Stakeholder engaged energy systems modelling: three Canadian case studies
Bibliographic record
Abstract
Meeting Canada's emission reduction targets requires a fundamental shift, not only in the infrastructure underpinning the supply and delivery of energy services, but also in the institutional frameworks that govern policy and investment decisions. Stakeholders' engagement in the energy transition is gaining momentum, but a communication gap between experts and decision makers is impeding the impact of model-based decarbonization analyses. This represents a substantial missed opportunity. We present three case studies that adopt a two-pronged strategy to co-create and co-deliver model-based insights. The first prong entails the development of an integrated energy modelling suite that provides a holistic perspective of energy systems that spans sectors, spatial-temporal scales, and energy vectors. The second prong entails a model implementation process, in which stakeholders and researchers co-develop 'Scenario Bundles' to analyze a particular project, policy, or target through a series of collaborative activities. We present three case studies, at the federal, inter-provincial and municipal scales respectively, that apply these two prongs in distinct stakeholder-driven modelling projects. We conclude with a critical analysis of the role of modelling and stakeholder engagement in effective decision making.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 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".