Assessing and predicting the illegal dumping risks in relation to road characteristics.

Using historical data to assess illegal dumping risks has significant potential to enhance the effectiveness of waste management in low-population density counties where the ability to patrol and regulate illegal dumping is limited. Using big data and geographical analysis to identify high-risk areas plays an important role in improving the effectiveness of supervision related to illegal dumping. However, current methods for classifying risk areas have limited accuracy. Taking an area in South Australia as an example, this study aims to improve the accuracy of classifying risk areas by using geo-information technology and machine learning methods. The results show that combining illegal dumping locations with road characteristics allows the high-risk areas to be refined to road sections. Compared with identifying the whole road or area as a high-risk spot, this result could be beneficial for monitoring illegal dumping in real life. Moreover, this model allows the analysis of factors that affect illegal dumping locations. Results show that the influencing factors for different risk levels of illegal dumping vary significantly. The model developed in this research can effectively distinguish risk levels according to these factors, and the model classification accuracy can reach 85%. In addition, there are priorities amongst these factors. This finding could help environmental authorities to allocate equipment and personnel with consideration of varying level of importance of those factors. This study has both technical contributions to identify high risk areas of illegal dumping, and theoretical implications for its management.

Cost-benefit analysis of construction waste source reduction: a system dynamics approach.

Construction waste (CW) source reduction is a crucial strategy to address the sustainability issue of the construction industry. The economic benefit is a key factor affecting project decision-makers on whether to implement this strategy. However, limited studies analysed the cost-benefit of CW source reduction from a system dynamic perspective. Therefore, by considering the design and construction phase as a whole, this study constructed a system dynamics (SD) model based on the identification of the factors affecting the cost-benefit of CW source reduction to analyse the cost-benefit of CW source reduction. A residential building project in China's Chengdu was used for the study case. The results show that the net benefit of CW source reduction is positive, and BIM implementation, design for standard material size, and building material storage are the three strategies to effectively improve the economic benefits of CW source reduction. Furthermore, the best investment level of CW source reduction was also determined. This study provides a model which can be used to simulate the cost-benefit under different implementation levels of reduction strategies and different investment levels in advance, thereby providing a reference for project decision-makers to plan CW source reduction.