Enhancing environmental, social, and governance, performance and reporting through integration of life cycle sustainability assessment framework
Bibliographic record
Abstract
Abstract We introduce an innovative framework integrating Life Cycle Sustainability Assessment (LCSA) impact categories with Environmental, Social, and Governance (ESG) factors, offering a unified approach for ESG assessment and reporting. It covers sustainable development's key aspects, enabling a detailed evaluation of environmental, economic, and social performance across product and system life cycles, in line with the Sustainable Development Goals (SDGs). Incorporating the UN's 10 principles, the framework fosters a synergy to improve ESG reporting, adaptable across industries. To demonstrate its practicality, a theoretical application in Canada's oil and gas sector highlights how this framework can provide actionable insights for SDG‐aligned performance improvements. This example illustrates how the framework can identify and address sustainability issues, thereby improving ESG performance. Beyond its theoretical contributions, the framework serves as a valuable tool for practitioners and investors, promoting informed and comprehensive ESG reporting. Ultimately, it aims to enhance organizations' contributions towards achieving the SDGs and advancing global sustainability.
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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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".