Cost function analysis for solid waste management: a developing country experience.

The need for improved cost planning methods for solid waste management (SWM) is particularly strong in emerging economies where problems are severe, expectations for improvements are high, but finances are constrained. Estimating cost functions is suggested as an improved cost planning method. The research uses 1999 data from 298 Indian municipalities, covering over 140 million people. Stepwise multiple regression involving eight predictor variables was conducted on costs to detect any statistically significant correlations. The average costs on either a per tonne or per capita basis are most influenced by the total number of staff employed per capita or per tonne. The results are believed to be due to labour costs being such a high fraction of total costs in developing countries. Due to high variability in labour intensity between municipalities the data showed no clear correlation between per tonne or per capita costs and population, indicating no economy-of-scale. The data used here are subject to significant conjecture over their quality and age; however, the unique nature of the study should help future researchers investigating costs in emerging economies.

Cost estimation for solid waste management in industrialising regions--precedents, problems and prospects.

The importance of cost planning for solid waste management (SWM) in industrialising regions (IR) is not well recognised. The approaches used to estimate costs of SWM can broadly be classified into three categories - the unit cost method, benchmarking techniques and developing cost models using sub-approaches such as cost and production function analysis. These methods have been developed into computer programmes with varying functionality and utility. IR mostly use the unit cost and benchmarking approach to estimate their SWM costs. The models for cost estimation, on the other hand, are used at times in industrialised countries, but not in IR. Taken together, these approaches could be viewed as precedents that can be modified appropriately to suit waste management systems in IR. The main challenges (or problems) one might face while attempting to do so are a lack of cost data, and a lack of quality for what data do exist. There are practical benefits to planners in IR where solid waste problems are critical and budgets are limited.