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• W4220713072 endingPage "131246" @default.
• W4220713072 startingPage "131246" @default.
• W4220713072 abstract "The current aviation sector has been shaken by COVID-19, but a few years prior, the industry was experiencing a time of prosperity never seen before. These years were marked by the introduction of new models and record sales. In particular, two cases stood out from the rest: the Boeing 737 MAX and the Airbus A320neo. Such overwhelming success in sales was partly because in essence, these are quite traditional and familiar aircrafts that featured improvements in some critical systems, notably in the use of newer engines. Current projections suggest that the pre-COVID growth rate of aviation will resume in a few years, which raises global concerns regarding the ecological burden of conventional aircraft and their resulting limitations. By reviewing the green technologies likely to be incorporated into conventional aviation over the next 30 years, we explore the limits of the industry's current approach. To this end, we reconstruct an already validated life cycle analysis model to assess a fleet of aircraft and analyze the impacts of these new technologies on emissions. Based on data from the literature, predictions are made for optimistic and pessimistic scenarios in a post-COVID world. The results are compared with the globally established targets set by the International Air Transport Association (IATA). Simulations show that a future based solely on conventional aircrafts using evolutionary technologies is of great concern. There is a need to promote a radical departure from the current aviation models to accommodate the growing demand for aviation with a green future." @default.
• W4220713072 created "2022-04-03" @default.
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• W4220713072 date "2022-05-01" @default.
• W4220713072 modified "2023-09-27" @default.
• W4220713072 title "Emissions of future conventional aircrafts adopting evolutionary technologies" @default.
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• W4220713072 doi "https://doi.org/10.1016/j.jclepro.2022.131246" @default.
• W4220713072 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36245890" @default.
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