Advances in quantifying power plant CO2 emissions with OCO-2
Bibliographic record
Abstract
We present CO2 emission estimates for twenty power plants and related facilities in the United States, India, South Africa, Poland, Russia and South Korea, derived from space-based CO2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Improvements to OCO-2 data quality and to our methodology yield improved results relative to earlier work. These new results include emission quantification for both larger and smaller power plants, the first power plant emission estimate based on ocean glint data and emissions from a small city with multiple industrial facilities. CO2 emission estimates are compared against reported facility emissions where available, including high temporal resolution data for the eight US sites. The difference with respect to reported values for the US sites ranges from 1.4% to 26.7%, with a mean of 15.1%, although the estimated emission sum for all US sites is within 0.8% of the reported value, suggesting the errors are largely random. This finding reinforces the importance of revisit rate for future space-based emission monitoring systems and furthermore confirms that making multiple overpasses of a power plant can reduce errors to an accuracy useful to support climate policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".