Disparities in ESG reporting by emerging Chinese enterprises: evidence from a global financial center
Bibliographic record
Abstract
Purpose This study aims to examine the potential disparities in environmental, social and governance (ESG) reporting among emerging Chinese enterprises (ECEs). ECEs are subject to a set of internationally oriented ESG requirements imposed by the regulator of a global financial center that is exposed to diverse stakeholders. The authors also consider ECEs’ underlying institutional ownership, which exhibits influence over governance as a salient component of ESG. Design/methodology/approach This study is based on a random sample of 500 ECEs listed on the Stock Exchange of Hong Kong (SEHK) – the global financial center of China. ESG reporting is measured by using the key performance indicators of the SEHK’s ESG Reporting Guide. The data are collected from annual reports that contain ESG disclosures or standalone ESG/sustainability reports published during the 2018–2019 fiscal year. The authors adopt binary logistic regressions and Chi-square tests to test the proposed hypotheses. Findings The authors find that ECEs’ heterogeneous institutional ownership and the extent of overseas development are associated with their disclosures on climate change. ECEs with international institutional ownership are found to be a significant factor for reporting aligned with the United Nations sustainable development goals (SDGs), using external assurance and stakeholder engagement, rather than state-owned enterprises (SOEs) and private companies. The authors also document that the presence of independent nonexecutive directors (INEDs) is significantly associated with reporting on meeting the SDGs and its use of external assurance, while the presence of female directors is a significant factor influencing disclosure emphasis on energy-saving initiatives. Practical implications The authors provide an empirical study of ECEs beyond the focus on SOEs that are expected to produce comprehensive ESG reporting in addressing a broader international community of stakeholders apart from the regime of their home country. The authors document the pertinence of ECEs’ institutional ownership and governance diversity to ESG reporting. In particular, international stakeholders need to recognize such underlying differences among ECEs rather than viewing them as a homogeneous group. Social implications The authors suggest that policymakers and practitioners in Asian countries consider increasing the presence of INEDs and gender diversity on ECE boards to enhance ESG reporting, which reinforces the findings of prior international studies suggesting such governance practices. Originality/value This study contributes to the existing body of knowledge about ESG reporting by documenting the underlying heterogeneity within ECEs, which are subject to a set of internationally oriented standards, as evidenced by their disparities in ESG reporting.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".