A Wind Offset Paradox: Alberta’s Wind Fleet Displacing Greenhouse Gas Emissions and Depressing Future Offset Values
Bibliographic record
Abstract
The introduction of a significant industrial carbon price in Alberta, Canada, has precipitated major changes in its electricity market, both for fossil fuel generators, which has resulted in a rapid transition from coal to natural gas, as well as for renewable energy projects, which can monetize emission offset credits. Coal, which generated close to half of the electricity in the province in 2016 before the major changes were introduced, had fallen to less than 8 percent by the end of 2023 and was completely phased out by June 2024. Conversely, wind energy grew from 6 to 12 percent of the annual supply, in part due to the increasing value of the carbon credits whose value is connected to the deemed greenhouse emissions they are displacing. As wind energy increased in penetration, it lowered its own market price, which was discounted from the average market price by 10–43 percent, but in turn increased the relative importance of its offset. This paper examines the evolution of emissions displaced by wind energy in Alberta by considering 10 years of historical merit order data and creating a counterfactual scenario where historical wind generation is replaced by next-in-merit units. On average, coal made up 84 percent of the marginal energy and 93 percent of the marginal emissions in 2018. As the coal capacity declined, natural gas units replaced coal on the margins, jumping from 21 percent of next-in-merit generation in 2020 to 84 percent in 2023. Alberta uses a deemed emissions displacement factor, which is a combination of historical build and operating margins that declined from 0.65 tCO2e/MWh in 2010 to 0.52 tCO2e/MWh in 2023. Using the counterfactual scenario, an alternative offset value is considered, which had a maximum difference of 57 percent (9 CAD/MWh) of increased value over the actual historical offset. However, the counterfactual rate of emission offsets fell to near parity with the deemed grid displacement factor by 2022 as natural gas became increasingly dominant in the market. As the carbon price is scheduled to increase from 65 CAD/tCO2e in 2023 to 170 CAD/tCO2e by 2030, the provincial offset could reach a maximum value of 53 CAD/MWh in 2030 but begin to decline thereafter as the carbon price drives decarbonization, thereby lowering displaced emissions in either method of calculation. The introduction of significant carbon pricing into a thermally dominated electricity market resulted in more emissions being displaced by renewable energy than they were credited for in the short term, but the resultant decarbonization of the grid decreases the long-term value of emission offsets.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".