Environmental Audit and Policy Compliance of Tobacco Farming Practices in Migori County, Western Kenya
Bibliographic record
Abstract
There is growing evidence that tobacco farming and use, among other critical livelihood factors, has an impact not only on health but also on the environment. The tobacco industry in Kenya has been on the rise with new production areas emerging. However, the nature of the environmental friendliness of the production, processing and consumption technologies have yet to be evaluated in many parts of Kenya. This study undertook an environmental auditing of tobacco farming activities in the South Nyanza region of Kenya for purposes of evaluating their compliance levels and policy formulation. Data collection was achieved through a detailed environmental audit checklist, focused group discussions, interviews, and field observations. Generally, the environmental compliance level with the National Environment Management Authority, World Health Organization standards and world best farming practices was dismal, at 13.6%. Tobacco companies' compliance rating with standards in application of pesticides and agrochemicals, use and management of energy resources, tobacco leaf storage and disposal of uncollected leaves, occupational health and safety, and best established practices in corporate social responsibility stood at 19.8, 15.7, 27.7, 2.1 and 11.8%, respectively. The low compliance level implies unsustainable farming practices; thus the need for enhanced enforcement of good environmental practices in the sector. Development of policies and legislation on economically viable and environmentally sustainable alternative crops is therefore recommended.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".