Performance assessment for municipal solid waste collection in Taiwan

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.