INTEGRATING LIFE CYCLE ASSESSMENT AND LIFE CYCLE COSTING THROUGH CLUSTER ANALYSIS FOR ENHANCED DECISION-MAKING IN FOOD WASTE MANAGEMEN
DOI:
https://doi.org/10.11113/jurnalteknologi.v88.22751Keywords:
Cluster Analysis, Decision Making, Food Waste Management, Life Cycle Assessment, Life Cycle Costing AnalysisAbstract
Evaluating food waste (FW) treatment systems is challenging due to their complexity, encompassing processes like collection, sorting, preprocessing, treatment (e.g., composting and anaerobic digestion), and disposal or reuse. These systems involve numerous variables, including waste types, contamination levels, treatment methods, and environmental factors. Analyzing these systems requires an understanding of their efficiency, effectiveness, environmental impact, and economic feasibility. While life cycle assessment (LCA) is a standardized method for assessing environmental impacts, it does not consider economic performance. The methodology for combining LCA with life cycle costing (LCC) analysis is limited and inconsistent. To address this, a comprehensive framework is proposed, integrating LCA, LCC, and cluster analysis (CA) to evaluate six FW treatment scenarios, including landfilling. This framework involves three steps: LCA to assess environmental impacts, LCC to estimate annual costs, and CA to integrate both results. LCA evaluations has found that anaerobic dry digestion had the least environmental impact across 12 midpoint assessments, including global warming and ozone depletion, and ranked highest in minimizing damage to resource availability, human health, and ecosystem diversity. Economically, the LCC analysis showed that landfill technologies were the most cost-effective. The integration of LCA and LCC results using CA demonstrated that aerobic windrow composting was the most suitable FW management method. This approach showed a 65% reduction in harmful environmental impact points and a 46% increase in economic benefits compared to traditional landfilling. This framework enables decision-makers to evaluate and compare various FW treatment methods, leading to informed decisions regarding system selection and implementation.
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