A comparison of trends and magnitudes of household carbon emissions between China, Canada and UK
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
Household carbon emissions (HCEs) contribute a large proportion of global carbon emissions . For several reasons there are large differences in HCEs between countries. Using governments’ annual data, this study aims to compare the trends and magnitudes of HCEs between China, Canada and the UK and pinpoint where these countries are heading and what lessons they can learn from others. In the years when HCEs were first reported (1995 in China, 1990 in Canada and 1997 in UK), per person HCEs in China, Canada and the UK were 0.54 tCO 2 , 13.54 tCO 2 and 9.63 tCO 2 , respectively. These values had changed to 1.77 tCO 2 , 13.14 tCO 2 , 8.20 tCO 2 by the end of reporting (2011 in China and UK and 2007 in Canada), representing an increase of 7.7%/yr in China and a decrease of 0.18%/yr in Canada and 1.14%/yr in the UK. Although the rate of increase in China was high, in absolute terms China’s per person HCE remained many times lower than that of Canada and the UK. The reasons why China may not follow Canada and UK’s emissions pathways are discussed. In comparison with several other studies, China’s average HCEs were found to be much lower than that of developed countries. Among the developed world, Sweden and Norway had much lower HCEs, probably due to the production of electricity by hydro and nuclear power generation and the use of centralised heating systems in Sweden, and production of electricity by hydropower in Norway. Where possible, countries all around the world can learn lessons from these two countries.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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