Towards real energy economics: Energy policy driven by life-cycle carbon emission
Bibliographic record
Abstract
Alternative energy technologies (AETs) have emerged as a solution to the challenge of simultaneously meeting rising electricity demand while reducing carbon emissions. However, as all AETs are responsible for some greenhouse gas (GHG) emissions during their construction, carbon emission "Ponzi Schemes" are currently possible, wherein an AET industry expands so quickly that the GHG emissions prevented by a given technology are negated to fabricate the next wave of AET deployment. In an era where there are physical constraints to the GHG emissions the climate can sustain in the short term this may be unacceptable. To provide quantitative solutions to this problem, this paper introduces the concept of dynamic carbon life-cycle analyses, which generate carbon-neutral growth rates. These conceptual tools become increasingly important as the world transitions to a low-carbon economy by reducing fossil fuel combustion. In choosing this method of evaluation it was possible to focus uniquely on reducing carbon emissions to the recommended levels by outlining the most carbon-effective approach to climate change mitigation. The results of using dynamic life-cycle analysis provide policy makers with standardized information that will drive the optimization of electricity generation for effective climate change mitigation. © 2009 Elsevier Ltd. All rights reserved.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".