Predictive modelling as a tool for effective municipal waste
management policy at different territorial levels

J 2021

Predictive modelling as a tool for effective municipal waste management policy at different territorial levels

ROSECKÝ, Martin; Radovan ŠOMPLÁK; Jan SLAVÍK; Jiří KALINA; Gabriela BULKOVÁ et al.

Základní údaje

Originální název

Predictive modelling as a tool for effective municipal waste management policy at different territorial levels

Autoři

ROSECKÝ, Martin; Radovan ŠOMPLÁK; Jan SLAVÍK; Jiří KALINA; Gabriela BULKOVÁ a Josef BEDNÁŘ

Vydání

Journal of Environmental Management, Amsterdam, Elsevier, 2021, 0301-4797

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10511 Environmental sciences

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 8.910

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/21:00124368

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Municipal waste generation; Territorial levels; Regression modelling; Machine learning; Socio-economic factors; Public policy

Štítky

143180, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 3. 2022 09:02, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

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.

Citovat

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. Journal of Environmental Management. Amsterdam: Elsevier, 2021, roč. 291, August 2021, s. 1-13. ISSN 0301-4797. Dostupné z: https://doi.org/10.1016/j.jenvman.2021.112584.

@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 = {https://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://doi.org/10.1016/j.jenvman.2021.112584.

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