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Performance assessment for municipal solid waste collection in Taiwan.

You-Ti Huang1, Tze-Chin Pan, Jehng-Jung Kao

  • 1Institute of Environmental Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, 30039 Taiwan, ROC.

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|January 7, 2011
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Summary

This study introduces an aggregate indicator (AI) to measure municipal solid waste (MSW) collection efficiency. The AI uses key performance indicators (KPIs) and data envelopment analysis (DEA) to assess services for 307 Taiwanese local governments.

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Area of Science:

  • Environmental Science
  • Operations Research
  • Public Administration

Background:

  • Municipal solid waste (MSW) collection is a significant and costly challenge for local authorities.
  • Efficient MSW collection is crucial for effective waste management and urban sustainability.
  • Existing assessment methods may not fully capture the multifaceted nature of collection efficiency.

Purpose of the Study:

  • To develop a robust aggregate indicator (AI) for assessing municipal solid waste (MSW) collection efficiency.
  • To identify and integrate key performance indicators (KPIs) relevant to MSW collection.
  • To provide a standardized methodology for evaluating the performance of local waste management services.

Main Methods:

  • Evaluation of key performance indicators (KPIs) using five distinct selection criteria.
  • Development of an aggregate indicator (AI) comprising five selected KPIs.
  • Application of data envelopment analysis (DEA) to determine the relative efficiencies of MSW collection services.
  • Generation of common weights for KPIs based on DEA and modified selection rules.

Main Results:

  • Five key performance indicators (KPIs) were identified and integrated into the aggregate indicator (AI).
  • Data envelopment analysis (DEA) successfully evaluated the relative efficiencies of MSW collection services.
  • The aggregate indicator (AI) was applied to 307 local governments in Taiwan, providing comparative efficiency assessments.
  • A modified approach generated common weights for the selected KPIs, enhancing comparability.

Conclusions:

  • The proposed aggregate indicator (AI) offers a comprehensive method for evaluating MSW collection efficiency.
  • The methodology provides a valuable tool for local governments to benchmark and improve their waste management operations.
  • The study demonstrates the practical application of the AI in assessing real-world municipal solid waste collection services.