Experience Of Foreign Countries In Implementing Digital Technologies In Waste Management
Bibliographic record
Abstract
The foreign experience of implementing digital technologies in waste management has been analyzed, and the potential for their adaptation in Ukraine has been identified. The study covers countries with varying levels of economic development and diverse technological solutions, providing a broad range of approaches to waste management. The analysis of selected countries is based on reports such as the Global Waste Management Outlook 2024, What a Waste 2.0 Update, and the European Environment Agency 2024. Primary focus is given to Internet of Things (IoT), artificial intelligence (AI), blockchain, big data, and mobile applications, their key applications in waste management, quantitative outcomes, and institutional and financial mechanisms for implementation. IoT is applied for monitoring container fill levels, waste sorting, logistics optimization, and environmental monitoring. For instance, in Barcelona (Spain), IoT-enabled containers reduced waste collection frequency by 20–50 %, lowering CO2 emissions, while in Singapore, IoT systems with GPS trackers cut transport costs by 15 %, saving $2 million annually. AI is utilized for automated sorting (Tokyo, Japan: 95 % accuracy, 30 % increase in plastic recycling) and logistics and recycling optimization (Munich, Germany: 65 % waste recycling, €5 million annual savings). Blockchain ensures transparency in the recycling chain, as in China, where the AntChain platform tracks 1.2 million tons of plastic, reducing illegal dumping by 12 %. Big data facilitates waste volume forecasting and process optimization, as seen in the USA (Rubicon Global), while mobile apps like Recycle Coach (Canada/USA) and TrashOut (Slovakia/Czechia) enhance citizen engagement in sorting and environmental initiatives. In Ukraine, where waste management is hindered by war and limited resources, adapting these technologies holds significant potential. Pilot implementation of IoT sensors in cities, expansion of the Sortuy app’s functionality by integrating a map of construction waste disposal sites, IoT monitoring, and blockchain for transparency are proposed. Implementation requires international funding, grants, infrastructure modernization, and legislative support. Adopting digital technologies could lead to savings (up to 30 % of disposal costs), reduced environmental impact, and the development of a circular economy in Ukraine.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".