NoteForecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling
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 population 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 advance in the practice of solid waste management.
Traditional forecasting methods for solid waste generation frequently count on the demographic and socioeconomic 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 effects 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 generation. But dynamic properties in the process of solid waste generation cannot be fully characterized in those model formulations. Econometric forecasting, one of the alternatives to static models, is an approach in which the future forecasts are derived from current forecasts of the independent variables themselves (Chang et al., 1993). It covers part of the dynamic features in forecasting analysis. When recycling impact is phenomenal, intervention analysis may account for the varying trends of solid waste generation under uncertainty (Chang and Lin, 1997). Such an analysis creates profound impacts in dealing with the possible structure change of solid waste generation trends in metropolitan regions. To implement those traditional statistical forecasting methods, however, it would require collecting thorough socioeconomic and environmental information before the forecasting analysis can be performed. In many cases, municipalities might not have sufficient budget and management capacity to maintain a complete database of solid waste quantity and quality in support of such needs on a long-term basis.
Most traditional statistical forecasting models, such as the geometry average method, saturation curve method, least-squares regression method, and the curve extension method, are designed based on the configuration of semi-empirical mathematical models. The structure of these models is simply an expression of cause-effect or an illustration of trend extension in order to verify the inherent systematic features that are recognized as related to the observed database. In light of the evolution of structured or semi-structured forecasting techniques, the synergy of fuzzy forecasting and grey dynamic modeling is viewed as a promising approach for handling forecasting issues under uncertainty. The grey dynamic model was developed earlier simply to resolve the data scarcity issue (Deng, 1982). It is particularly designed for handling situations in which only limited data are available for forecasting practice and system environments are not well-defined or fully understood. In conjunction with fuzzy regression analysis, a revised dynamic forecasting method – grey fuzzy dynamic modeling – that is suitable for the situation when only very limited samples are available for forecasting practice, was demonstrated to handle the dynamic prediction analysis of municipal solid waste generation with reasonable accuracy (Chen and Chang, 2000).
When the database is not sufficient to support traditional statistical forecasting analyses yet ample enough to run several grey dynamic models with different natures, there is a need to integrate those separate dynamic efforts as a whole that may be able to account for the interrelationships among relevant dynamic features influential for municipal solid waste generation. Such concatenation enables us to explore the interactions among a variety of socio-economic, environmental, and managerial factors when we still have to handle the data scarcity issue. This study presents a new approach – system dynamic modeling – for the prediction of municipal solid waste generation in an urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was accessed by a case study in the city of San Antonio, Texas (USA), which is one of the fast-growing regions in North America. It presents various trends of municipal solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool – Stella®. Discrepancies embedded in the prediction matrix based on those models may provide obvious clues in a form of interval number to address possible ranges of uncertainty in municipal solid waste generation.
Section snippets
Methodology
The method of system thinking has been used for over 30 years (Forrester, 1961). It provides us with effective tools for better understanding those large-scale complex management problems. System dynamics, being designed based on system thinking, is a well-established methodology for studying and managing complex feedback systems. It requires constructing the unique “causal loop diagrams” or “stock and flow diagram” to form a system dynamics model for applications. Relevant work of how to
Overview of the study area
NAFTA is a comprehensive trade agreement that improves virtually all aspects of doing business within North America after being implemented on January 1, 1994. Tremendous economic growth along the US–Mexico border region has been observed since then. Growth associated with population increases in Mexico (the Maquiladoras), and in the US (the river corridor along Laredo, McAllen, and Brownsville) very recently due to NAFTA related activities is apparent in the Rio Grande/Rio Bravo region. Active
Conclusion
In this analysis, system dynamics models were developed for the prediction of solid waste generation in an urban setting having a high economic growth potential. A case study for the City of San Antonio, Texas presented its unique solutions based on system dynamics outputs. Five planning models were considered based on different types of system dynamics models, while the base case was designed according to a traditional regression analysis. All of the five planning models were based on an
Acknowledgements
The authors are thankful for the data reports cited and used in this analysis as well as the technical and administrative support from many people in the Environmental Services Department of the City of San Antonio, including Daniel Cardenas, Arturo Alvarez, Pamela Bransford, David McDaniel, Juana Perez, and Rose Ryan.
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