Applying GRI Sustainability Reporting in the Water Sector: Evidences from an Italian Company
Bibliographic record
Abstract
Sustainability reporting is considered the most applicable and reliable tool for disclosing financial and non-financial information to stakeholders and a means for strengthening company credibility. The Global Reporting Initiative (GRI) is the most common standard followed to implement and to develop sustainability reporting. Nevertheless, a few studies have focused on the real adoptability of the standard. The aim of the present study is to illustrate how GRI indicators could be applied and interpreted by managers of a water company to implement sustainability reporting. A single case study is developed to identify which factors could affect the standardized implementation of the G4 guidelines by a water utility company. The research was conducted in an Italian medium-sized enterprise, an entirely publicly owned joint-stock company under the in-house providing rules. Evidence from the case study was gathered through direct observation as well as semi-structured interviews and focus groups with the human resources responsible for the data collection. The research highlights that the process generated an internal validation of the practices carried out by the managers to achieve sustainable development. The case study presented shows that GRI Guidelines adoption is left to interpretation: certain practices and recommendations contained in the G4 implementation manual are, in fact, operationalized within the organization in consideration of the company’s activities and governance. The illustration of the case study may guide practitioners in the GRI implementation process in all sectors, especially in public utilities.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".