Integrating sustainability with corporate governance: a framework to implement the corporate sustainability reporting directive through a balanced scorecard
Bibliographic record
Abstract
Purpose The growing importance of environmental, social and governance (ESG) issues, as well as related performance planning, measuring and reporting, has spurred interest in linking corporate sustainability and performance management systems (PMSs). In this context, the aim of this paper is to provide companies with a framework for implementing the requirements of the corporate sustainability reporting directive (CSRD) through a sustainability balanced scorecard (SBSC). The framework will further the integration of sustainability with corporate governance. Design/methodology/approach The framework was grounded in the relevant literature and the CSRD requirements. Findings This paper provides companies with a novel framework for implementing the requirements of the CSRD through a SBSC. The framework specifies four key steps (i.e. identifying material themes, initial assessment, strategic formulation and action, and sustainability reporting) to integrate sustainability with corporate governance. Practical implications The framework supports managers’ decision-making processes in linking sustainability with strategy and providing a basis for integrating sustainability with corporate governance in organizations. The paper provides a way to practically address the CSRD requirements. Originality/value This is the first study integrating the emerging CSRD requirements with corporate governance. The paper advances discussion and debate by management scholars on how a SBSC can be practically implemented, providing details on how this may be achieved.
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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 | 0.106 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".