Assessing the Maturity of Sustainable Business Model and Strategy Reporting under the CSRD Shadow
Bibliographic record
Abstract
The present work is amongst the few that attempt to critically assess the maturity of Business Model (BM) and strategy disclosures of listed firms under the shadow of the new EU reporting directive, the Corporate Sustainability Reporting Directive (CSRD). The novel Practices Evaluation Approach (PEA), developed recently by the Project Task Force on Reporting of Non-Financial Risks and Opportunities (PTF-RNFRO), offers the evaluation framework for this assessment. The PEA delineates and evaluates the maturity of BM and strategy disclosures against qualitative characteristics and content elements drawn from well-accepted, financial and non-financial, reporting frameworks, standards and directives (including the CSRD). Therefore, the PEA provides the advantage of a contemporary and integrated/holistic assessment tool. Specifically, the following seven evaluation criteria are used for the assessment: clarity and comprehensiveness of the overall BM, strategy disclosure, disclosure of the BM’s potential across-time horizons and its dependencies, impacts on sustainability issues, material sustainability issues that are likely to affect the company’s performance, the BM’s exposure to sustainability risks and sustainability opportunities, and sustainability strategy, targets, KPIs and their monitoring and progress. The analysis covered 30 CSR/sustainability reports and connected documents of listed companies operating in 6 key sectors of the Greek economy, i.e., information technology, construction, tourism and transportation, cosmetics, banking and energy. The results of our analysis offer evidence that BM reporting is not holistically developed (i.e., critical components are missing), and the level of development varies across the examined sectors. Moreover, sustainability risks are more stressed, in relevance to opportunities, whilst positive (rather than negative) impacts are mainly disclosed. Also, the quantification of sustainability risks and opportunities does not appear frequently, whilst the interconnections between sustainability strategy and companies’ financial objectives is relatively restricted. The paper concludes by pointing out some critical hints useful for enhancing the maturity of BM and strategy disclosures.
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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 | 0.097 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".