An economic–environmental–energy efficiency analysis for optimizing organic waste treatment of a livestock-orchard system: a case in the Mekong Delta, Vietnam

Abstract

BackgroundEconomic benefit has been analyzed for the yield of farming products when designing a farming system, while waste treatment also generates profitable energy products for this system. The economic factor is decisive in decision-making for applying waste treatment solutions for a small-scale farming system. A household farming system in the Mekong Delta generates many kinds of organic wastes, but most of the agricultural waste resources are disposed of into the environment.MethodsThis study approaches an analysis of economic–environmental–energy (EEE) efficiency for waste treatment of an integrated livestock-orchard (LO) system on a household scale in the Mekong Delta. This novel analysis method is based on the energy content of biomass and its cost. The EEE efficiency is optimized to gain objective functions regarding energy yield efficiency, system profit, and CO2 sequestration for the treatment model. The algorithms are built for optimizing these objective functions.ResultsThe optimization results show the treatment model of pyrolysis and pelleting gain all the objective functions with high efficiency. The model is efficiently applied for the LO system that generates more than 100 kg of orchard residues and 3000 kg of pig manure. The system with a charcoal oven and pellet machine is capable to gain energy efficiency due to its potential biofuel products, such as biochars and pellet products. A treatment model of composting, pyrolysis, and pelleting gives the best performance of overall EEE efficiency.ConclusionsThis work has proven economic benefits from integrating biogas tank, charcoal oven, and pellet machine in an integrated LO system. The system contributes not only for reducing CO2 emissions but also for supplementing secondary renewable bioenergy, as well as for increasing incomes and thus supporting livelihoods for the local farming households.