Global Warming and Atmospheric Carbon: Is Carbon Sequestration a Myth or Reality?
Bibliographic record
Abstract
Abstract Biotic and abiotic carbon sequestration currently seems to be the only viable tools at the disposal of mankind for mitigating greenhouse gas (GHG) emissions and thus a remedy for tackling global warming challenges. This study accesses the global carbon capture and storage (CCS) programme: the level of success in its implementation and its impact using panel data from eight countries, the majority of which have begun one or more operational CCS facilities. To achieve this objective, fifteen years period time series data was sourced for the eight selected countries based on data availability, namely the United States (US), the United Kingdom (UK), Canada, China, Australia, Norway, South Africa, and Nigeria; ranging from 1990 to 2015. The panel ARDL results show that the explanatory variables, global industrial production (LIP), Electricity production (LEP), Agricultural production (LAP), transportation (LTR), and energy supply (LES) have a long-run relationship with the dependent variable (LGHG emissions). While the short-run results show that none of the variables have a significant contribution to LGHG emissions. In the long-run results, LIP and LTR significantly contribute to the reduction of LGHG courtesy of the CCS programme while LEP, LAP, and LES contribute to a rise in the LGHG emission. The cross-sectional results show that all the variables have significant impacts on LGHG in all the sampled countries except Australia. Suggesting that, the CCS programme is viable for mitigating global warming and climate change and therefore should be considered by the various countries of the world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".