Decarbonising buildings by indirect use of gas and biomass
Bibliographic record
Abstract
Current efforts to reduce carbon dioxide emissions associated with energy services in buildings have focused on efficiency and a ‘distributed energy’ approach, using renewables and small-scale combined heat and power plants fuelled by natural gas or biomass. The same fuels can, however, give similar results if used in centralised plants to generate electricity that can then power heat pumps. A greater emphasis on the use of heat pumps would also facilitate the use of non-fossil electricity and the future application of carbon capture and storage to reduce emissions from any centralised fossil fuel use. Carbon capture and storage and electricity can give cuts in carbon dioxide emissions of 75% or more compared with local direct use of gas and overall negative emissions with biomass. Centralised hydrogen production with carbon capture and storage can also be combined with distributed combined heat and power. Given the potential effectiveness of these combined centralised/distributed approaches for emissions reductions, it is important that further work is undertaken so that they can be accurately evaluated as additional policy options. Infrastructure developments should also take into account the likely importance of decarbonised electricity and/or hydrogen in the future and not be locked into direct use of natural gas with its minimum emission limit of approximately 0·2 kgCO2/kWh.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".