Eco-efficiency evaluation in wastewater treatment plants considering greenhouse gas emissions through the data envelopment analysis-tolerance model

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hdl:2117/344719
Tipo de documentoArtículo
Fecha de publicación2021-05
Condiciones de accesoAcceso abierto
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Resumen
The eco-efficiency evaluation in wastewater treatment plants (WWTPs) is used to know and improve the environmental and economic efficiency of these processes, systems, products, and services. The eco-efficiency evaluations in WWTP contemplate the inputs to be minimized, the desirable results to be maximized, and the undesired results to be minimized. Data envelopment analysis (DEA) is a widely used method to evaluate the eco-efficiency of WWTPs; integrating several approaches in a single index, traditional DEA models do not take into account the uncertainty in the data. This study evaluates the eco-efficiency of a sample of Catalan WWTPs, considering the uncertainty of the data (DEA tolerance model), and it is for the first time that together with CO2, other greenhouse gas (GHG) such as CH4 and N2O are considered as part of the process outputs. GHG emissions were quantified using methods reported in the literature. Seven hundred twenty-nine eco-efficiency scores were estimated for each WWTP instead of a single score like conventional DEA models, analyzing optimistic and pessimistic scenarios. The WWTPs were classified according to the estimated eco-efficiency scores, accounting for the uncertainty in each of the scenarios, and demonstrating the changes in the performance of the WWTPs in the different scenarios. Only two WWTPs were eco-efficient in all the scenarios evaluated. This approach provides essential information to improve efficiency and innovation in the wastewater sector.
Descripción
The final publication is available at Springer via http://dx.doi.org/10.1007/s10661-021-09063-5
CitaciónRamírez, M. [et al.]. Eco-efficiency evaluation in wastewater treatment plants considering greenhouse gas emissions through the data envelopment analysis-tolerance model. "Environmental monitoring and assessment", Maig 2021, vol. 193, núm. 5, p. 301:1-301:15.
ISSN0167-6369
Versión del editorhttps://link.springer.com/article/10.1007/s10661-021-09063-5
Otros identificadoreshttps://rdcu.be/cjooc
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