Global energy burden atlas

Three lenses on the world’s household energy

Household energy burden has never been comparably measured across most of the world. We built the first open-code, open-data pipeline that estimates it for 129 countries and 17,028 grid cells covering 8.2 billion people. Three findings that no single prior study surfaces: a quality-of-energy gap Odum’s emergy framework exposes, an access + affordability + QoS gap that single-axis indicators miss, and a climate-justice mismatch sharper than any prior estimate.

variation in grid
transformity (sej/J)

67× per-capita cumulative CO2
climate-justice disparity

129 countries with
cell-level microdata

8.2bn people covered
by the atlas

Three findings

Finding 1 — Emergy

Grid quality varies 6× across 213 countries

Nuclear-heavy grids (France, Slovakia) deliver electricity with ~15× the emergy per joule of wind-heavy grids (Denmark, Germany). Two grids with identical per-capita kWh can carry radically different prior-emergy investment — invisible in CO2 or kWh accounting. Odum’s framework separates quality from quantity.

The Odum lens →

Finding 2 — Access + QoS

“Connected” isn’t “served”

Global access to electricity is 91%; global access to clean cooking fuel is 74%. On top of that, quality of service varies enormously — outage frequency, voltage sag, tariff affordability. Households in low-QoS grids are functionally under-served regardless of the binary access statistic.

Access, affordability, QoS →

Finding 3 — Climate justice

The 67× per-capita CO2 disparity

The 20 least-electrified countries hold 498M people (6% of humanity) and 0.13% of cumulative CO2 since 1750. The 20 largest historical emitters hold 4.8bn people and 82%. Per-capita cumulative disparity is approximately 67-fold.

The climate-justice scatter →

Why publish this now

Each finding on its own has been sketched in prior work, but no prior study combines all three at cell-level for 129 countries with fully open data + open code. The combination reveals patterns that single-lens studies miss:

  • High-transformity + high-burden — a country like South Africa (coal + nuclear grid, ~90k sej/J, 8% burden) is high-emergy AND high-burden — quality-adjusted energy is still expensive relative to income.
  • Low-transformity + low-CO2 + high-suppression — wind-heavy small emitters (Portugal, Denmark) have low emergy per joule + low CO2 contribution, yet cells within them still show suppression pockets.
  • Climate-justice + emergy inversion — the 20 countries most burdened by climate change (SSA + SAR) also receive lowest- transformity energy (biomass + low-access grids), a double penalty invisible when either lens is used alone.
Atlas

39 figures across 215 countries

Cell-level maps, cross-signal PCA, climate-justice scatter, country archetypes, emergy panels.

Browse the atlas →

Data

17 open sources, 1750–2025

WB, Ember, OWID, EIA, WHO, FAOSTAT, UNDP, NASA GISS, WGI, and more. Every number traceable to a script + cache.

See the sources →

Papers

Seven papers in flight

Ecological Economics, Science Policy Forum, Joule, Nature Energy scope, JSS ecosystem umbrella, JOSS Wave 1, Scientific Data descriptor.

See the papers →

Code

The emburden ecosystem

13 R packages, all MIT-licensed. Includes emburden::neb_func (Net Energy Return), emburdensynth::grid_transformity (Odum emergy), emburdensynth::run_global_pipeline.

Explore the code →

Caveats worth naming

The emergy computation inherits the country’s grid mix onto every cell (no cell-level fuel mix data exists at this scale). Cumulative CO2 is territorial-only (consumption-based would tighten rather than loosen the disparity). Physics baseline uses global-average building envelope. Full caveats in the technical report.