Experience Of Foreign Countries In Implementing Digital Technologies In Waste Management
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it