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Record W4410307921 · doi:10.3390/jrfm18050261

Integrating Circular Economy Principles in Water Resilience: Implications for Corporate Governance and Sustainability Reporting

2025· article· en· W4410307921 on OpenAlexvenueno aff
Ronald C. Beckett, Milé Terziovski

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Corporate governanceSustainabilityCorporate sustainabilityCircular economySustainability reportingBusinessWater sectorEnvironmental resource managementAccountingEconomicsEnvironmental scienceWater supplyFinanceEcologyEnvironmental engineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.236
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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