Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling.
Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach--system dynamics modeling--for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool--Stella. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues.
Dyson
Brian
B
Department of Environmental Engineering, Texas A&M University-Kingsville, MSC 213, Kingsville, TX 78363, USA.
Chang
Ni-Bin
NB
D002947:Cities; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D005544:Forecasting; D006801:Humans; D007182:Income; D008962:Models, Theoretical; D011156:Population Density; D012037:Refuse Disposal; D013597:Systems Analysis; D013781:Texas