ABSTRACT
Air transport challenges the world's net-zero carbon ambitions. The sector has consistently grown and causes warming as a result of both CO2 and other, short-lived emissions. Two principal solutions have been proposed to reduce the contribution of aviation to climate change: innovations of technology and the development of interventions to trigger behavioral change. Technological innovations include new propulsion technologies and the use of sustainable aviation fuels. Behavioral change includes flight avoidance, substitution with other means of transport, the choice of efficient flight options, and carbon offsetting. This article focuses on behavior;it offers an overview of factors that lead to consumers traveling by air and discusses demand distribution complexities. The importance of price for air travel decisions is assessed, and evidence of travel "wants” are contrasted with "needs,” the latter investigated in light of the COVID-19 pandemic. The review of relevant scholarly work culminates in an action list enabling air travelers, policy makers, the aviation industry, researchers and society to meaningfully advance low-carbon air transport trajectories. This article is categorized under: Perceptions, Behavior, and Communication of Climate Change > Behavior Change and Responses The Carbon Economy and Climate Mitigation > Policies, Instruments, Lifestyles, Behavior. © 2022 The Authors. WIREs Climate Change published by Wiley Periodicals LLC.
ABSTRACT
Music listening choices are considered to be a factor capable of measuring people's emotions. Thanks to the explosion of streaming music applications in recent years, it is possible to describe listening trends of the global population based on emotional features. In this paper we have analysed the most popular songs from 52 countries on Spotify through their features of danceability, positivity and intensity. This analysis allows exploring how these song features reflect mood trends along with other contextual factors that may affect the population's listening behaviour, such as the weather or the influence of the COVID-19 pandemic. Finally, we have proposed a multivariate time series model to predict the preferred type of music in those countries based on their previous music listening patterns and the contextual factors. The results show some relevant behavioural changes in these patterns due to the effect of the pandemic. Furthermore, the resulting prediction model enables forecasting the type of music listened to in three different groups of countries in the next 4 months with an error around 1%. These results may help to better understand streaming music consumption in businesses related to the music and marketing industry. © 2022 Elsevier B.V.