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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13510%2F21%3A43896189" target="_blank" >RIV/44555601:13510/21:43896189 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/21:00124368 RIV/00216305:26210/21:PU141154

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0301479721006460?dgcid=coauthor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0301479721006460?dgcid=coauthor</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jenvman.2021.112584" target="_blank" >10.1016/j.jenvman.2021.112584</a>

Result language

angličtina

Original language name

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

Original language description

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 micro-regions are weak and should be treated as such.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50704 - Environmental sciences (social aspects)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Environmental Management

ISSN

0301-4797

e-ISSN

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Volume of the periodical

291

Issue of the periodical within the volume

1 August 2021

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

1-13

UT code for WoS article

000684997800010

EID of the result in the Scopus database

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