Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling Brian Dyson, Ni-Bin Chang * Department of Environmental Engineering, Texas A&M University-Kingsville, MSC 213, Kingsville, Tx 78363, USA Accepted 25 October 2004 Available online 1 January 2005 Abstract 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 gen- eration 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 Ò . 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 regres- sion methods are unable to handle such issues. Ó 2004 Elsevier Ltd. All rights reserved. 1. Introduction The prediction of municipal solid waste generation plays an important role in a solid waste management. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast- growing regions is quite challenging. In addition to pop- ulation growth and migration, underlying economic development, household size, employment changes, and the impact of waste recycling would influence the solid waste generation interactively. The development of a reliable model for predicting the aggregate impact of economic trend, population changes, and recycling impact on solid waste generation would be a useful ad- vance in the practice of solid waste management. Traditional forecasting methods for solid waste gen- eration frequently count on the demographic and socio- economic factors on a per-capita basis. The per-capita coefficients may be taken as fixed over time or they may be projected to change with time. Grossman et al. (1974) extended such considerations by including the ef- fects of population, income level, and the dwelling unit size in a linear regression model. Niessen and Alsobrook (1972) conducted similar estimates by providing some other extensive variables characterizing waste genera- tion. But dynamic properties in the process of solid waste generation cannot be fully characterized in those model formulations. Econometric forecasting, one of 0956-053X/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2004.10.005 * Corresponding author. Tel.: +1 361 5933898 (o)/4553179 (c). E-mail address: nchang@even.tamuk.edu (N.-B. Chang). www.elsevier.com/locate/wasman Waste Management 25 (2005) 669–679