Designing energy-positive neighborhoods: A modular framework for integrated planning and policy guidance
Bibliographic record
Abstract
This study proposes a modular and scalable framework for evaluating energy performance at the neighborhood scale, supporting the design of net-zero and energy-positive urban districts. Using archetype-based models of 11 representative Canadian neighborhood typologies, varying in density, land use, and form, the framework analyzes energy outcomes under multiple configurations, including microgrid coordination, renewable-powered district heating (geothermal and PV/T), landscape-integrated photovoltaics (LPV), and electric vehicle (EV) integration with vehicle-to-grid (V2G) functionality. Results show that integrating LPV over 20 % of public open space increases local generation reducing energy deficits by 20–30 % in high-density neighborhoods. Scenarios using PV/T based district heating (Scenario 4) achieved up to 90 % and 80 % reductions in net energy deficits in dense mixed-use neighborhoods (MU1 and MU2, respectively), compared to the baseline. V2G integration further reduced EV-related energy deficits by up to 25 % relative to uncontrolled charging scenarios. Additionally, strategic aggregation of complementary neighborhoods enabled the formation of energy-positive clusters, even when individual neighborhood units remained energy deficit. Although the analysis relies on simplified, correlation-based models and average annual inputs, the framework provides actionable insights during early planning phases, enabling rapid assessment of cross-sectoral design strategies for resilient, low-carbon urban development.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".