Potential benefits of developing and implementing environmental and sustainability rating systems: Making the case for the need of diversification
Bibliographic record
Abstract
In this paper, we explore the potential benefits of implementing environmental and sustainability rating systems (ESRS) in industrial sectors other than the building industry. The increasing demand for natural resource exploration and exploitation has generated greater attention to the impact of such activity on both the organization and its stakeholders. One solution to mitigate the negative impacts is to regulate it through government agencies and legal requirements. While providing general guidelines, these processes often provide little practical help for firms to address triple bottom line goals in sustainability (i.e. social, economic, environment). More recently, a variety of environmental and sustainability rating tools have been developed to assist firms in making decisions that best fit these goals. While readily used and championed by the building industry, these rating tools have yet to be adopted by adjacent industries like mining, energy, oil & gas, and heavy industrial. This paper outlines potential benefits that these industries could realize in choosing to use such tools for the assessment of sustainability performance.
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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 | 0.101 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".