The Impact of Epidemics on Green Innovation: Global Analysis

Epidemics and their resulting pandemics have become essential factors influencing economic development, financial stability, poverty, and ultimately a country's innovation level, including green technology innovation. This research thus investigates epidemic events' correlation to green innovation by operating with skewed panel data involving 134 countries from 1971 to 2018 and provides compelling proof that Epidemics have a detrimental effect on green innovation, not only for the current year but also for the next six years. We also show that the quality of institutions and financial development levels weaken epidemics' detrimental effects on green innovation. Overall, the findings would draw particular attention from policymakers.

Strategies for the sustainable development of China in the post-epidemic era

Countries around the world are facing enormous challenges in their economic and social development as COVID-19 continues to spread, resulting in slower economic recovery in the post-pandemic era. Considering the impact of economic growth on future sustainable development in this new era, green economic recovery (GER) can achieve a win-win situation between economic recovery and environmental improvement and bring forth environmentally sustainable economic growth. This research first lists related COVID-19 literature surveys and GER policies in the post-pandemic era in China. Based on a comparative study of the international experience of GER policy practices, this paper then analyzes the opportunities and challenges China faces for GER and puts forward countermeasures and suggestions on how to promote its sustainable development in the post-epidemic era. We believe our research presents useful enlightenments for sustainable economic and social development in the post-epidemic era. © 2022 ERP Environment and John Wiley & Sons Ltd.

Compensatory travel in the post COVID-19 pandemic era: How does boredom stimulate intentions?

To promote tourism recovery in the post-COVID-19 pandemic era, it is critical to understand the psychological factors that either boost or suppress travel demands. However, little is known about the underlying psychological mechanism that affects compensatory travel intention. Therefore, by scrutinizing the roles that autonomous self -motivation, sensation seeking, and perceived susceptibility to COVID-19 play, this study conducted two scenario -based experiments (N = 223 + 200) to explore the psychological mechanism and boundary conditions behind the influence of boredom on compensatory travel intention. The findings reveal that people are more likely to generate compensatory travel intention when there is a higher level of boredom during the COVID-19 pandemic due to their desire for sensation seeking. This effect is magnified when people adopt autonomous self-motivating strategies. However, for people with high (vs. low) perceived susceptibility to COVID-19, a high level of boredom evokes lower compensatory travel intention through sensation seeking.

Predicting hospital emergency department visits with deep learning approaches

Overcrowding in emergency department (ED) causes lengthy waiting times, reduces ade-quate emergency care and increases rate of mortality. Accurate prediction of daily ED visits and allocating resources in advance is one of the solutions to ED overcrowding problem. In this paper, a deep stacked architecture is being proposed and applied to the daily ED visits prediction problem with deep components such as Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and simple Recurrent Neural Network (RNN). The proposed architec-ture achieves very high mean accuracy level (94.28-94.59%) in daily ED visits predictions. We have also compared the performance of this architecture with non-stacked deep mod-els and traditional prediction models. The results indicate that deep stacked models out-perform (4-7%) the traditional prediction models and other non-stacked deep learning models (1-2%) in our prediction tasks. The application of deep neural network in ED visits prediction is novel as this is one of the first studies to apply a deep stacked architecture in this field. Importantly, our models have achieved better prediction accuracy (in one case comparable) than the state-of-the-art in the literature.(c) 2022 Published by Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Bio-medical Engineering of the Polish Academy of Sciences.