Advancing Institutional Sustainability Through Green Roofs: Performance-Based Policy for RIT
Bibliographic record
Abstract
Utilization of green roof systems continues growth as a sustainable architectural strategy, offering enhancements to key building performance metrics while contributing to overall community resilience. The ability of green roofs to deliver a wide range of performance benefits, even in environments with harsh climatic conditions, positions them as an asset for advancing sustainable development. Within this context, university campuses represent an underutilized, yet important, setting for green roof adoption. Functioning much like small cities, campuses contain diverse building types and extensive infrastructure networks, which draw significant energy demands, positioning them to lead in sustainable building practices. Yet, most lack rigorous, campus-wide policies to guide the integration of green roofs into both new and existing facilities. This thesis responds to that gap by focusing on the institutional scale. It develops a performance-based policy framework for universities in plant hardiness zone 6 to implement green roofs on both new construction and existing facilities. The research examines existing green roof regulations from cities such as Toronto (6b) and Chicago (6a) to identify key transferable strategies and best practices in policy design and enforcement types. Key performance factors including vegetation selection, thermal insulation, freeze–thaw resilience, and drainage efficiency were analyzed for their influence on green roof success in cold climates. Comparative analysis of municipal policies highlighted gaps and opportunities for adaptation into the higher education setting. The Golisano Institute of Sustainability at the Rochester Institute of Technology served as the primary case study, offering modeled insights into how a cold-climate green roof performs at the building scale. The results confirmed meaningful reductions in runoff and improvements to seasonal energy performance, though these gains were relatively modest when compared to the campus’s overall demand. More importantly, the study showed that long-term value lies not only in technical performance, but also in the policy and operational frameworks that support it, since cost, maintenance, and cold-weather durability remain significant factors shaping feasibility. By integrating lessons from leading municipal policies with building science and institution-specific sustainability goals, this study offers a comprehensive green roof policy model tailored to higher education. Framing universities as both living laboratories and replicable models for urban sustainability, the proposed framework addresses the environmental performance gap in campus infrastructure while demonstrating how institutional policies can scale outward to support sustainable city 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".