RESUMO
The rapid increase in waste generation in developing countries presents significant challenges, necessitating effective waste management strategies. This study examines the influence of individual, household and institutional factors on waste sorting behaviours in Ecuador, employing an ordered logistic regression model. Data were sourced from the 2019 National Multipurpose Household Survey (NMHS) and the Census of Economic Environmental Information in Decentralised Autonomous Governments (CEEIGAD). The NMHS uses a two-stage probabilistic sampling methodology, with census sectors as the primary sampling units and households as the secondary units. After excluding outliers and selecting individuals aged 15-65 years, the final sample consisted of 8601 households, including 26,175 individuals. The findings reveal that personal attributes such as gender, ethnicity, age, marital status and environmental concern significantly influence waste sorting behaviours. Household characteristics, including urban or rural location, are also critical. Institutional factors, such as municipal regulations, waste collection fees and waste separation at source, play essential roles in promoting waste separation. The study highlights the necessity for targeted governmental policies. Recommendations include improving environmental education, increasing sorting infrastructure in urban areas and ensuring waste collection systems maintain the separation of waste streams.
RESUMO
Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.