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@article{1839326, author = {Rosecký, Martin and Šomplák, Radovan and Slavík, Jan and Kalina, Jiří and Bulková, Gabriela and Bednář, Josef}, article_location = {Amsterdam}, article_number = {August 2021}, doi = {http://dx.doi.org/10.1016/j.jenvman.2021.112584}, keywords = {Municipal waste generation; Territorial levels; Regression modelling; Machine learning; Socio-economic factors; Public policy}, language = {eng}, issn = {0301-4797}, journal = {Journal of Environmental Management}, title = {Predictive modelling as a tool for effective municipal waste management policy at different territorial levels}, url = {https://www.sciencedirect.com/science/article/pii/S0301479721006460?via%3Dihub}, volume = {291}, year = {2021} }
TY - JOUR ID - 1839326 AU - Rosecký, Martin - Šomplák, Radovan - Slavík, Jan - Kalina, Jiří - Bulková, Gabriela - Bednář, Josef PY - 2021 TI - Predictive modelling as a tool for effective municipal waste management policy at different territorial levels JF - Journal of Environmental Management VL - 291 IS - August 2021 SP - 1-13 EP - 1-13 PB - Elsevier SN - 03014797 KW - Municipal waste generation KW - Territorial levels KW - Regression modelling KW - Machine learning KW - Socio-economic factors KW - Public policy UR - https://www.sciencedirect.com/science/article/pii/S0301479721006460?via%3Dihub N2 - Nowadays, the European municipal waste management policy based on the circular economy paradigm demands the closing of material and financial loops at all territorial levels of public administration. The effective planning of treatment capacities (especially sorting plants, recycling, and energy recovery facilities) and municipal waste management policy requires an accurate prognosis of municipal waste generation, and therefore, the knowledge of behavioral, socio-economic, and demographic factors influencing the waste management (and recycling) behavior of households, and other municipal waste producers. To enable public bodies at different territorial levels to undertake an effective action resulting in circular economy we evaluated various factors influencing the generation of municipal waste fractions at regional, micro-regional and municipal level in the Czech Republic. Principal components were used as input for traditional models (multivariable linear regression, generalized linear model) as well as tree-based machine learning models (regression trees, random forest, gradient boosted regression trees). Study results suggest that the linear regression model usually offers a good trade-off between model accuracy and interpretability. When the most important goal of the prediction is supposed to be accuracy, the random forest is generally the best choice. The quality of developed models depends mostly on the chosen territorial level and municipal waste fraction. The performance of these models deteriorates significantly for lower territorial levels because of worse data quality and bigger variability. Only the age structure seems to be important across territorial levels and municipal waste fractions. Nevertheless, also other factors are of high significance in explaining the generation of municipal waste fractions at different territorial levels (e.g. number of economic subjects, expenditures, population density and the level of education). Therefore, there is not one single effective public policy dealing with circular economy strategy that fits all territorial levels. Public representatives should focus on policies effective at specific territorial level. However, performance of the models is poor for lower territorial levels (municipality and micro-regions). Thus, results for municipalities and microregions are weak and should be treated as such. ER -
ROSECKÝ, Martin, Radovan ŠOMPLÁK, Jan SLAVÍK, Jiří KALINA, Gabriela BULKOVÁ a Josef BEDNÁŘ. Predictive modelling as a tool for effective municipal waste management policy at different territorial levels. \textit{Journal of Environmental Management}. Amsterdam: Elsevier, 2021, roč.~291, August 2021, s.~1-13. ISSN~0301-4797. Dostupné z: https://dx.doi.org/10.1016/j.jenvman.2021.112584.
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