An Analytical Literature Review on Environmental Innovations Concepts
Bibliographic record
Abstract
Abstract Background: The importance of environmental innovation concepts is growing in the private sector, in academia and at the level of government policies. The concept of environmental innovation is closely related to two other notions: eco-innovation and green innovation. The purpose of this paper is to contribute to a clarification of the concept of environmental innovation and to provide an overview of the existing scientific literature in this field, identifying the most active authors, countries, publishers and relevant publications.Results: We created a matrix with a proposed eco-innovation model that is focused on the correlation between the measures related to the model application in the process of eco-innovation and the main areas of application.Conclusion: This review draws from the resource-based theory and investigates the interrelationships between three types of innovation (environmental innovation, eco-innovation, green innovation) and their impact on firm’s business performance using the proposed eco-innovation model. We found that the most active scholars are situated in US and Europe (i.e. USA, UK. Swiss, Germany, Singapore, Netherlands, Canada and France) and identified OECD as author of seven publications and Jens Horbach as author of six publications in the field of environmental innovations journals, leading the field.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".