Rethinking ESG Credibility: Conceptual Gaps, Normative Assumptions, and the Future of Sustainable Capitalism
Bibliographic record
Abstract
Environmental, Social, and Governance (ESG) frameworks have become central to sustainable finance and corporate accountability, however, their credibility remains contested. This paper critically examines the conceptual gaps, normative assumptions, and empirical challenges that undermine ESG’s capacity to drive substantive sustainability outcomes. Drawing on recent literature, we identify four core credibility challenges: definitional fragmentation across rating providers and regulatory regimes, measurement heterogeneity that prevents meaningful comparability, pervasive greenwashing enabled by disclosure-oriented rather than impact-oriented metrics, and embedded market-centric assumptions that privilege voluntary compliance over mandatory verification. Empirical evidence demonstrates that mandatory reporting requirements significantly reduce deceptive disclosure practices, while rating divergence and symbolic compliance persist under voluntary regimes. We propose a multi-level governance framework integrating harmonized taxonomies, outcome-based metrics, mandatory third-party audits, and ecocentric principles that extend beyond anthropocentric stakeholder models. This synthesis contributes to ongoing debates about ESG’s role in sustainable capitalism by articulating pathways from symbolic to substantive implementation and identifying priority areas for regulatory reform, corporate practice, and future research.
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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 | 0.131 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.066 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".