The effects of national culture on environmental disclosure: A cross-country analysis
Bibliographic record
Abstract
ABSTRACT This study aims to answer the following research question: How does national culture affect environmental disclosure in liberal economies? Several previous studies have shown that formal institutions, such as the characteristics of the government, the country's financial system, have an influence on environmental disclosure. However, there is still a gap in the literature on how informal institutions (the country's culture) can influence the behavior of companies. The results of this study provide a solid understanding of environmental disclosure in liberal economies. In these economies, it is common for companies to be more interested in disclosing financial and governance information. Thus, analyzing environmental disclosure in these companies presents an additional gain for the literature. Managers can use our findings to understand how the country's culture can influence their business. The adoption of the Global Reporting Initiative (GRI) disclosure guidelines and the presence of a sustainability committee in the company can help companies achieve better environmental performance. We examined the level of environmental disclosure for a sample of 1,037 companies based in Australia, Canada, Ireland, New Zealand, United Kingdom, and United States of America (USA) for the period 2015-2018. To measure national culture, we used the proposed cultural dimensions by Hofstede (1983): distance to power, individualism, masculinity, aversion to uncertainty, long-term orientation, and indulgence. Our evidence shows that power distance, individualism, and masculinity have a positive effect on environmental disclosure. The results show that in cultures with less aversion to uncertainty, companies disclose more environmental information. Our findings also show us that companies that carry out an environmental disclosure following GRI guidelines disclose more sustainability information. The results show that sectors such as energy, materials, and utilities have greater environmental disclosure.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".