Solar has greater techno-economic resource suitability than wind for replacing coal mining jobs
Bibliographic record
Abstract
Abstract Coal mining directly employs over 7 million workers and benefits millions more through indirect jobs. However, to meet the 1.5 °C global climate target, coal’s share in global energy supply should decline between 73% and 97% by 2050. But what will happen to coal miners as coal jobs disappear ?Answering this question is necessary to ensure a just transition and to ensure that politically powerful coal mining interests do not impede energy transitions. Some suggest that coal miners can transition to renewable jobs. However, prior research has not investigated the potential for renewable jobs to replace ‘local’ coal mining jobs. Historic analyses of coal industry declines show that coal miners do not migrate when they lose their jobs. By focusing on China, India, the US, and Australia, which represent 70% of global coal production, we investigate: (1) the local solar and wind capacity required in each coal mining area to enable all coal miners to transition to solar/wind jobs; (2) whether there are suitable solar and wind power resources in coal mining areas in order to install solar/wind plants and create those jobs; and (3) the scale of renewables deployment required to transition coal miners in areas suitable for solar/wind power. We find that with the exception of the US, several GWs of solar or wind capacity would be required in each coal mining area to transition all coal miners to solar/wind jobs. Moreover, while solar has more resource suitability than wind in coal mining areas, these resources are not available everywhere. In China, the country with the largest coal mining workforce, only 29% of coal mining areas are suitable for solar power. In all four countries, less than 7% of coal mining areas have suitable wind resources. Further, countries would have to scale-up their current solar capacity significantly to transition coal miners who work in areas suitable for solar development.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".