INCENTIVIZING LANDLORD ENGAGEMENT IN A CLEAN ENERGY TRANSITION IN NOVA SCOTIA, CANADA: A CASE STUDY OF ENERGIZE BRIDGEWATER
Bibliographic record
Abstract
ABSTRACT Split incentives, in which the costs and benefits of energy efficiency upgrades are unevenly felt between landlords and tenants, are a powerful barrier that prevents retrofits from taking place. The problem is clearly playing out across the low-income, and inefficient homes of Bridgewater, Nova Scotia, Canada, where greenhouse emissions are high, and a large proportion of residents are burdened by energy poverty. To help solve the problem and facilitate home energy efficiency renovations, a project called Energize Bridgewater was developed. Guided by 10 semi-structured interviews with Energize Bridgewater team members ( n = 4) and property managers ( n = 6), we present a case study which sought to contextualize and better understand how Energize Bridgewater may incentivize owners of rental houses to participate in this clean energy transition. Our results focus on three main findings: i) the reality of the split incentive problem in Bridgewater; ii) barriers to landlord engagement; and iii) reactions to proposed programming. We close this paper with a discussion of the theoretical and practical implications of our work, including a set of recommendations for Energize Bridgewater and similar projects facing the split incentive problem while working toward a clean and cost-effective energy transition.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".