Are There Differences between Estimate (Theoretical) and Actual MACC Approaches of Emission Reduction?
Bibliographic record
Abstract
The global warming phenomenon has become an international issue which requires effort to avoid and control the concentration of greenhouse gases (GHGs). At the same time, despite various attempts, developed countries need to put more effort and attention into dealing with this issue. Many studies have been conducted on reducing GHGs globally and nationally. The majority of these studies have focused at a national or sectorial level, particularly in the industrial sector. This study focuses on stationary energy. There are two main ways to reduce GHGs, particularly CO2. One is to replace carbon-based fuels with renewables. The other is to reduce consumption. To achieve further GHG emission reductions, improvements to regarding the use of energy are an emerging area of research that has significant implications for policy. Quantitative and qualitative research methodologies were used for this research. The findings of this research indicate that organisations are seeking a more accurate approach to save energy, reduce emissions, and determine the impact of users.’ Organisations are planning to use management accounting methods such as Marginal abatement cost curves (MACCs) when measuring the cost of abatement or reduction in environmental costs for more effective decision-making. This study developed a concept by using actual data in MACC. The design established support for organisations to meet data accuracy needs.
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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 | 0.088 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".