An in-depth analysis of factors and forecasting techniques for emerging solid waste streams.

ABSTRACT

Technological advances have led to the generation of novel streams of solid wastes, comprising materials previously excluded from traditional waste considerations. The absence of proper handling and management policies for these Emerging Solid Waste Streams (ESWSs) poses a great cause of concern. Proper estimation of current and future quantities is necessary for efficient policy making. This study, through a systematic literature review, analyses forecasting models for four major ESWSs PV waste, e-waste, battery waste, and biomedical waste. A total of 40 modelling methodologies which successfully forecast the quantities of these ESWSs are identified and analyzed in this review. These highly heterogeneous models are classified into several crucial categories based on the modelling method, independent variable, geographical scale and data type involved. This categorization proves to be pivotal in the selection of an appropriate forecasting model. Around 40 modelling methods and 100+ independent variables, crucial for a successful forecast are identified and categorized. This study also focuses on the uncertainty involved in input data, a factor contributing to inaccurate predictions. It further entails identifying and analysing potential data sources, examining the rationale behind their selection, and providing recommendations for choosing suitable data sources. Beyond analysis, potential future areas of research and gaps involved in the field of forecasting ESWSs have also been highlighted. Serving as a valuable guide for beginners, the research also proposes a methodology to navigate the intricacies of forecasting ESWSs, contributing to both our understanding of forecasting models and the development of robust waste management policies in the evolving technological landscape.