A Predictive Model for the Determination of the Economic Feasibility of Construction and Demolition Waste Recycling in the Air Force

Abstract

This study created a model to be used at a CONUS Air Force base to determine the economic feasibility of Construction and Demolition (C&D) waste recycling. Three areas investigated to develop this model: the methods to determine amounts and types of C&D waste generated at a specific location, the markets for recycled C&D wastes, and the recycling methods currently available. From this data, gathered through records searches and interviews, a procedure was developed to perform cost/benefit analyses on the available recycling options. A model was then created based on these calculations which can arm a manager with information to either support or reject a recycling program by indicating cost savings or losses from recycling C&D waste. Also, the model aids managers in determining the approximate quantities of recyclable materials being generated, which could be valuable in reaching base recycling goals. To demonstrate the model, the feasibility of recycling C&D waste at Hill AFB, Utah in 1994 was evaluated. In addition to determining recycling feasibility, a method was presented to perform sensitivity analyses on the base-specific input variables. This procedure can help determine when it will become feasible to create a C&D waste recycling program.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA270704

Entities

People

  • Byron L. Dixon

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Facilities
  • Concrete
  • Construction
  • Construction Materials
  • Contracts
  • Economic Models
  • Environment
  • Environmental Protection
  • Governments
  • Law
  • Materials
  • National Governments
  • Natural Resources
  • Predictive Modeling
  • Solid Waste
  • Urban Areas
  • Waste Management

Readers

  • Computational Modeling and Simulation
  • Environmental Engineering.
  • Environmental Remediation and Restoration.