Accelerate Large-Scale Biomass Residue Utilization via Cofiring to Help China Achieve Its 2030 Carbon-Peaking Goals
Bibliographic record
Abstract
Cofiring biomass with coal for power generation is an affordable and ready-to-deploy technology to help reduce carbon emissions and resolve residual biomass. Cofiring has not been widely applied in China primarily because of some practical limitations, i.e., biomass accessibility, technological and economic constraints, and lack of policy support. We identified the benefits of cofiring with consideration of these practical limitations based on Integrated Assessment Models. We found that China produces 1.82 Bts/year of biomass residues, 45% of which is waste. 48% of the unused biomass can be utilized without fiscal intervention and 70% can be utilized with the subsidized Feed-in-Tariffs for biopower and carbon trading. The average Marginal Abatement Cost of cofiring is twice that of China's current carbon price. Cofiring can help China create 153 billion yuan of farmers' income annually and reduce 5.3 Bts of Committed Cumulative Carbon Emissions (CCCEs, 2023-2030), contributing to the needed CCCE mitigations to China's overall sector and the power sector by 32 and 86%, respectively. About 201 GW of coal-fired fleets are not compliant with China's 2030 carbon-peaking goals, and 127 GW can be saved by implementing cofiring, representing 9.6% of the total fleets in 2030.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".