Quantifying Health Benefits of Coal Power Plant Phase-Out in Canada and the U.S.: An Adjoint Sensitivity Analysis
Bibliographic record
Abstract
We estimate monetized health benefits of phasing out the coal-fired power plants in Ontario and Alberta as well as in the US. We quantify these health impacts by accounting for reduced mortality due to chronic exposure to NO2 (In Canada) and PM2.5 (In Canada and the US).We apply the US EPA’s Community Multi-Scale Air Quality (CMAQ-5.0) model and its adjoint to quantify the marginal benefits (MB) of NOx and PM2.5 emissions. The adjoint model traces mortality counts back to emissions for each single source location and time. The backward simulations of the model rely on non-linear concentration-response (C-R) functions of single (PM2.5) and three-pollutant (PM2.5, NO2 and O3) epidemiologic models. The simulations are done over a nested 12 km and 36 km domain, covering North America and for July 2010.Our preliminary results show health benefits for specific plants in Ontario and Alberta are between C$ 30k-310k/ton of PM2.5 and $30-270k/ton of NOx. These values range between $30k-580k/ton of PM2.5 for plants in the US. Retrospective analysis of coal phase-out in Ontario suggests benefits of $3.1 billion/yr, while societal benefits of the proposed phase-out in Alberta is approximated at $2.4 billion/yr.We find significant benefits from coal phase-out in both Ontario and Alberta, and even larger benefits in the US. For Ontario, our results suggest that most of the health benefits from Ontario coal phase-out materializes in the province, whereas Alberta phase-out entails larger out-of-province benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".