Carbon Dioxide Emission Savings Potential of Household Water Use Reduction in the UK
Bibliographic record
Abstract
The relationship between household water use and energy consumption was examined to establish whether the conservation of water within a domestic environment offers significant potential for saving energy, thereby reducing household carbon dioxide emissions. Average UK water usage is 55,121 L ca-1yr-1. The supply of this volume of water and its subsequent treatment by the water companies is equivalent to just 38.6 kg CO2 ca-1 yr-1, although this is not currently included in the primary footprint. So water consumption per se does not significantly effect CO2 emissions. However, the heating water within the household using electricity requires 5,036 kWh ca-1yr-1, equivalent to a further 2,830 t CO2 ca-1yr-1 with 57% of energy associated with use of heated tap water. Using gas instead of electricity to heat water can reduce emissions by 63%, equivalent to an average reduction of 4.36 t CO2 yr-1 for a standard household (2.4 occupants). Water efficient appliances and the careful use of heated water in the home could reduce average household water use from 151 to 73 L ca-1d-1 as well as the volume of water required to be heated thereby reducing related emissions by 58% or 1,662 kg CO2 ca-1yr-1, where electricity is used. Maximum CO2 emission reduction is achieved by the use of solar collectors using gas as standby heating fuel. This, coupled with simple water conservation measures, emits as little as 130 kg CO2 ca-1yr-1 a potential saving of 2.7 t CO2 ca-1yr-1.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".