Integrating Circular Economy Principles in Water Resilience: Implications for Corporate Governance and Sustainability Reporting
Bibliographic record
Abstract
This paper makes an empirical contribution to the relatively sparse literature on the relationship between corporate governance and sustainability disclosure in mandatory reporting. We study the pursuit of UN SDG 6—clean water and sanitation—as an instance of sustainability and make observations from the literature considering water resilience scenarios, circular economy perspectives, as well as governance and integrated reporting requirements. The term “water governance” has been used to characterize operational actions needed to maintain a balance with water scarcity being a dominant theme. Continuing adaptation to emergent conditions is needed and we draw on an agile structuration theory model to help understand how a succession of innovation projects supports the transition to a circular economy. Our theoretical discussion is reinforced by an in-depth longitudinal case study of Yarra Valley Water (YVW), an innovative Australian water utility. The longitudinal case study analysis provides insights into several different types of innovative projects that demonstrate how circular economy principles in water resilience are integrated for corporate governance and sustainability reporting. Several case studies could be a topic for future research drawing on the agile structuration theory model presented in this paper.
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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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".