Probabilistic multicriteria environmental assessment of power plants: A global approach

Juan José Cartelle Barros, Manuel Lara Coira, María Pilar de la Cruz López, Alfredo del Caño Gochi and Isabel Soares

Applied Energy, 2020, vol. 260, issue C, No S0306261919320318

Abstract: This paper presents a probabilistic model to assess the environmental performance of power plants. The entire life-cycle is considered, from the fuel extraction to the dismantling phase. The model is based on the use of requirement trees, value functions, the analytic hierarchy process and Monte Carlo simulation. The data to feed the model were established after an extensive literature review and also after collecting more than 350 sets of life cycle assessment (LCA) results. The midpoint impact methods recommended by the International Reference Life Cycle Data System Handbook were employed. The results can be considered as representative for the world at large, including the most common types of energies. Wind and hydro power plants are the best options, with environmental indices always above 0.95, 1 and 0 being the highest and lowest levels of satisfaction. In contrast, power plants fired by coal, lignite and fuel oil are at the bottom of the ranking, with indices typically below 0.5. However, not all the renewables present high-performing environmental results. Furthermore, some non-renewable power plants can be environmentally competitive in certain situations. The model was used to assess case studies related to natural-gas and biomass power-plants, previously analysed in the literature. The environmental indices fell within the expected intervals for such technologies, which served to validate the model. This study can be useful for researchers, professionals of all kinds in the energy sector, politicians, Non-Governmental Organizations (NGOs) and the general public as well as for energy policy decision-making processes at different geographical scales.

Keywords: Environmental assessment; Cradle to grave; Non-renewables; Renewables; Multicriteria decision method; Monte Carlo (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2019.114344

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