Implication of Environmental Certification and CSR for Companies’ Sustainable Performance in Developing Countries
Bibliographic record
Abstract
This article presents a new approach to measuring level of social and ecological consciousness in developing countries based on the example ofUkraine. The paper documents a relationship between introduction of eco-certification and corporate social responsibility practices into companies’ daily activities, and the possible subsequent increase of their revenues, social reputation and competitiveness on international markets. Environmental certification will be also considered as a systematic tool to guarantee the quality of products, production and company’s business processes. The research was conducted during 2014-2015 and has covered 35% of Ukrainian companies from various branches that had valid environmental certificates. Based on regional distribution, distribution according to the industry a company operates in, in this paper we explore correlation between implementation of eco-certificates and CSR and their influence on company’s performance. The research is aimed to demonstrate how the introduction of green policy, environmental certification and corporate social responsibility influence company’s societal value, its reputation and competitiveness on the market, and whether it helps receive financial benefits in short- or long-term period.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".