CanAdopt—coupling agent-based and energy systems models for decarbonisation pathway analysis
Bibliographic record
Abstract
Abstract Energy system models (ESMs) inform the transition from fossil fuels to renewable energy. Optimal system design is influenced by the shape and magnitude of electricity demand. Demands will change as decarbonisation efforts across sectors aim at electrification. However, many ESMs oversimplify the complexity of demand changes driven by individual adoption decisions. Agent-based models (ABMs) allow for incorporating behavioural theories capturing this complexity, but rely on assumptions about factors like energy prices and emissions affecting adoption behaviour. This work introduces a novel framework, CanAdopt, that integrates an ABM with an ESM, alleviating assumption requirements in both models. The capabilities of the novel framework are demonstrated for scenario analysis of policy impacts in the energy and residential heating sectors. The ABM models heating technology adoption and residential electricity demands for the ESM, which optimises capacity expansion and yields electricity prices and embedded emissions for the ABM. Both models cover 2020–2050 and are executed sequentially eight times. Applied to Ontario, Canada, the most progressive scenario achieves net-zero by 2035 and 2040 in the residential sector and power sector, respectively. The total transition costs are 327 billion CAD in the residential sector and 395 billion CAD in the power sector. Cumulative heating related emissions increase by 43% through a five-year delay in achieving net-zero, underscoring the urgency of the transition. The governmental carbon abatement costs in the residential sector range from 72 to 110 CAD/(t CO 2 ), well below the federal carbon tax of 170 CAD/(t CO 2 ). The coupling of both models showed, that increased residential heat pump adoption may reduce total transition cost by 8 to 10 billion CAD in the power system, but the increases in demand may be challenging to meet if additionally electric furnaces are widely adopted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".