A cross-country comparison of green initiatives, green performance and financial performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the relationship between green initiatives, green performance, and a firm’s financial performance in the world. The existing literature on environmental initiatives and their impacts is limited to the context of a particular country. This gap points to a lack of clarification of variations in environmental regulation and in economic disparity which may affect the impact of green initiatives on green performance and on financial performance. Design/methodology/approach Data on the world top 500 publicly traded companies are collected from Compustat, a database of financial, statistical and market information on global companies, and from Newsweek, an information gatekeeper that enables consumers to access a list of environmentally friendly companies. The paper adopts linear regression to test the relationships between variables. Findings The results show that green initiatives have a positive impact on green performance, which in turn has a positive impact on financial performance. However, the impact of green initiatives varies by country. The study revealed that companies in European countries and Canada lead in the green initiatives and green performance, followed by the USA and Japan. China and Hong Kong lag behind compared to other countries. Research limitations/implications The small sample size in some of the countries used in this study may impact the validity of the results. Practical implications This study suggests that companies that seek financial benefits of pursuing green initiatives should have a long-term orientation when implementing these initiatives and should consider the country where they operate. Originality/value The current study provides a global understanding of the relationship between green initiatives, green performance, and financial performance, and contributes to the literature by highlighting variation among countries and by year.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.004 | 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".