Bibliographic record
Abstract
Summary Ever-increasing global demand for energy, and the world's supply predominantly being fossil-based, implies continued growth of emissions. Efficiency improvements and employment of non-fossil energy will definitely help mitigate the problem, but it is generally recognized that "pure carbon offsets" will have to play a major role if the problem has to be combated in a timely fashion. Discussion on pure offsets employing geological storage [namely, carbon capture and storage (CCS)] is advancing rapidly. However, major issues with this approach are its high cost and the long-term post operation liability. The author has previously proposed an alternate approach of pure offset-charcoal sequestration (CS), which essentially employs conversion of dead plant material into inert solid carbon. CS promises to be both less expensive and a better option as far as the operational and post-operation liability is concerned. Among the numerous advantages of the charcoal approach is its easier reversibility, both in terms of liability and costs. Although implementation of this approach at a scale where it can make a significant impact on global CO2 concentration needs to be preceded by a substantial information dissemination and public preparedness, a practical way to introduce it is through using municipal solid waste (MSW) as the feed biomass for CS. This will not only allow time for public acceptance to evolve, and evaluation of potential associated risks, but immediately help mitigate the growing problem of space requirement for waste landfills, waste transport costs and emission of methane from the rotting municipal waste associated with the continued urban sprawl. This paper, aside from describing the carbon sequestration from waste (CSW) method, estimates the cost of carbon offset with this and other competing approaches, such as the use of MSW for conversion to bio-alcohol and for power generation. It highlights the difference between carbon credits associated with mobile energy needs (pure offsets) and stationary energy needs and makes a case for price duality of carbon credits. It also compares the global potential of CSW in combating the greenhouse gas (GHG) problem, making more than two Socolow wedges with use of charcoal for soil enhancement and other purposes amounting to less than 0.04 such wedges. In this work, the cost of carbon offset with CSW is estimated to be as low as CAD 2.6/tonne CO2 equivalent (CO2e).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".