Optimization of municipal solid waste management using a coordinated framework

Within cluster pattern identification: A new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia

Despite the government's policies and objectives, Malaysia lags behind in sustainable waste management techniques, particularly recycling. Bins should be located conveniently to encourage recycling and reduce waste. The current model of bin location-allocation is mostly determined by distance. However, it has been identified that previous studies excluded an important factor: litter pattern identification. Litter pattern is important to identify waste generation hotspots and litter distribution. Thus, we proposed the within cluster pattern identification (WCPI) approach to optimize the recycle point distribution. WCPI gathers the information on litter distribution using geotagged images and analyses the pattern distribution. The optimal location for recycle bin can be identified by incorporating k-means clustering to the pattern distribution. Since k-means faces the non-deterministic polynomial-time-hard challenge of selecting the ideal cluster and cluster centre, WCPI used the total within-cluster sum of square on top of k-means clustering. The proposed location by WCPI is validated in terms of accessibility and suitability. Furthermore, this study provides further analysis of carbon footprint that can be reduced by simulating the data from geotagged images. The results show that 10,323.55 kg of carbon emission can be reduced if the litter is sent for recycling. Thus, we believe that locating bins at an optimal location will embark on consumer motivation to dispose of recycled waste, reduce litter and lessen the carbon footprint. At the same time, these efforts could transform Malaysia into a clean and sustainable nation that aims to achieve Agenda 2030.

Optimal waste management during the COVID-19 pandemic

There have been many problems generated by the COVID-19 pandemic. One of them is the worrying increase in the generation of medical waste due to the great risk they represent for health. Therefore, this work proposes a mathematical model for optimal solid waste management, proposing a circular value chain where all types of waste are treated in an intensified industrial park. The model selects the processing technologies and their production capacity. The problem was formulated as a mixed-integer linear programming problem to maximize profits and the waste processed, minimizing environmental impact. The proposed strategy is applied to the case study of the city of New York, where the increase in the generation of medical waste has been very significant. To promote recycling, different tax rates are proposed, depending on the amount of waste sent to the landfill. The results are presented on a Pareto curve showing the trade-off between profits and processed waste. We observed that the taxes promote recycling, even of those wastes that are not very convenient to recycle (from an economic point of view), favoring profits, reducing the environmental impact, and the risk to health inherent to the medical waste.

Behavioral change in waste separation at source in an international community: An application of the theory of planned behavior

Analysing and understanding the factors that influence the behavior of waste separation is critical to improved waste management. This study identified the factors behind behavioral change in waste separation at source in an international community. The theory of planned Behavior (TPB) was adopted to conduct a self-report questionnaire survey twice, in which 354 residents participated at Time1 and 331 at Time 2, with a retention rate of 93.5%. The questionnaires were designed based on TPB with additional questions regarding waste separation rules in the community, experience of waste separation, and environmental knowledge. With the questionnaire data and follow-up qualitative interviews, factors related to waste separation through factor analysis and structural equation modelling and text mining were specified. The panel data collected over three months demonstrated the changes in attitudes, subjective norms, and perceived behavioral control that affect behavioral intention in the process of behavior adaption. Combined with interview data, the reasons for these changes can be further attributed to individuals' environmental knowledge and community guidelines. The findings imply that policies targeting environmental education and rulemaking are effective to help residents form more positive intentions towards waste separation.