Identifying key process parameters for uncertainty propagation in environmental life cycle assessment for sewage sludge and food waste treatment

Life cycle assessment (LCA) results are often subject to uncertainty, which may lead to erroneous conclusions. This paper aims to tackle the parameter uncertainty involved in LCA. Hong Kong is selected as the reference city for the treatment of sewage sludge and food waste. A selection approach with sensitivity analysis is proposed to identify the key process parameters and Monte Carlo simulation is further conducted to propagate the uncertainty of the key process parameters identified. The results show that climate change is the major impact category among various impact categories that are generally considered in waste LCA studies. Scenarios 5 and 6, which consider anaerobic co-digestion (coAD) treatment, achieve the best performance in regard to the climate change impact. Scenario 6 which includes a combined cycle gas turbine system for biogas utilization has −6.75 × 104 kg avoided CO2e emissions and it is equal to 44% more avoided emissions compared to scenario 5 which applies a combined heat and power system. For the key process parameters identified, it is found that the electricity generation efficiencies in different waste treatment facilities, such as the incineration plant and the anaerobic digestion plant, have the greatest sensitivity to the result. Uncertainty propagation is then conducted to obtain the probability distribution functions in environmental performance of different scenarios. Scenario 6 has a 95% probability of achieving at least −5.32 × 104 kg avoided CO2e emissions, while the probability of scenario 5, which is the second best scenario, achieving the same avoid emissions is below 5%. It indicates a significant advantage of using combined cycle gas turbine over combined heat and power unit for biogas utilization in Hong Kong. The methodologies and results of this study provide comprehensive material that can be adapted for other areas planning sustainable sewage sludge and food waste treatment, as well as in tackling parameter uncertainty in general LCA studies.