Shifting from net-zero to net-positive energy buildings
Bibliographic record
Abstract
Numerous building projects have been presented as having ‘net-zero’ energy performance. Such claims use a variety of different approaches: on- and off-site renewable energy technologies, purchasing green energy credits, etc. Efforts have subsequently been directed at formulating clear definitions of ‘net-zero’ that provide some degree of clarity and theoretical framing. The emerging notion of ‘net-positive energy’ buildings raises new theoretical and practical issues and introduces several new design considerations and possibilities. Net-positive energy is explored though viewing the role of a building for adding value to its context and systems in which it is part. Rather than considering only the generation of more exported energy versus its importation to individual buildings or the grid, the emphasis shifts to the maximization of energy performance in a system-based approach. Net-positive energy approaches open a host of new technical, behavioural, policy, and regulatory issues and opportunities not currently evident with net-zero energy buildings. These challenge the primacy of ‘individual’ buildings as the most effective unit to make significant energy gains and the current prevalent expectation that each and every new building should be required to attain net-zero performance. More generally, it highlights the importance of extending the systems limits of energy analysis.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".