Boiler Retrofits and Decarbonization in Existing Buildings: HVAC Designer Interviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, we investigate methods to reduce carbon emissions from existing large commercial buildings with central natural gas-fired boilers used for space heating. This research explores opportunities to reduce natural gas use through improved building operations and through building decarbonization. We conducted one-hour interviews with 17 mechanical HVAC designers, together having over 350 years of industry experience, professional tenures at engineering consulting firms and design/build firms, and project work in California, New York, Texas, Alaska, the United Kingdom, and Canada. We asked a mix of quantitative and qualitative questions, covering four topic areas: General Background, Peak Heating Load and Boiler Selection, Boiler Controls, and Existing Building Decarbonization. The interviews yielded insight into industry practices, including determining peak heating load, equipment redundancy, boiler staging controls, Heating Hot Water temperature resets, challenges of building electrification, and design considerations for building decarbonization. From the interview results, we developed five key findings: (1) New boilers are oversized, (2) Actual building load distributions are not available, (3) Heating Hot Water temperatures are too high, (4) Boiler end-of-life is not the best electrification opportunity, (5) Reduce building emissions even if all-electric is infeasible. There are many challenges to reducing carbon emissions from existing buildings, but we conclude there are also many opportunities to make immediate positive change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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