Public Understanding of Climate Change and the Gaps Between Knowledge, Attitudes, and Travel Behavior
Bibliographic record
Abstract
Climate change is the most serious threat facing mankind in the 21st century; it has been linked to human activities and the impacts of global climate change will persevere for years to come. The transport sector is responsible for a quarter of global greenhouse gas emissions linked to climate change and it is the only sector with rising emissions. Public awareness of the impacts of transport on climate change appears to be high, and a high degree of concern on environmental issues is expressed. However this is not reflected in corresponding lifestyle choices implying the existence of an attitude-behavior gap.<br/>A series of postal questionnaires on climatic awareness and attitudes were distributed to a random population in an area of the UK. A representative sample of the UK population was obtained in terms of demographic and socio-economic characteristics, transport use, availability of public/private transport options, and views on climate change and travel behavior.<br/>This paper shows that the role of information on climate change needs to change. Knowledge and concern for climate change is evident as well as an acceptance of the role individuals have in this. Providing more tailored information therefore, has significant potential in providing the type of information required and encouraging sustainable travel behavior. Three knowledge groups were identified as well as three age categories which can be significant in terms of who to direct information at as well as what information to provide. Attempts to increase public awareness of climatic issues now need to be re-focused on encouraging people to act voluntarily on their attitudes, values and beliefs. Behavioral change is highly supported<br/>and preferred over fiscal measures, but requires more information than is currently available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".