Influence of population, income and electricity consumption on per capita municipal solid waste generation in So Paulo State, Brazil
Predicting municipal solid waste (MSW) generation is fundamental in choosing and scaling the processes involved in municipal management. The challenge for financial sustainability is to create indicators that enable MSW fees to be charged in proportion to the amount generated by each resident. Mathe...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Recursos: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/164060 |
| Acesso em linha: | http://dx.doi.org/10.1007/s10163-017-0687-0 http://hdl.handle.net/11449/164060 |
| Access Level: | acceso abierto |
| Palavra-chave: | Municipal solid waste Per capita generation rate Regression analysis Forecasting |
| Resumo: | Predicting municipal solid waste (MSW) generation is fundamental in choosing and scaling the processes involved in municipal management. The challenge for financial sustainability is to create indicators that enable MSW fees to be charged in proportion to the amount generated by each resident. Mathematical functions were tested to adjust the per capita waste generation rate (PCWG) in the municipalities of the state of So Paulo, based on population (P), per capita income (PCI) and per capita energy consumption (PCE). The dataset involved 238 municipalities in 2013 and 251 municipalities in 2014 that routinely weighed their wastes. The averaged PCWG increased from 0.65 to 0.90 kg inh.(- 1) day(- 1) (increment of 38%) when population enhanced from the range of 0-25,000 to 100,001-500,000 inh., mean per capita income grew from 10.1 to 13.6 USD inh.(- 1) day(- 1), and mean per capita electricity consumption expanded from 6.9 to 10.9 kWh inh.(- 1) day(- 1). The equation that best represented the data set resulted in r of 0.49, R (2) of 0.24, RMSE of 0.224 kg inh.(- 1) day(- 1) and E (p) of - 12.3%. Despite the relatively low R (2), it was demonstrated by Student's t test that the proposed equation was able to represent mean values and result in the same variance with more than 99% probability. |
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