Understanding energy-economy models: survey evidence from model users and developers in Canada
Bibliographic record
Abstract
Energy-economy models are important tools used by policy-makers and researchers to design effective climate policy. However, there has been limited research that compares models against consistent characteristics to understand their impacts on climate policy projections. This can make it difficult for policy-makers to identify suitable models for their specific policy questions and develop effective climate policies. A web-based survey of energy-economy model users and developers in Canada’s public, private, and non-profit sectors (n=14) was conducted to systematically compare seventeen models against a framework of seven characteristics: technology characteristics, micro-, and macro-economic characteristics, policy representations, treatment of uncertainty, high-resolution spatial and temporal representations, and data transparency. It was found that for the most part, models represent technology, micro-, and macro-economic characteristics according to the classic typology of bottom-up, top-down, and hybrid models. However, our findings show that several modelling evolutions have occurred. Some top-down models can explicitly represent technologies and some bottom-up models incorporate microeconomic characteristics. Models differ in the types of policies they can simulate, sometimes underrepresenting performance regulations, government procurement, and research and development programs. All models incorporate at least one type of uncertainty analysis, models infrequently have high-resolution spatial and/or temporal representations, and most models lack publicly accessible methodological documents. Implications for researchers and policy-makers that use energy-economy models and/or develop policies are discussed.
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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 | 0.017 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| 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".