Översiktsplaneringens roll för att minska växthusgasutsläpp i det lokala klimatarbetet - En fallstudie av Lunds kommuns nya översiktsplan
Bibliographic record
Abstract
To reduce the emissions of greenhouse gases by local efforts, physical planning plays an essential part. It can have a great impact on our greenhouse gas emissions, by deciding important things as transport and energy planning. This study aims to examine the role of municipal comprehensive planning in the work to reduce greenhouse gases. The study is based on a case study of Lund municipality, carried out as an analysis of the master plan of Lund municipality, and interviews with six municipal officers. The results show that one third of the goals set in the comprehensive plan concern reduced greenhouse gas emissions (3 out of 9), slightly more than a quarter of the strategies (13 out of 51), and slightly more than a fifth of the standpoints (59 out of 278). Furthermore, the interviews show that the goals set in the comprehensive plan work as pointers for further planning, and an independent environmental impact statement was made of the plan. Quite contrary to this, the plan also includes formulations about accessibility for cars in the plan, as well as a preservation of road reserves, several expansion areas, and plans for expansion in smaller villages and rural parts of the municipality, where more housing tends to lead to more transportation by car. All of these were political add-ons to the plan, concluding that to further prioritize reduced greenhouse gases in master plans, a political ambition to do so is needed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.026 |
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".