--- license: cc-by-4.0 pretty_name: Ember tags: - electricity - generation - capacity - emissions - global configs: - config_name: capacity_wind_solar_monthly data_files: datasets/ember_capacity_wind_solar_monthly/data/*.parquet - config_name: cleantech_exports_by_code_monthly data_files: datasets/ember_cleantech_exports_by_code_monthly/data/*.parquet - config_name: cleantech_exports_monthly data_files: datasets/ember_cleantech_exports_monthly/data/*.parquet - config_name: generation_monthly data_files: datasets/ember_generation_monthly/data/*.parquet - config_name: generation_yearly data_files: datasets/ember_generation_yearly/data/*.parquet - config_name: india_monthly data_files: datasets/ember_india_monthly/data/*.parquet - config_name: india_yearly data_files: datasets/ember_india_yearly/data/*.parquet - config_name: interconnection_country_profile data_files: datasets/ember_interconnection_country_profile/data/*.parquet - config_name: interconnection_flows data_files: datasets/ember_interconnection_flows/data/*.parquet - config_name: interconnection_import_potential data_files: datasets/ember_interconnection_import_potential/data/*.parquet - config_name: interconnection_ntc data_files: datasets/ember_interconnection_ntc/data/*.parquet - config_name: interconnection_peak_demand data_files: datasets/ember_interconnection_peak_demand/data/*.parquet - config_name: price_daily data_files: datasets/ember_price_daily/data/*.parquet - config_name: price_hourly data_files: datasets/ember_price_hourly/data/*.parquet - config_name: price_monthly data_files: datasets/ember_price_monthly/data/*.parquet - config_name: targets_2030 data_files: datasets/ember_targets_2030/data/*.parquet - config_name: targets_sources data_files: datasets/ember_targets_sources/data/*.parquet - config_name: us_states_monthly data_files: datasets/ember_us_states_monthly/data/*.parquet - config_name: us_states_yearly data_files: datasets/ember_us_states_yearly/data/*.parquet --- # Ember electricity data Global electricity generation, capacity, demand and emissions from Ember's public bulk releases, reshaped onto one long schema and stored as plain parquet. Area codes, labels and the geoscale that joins them are in `scales/`. ## Terms - **Licence**: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/) - **Attribution**: Data: Ember - CC-BY-4.0 - **Cite as**: Yearly and monthly electricity data, Ember. https://ember-energy.org/data/ - **Source**: https://ember-energy.org/data/ Redistributed by OptimalSolution LLC -- see `DISCLAIMER.md`. Redistribution only: no responsibility for the content, no endorsement or position, no affiliation with the publisher, no warranty. What was changed from the published data is recorded per dataset under `bank.modifications` and `bank.changes`. ## Datasets | dataset | rows | coverage | |---|---|---| | `ember_generation_yearly` | 543,302 | 1985 to 2025, annual | | `ember_generation_monthly` | 838,831 | 1998-12 to 2026-08, monthly | | `ember_capacity_wind_solar_monthly` | 52,910 | 2016-01 to 2026-08, monthly | | `ember_price_monthly` | 4,004 | 2015-01 to 2026-09, month | | `ember_price_daily` | 121,418 | 2015-01-01 to 2026-09-17, day | | `ember_price_hourly` | 2,924,722 | 2015-01-01 to 2026-09-17, hour | | `ember_targets_2030` | 2,064 | 2022 to 2050, year | | `ember_targets_sources` | 223 | reference table | | `ember_cleantech_exports_monthly` | 293,804 | 2018-01 to 2026-07, month | | `ember_cleantech_exports_by_code_monthly` | 1,183,132 | 2018-01 to 2026-06, month | | `ember_india_yearly` | 17,055 | 2019 to 2024, year | | `ember_india_monthly` | 234,687 | 2019-01 to 2025-11, month | | `ember_us_states_yearly` | 86,553 | 2001 to 2025, year | | `ember_us_states_monthly` | 1,052,031 | 2001-01 to 2026-06, month | | `ember_interconnection_ntc` | 1,702 | 2024 to 2040 | | `ember_interconnection_flows` | 22,500 | scenarios 2024/2030/2040 | | `ember_interconnection_country_profile` | 8,648 | scenarios 2024/2030/2040 | | `ember_interconnection_import_potential` | 368 | target years 2030/2040 | | `ember_interconnection_peak_demand` | 134 | 2024 to 2040 | ## Read this before you aggregate - `is_derived` marks Ember's OWN aggregate series -- Total generation, Clean, Fossil, Renewables, Demand. They sit in the same `series` column as the fuels they summarise, so summing the column without filtering counts the fuels twice. - `entity_kind` separates the 209 countries from Ember's 15 precomputed aggregates, and those aggregates OVERLAP -- every EU member is also in OECD. Summing `entity_id` double-counts differently again. - `unit` is part of the key. Solar capacity is published twice, once in GWAC and once in GWDC, so (entity, period, series, variable) is NOT unique. The generation tables also mix TWh with percent in one `value` column. - `AU` and `MENA` exist only as precomputed rows: Ember publishes no membership for them, so they cannot be broken down into countries. `scales/ember_geoscale.json` lists them under `unmappable`. - Geometry is NOT here. It is Natural Earth, public domain, shared with every other source and referenced as `basemaps:ne_countries_50m`. Without it the geoscale still aggregates and recasts; only maps need it. - **The sub-national tables carry the national total too.** India and the US both ship their country row alongside their states, coded with the ISO3 and marked `entity_kind = "country"`. Summing every row double-counts the country exactly once. India's `OT` is Ember's 'Others' residual, not a territory: it has no geometry and never will. - **The China cleantech tables are keyed on the DESTINATION.** Every row is an export from China to `entity_id`; it says nothing about that country's own production. `exports_12m_rolling` is a rolling twelve-month sum of the column beside it, so summing it over periods counts each month twelve times -- it is flagged `is_derived`. The by-code table is the same trade one level finer than the by-category table; stacking them doubles. - **The interconnection `Hour` and `Month` columns are not clocks.** Ember's own README calls them hour-of-day and month-of-year averages, so `period_index` runs 1-24 or 1-12 and describes a PROFILE, not a timeline. The tables are also scenario output, not observation: 2024 is a modelled reference and 2030/2040 are projections under Reference, Projects and Needs. - **Three interconnection zones are not countries.** `NW`, `BW` and `CW` are the North Sea, Baltic and Celtic offshore wind hubs. `scales/ember_interconnection_zones.csv` marks them `offshore_hub` and leaves their ISO3 empty. Ember also writes `UK` where ISO 3166-1 writes `GB`. - **`ember_targets_2030` is not only about 2030.** The tracker is named for it, but `TARGET_YEAR` in the underlying data ranges from 2022 to 2050. Filter on `year` rather than assuming. The targets are also stated ambitions, not outcomes, and `source_id` joins to `ember_targets_sources` for the document each one came from. - **Prices are wholesale day-ahead, not retail.** They exclude taxes, levies and network charges. The hourly table carries Ember's local wall-clock reading as text beside the UTC timestamp, because a local time with no offset cannot be parsed without inventing a timezone. ## Using it ```r # the tables (plain parquet -- no special reader needed) library(arrow) d <- read_parquet("datasets/ember_generation_yearly/data/part-0.parquet") # the geoscale, rebuilt from the shipped CSV + JSON source("scales/ember_geoscale.R") gs <- ember_geoscale() # no geometry gs <- ember_geoscale("../naturalearth") # with geometry # split a regional aggregate down to countries, conserving the total geoscales::recast_geoscale(x, gs, from = "ember_region", to = "region", values = "value", rule = "sum", weight = "pop_est") ```