Assessing the potential of surplus clean power in reducing GHG emissions in the building sector using game theory; a case study of Ontario, Canada
Bibliographic record
Abstract
This work assesses the potential of surplus electricity in reducing greenhouse gas (GHG) emissions in the building sector. The assessment is done by modelling the interaction of government and energy consumer using game theory. The government can provide discounted power to energy consumer by covering a fraction of the off‐peak price to encourage the replacement of natural gas consumption with electricity. This replacement reduces GHG emissions from the building sector. Energy consumer adopts electricity‐based technologies only if it leads to a lower heat and electricity supply cost. Cost‐effectiveness of solid oxide fuel cell, air–source heat pump (ASHP), and battery and hydrogen storage are assessed as alternatives to natural gas combined heat and power (CHP) and boiler technologies. The modelling results show that ASHP is the only technology that can compete with natural gas CHP and boiler. ASHP is chosen by the energy consumer when discounts of 4.5 cents/kWh or more for off‐peak electricity are available. The analysis also showed that CHP could be completely replaced by grid power at discount value of 4.5 cents/kWh and up. Natural gas boilers continue playing a role in building heating supply even under increased discount for off‐peak electricity price.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".