Green Innovation and Financial Performance
Bibliographic record
Abstract
Green innovation incorporates technological improvements that save energy, prevent pollution, or enable waste recycling and can include green product design and corporate environmental management. This type of innovation also contributes to business sustainability because it potentially has a positive effect on a firm’s financial, social, and environmental outcomes. However, the specific effect of green innovation on these outcomes can be highly influenced by the national context in which firms develop their activities. Using an institutional approach and employing a sample of 88 green innovative firms and 70 matched pairs (green innovative and non–green innovative firms), we find that green innovative firms are situated in contexts characterized by more stringent environmental regulations and higher environmental normative levels.Nevertheless, when compared to non–green innovative firms, we observe that green innovative firms do not experience improved financial performance. In focusing on green innovative firms, we note that the intensity of green innovation is positively related to firm profitability. Finally, we study whether national institutional conditions (stringency of environmental regulations and normative levels) impose a moderating effect on the relationship between green innovation intensity and the financial performance improvement of innovative firms. Our results show that regulatory and normative dimensions do not have the same influence on that relationship, creating implications for academia, managers, and policy makers.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".