Integrating Waste Legislation and Circular Economy Strategies in Climate Policy: International Lessons and Georgia's Experience
Bibliographic record
Abstract
The transition toward a circular economy (CE) has gained momentum worldwide, supported by regulatory reforms, economic incentives, and technological innovations. Yet progress remains uneven, particularly in transition and developing economies where enforcement, infrastructure, and public engagement are limited. This review synthesizes international literature and compares selected jurisdictions using a structured set of parameters: (i) national CE policies and regulatory frameworks, (ii) Extended Producer Responsibility (EPR) and economic instruments, (iii) recycling and separate collection systems, (iv) integration with greenhouse gas (GHG) reduction targets, (v) enforcement capacity, and (vi) public participation. The analysis shows that advanced economies such as Austria, South Korea, and Japan achieve higher recycling rates and measurable climate benefits through integrated policy mixes that combine legislation, financial incentives, and citizen engagement. In contrast, fragmented approaches in the United States, Canada, and Australia deliver uneven results, while transition economies— including Georgia—remain at early stages of CE implementation. Georgia has introduced the Waste Management Code (2014), Vision 2030, and a National Waste Management Strategy, but continues to face barriers of weak enforcement, limited infrastructure, and low circularity (≈ 1.3%). The review highlights that Georgia' s progress will depend on embedding existing legislation into a coherent policy mix, investing in infrastructure and enforcement, strengthening public-private partnerships, and aligning CE more closely with climate objectives. These findings provide insights for policymakers in Georgia and other middle-income countries on how international best practices can guide effective CE transitions and contribute to climate change mitigation.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".