Identifying Effective Management Instruments and Human Behavioural Changes to Manage Energy Use and Abate Emissions at Firm Level
Bibliographic record
Abstract
Despite recent efforts to reduce the growth of greenhouse gas emissions (GHGs) at the national level, the negative environmental impacts of GHGs are likely to increase more in the future, especially at the level of the company. This paper presents an extension to current integrated management efforts with traditional assessments to improve current situations. The extension can be used to determine strategies of cost-effectiveness to control emissions and achieve environmental goals in reducing concentrations of GHGs. Methods developed to demonstrate significant cost savings in companies and sectors are command and control (CAC), and innovation; these instruments can contribute to the allocation of emission controls when market instruments do not fully work. To achieve further GHG emission reductions, improvements to behavioural change regarding the use of energy are an emerging area of research that has significant implications for policy. There is increasing evidence that human behaviour has been producing environmental problems. This paper introduces elements and interventions that could affect that behaviour. Governments and businesses have agreed that there is a need for more emphasis on proper assessment of behavioural change interventions. Therefore, increasing efficiency and reducing barriers within marginal abatement cost curves (MACCs) has improved the results for the management of abatement energy and emissions at firm level.
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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 | 0.006 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".