 6 datasets from EECA — covering the data the EV and decarbonisation policy worlds depend on.

If you're doing energy-transition modelling, EV-infrastructure planning, or industrial-decarbonisation research — EECA is the source.

What's in the catalogue

EV charging

Dataset Description
eeca_ev_chargers_public All publicly accessible EV charging units in NZ with location, owner, operator, plug type, power.
eeca_ev_chargers_cofunded EV charging sites that received EECA co-funding support — applicant, status, funding.
eeca_ev_metrics_region Summary EV adoption + charging infrastructure metrics by NZ region — registrations, charger counts.
eeca_ev_metrics_district Same EV metrics by territorial authority.

Energy + industrial

Dataset Description
eeca_energy_end_use EECA's Energy End Use Database (EEUD) — energy consumption by sector + end use, 2017+.
eeca_regional_heat_demand Industrial heat demand + boiler capacity by region + sector, broken down by fuel + temperature band.

Refresh schedule

Quarterly. EECA publishes EEUD annually; EV charger + metrics quarterly; regional heat demand annually after EECA's industrial decarbonisation modelling cycle. Our refresh runs weekly to catch new releases promptly.

meta = client.info("eeca_ev_chargers_public")
meta["last_refreshed_at"]
meta["source_last_modified_at"]

License

All EECA data is published under CC-BY 4.0. Commercial use is fine; attribution required.

Recommended attribution: "Source: EECA (Energy Efficiency and Conservation Authority), served via eolas (eolas.fyi). CC-BY 4.0."

Common patterns

EV charging coverage by region

import matplotlib.pyplot as plt

chargers = client.eeca("eeca_ev_chargers_public", as_sf=True)
by_region = chargers.groupby("region").size().sort_values(ascending=False)
by_region.head(10).plot.barh(title="Public EV chargers by region")
plt.show()

library(dplyr)
library(ggplot2)

chargers <- eolas_get_eeca("eeca_ev_chargers_public", as_sf = TRUE)
by_region <- chargers |> count(region) |> arrange(desc(n))
ggplot(head(by_region, 10), aes(reorder(region, n), n)) +
  geom_col() + coord_flip() +
  labs(title = "Public EV chargers by region")

EV adoption + charging gap

metrics = client.eeca("eeca_ev_metrics_district")
# Districts with EV registrations growing faster than charger rollout
metrics["ev_per_charger"] = metrics["ev_registrations"] / metrics["chargers_per_district"]
print(metrics.sort_values("ev_per_charger", ascending=False).head(15)[["district", "ev_per_charger"]])

Industrial heat demand by sector

heat = client.eeca("eeca_regional_heat_demand")
# By sector + temperature band (drives boiler-replacement priorities)
pivot = heat.pivot_table(values="heat_demand_gwh", index="sector", columns="temp_band", aggfunc="sum")
print(pivot)

Energy end-use by sector

eeud = client.eeca("eeca_energy_end_use")
# Latest year, by sector
latest = eeud[eeud["year"] == eeud["year"].max()]
print(latest.groupby("sector")["energy_pj"].sum().sort_values(ascending=False))

Source-specific notes

  • EECA public chargers vs NZTA: EECA's eeca_ev_chargers_public is similar to NZTA's nzta_ev_charging but published independently. EECA's data is more current for sites EECA co-funded; NZTA is broader. Use both for a complete picture.
  • EEUD methodology: the Energy End Use Database is a modelled product — it combines census data, energy retail data, and end-use surveys to estimate consumption. Not measured at the meter; expect ±10-15% uncertainty on individual cells.
  • Heat demand temperature bands: industrial heat is classified by required temperature (typically <100°C, 100-300°C, >300°C). Lower-temperature processes (food, drying) are more electrifiable than high-temperature ones (cement, steel).
  • Co-funding status: charger sites that received EECA funding are flagged in _cofunded. Useful for policy evaluation (did co-funding fill the right gaps?).
  • District-level EV metrics: a useful proxy for council-level decarbonisation reporting. EECA also publishes vehicle-fleet data not (yet) in eolas.
  • Charging "speed" tiers: data distinguishes Level 1 (slow), Level 2 (medium), DC fast (50-150kW), DC ultra-fast (150+kW). Different infrastructure types serve different use cases (overnight at home vs road trip).

Where to find more