Modeling the transition to a zero emission energy system: A cross-sectoral review of building, transportation, and electricity system models in Canada
Bibliographic record
Abstract
Models have long been effective tools in the planning and policy making of energy systems, but low-carbon electrification—decarbonizing generation supply while expanding electrical demand—poses new challenges for the modeling community. At its core, electrification relies on integrating insights that span the supply and demand side of power systems, resolving operational characteristics and long-term climate change, and the integration of previously independent engineered systems. However, our modeling landscape consists of a suite of models that focus on distinct sectors (power, buildings, transport) and spatial–temporal scales (municipal, provincial, federal). This paper probes whether the existing suite of energy system models, which span sectors, disciplines, and jurisdictions, is up to the task of charting net-zero pathways, specifically in the Canadian context. To do so, we analyze an inventory of energy system models that are being used in practice using a recently assembled model database. Next, we supplement our analysis with a web-based search and literature review. For each model category, we describe the key modeling approaches, strengths and weaknesses, and typical ways and areas in which these models are applied. We find that by focusing on a specific scale and sector, these models by their very definition, omit out-of-scope interactions leaving critical information gaps. Many of the most imperative areas for future research straddle multiple sectors or multiple scales—electric vehicle charging, carbon policy coordination, regional electricity trading, to name a few. Future research should focus on identifying ways in which different models could be used together to produce policy-related conclusions that are as detailed but more holistic than conclusions that can be gleaned from an individual model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".