Mass social media data enabled an extensive investigation into greenspace use patterns. • Urban greenspace use increased in four Asian cities during the COVID-19 outbreak. • During the COVID-19 outbreak, residents preferred large nature parks close to city centers.

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The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of the study are robust with VECM technique. The outcomes of the study revealed that climate change mitigation indicators significantly affect the GDP of G7 countries than that of E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques helps to clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, study results shown that a 1% rise in green financing index improves the environmental quality by 0.375% in G7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.The study also provides different policy implications for the stakeholders guiding to actively promote financial hedging for green financing. So that climate change and envoirnmental pollution reduction could be achieved effectively. The novelty of the study lies in study framework.

Cities implemented social distancing measures to cope with COVID-19, which kept people away from nature. A steep drop in the greenspace use was observed in western cities. Surprisingly, news revealed a surging greenspace use in Asian cities. In this study, we used COVID-19 outbreak stages as natural experiments to investigate individual behavioural changes of greenspace use before and during the pandemic. Our case cities are Hong Kong, Singapore, Tokyo and Seoul. We extracted panel data which consisted of longitudinal posts of 100, 232 users, being posted in 1,185 greenspaces in the four Asian cities. Our statistical models found a 5.3% increase in the odds of people using greenspaces for every 100 additional weekly new cases. The analyses also revealed people’s preference in larger, nature parks that are close to city centres. Due to the established physical and mental health benefits of greenspaces, people have been escaping to nature to cope with the pandemic in Asian cities.

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The coronavirus disease 2019 pandemic has stimulated intensive research interest in its transmission pathways and infection factors, e.g., socioeconomic and demographic characteristics, climatology, baseline health conditions or pre-existing diseases, and government policies. Meanwhile, some empirical studies suggested that built environment attributes may be associated with the transmission mechanism and infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, no review has been conducted to explore the effect of built environment characteristics on the infection risk. This research gap prevents government officials and urban planners from creating effective urban design guidelines to contain SARS-CoV-2 infections and face future pandemic challenges. This review summarizes evidence from 25 empirical studies and provides an overview of the effect of built environment on SARS-CoV-2 infection risk. Virus infection risk was positively associated with the density of commercial facilities, roads, and schools and with public transit accessibility, whereas it was negatively associated with the availability of green spaces. This review recommends several directions for future studies, namely using longitudinal research design and individual-level data, considering multilevel factors and extending to diversified geographic areas.