During the COVID-19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their (...) structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior; therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states, we introduce a novel information-theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics. (shrink)

The paper presents the results of a study on the analysis of the perception of coronavirus disease 2019 by Spanish-, German- and Russian-speaking social media actors after the emergence of vaccines and attitudes toward vaccination. The empirical base of the study was corpus data, materials from online media, social networks, microblogging, blogs, instant messengers, forums, reviews, and video hosting data. The Spanish-language database included 6,640,912 tokens and 43,251,900 characters; the German-language database included 16,322,042 tokens and 109,139,405 characters; and the Russian-language (...) database included 16,310,307 tokens and 109,060,935 characters. With a neural network approach, a multilingual analysis was performed, which made it possible to analyze the topic structure and the semantic network with the allocation of the semantic core and the associative network. Differential and integral features of the identified structures based on the material of these three databases made it possible to determine the general and different characteristics of the perception by Spanish-, German-, and Russian-speaking users of the development of the pandemic, a number of social problems, attitudes toward various types of vaccines, observance of preventive measures, and readiness for vaccination. (shrink)

The study aims to ascertain how different levels of society have been influenced by the impact of pandemics over the last many years. The study also determines the societal implications of the COVID-19 pandemic. The integrative literature survey method is adopted to extract the secondary data pertinent to the socio-economic effect of pandemics and COVID-19 on society. Primary data is collected to diagnose the impact of the COVID-19 pandemic and lockdown on employees (N = 210) working in the Indian organized (...) sector. Findings of the study suggest that pandemics have been a cause of economic slowdown measured with a fall in gross domestic product (GDP) at the macro level. The reduced level of economic activity is found to be negatively related to an individual socio-economic status building their psychological distress. The descriptive analysis of the data reveals that although employees have managed their jobs and finances during COVID-19, they have experienced stress in their lives due to the pandemic. The study assimilates the pandemic effect using inductive reasoning to develop a COVID quadrilateral conceptual framework. (shrink)