Bibliographic record
Abstract
Despite that transports contribute to around a quarter of all global energy-related CO2 emissions, the question of how the sector’s emissions are best reduced remains unclear. To fill this gap, this thesis investigates what both academia and the Swedish Climate Policy Council recommend to be the best ways of reducing transport emissions. These findings are then compared with the mobility strategies of six Swedish municipalities. The review of academic literature found that going car-free has the highest emission mitigation potential among the reviewed options – at a median of 2.1 tCO2eq/cap pear year. Both academic literature and the Swedish Climate Policy Council argues that shifting over to electric vehicles – which was found to have a median emission mitigation potential of 2.0 tons of CO2eq/cap per year – is necessary if transport emissions are to be substantially reduced. While the general strategy of most Swedish municipalities matches well with going car-free, the review of municipal transport strategies showed an evident lack of planning and consideration for the electrification of vehicle fleets. Based on the Swedish Climate Policy Council’s recommendations, academic literature, and the municipalities' strategies, the thesis identifies obstacles that municipalities face in reducing emissions, areas in which Swedish municipalities are lagging, and policies and strategies which could help municipalities reach their environmental goals. Of particular importance are the municipalities’ lack of long-term planning and clarity in their documents, their lack of evaluation of the effect of specific policies, and their lack of ability to promote electric vehicles due to laws and regulations at the national level.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".