Firm- and country-level determinants of green investments: an empirical analysis
Bibliographic record
Abstract
Purpose This study aims to examine the determinants of corporate green investments (GI) by using a series of both firm- and country-level factors. Design/methodology/approach The authors collect information on environmental expenditures of 763 firms from 40 countries and use random effects regressions to identify the determinants of GI. Findings The authors find that larger firms tend to invest more in green projects, whereas firms that are highly valued or more profitable are less likely to go green. In terms of country-level determinants, we find that the gross domestic product (GDP) per capita and population are positively related with GI, while GDP growth and surface area are negatively associated with GI. Additionally, firms in common-law countries and English-speaking countries make fewer GI than firms in other countries. Social implications The findings of this research not only contribute to the academic literature in these areas, but also have important implications for both regulators and policymakers in countries that exhibit sub-par GI or who otherwise aim to increase GI by firms operating in their country. Originality/value The authors identify and explore the key determinants of GI from both a firm- and country-level perspective.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".