Price vs policy: The impact of cost uncertainty on decarbonization pathways
Bibliographic record
Abstract
The future costs of many low-carbon power generation technologies are highly uncertain. Capturing these uncertainties for current and emerging technologies can help to understand potential policy impacts and the roles that emerging technologies could play. Energy system studies often use a scenario-based approach, which requires modelers to choose specific values from the range of possibilities, which can bias results and report unlikely scenarios. This paper combines a stochastic capital-cost forecasting methodology, based on Wright’s law of experiential learning, with a range of cost values for emerging technologies. The set of inputs generated is linked with the COPPER power system capacity expansion model to generate a database of 400 model runs, using 100 sampled combinations of cost input parameters and four policy scenarios. The impacts of policy on future emissions, system costs and generation mixes are presented. The incorporation of uncertainty into the model demonstrates the consistent deployment of transmission across all scenarios, in contrast with the inconsistent deployment of emerging technologies. This study finds that neither the carbon tax nor proposed clean electricity regulations achieve power-system decarbonization by 2050 across all scenarios. The results from this study are consistent with the results of national and provincial energy studies in Canada, however some results were outliers compared to the full distribution of potential model outcomes. These findings underscore the critical need to incorporate uncertainty into power system models, particularly when discussing policies and emerging technologies. • Links stochastic cost forecasts with the COPPER capacity expansion model. • Conducts 400 probabilistic simulations under four Canadian policy scenarios. • Finds transmission expansion robust across all cost and policy uncertainties. • Shows CCS and SMR deployment highly sensitive to cost and policy choices. • Reveals current policies insufficient for full power sector decarbonization by 2050.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".