A comprehensive review of recycling and reusing methods for plastic waste focusing Indian scenario
Bibliographic record
Abstract
Plastics are integral to modern life but present significant environmental and economic challenges due to ineffective waste management systems. This article provides a comprehensive review of global plastic waste management (PWM) strategies, focusing on advancements in processing technologies, policy frameworks and their practical applications. It highlights the role of the World Intellectual Property Organization (WIPO) and regulatory bodies across the United States, Canada, Europe, Britain, India, Japan, Australia and China in fostering sustainable PWM practices. The study evaluates key processing techniques, including pyrolysis, gasification, supercritical water conversion, plasma-assisted processes, mechanical reprocessing and landfilling, emphasizing their technological advancements, limitations and scalability. Supercritical water conversion (operating at >374°C and 22.1 MPa) and plasma-assisted processing (using ionized gas at >3000°C) are identified as advanced methods capable of converting plastics into simpler molecules or valuable by-products. However, these technologies face challenges such as high energy requirements, operational costs and limited scalability. Persistent issues, including microplastic pollution, environmental impacts and the chemical-intensive nature of certain processes, are critically analysed. Drawing on extensive reviews of patents, case studies and real-world implementations, the study also examines the reuse potential of plastic by-products in diverse industries and evaluates state-level PWM initiatives in India. This review provides actionable insights for policymakers, researchers and industry stakeholders, highlighting critical gaps and opportunities to enhance the sustainability and scalability of PWM systems. By addressing persistent challenges, it contributes to advancing a circular economy for plastics and sustainable waste management practices globally.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".