The impact of regulatory reforms on corporate climate-related environmental reporting in China
Bibliographic record
Abstract
Purpose This study examines how China’s first and only nation-wide institutional reforms, Open Government Information (OGI) and Open Environment Information (OEI), both effective in 2008, influence the change in corporate climate-related environmental reporting (CER). It explores the role of institutional pressures in moderating the relationship between company characteristics and information disclosure. Design/methodology/approach The study uses multivariate regression analysis with general estimating equations to analyse 471 annual and CSR reports from 100 Chinese companies. Data were collected for three key years: 2006 (pre-reform), 2008 (immediate post reform-implementation), and 2010 (extended post reform-implementation). The study assesses the impact of regulatory reforms on overall reporting and category-specific reporting on climate-related environmental information in the short and medium term. Findings The findings reveal a significant increase in environmental reporting post-reform. Coercive institutional pressures from OGI and OEI moderate the relationship between Chinese company characteristics, such as political connections, ownership structure, and international operations, and reporting practices. Distinct drivers were identified for category and overall disclosures, highlighting the role of even voluntary regulatory reforms in shaping reporting behaviour. Practical implications The study offers policymakers insights into designing effective regulatory frameworks to enhance corporate transparency. Originality/value This study uniquely evaluates the moderating effects of OGI and OEI on the relationship between Chinese company characteristics and CER across overall and six category-specific disclosures. By addressing critical gaps, it captures the nuanced variability of factors influencing disclosure practices. The study also contributes a dynamic empirical model, resolving a long-standing issue of a “stable” functional relationship between company characteristics and CER. These insights deepen understanding of regulatory impacts on the change in corporate behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".