Boiler Retrofits and Decarbonization in Existing Buildings: HVAC Designer Interviews
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".