Carbon Reduction Intentions and Behaviours for Sustainability
Bibliographic record
Abstract
Increases in emissions of greenhouse gases resulted in rising global average temperature in the past few decades. In Taiwan, annual emissions of CO2 increased from 114.4 million tons in 1990 to 270.2 million tons in 2010, a staggering 136.2% increase in two decades. Strategic implementation of carbon reduction and environmentally sustainability becomes a top priority for government administration in public education in Taiwan. This study aims to examine information search behaviour and to reveal how carbon reduction behaviours differ from intentions. Carbon reduction intentions related to what respondents thought they should be doing for environmental sustainability. Behaviours referred to actions respondents had taken in particular ways to reduce carbon emissions. A survey was administered using personal interviews in March, 2010, in Taipei, Taiwan. A stratified sampling was utilised in this study following gender and age distributions of the population between the ages of 20 to 59. Findings in this study revealed that people search different types of carbon reduction information via various media channels. For those who prefer technology and policy related carbon reduction information, they would be more likely to take actions than those who prefer practical carbon reduction information. Technology/policy information seekers would have carbon reduction behaviour similar to their intentions, especially in energy usage and shopping activities. Practical information seekers seem to have higher level of behavioural discrepancies in carbon reduction. Strategic implications based on findings in this study are suggested.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".