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Record W2065746957 · doi:10.2118/123864-ms

Are there <i>Less Costly</i> Ways to Sequester Carbon than CCS?

2009· article· en· W2065746957 on OpenAlexaff
Subodh Gupta

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2009
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCarbon sequestrationCarbon capture and storage (timeline)Flexibility (engineering)Fossil fuelCapital costEnvironmental economicsGreenhouse gasNatural resource economicsEnvironmental scienceBiomass (ecology)Waste managementEconomicsEngineeringCarbon dioxideClimate changeChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.002

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.

Opus teacher head0.039
GPT teacher head0.271
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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