Coming Clean: The Impact of Firm Internationalization on Environmental Information Disclosure in the Construction Industry
Bibliographic record
Abstract
Environmental information disclosure (EID) as an essential component of corporate social responsibility (CSR) has become a business imperative worldwide. Despite the growing interest in CSR activities of construction companies on an international scale, little is known about the extent to which firms’ EID practices are influenced by their internationalization. This study thus fills the gap using a sample of 1,571 observations from 250 public construction companies headquartered in 36 countries. The level of internationalization is measured as the ratio of foreign sales to total sales. The empirical results suggest that the level of internationalization has a significant and positive impact on the quality of EID—the extent to which a firm’s disclosed information represents its overall environmental performance. The findings remain robust after conducting a batch of robustness checks and addressing potential endogenous concerns. Additionally, the results demonstrate that the positive effect of internationalization on the quality of EID is more pronounced for firms that are (1) more exposed to adverse CSR events, (2) headquartered in countries with less stringent environmental policies, and (3) headquartered in countries with fewer ties to global information trends. Collectively, this study sheds light on the implications of internationalization for environmental disclosure and transparency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.032 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".