Are there <i>Less Costly</i> Ways to Sequester Carbon than CCS?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Given the expected rise in global demand for energy, which in turn is dominated by reliance on fossil fuels, carbon emissions are only expected to increase, raising the CO2 concentration of the atmosphere even further. Efficient use of energy will help but if we have to reduce atmospheric CO2, we are forced to consider methods of carbon sequestration and associated costs. Carbon Capture and Storage (CCS) is the most discussed method of sequestration. However, there are two major issues with this approach of sequestration. Firstly, it is the high direct cost associated with capital and operation of capture, transport and subsurface injection. And secondly, it is the undetermined cost of post-operation liability associated with measurement, monitoring, verification and problem-rectification. Committing to the needed large scale sequestration projects without properly considering alternatives can prove costly at both economic and social levels. There are alternatives to CCS, however. Use of biomass derived fuels such as bio-ethanol has been talked about in literature as one of them. But biofuels are shown to be more expensive for carbon abatement than the base alternative. Charcoal sequestration (CS), discussed earlier by the author carries with it the benefit of being a pure sequestration (similar to CCS), is less costly both in terms of its direct costs and lacking any post operation liabilities, and above all is reversible, allowing flexibility of policy and operation, avoiding long term or large scale commitments. In this paper the author outlines how municipal solid waste (MSW) processing into charcoal can be a good way to initiate demo projects for charcoal sequestration approach potentially opening door for employing other larger resources of biomass and shows as such use of MSW has to be a major component of our carbon abatement package of actions. This is for reasons of both cost effectiveness as well as to reduce the landfill methane (a more potent GHG) from entering into atmosphere. The over all discounted cost of a suite of proposed measures including improvement of efficiency, conversion of waste to charcoal for use both as fuel as well as sequestration, increased use of nuclear energy, limited application of CS using larger dead-stock biomass pool, and CCS, is assessed to be in the range of US$7.5trillion in this paper, reducing the equivalent CO2 concentration by 465 ppm (compared to the projected business as usual) by the end of 21st century. The outlined approach, with its prominent use of MSW, promises not only to mitigate the growing urban waste problem but also to help evaluate this novel method of sequestration and enhance public awareness on the subject. This in turn will help the larger society make an informed choice to embark on a right course of action for atmospheric carbon abatement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it