Environmental Management
S. Sunarti; R.S.Y. Zebua; J.H. Tjakraatmadja; A. Ghazali; B. Rahardyan; K. Koeswinarno; S. Suradi; N. Nurhayu; R.H.A. Ansyah
Abstract
BACKGROUND AND OBJECTIVES: Community engagement is crucial to overcome environmental issues, including waste management. Several education-based initiatives have been employed to improve community engagement in waste management programs, but the effects were not satisfied in changing resident behavior ...
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BACKGROUND AND OBJECTIVES: Community engagement is crucial to overcome environmental issues, including waste management. Several education-based initiatives have been employed to improve community engagement in waste management programs, but the effects were not satisfied in changing resident behavior for sustainable engagement. Some studies suggested social learning as the solution to improve community engagement through practice-based and dialogue-based learning activities. Nevertheless, it needed more empirical evidence to show the effect. This study aimed to measure the effect of social learning on improving individual waste management behavior and how social learning influence it.METHODS: Using SmartPLS 3.2.9, this study measured the causal relationship of social learning activities to individual affective and behavioral factors. This study involved 504 residents exposed to social learning activities in Kawasan Bebas Sampah/ Zero Waste Area program in Bandung City, Indonesia as the respondents to gather the data using survey method.FINDINGS: The study found that social learning activities have significantly influenced waste management behavior indirectly through Affective factors. The data showed that Dialogue-based learning has no significant effect on Affective factors for all significance levels (β = -0.0862, P > 0.01). Instead, path model analysis indicated the mediating effect of Practice-based learning for Dialogue-based learning and Affective Factors, with the accuracy model at a moderate level (R2 = 42%; Q2 = 0.2258). Meanwhile, supporting facilities influenced both Practice-based learning (β = 0.3116, P < 0.001) and Affective factors (β = 0.4419, P < 0.001) significantly. Further path model analysis demonstrated that without “Affective Factors” being nurtured, learning activities and Facilities would not be able to improve behavior significantly, as all paths directing to Behavioral Domain (Intention and WMB) had an insignificant effect (P value > 0.05).CONCLUSION: This study offered empirical evidence, showing the mechanism of social learning to improve waste management behavior. The Learning activities should combine Dialogue and Practice-based learning to influence waste management behavior significantly, while Affective factors become the direct effect of Learning Activities. Supporting facilities were required to support the learning by providing routine waste collection systems and recycling facilities beneficial for the residents. In order to improve the learning activity effectiveness, the facilitators need to pay more attention to the learning contents to nurture the expected Affective Factors factors.
M.G. Hoang; T. Fujiwara; S.T. Pham Phu; K.T. Nguyen Thi
Abstract
A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated ...
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A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate the impacts of significant factors on household waste production. The model obtained from this study indicated that household location, household size, house area per person, and family economic activity are important determinants of the waste generation rate. The models could explain about 34% of the variation of the per capita daily waste generation rate. Diagnostic tests and model validation results showed that the regression model could provide reliable results of estimated household waste. The study revealed that per capita urban household waste generation is 70–80% higher compared to a rural household. The models also showed that if a family ran a business from home, the household waste generation rate would increase by about 35%. This result provides reliable information for better waste collection and management planning. Two other significant variables (family size and house area per capita) do not contribute much (less than 20%) to waste generation. Variables accounting for household income, presence of a garden, number of rooms in a house, and percentage of members of different ages were proven to be not significant. The study provides a reliable method for estimating household waste generation, providing decision makers useful information for waste management policy development.
