Just transition toolbox for coal regions
Bibliographic record
Abstract
As the worldwide remaining carbon budget decreases rapidly, countries across the globe are searching for solutions to limit greenhouse gas emissions. As the production and use of coal is among the most carbon-intensive processes, it is foreseeable that coal regions will be particularly affected by the consequences of a transformation towards a climate-neutral economy and energy system. Challenges arise in the area of energy production, environmental protection, but also for economic and social aspects in the transforming regions - often coined with the term "Just Transition". For the decision makers in coal regions, there is an urgent need for support tools that help to kick off measures to diversify the local economies while at the same time supporting the local workers and communities. The Wuppertal Institute aims to support coal regions worldwide by developing a Just Transition Toolbox, which illustrates the challenges and opportunities of a sustainable transition for a global audience. It comprises information about strategy development, sets recommendations for governance structures, fostering sustainable employment, highlights technology options and sheds light on the environmental rehabilitation and repurposing of coal-related sites and infrastructure. The toolbox builds on the work of the Wuppertal Institute for the EU Initiative for Coal Regions in Transition and takes into account country-specific findings from the SPIPA-partner countries India, Indonesia, South Africa, Japan, South Korea, Canada and the USA. The acronym SPIPA is short for "Strategic Partnerships for the Implementation of the Paris Agreement" an EU-BMU programme co-financed by the GIZ.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.165 | 0.070 |
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".