Motives for Firms to Adopt Solid Waste Management Controls: The Case of Food Processing Sector in Sri Lanka
Bibliographic record
Abstract
This study offers an empirical analysis of the economic incentives available for food processing firms in Sri Lanka to adopt environmental controls for solid waste management. A series of in-depth interviews (n=325) were carried out with managers responsible for environmental quality in five types of food processing firms (coconut-based products, essential oils, non-alcoholic beverages, processed fruits and vegetables, and other processed products). Confirmatory Factor Analysis techniques were applied to the data to quantify the effect of six market-based incentives (cost/financial implications, sales, reputation, commercial pressure, human resources and technical efficiency), two regulatory incentives (existing and anticipated government regulations), and the liability incentive on the firm’s adoption of solid waste management practices. The level of adoption of environmental practices at the firm level is low -- on average firms adopt only 1.2 compared to the recommended eight different possible practices. Costs of adoption and perceived improvements in technical efficiency are two factors that motivate adoption. Liability laws and anticipated future regulations also matter. The analysis suggests that older and larger firms are more responsive to environmental considerations. Interestingly, export oriented firms do not do better than domestic firms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".