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A European household waste management approach: Intelligently clean Ukraine

机译:欧洲家居废物管理方法:智能清洁乌克兰

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The European-wide environmental obstacles of inefficient and unsustainable recycling systems and flows constrain household waste (HW) management, endangering the circular economy. The European 2020 strategy and ongoing environmental disasters indicate the ineffectiveness of the current HW sustainability practices. This paper introduces an artificial intelligence (AI) approach for calculating urban residual waste, based on its generation level. It reforms the current diverse and high discrepancy levels of HW residual for EU-countries and Ukraine. Adopting a k-means clustering method with a multi-criteria taxonomic development level index (TIDL), it produces uniform clusters with higher accuracy and manageability. Findings discover and remedy opaque managerial practices, enabling sustainable and environment-friendly development at national and regional levels for EU-countries. Results reveal an increased number of clusters in crisis, contributing to a methodological reference for environmental planning. In conclusion, this AI approach could have a European-wide impact on sustainable economic value-chain, converging toward an eco-friendly economy.
机译:欧洲广泛的环境障碍低效率和不可持续的回收系统,流动限制家庭废物(HW)管理,危及循环经济。欧洲2020年的策略和持续的环境灾害表明目前的HW可持续发展实践的无效。本文介绍了一种人工智能(AI)方法,用于计算城市剩余废物,基于其生成水平。它改革了欧盟国家和乌克兰的当前多样化和高差异水平的HW残差。采用具有多标准分类系统开发水平指数(TID1)的K-Means聚类方法,它产生具有更高准确性和可管理性的均匀簇。调查结果发现和补救的不透明管理实践,在欧盟国家的国家和地区各级实现可持续和环保的发展。结果揭示了危机中增加的群集,有助于环境规划的方法论参考。总之,这种AI方法可能对可持续经济价值链产生欧洲广泛的影响,趋于环保经济。

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