Meeting well-below 2°C target would increase energy sector jobs globally
Bibliographic record
Abstract
To limit global warming to well-below 2°C (WB2C), fossil fuels must be replaced by low-carbon energy sources. Support for this transition is often dampened by the impact on fossil fuel jobs. Previous work shows that pro-climate polices could increase employment by 20 million net energy jobs, but these studies rely on Organisation for Economic Co-operation and Development (OECD) jobs data, assumptions about jobs in non-OECD countries, and a single baseline assumption. Here we combine a global dataset of job intensities across 11 energy technologies and five job categories in 50 countries with an integrated assessment model under three shared socioeconomic pathways. We estimate direct energy jobs under a WB2C scenario and current policy scenarios. We find that, by 2050, energy sector jobs would grow from today's 18 million to 26 million under a WB2C scenario compared with 21 million under the current policy scenario. Fossil fuel extraction jobs would rapidly decline, but losses will be compensated by gains in solar and wind jobs, particularly in the manufacturing sector (totaling 7.7 million in 2050).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".