Incorporating non-climate externalities receiving public attention in the optimisation of grid decarbonisation: the case of Ontario
Bibliographic record
Abstract
Abstract This paper extends a cost-optimising electricity capacity expansion model for the Canadian province of Ontario to incorporate several non-climate externalities, reflecting the way such externalities have been shown to impact the public acceptance of clean electricity policy. A Textometrica analysis of 225 news articles identified employment and health as the externalities receiving the most public attention, followed by land use and ecological impacts. Multiple sets of monetary valuations of the impact of different generation technologies on each externality were generated from the literature. The model was run with each combination of valuations incorporated into the cost function and with no externalities under both partial (carbon cost of $200 CAD/tonne) and full decarbonisation. Incorporating externalities was found to decrease optimal emissions by 21% on average across partial decarbonisation scenarios, and generally to increase optimal wind capacity expansion and retirement of gas generation (solar expansion and nuclear expansion were not optimal in any modelled scenario). Significantly, the incorporation of externalities only modestly increases the accounting cost of the system. All these impacts are much smaller in the case of full decarbonisation. Results are highly sensitive to the health impact valuations used and the presence of community support for wind expansion, which determines whether property values are impacted. Application of this modelling approach and these findings should not only reduce the social cost of electricity system decarbonisation measures but also facilitate their implementation by decreasing the risk of politicised opposition.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".