Integration of an energy– economy model with an urban energy model
Why this work is in the frame
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Bibliographic record
Abstract
A proliferation of energy models has been developed across disciplines to explore energy and greenhouse gas (GHG) emissions-reduction strategies in cities. Hybrid models are especially useful because they incorporate more dynamics to simulate realistic results informed by relevant high-level policy decisions and building-level factors. Spatial and aspatial energy models, however, are not often linked, which overlooks the spatial impact of energy and emissions policies in urban environments. A new method is presented that links these types of models to understand how building stocks change over time in response to policies. This approach integrates outputs from an aspatial economic model, CIMS, with buildings in a spatially explicit urban building energy model (UBEM), UMI. The energy–economy model is parameterised against the UBEM using identified baseline condition and proposed future policy interventions. Building stock replacement and retrofit change are downscaled and disaggregated to individual buildings based on existing stock age and a probability-based Markov chain model (MCM). This integration enables simulations of cross-scale policy interventions that are sensitive to both economically and mechanically driven factors. An application of this approach shows how it can be used to evaluate how different policies interact with and influence building energy demand and GHG emissions. <em><strong>Practice relevance</strong></em> The results are integrated as a series spatially explicit energy modeling procedure (UMI) at the neighborhood scale. This process enables local assessments of efficacy of the proposed city scale and even regional policies in municipalities with various energy and GHG emission agendas. In the presented case study (of the Sunset neighborhood of Vancouver, BC, Canada) this method can quantify the elasticity of emission reductions from various urban form changes (<em>e.g.</em> infill, transportation-oriented development, <em>etc.</em>), new building code (<em>i.e.</em> BC Energy Step Code), active transportation and retrofit strategies from 2020 to 2050.
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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 | 0.000 | 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 it