Advancing just transition: The role of biomass co-firing in emission reductions and employment for coal regions
Bibliographic record
Abstract
As efforts to mitigate carbon emissions intensify, the issue of justice in the transition process has gained significant attention. The environmental impacts of low-carbon technologies, such as renewable energy, have been widely evaluated. This study develops an integrated model to quantify the employment effects of biomass co-firing retrofitting for coal plants and to explore its optimal ratio. We also modified a plant-level carbon emission calculation method to analyze the trade-off between carbon emissions and social benefits. Our results indicate that when the blend rate of coal and biomass exceeds 90%, there are noticeable changes in job creation. However, the maximum job creation value is negative, suggesting that biomass co-firing technology is not an effective choice for achieving a just transition during the large-scale coal phase-out process, and it is less competitive compared to renewable energy. Additionally, we find that generation efficiency is linearly negatively correlated with the blend rate. Moreover, carbon emission intensity and job intensity are positively correlated linearly, while there is a nonlinear negative correlation between job intensity and the blend ratio. Overall, biomass co-firing appears insufficient for a just transition in coal phase-out, indicating a need for regionally adaptive blend ratios for optimal operation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".