Cities in Germany and their climate commitments: More hype than substance?
Bibliographic record
Abstract
While nation states debate climate policy at an international scale, on a local level, cities across the globe have committed to emission targets and mitigation activities. This study analyses the actual performance of municipal climate action against their targets. Official information material from large cities in Germany was collected and complemented with questionnaires from officials in 40 municipalities. While 77% of cities have adopted emission targets in a voluntary act, and 80% of these cities are engaged in at least basic emission reporting, only a quarter of them are on course to reach their targets. All of these ‘successful’ cities are situated in Eastern Germany – and their emission reductions can mainly be explained by the industrial decline in the 1990s after the German Reunification. Not a single city in Western Germany is on course to reach its reduction commitment. Cities average mitigation performance is slightly worse than the German average, and the effect of city networks on cities is not very clear. It can be concluded that cities are currently not living up to their ambitions. The practice of urban emission reporting does in many cases not allow for proper quality management of greenhouse gas policies. For a more meaningful contribution to the battle against climate change, cities could follow a double strategy: Firstly they could report emissions regularly and adopt realistic and city-specific targets and action plans based on their emission patterns. Secondly, they could complement their targets with a visionary approach: This would include pilot projects that demonstrate how low carbon cities could look like, as well as a more ambitious target which they would be able to reach – provided that optimal framework conditions for local mitigation activities would be put in place by other policy levels.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".