Policy pathways utilizing extended producer responsibility and eco-modulation frameworks for sustainable food packaging waste management in India: A review
Bibliographic record
Abstract
• Multilayer packaging (MLP) waste is difficult to recycle, posing a major environmental threat. • Some countries have implemented effective policies to manage waste sustainably. • EPR and Eco-modulation encourage producers to design eco-friendly packaging. • There is a Need for Joint Consumer-Producer Policies for Waste management strategies. Despite the growing concerns surrounding food packaging waste, India still lacks a well-defined and effectively enforced policy framework to tackle this issue. Multilayered packaging, constituting 35 % of total packaging in India, is currently deemed unrecyclable due to its complex structure and is currently landfilled. While countries like the UK, Belgium, France, Italy, and Canada have successfully implemented measures such as landfill taxes and "pay-as-you-throw" policies, India has yet to establish strict regulations and economic incentives to promote sustainable waste management. The absence of strong Extended Producer Responsibility (EPR) enforcement, inadequate eco-modulation strategies, and limited consumer incentives such as reduced GST on recycled products or rewards for eco-conscious practices highlight the urgent need for India to bridge this policy gap and adopt a more structured approach towards a circular economy. Strengthening of regulatory frameworks and a collaborative approach involving the government, producers, informal sector, and consumers is essential for developing an efficient waste management system. Through this review, we aim to highlight successful global strategies and motivate the adoption of similar policies in India to mitigate the growing environmental threat posed by food packaging waste.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".