You want to build something on top of energy data. A dashboard, a model, a map, a research memo, maybe just a well-sourced argument at dinner. The hard part isn't finding numbers. It's finding numbers you can actually trust, reuse legally, and cite without hand-waving.
This is a curated shortlist of open datasets, dashboards, and trackers for clean-energy progress. We chose these for reputable provenance, clear licensing where we could confirm it, and a track record of being updated. To be clear about method: this is curated, not tested. We did not benchmark accuracy, re-run every pipeline, or independently validate figures. Where a source's exact license or update cadence isn't obvious, we say so and point you to check it yourself before you build.
Some links below may be monetized in the future. When that happens we mark it and explain it on our disclosure page. Right now this list is editorial and unpaid.
What Makes An Energy Dataset Actually Trustworthy
Before the list, the criteria. These are what we looked for, and what you should look for in anything not on this page.
- Named methodology. A good source tells you how the number was produced, not just what it is. Modeled estimates and metered measurements are both useful, but they are not the same thing, and a trustworthy dataset labels which it is.
- A real license. "Free to look at" is not the same as "free to redistribute in your product." You need to know if it's public domain, Creative Commons, open-government, or a custom terms-of-use that restricts commercial reuse.
- A freshness signal. When was it last updated? Is it annual, monthly, hourly, or a one-off snapshot? A dataset with no visible update date is a liability for anything you plan to maintain.
- Coverage you can define. Which countries, which years, which sectors, what resolution. A source that is excellent for Europe may be thin for Sub-Saharan Africa.
- Stable access. A downloadable file or a documented API beats a chart you can only screenshot.
No single source scores perfectly on all five. That's normal. The skill is matching the source to the job.
Open Datasets For Generation, Emissions, And Grid
These are primary or near-primary data sources you can download and build on. License and freshness notes are our best current read; treat them as a starting point and verify on the source site, because these terms change.
- Our World in Data — Energy and Emissions. Cleaned, harmonized, chart-and-download datasets covering generation mix, per-capita energy, and emissions across countries and decades. Data is generally offered under a permissive open license, with underlying sources credited. Good for long time-series and cross-country comparison. As of 2026, the data and visualizations OWID produces are released under a Creative Commons BY license, though third-party inputs (such as the Energy Institute's Statistical Review and Ember) keep their own terms, so credit both OWID and the underlying source and confirm the specifics on the dataset page before commercial reuse (OWID FAQ on reuse; energy-data repo).
- EMBER — Electricity Data. A think tank focused on the electricity transition, with country-level generation-by-source data and downloadable datasets. Strong for tracking the coal-to-clean shift. As of 2026, Ember's electricity datasets are free to reuse under a CC BY 4.0 license, with the monthly series refreshed roughly twice a month and yearly data updated annually; check the note attached to each download to confirm (Ember data).
- U.S. EIA Open Data. The U.S. Energy Information Administration publishes a large, well-documented API and bulk files covering U.S. generation, consumption, prices, and more. U.S. government works are generally not copyright-restricted, which makes this attractive for reuse, but verify for any third-party data folded in. Freshness ranges from hourly to annual depending on the series.
- Global Power Plant Database. A plant-level dataset of power stations worldwide with location, capacity, and fuel type. Useful when you need asset-level rather than country-level data. Coordinated by the World Resources Institute (with Google Earth Outreach and other partners) and released under a CC BY 4.0 license; note that some capacity and generation figures are modeled rather than measured, and the public release has not been refreshed frequently in recent years, so read the methodology and check the repo for the latest version (WRI global-power-plant-database).
- National and regional grid operators. Many transmission operators publish near-real-time generation and demand data (for example, several European TSOs and U.S. ISOs). Coverage, format, and license vary widely by operator, so each is its own integration.
A recurring theme: modeled vs. measured. Plant databases and global estimates often infer generation from capacity and assumptions. Grid-operator feeds tend to be metered. Both belong in your toolkit, but don't blend them without noting the difference.
Dashboards And Trackers Worth Bookmarking
These are more "look and understand" than "download and build," though several expose data too. Good for orientation, sanity checks, and spotting when your own numbers drift.
- Electricity Maps. A live map of grid carbon intensity and generation mix by region. Excellent for an at-a-glance sense of how clean the grid is right now in a given place. The live visualization is free to view; programmatic and commercial access typically run through a paid API, so check terms before you wire it into anything. This is a case where a partly-proprietary source earns its place: the real-time coverage and consistency are hard to reproduce from raw feeds yourself.
- EMBER's dashboards. Browsable views layered on the electricity datasets above, handy for quick country comparisons without writing code.
- Our World in Data's explorers. Interactive charts you can reconfigure by country and metric, with a download button on most views. Good for building intuition before you commit to a pipeline.
- Government and agency trackers. Many national energy agencies and international bodies publish progress dashboards. These are authoritative for their own jurisdiction and often lag on cross-country comparability.
If you want a broader, browsable set of future-building projects and tools beyond data alone, our Optimism Directory collects them, and the open-source picks for climate builders covers the ones you can fork or self-host.
Licenses And Freshness — Read Before You Build
This is the section people skip and later regret. Two questions decide whether a dataset is safe to build on.
Can you legally reuse it? Rough tiers, loosest to strictest:
| License type | What it usually allows | Watch for |
|---|---|---|
| Public domain / CC0 | Use, modify, redistribute, even commercially, no attribution required | Rare; verify it's truly CC0 |
| Open-government (e.g. many U.S. federal works) | Broad reuse of the government-produced portion | Third-party data mixed in may carry its own terms |
| CC BY | Reuse with attribution | You must credit correctly |
| CC BY-SA | Reuse with attribution, share alike | Your derivative may need the same license |
| CC BY-NC | Non-commercial only | Blocks most product use |
| Custom terms-of-use | Whatever the page says | Read every line; may forbid redistribution |
We've given our best read per source above, but licenses get revised, so the source page is the authority, not this table.
Is it fresh enough for your use? Match cadence to purpose. A carbon-intensity app needs near-real-time; a decade-trend chart is fine with an annual dataset that's a year behind. What you never want is an undated number in production. If a source won't tell you when it was last updated, treat that as a red flag and find one that will.
When To Combine Sources (And Where They Disagree)
Serious analysis almost always pulls from more than one source. That's healthy, but it introduces a trap: two reputable datasets can report different numbers for the same country and year, and both can be defensible.
Common reasons they diverge:
- Boundary definitions. Does "electricity" include heat and transport? Does "renewables" count large hydro? Different scopes, different totals.
- Estimation vs. measurement. Modeled generation from a plant database won't match metered grid output.
- Revision timing. One source may have incorporated a late correction the other hasn't yet.
- Coverage gaps. A source may impute missing countries, and imputation methods differ.
The move is not to pick a winner and hide the disagreement. It's to name your primary source, note where a secondary source diverges, and explain why if you can. That single habit is the difference between an honest chart and a misleading one. If you're assembling a public-facing view, disclose which dataset each figure came from.
For the wider story these numbers tell, see our plain-language walkthrough of solar, battery, and nuclear progress, the startups worth watching in energy and grid, and a roundup of climate progress you probably missed this year. All three lean on sources like the ones above.
In Plain English
What this is: a curated shortlist of mostly-open energy datasets, dashboards, and trackers, with notes on license, freshness, and where sources disagree. Curated by us for provenance and reputation, not benchmarked for accuracy.
Who it helps: builders, analysts, journalists, and students who need energy data they can trust, reuse legally, and cite honestly.
When to use it: when you're starting a dashboard, model, map, or research piece and want to pick sources deliberately instead of grabbing the first chart you find.
What to do next: shortlist two or three sources that fit your coverage and license needs, confirm each one's current terms and update date on its own site, and write down which source each figure comes from before you publish.
This connects to the bigger picture in our pillar on the clean-energy and build-more-stuff projects making the future look bright, where the Abundance thesis is that building and measuring more, in the open, is how progress compounds.
FAQs
Is open energy data reliable enough to build a product on? Often yes, if you match the source to the job and respect the modeled-vs-measured distinction. Reliability is per-dataset, not a blanket property. Read the methodology and the license.
Why do two trusted datasets show different numbers? Usually different boundary definitions, estimation methods, revision timing, or coverage. Divergence isn't proof one is wrong. Name your primary source and note the difference.
When is a proprietary source worth paying for? When it delivers coverage or freshness that's genuinely hard to reproduce from raw feeds, like consistent real-time grid carbon intensity across many regions. We flag those cases rather than pretend open sources always win.
How do I keep a dataset from going stale? Prefer sources with a visible update cadence and, ideally, an API or versioned file. Record the date and version of every figure you use so you can refresh and audit later.
Ready to pick your sources? Start with two or three from the list above, check each one's license and last-updated date, then explore more future-building tools in the Optimism Directory. Any monetized links we add later are disclosed on our disclosure page.