Potential benefits of developing and implementing environmental and sustainability rating systems: Making the case for the need of diversification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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 it