The impact of socioeconomic factors on municipal solid waste generation in São Paulo, Brazil

Abstract

Social factors have not been sufficiently explored in municipal solid waste management studies. Latin America has produced even fewer studies with this approach; technical and economic investigations have prevailed. We explored the impacts of socioeconomic factors on municipal solid waste generation in Greater Sao Paulo, which includes 39 municipalities. We investigated the relations between municipal solid waste generation and social factors by Pearson's correlation coefficient. The Student's t-test (at p ← 0.01) proved significance, and further regression analysis was performed with significant factors. We considered 10 socioeconomic factors: population, rural population, density, life expectancy, education (secondary, high and undergraduate level), income per capita, inequality and human development. A later multicollinearity analysis resulted in the determination of inequality (r = 0.625) and income per capita (r = 0.607) as major drivers. The results showed the relevance of considering social aspects in municipal solid waste management and isolated inequality as an important factor in planning. Inequality must be used as a complementary factor to income, rather than being used exclusively. Inequality may explain differences of waste generation between areas with similar incomes because of consumption patterns. Therefore, unequal realities demand unequal measures to avoid exacerbation, for example, pay-as-you-throw policies instead of uniform fees. Unequal realities also highlight the importance of tiering policies beyond the waste sector, such as sustainable consumption.

Identification and Analysis of Sets Variables for of Municipal Waste Management Modelling

“…The novelty of the undertaken research is an attempt to develop a universal model describing the mass accumulation index of waste and using nonstandard variables for this purpose, such as the functional structure of municipalities (C2) and typology of municipalities according to the scope of impact (C10). These variables, unlike the indicators presented, for example, in [2,4,13,21], are relatively easily available at the commune level based on the methodology described in Bański's [31]. In his work [31], Bański assigned municipalities to sets of predefined classes (subsets) based on selected criteria.…”

“…administrative, functional, and economic type of the municipality [2] affluence ("standard of living") and inhabitants' lifestyle [15,16] average size of a household [5] buildings type and heating system [2,5,12] climate factors (the temperature and precipitation) and the season of the year [7,12,17,18] eating habits and health indicators, such as lifespan and infant mortality, as well as the age structure of population [17,19] fees for waste collection and disposal calculated per one inhabitant or per one ton and the frequency of waste collection [7,12] household size [2,5,15,20] level of contamination in selectively collected waste [7,12] municipality's income from taxes calculated per one inhabitant [13,21,22] other technical and sanitary equipment of buildings [5] participation in taxes comprising national budget income personal income tax [5] participation of ashes and/or biodegradable waste in the mixed municipal waste stream [7,12] participation of households composting organic waste [7,12]…”

“…participation of households equipped with furnace for solid fuels [5] participation of waste collected selectively [7,12] participation of waste from infrastructural facilities in the total weight of municipal waste [7,12] percentage of municipality's/city's inhabitants covered under waste collection system [7,12] population density [5,6,20,23] saturation of technical infrastructure facilities [2] social factors [2,5,6,13,19,23] the number and capacity of containers calculated per one household-furnishing houses with small capacity containers motivates the inhabitants to collect waste selectively [7,12] the number of unemployed people, the level and structure of employment [5,19,20] tourism-the number of accommodation places, hotels, guesthouses, etc. [17] tradition and people's habits [6,17] urbanization level [2,4,20,23] The approaches to analyzing the rate of mass accumulation of waste, presented in the chosen literature, use mainly statistical methods in the form of linear regression models [17,18,24], multiple regression [5,19,25,26], Rough Set Theory [22], multivariate grey models [20], and artificial neural networks [1,…”

“…Research on the forecasts of the amount of generated solid waste was carried out for the planning of municipal waste management at various levels, such as national [10][11][12], regional [13], and in households in rural areas and in cities [14].…”

Untitled

“…Los países desarrollados y en vías de desarrollo enfrentan un crecimiento poblacional y progreso industrial que se asocia a la producción de grandes cantidades de residuos sólidos (Akhtar et al, 2017;Oyekale, 2017;Yang et al, 2018) debido a que la sociedad de hoy explota diferentes materiales (Vázquez et al, 2016). Nuestras prácticas de consumo elevan las tasas de generación de residuos sólidos (Vieira & Matheus, 2018) puesto que, según Ribeiro y Kruglianskas (2015), continúa predominando el modelo económico lineal de extraer, manufacturar, usar y descartar (citados por Wadhy et al, 2017). Benton-Short y Short (2013) señalan que la generación de basura, junto con los problemas de recolección, disposición y los riesgos ambientales, continúan siendo un desafío para muchas ciudades (citados por Jiménez, 2015), más aún la gestión para los municipios que resulta una gran dificultad (Abdoli, Rezaei, & Hasanian, 2016;Meylan et al, 2018;Botello et al, 2018).…”