Integrating Thermal and Hydro Electricity Markets: Economic and Environmental Costs of not Harmonizing Pricing Rules
Bibliographic record
Abstract
The electricity sector is the largest source of greenhouse gases (GHG) emissions in the world, and reducing these emissions can often be costly. However, because electricity markets remain integrated at a shallow level (with different pricing regulations), many gains from deeper integration (adoption of marginal cost pricing everywhere) are yet to be realized. This paper assesses the benefits of deep integration between a “hydro” jurisdiction and a “thermal” one. It also underscores the inefficiency of trade when pricing rules differ. Our detailed hourly model, calibrated with real data from the provinces of Ontario and Quebec, Canada, estimates price, consumption, emissions and welfare changes associated with fully integrating electricity markets, under transmission constraints. A negative abatement cost of $37/tonne of CO2 was found (for more than 1 million tonnes), clearly illustrating the untapped potential of wealth creation in carbon reduction initiatives. Furthermore, given the inefficiency of shallow integration between markets, we found that removing interconnections between markets offers a relatively affordable CO2-reduction opportunity, at $21.5/tonne.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".