Marginal Abatement Cost Curves (MACCs): Important Approaches to Obtain (Firm and Sector) Greenhouse Gases (GHGs) Reduction
Bibliographic record
Abstract
The study aims to identify appropriate methods that can help organisations to reduce energy use and emissions by using an effective concept of sustainability. In different countries, estimates of marginal abatement costs for reducing GHG emissions have been widely used. Around the world, many researchers have focused on MACCs and reported different results. This may due to different assumptions used which in turn lead to uncertainty and inaccuracy. Under these circumstances, much attention has been paid to the need for the role of MACC in providing reliable information to decision makers and various stakeholders. By reviewing the literature, this paper has analysed MACCs in terms of the role of different approaches to MACCs, representations of MACCs, MACC applications, pricing carbon, verification, and sectors analysis for energy and emissions projections. This paper concludes that MACCs should depend on actual data to provide more reliable information that may assist (firms and sectors) stockholders to determine what appropriate method for reducing emission.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".