Are There Effective Accounting Ways to Determining Accurate Accounting Tools and Methods to Reporting Emissions Reduction?
Bibliographic record
Abstract
Over the last century, many studies have used accounting methods and tools in focusing on environmental issues. This paper introduces readers to developments within the appropriate accounting tools designed to support firms and sectors reduction energy use as well as reducing greenhouse gases (GHGs) emissions. Current practice of traditional accounting (to date) has not covered environmental costs. Using Activity Based Costing could help firms to increase their understanding of sustainability and how to develop way to incorporate opportunity costs of environmental activities which are becoming significant issues on stakeholders. Moreover, an environmental management accounting approach can enhance information available on emissions to be more accurate. The net present value and internal rate of return also are considered the biggest hurdles to enhancing sustainability in business. This paper concludes that there is considerable potential to use environmental management accounting approach which is based on actual data to obtain more accurate information.
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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 | 0.116 | 0.339 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".