<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Accurate forecasting of municipal solid waste (MSW) generation is important for the planning, operation and optimization of municipal waste management system. However, it’s not easy task due to dynamic changes in waste volume, its composition or unpredictable factors. Initially, mainly conventional and descriptive statistical models of waste generation forecasting with demographic and socioeconomic factors were used. Methods based on machine learning or artificial intelligence have been widely used in municipal waste projection for several years. This study investigates the trend of municipal waste accumulation rate and its relation to personal consumption expenditures based on the yearly data achieved from Local Data Bank (LDB) driven by Polish Statistical Office. The effect of personal consumption expenditures on the municipal waste accumulation rate was analysed by using the vector autoregressive model (VAR). The results showed that such method can be successfully used for this purpose with an approximate level of 2.3% Root Mean Square Error (RMSE).</p>
</abstract>

Accurate forecasting of municipal solid waste (MSW) generation is important for the planning, operation and optimization of municipal waste management system. However, it’s not easy task due to dynamic changes in waste volume, its composition or unpredictable factors. Initially, mainly conventional and descriptive statistical models of waste generation forecasting with demographic and socioeconomic factors were used. Methods based on machine learning or artificial intelligence have been widely used in municipal waste projection for several years. This study investigates the trend of municipal waste accumulation rate and its relation to personal consumption expenditures based on the yearly data achieved from Local Data Bank (LDB) driven by Polish Statistical Office. The effect of personal consumption expenditures on the municipal waste accumulation rate was analysed by using the vector autoregressive model (VAR). The results showed that such method can be successfully used for this purpose with an approximate level of 2.3% Root Mean Square Error (RMSE).

Forecasting municipal waste accumulation rate and personal consumption expenditures using vector autoregressive (VAR) model

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This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Accurate forecasting of municipal solid waste (MSW) generation is important for the planning, operation and optimization of municipal waste management system....

Forecasting municipal waste accumulation rate and personal consumption expenditures using vector autoregressive (VAR) model

Online veröffentlicht: 19. Mai 2022

Seitenbereich: 150 - 156

Eingereicht: 21. Dez. 2021

Akzeptiert: 23. Feb. 2022

DOI: https://doi.org/10.30657/pea.2022.28.17

Schlüsselwörter
waste accumulation rate, consumption expenditures, forecasting, time-series analysis, multivariate time series, vector autoregression model

© 2022 Jurand Bień, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Forecasting municipal waste accumulation rate and personal consumption expenditures using vector autoregressive (VAR) model

Online veröffentlicht: 19. Mai 2022

Seitenbereich: 150 - 156

Eingereicht: 21. Dez. 2021

Akzeptiert: 23. Feb. 2022

DOI: https://doi.org/10.30657/pea.2022.28.17

Schlüsselwörterwaste accumulation rate, consumption expenditures, forecasting, time-series analysis, multivariate time series, vector autoregression model

© 2022 Jurand Bień, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Schlüsselwörter
waste accumulation rate, consumption expenditures, forecasting, time-series analysis, multivariate time series, vector autoregression model