An Energy Performance Contract Optimization Approach to Meet the Competing Stakeholder Expectations under Uncertainty: A Canadian Case Study
Why this work is in the frame
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Bibliographic record
Abstract
Energy performance contracts (EPC) can address economic sustainability challenges associated with residential energy retrofitting projects, including funding limitations, poor quality of project delivery, and landlord-tenant dilemma. Literature has overlooked the impact of weighted average cost of capital (WACC) and funding sources in EPC planning. However, the WACC, stakeholder priorities, and uncertainties can alter the project outcomes. This study proposes a Monte-Carlo simulation based non-linear multi-objective optimization approach to address the aforementioned challenges. A case study conducted in British Columbia indicated that the maximum overall project profitability can vary between $18,035 and $20,626 with decision priorities. The overall project profitability can vary over 9% due to uncertainties. The project profits can change over $3000 due to changes in the WACC. These observations confirmed the criticality of accounting for WACC, stakeholder priorities, and uncertainties in EPC planning. The risk of compensating for the performance compromises and profits increases simultaneously for the energy services company with the increasing contract periods, while it is inverse for the owners. Therefore, the contract period must be decided considering the profit expectations and risk tolerance of the stakeholders. Extended contract periods allow lower capital contributions from the building owners, potentially solving the principal-agent disputes in rental buildings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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