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Approximate cost functions for solid waste treatment facilities.

Konstantinia Tsilemou¹, Demetrios Panagiotakopoulos

¹Laboratory of Project Management, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece. ntsilemo@civil.duth.gr

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA

|September 1, 2006

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This study addresses challenges in estimating municipal solid waste management costs using limited data. It proposes a method to create cost functions for various European waste treatment facilities, aiding decision-making.

Area of Science:

Environmental Engineering
Waste Management Economics
Operations Research

Background:

Accurate cost estimation is crucial for municipal solid waste management planning.
Economic analysis is complex due to diverse schemes, technological advancements, and scarce real cost data.
Variability in facility size, technology, and operational factors complicates cost data interpretation.

Purpose of the Study:

To explore challenges in obtaining cost estimates from fragmented and limited published data.
To propose a procedure for generating cost functions linking initial and operating costs to facility size.
To present cost functions for European solid waste treatment and disposal facilities.

Main Methods:

Statistical analysis of fragmented cost data.

Related Experiment Videos

Development of cost functions relating initial set-up and operating costs to facility size.

Identification and discussion of common statistical misuses in cost analysis.

Main Results:

Approximate cost curves were generated for waste-to-energy, landfilling, anaerobic digestion, and composting facilities.
A procedure for creating reliable cost functions from limited data was suggested.
The importance of statistical rigor in handling heterogeneous cost data was highlighted.

Conclusions:

The study provides a framework for developing more accurate cost estimations in municipal solid waste management.
The proposed cost functions can assist decision-makers in planning and economic analysis.
Careful statistical application is essential to avoid erroneous results with fragmented cost data.