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The lockdown policy deals a severe blow to the economy and greatly reduces the nitrogen oxides (NOx) emission in China when the coronavirus 2019 spreads widely in early 2020. Here we use satellite observations from Tropospheric Monitoring Instrument to study the year-round variation of the nitrogen dioxide (NO2) tropospheric vertical column density (TVCD) in 2020. The NO2 TVCD reveals a sharp drop, followed by small fluctuations and then a strong rebound when compared to 2019. By the end of 2020, the annual average NO2 TVCD declines by only 3.4% in China mainland, much less than the reduction of 24.1% in the lockdown period. On the basis of quantitative analysis, we find the rebound of NO2 TVCD is mainly caused by the rapid recovery of economy especially in the fourth quarter, when contribution of industry and power plant on NO2 TVCD continues to rise. This revenge bounce of NO2 indicates the emission reduction of NOx in lockdown period is basically offset by the recovery of economy, revealing the fact that China's economic development and NOx emissions are still not decoupled. More efforts are still required to stimulate low-pollution development. © 2022
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Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
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The article deals with the problem of personal reaction to the danger of COVID-19 virus infection and its influence on social processes. Based on the results, the survey proposes the answers to the central questions of public health services development: what is the correlation between the trust of citizens in a national healthcare system, the government's decision, and the effectiveness of lockdown measures taken to stop the coronavirus spreading with reference of Ukraine and India. This research analyses focus on personal and social attitude towards the immediate danger and the ways how different cultural environments react to the new factors of development and risk in general. It proves that personal and social responsibility is directly connected with a level of trust in the national healthcare system and government decisions. Indian and Ukrainian societies before a face of equal danger and experiencing similar personal emotions show the different social behaviour due to the opposite attitude to national healthcare policy and different social and personal evaluations of the government response. The comparison of the answers of Indian and Ukrainian respondents showed a higher level of passive social reaction and obedience in the Indian group and the lower level of obedience and a higher level of active-controlled and uncontrolled reaction in the Ukrainian group. The research paper proposes some conclusions and recommendations about effective social management of personal and public healthcare challenges. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The impact of the COVID-19 pandemic has accelerated the search for solutions to complex problems associated with the Sustainable Development Goals (SDGs). Main actors are turning to Digital Social Innovations (DSI), defined as collaborative innovations where enterprises, users, and communities collaborate using digital technologies to promote solutions at scale and speed, connecting innovation, the social world, and digital ecosystems to reach the 2030 Agenda. This study aims to identify how digital transformations and social innovations solve social problems and address SDGs. We conducted a systematic review combining a bibliometric study and a content analysis focusing on opportunities and threats impacting these fields. We expect that our findings advance the understanding of digital social innovations and different stakeholders' roles in promoting social advancements. © 2022 Elsevier Inc.
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As chatbots become more advanced and popular, marketing research has paid enormous attention to the antecedents of consumer adoption of chatbots. This has become increasingly relevant because chatbots can help mitigate the fear and loneliness caused by the global pandemic. Therefore, unlike previous work that focused on design factors, we theorize that social presence serves a mediating role between consumer motivations (i.e., hedonic and utilitarian) and intention to use a chatbot service based on self-determination theory. Our results from a structural equation model (n = 377) indicate that hedonic (but not utilitarian) motivation significantly affects chatbots' social presence, ultimately influencing intention to use the chatbot service. We also found that fear of COVID-19 amplifies the effect of social presence on intention to use the chatbot service. In this dynamic, we found an additional moderated moderation effect of generational cohorts (i.e., baby boomers and Generations X, Y, and Z) in experiencing different levels of fear of COVID-19. Overall, our findings emphasize the importance of motivation-matching features for consumer adoption of chatbot services. Our findings also indicate that marketers may utilize the fear element to increase adoption of chatbot services, especially when targeting the young generations (e.g., Generation Z). © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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During the past few decades, countries have experienced a remarkable increase in local expenditure levels to address rising local needs. However, the limited availability of financial resources, exacerbated first by the 2008 financial crisis and then by Covid19 crisis, has called for budget restrictions usually imposed by higher levels of government. In this paper, we evaluate the impact of a balance budget rule enforcement, exploring its effect on the local government cost efficiency and, in particular, considering the complex trade-off between efficiency and equity. Specifically, our identification strategy considers the exogenous introduction of a new budget balance rule that requires local governments to respect both an annual and a longer-term equilibrium criterion. The difference-in-differences analysis builds on a rich panel dataset covering all the public functions. We find that, on average, the budget rule enforcement exerted a positive effect on local government efficiency. © 2023 Elsevier B.V.
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When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order. © 2022
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The decisions of whether and how to evacuate during a climate disaster are influenced by a wide range of factors, including emergency messaging, social influences, and sociodemographics. Further complexity is introduced when multiple hazards occur simultaneously, such as a flood evacuation taking place amid a viral pandemic that requires physical distancing. Such multihazard events can necessitate a nuanced navigation of competing decision-making strategies wherein a desire to follow peers is weighed against contagion risks. To better understand these trade-offs, we distributed an online survey during a COVID-19 pandemic surge in July 2020 to 600 individuals in three midwestern and three southern states in the United States with high risk of flooding. In this paper, we estimate a random parameter discrete choice model in both preference space and willingness-to-pay space. The results of our model show that the directionality and magnitude of the influence of peers' choices of whether and how to evacuate vary widely across respondents. Overall, the decision of whether to evacuate is positively impacted by peer behavior, while the decision of how to evacuate (i.e., ride-type selection) is negatively impacted by peer influence. Furthermore, an increase in flood threat level lessens the magnitude of peer impacts. In terms of the COVID-19 pandemic impacts, respondents who perceive it to be a major health risk are more reluctant to evacuate, but this effect is mitigated by increased flood threat level. These findings have important implications for the design of tailored emergency messaging strategies and the role of shared rides in multihazard evacuations. Specifically, emphasizing or deemphasizing the severity of each threat in a multihazard scenario may assist in: (1) encouraging a reprioritization of competing risk perceptions;and (2) magnifying or neutralizing the impacts of social influence, thereby (3) nudging evacuation decision-making toward a desired outcome. © 2022 American Society of Civil Engineers.
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The COVID-19 pandemic has demonstrated the importance of large-scale campaigns to facilitate vaccination adherence. Social media presents unique opportunities to reach broader audiences and reduces the costs of conducting national or global campaigns aimed at achieving herd immunity. Nonetheless, few studies have reviewed the effectiveness of prior social media campaigns for vaccination adherence, and several prior studies have shown that social media campaigns do not increase uptake rates. Hence, our objective is to conduct a systematic review to examine the effectiveness of social media campaigns and to identify the reasons for the mixed results of prior studies. Our methodology began with a search of seven databases, which resulted in the identification of 92 interventions conducted over digital media. Out of these 92 studies, only 15 adopted social media campaigns for immunization. We analyzed these 15 studies, along with a coding scheme we developed based on reviews of both health interventions and social media campaigns. Multiple coders, who were knowledgeable about social media campaigns and healthcare, analyzed the 15 cases and obtained an acceptable level of inter-coder reliability (>.80). The results from our systematic review show that only a few social media campaigns have succeeded in enhancing vaccination adherence. In addition, few campaigns have utilized known critical success factors of social media to induce vaccination adherence. Based on these findings, we discuss a set of research questions that informatics scholars should consider when identifying opportunities for using social media to resolve one of the most resilient challenges in public health. Finally, we conclude by discussing how the insights drawn from our systematic reviews contribute to advancing theories, such as social influence and the health belief model, into the realm of social media–based health interventions. © 2022 Elsevier Ltd
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Purpose: The introduction in Italy in July 2021 of the "COVID-19 Green Certification”, known as the "Green Pass”, was a particularly important moment in the political and social history of the country. While its use for health reasons is debatable both logically and scientifically, its effects should be measured at the general sociological level. The "Green Pass” allowed Italian social life to be shaped according to a social and political profile that can be traced back to a "society of control”. This paper aims to discuss the aforementioned issue. Design/methodology/approach: This paper, of a theoretical nature, intends to verify such an interpretation through a critical survey of Gilles Deleuze's well-known Post-scriptum sur les sociétés de contrôle (1990) and relating the theories to it from cybernetic science, sociology of social systems and the continental philosophy, specifically Michel Foucault. After a short introduction on the history of the instrument's introduction, the paper, divided into parts reflecting the set-up of Deleuze's text, examines the systemic social effects of the "Green Pass” with regard to its logic, and concludes with a reflection on the program of the instrument's future developments. Findings: The "Green Pass” put into practice a model of a society of control as anticipated by Deleuze, verified with particular reference to some instances of Luhmann's theory of social systems, and in the perspective of a Foucault's "normalizing society” in the process of definition and affirmation. Social implications: The "Green Pass” has been a controversial tool that has caused forms of social discrimination and exclusion and has seriously questioned the architecture of the rule of law. The conceptual paper tries to reflect on the premises and implications of this instrument. Originality/value: The approach to the problem both in a critical key and according to concepts and theories of the sociology of social systems, cybernetics and continental philosophy. © 2023, Emerald Publishing Limited.
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The increased frequency and severe consequences of risks in the cruise industry have attracted increasing attention from both academics and practitioners, especially after the 2012 ‘Costa Concordia' disaster and the 2020 coronavirus outbreak on the ‘Diamond Princess'. Although the literature on risk studies associated with the cruise industry and supply-chain risk management is growing, the extant literature lacks a study to view risks in the cruise industry associated with the supply chain. This paper addresses this gap by reviewing the literature on risks related to the cruise industry and general supply-chain risks to create a framework of cruise supply-chain risks. Then, semi-structured interviews were conducted to validate the identified risks and explore potential undiscovered risks. A novel risk typology of the cruise supply chain was then built based on the literature review and the empirical study. This includes macro risks, safety, security, and health risks, information risks, and supply risks. This framework can be applied for the purpose of systematically identifying the risks and their impacts on the cruise supply chain. This paper contributes to the development of a comprehensive cruise supply-chain risk classification with a detailed explanation of each risk in the cruise supply chain, which can be used by stakeholders in the cruise industry to identify and measure the impact of each risk. Additionally, this paper provides avenues for future research by scholars interested in assessing and managing cruise supply-chain risks. © National Academy of Sciences: Transportation Research Board 2022.
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As current production and consumption patterns exceed planetary boundaries, many leaders have stressed the need to adopt green economic stimulus policies in the aftermath of the COVID-19 pandemic. This paper provides an integrated multi-stakeholder framework to design an economic recovery strategy aligned with climate stabilisation objectives. We first employ quantitative energy and economic models, and then a multi-criteria decision process in which we engage social actors from government, enterprises and civil society. As a case study, we select green recovery measures that are relevant for a European Union country and assess their appropriateness with numerous criteria related to climate resilience and socio-economic sustainability. Results highlight trade-offs between immediate and long-run effects, economic and environmental objectives, and expert evidence and societal priorities. Importantly, we find that a ‘return-to-normal' economic stimulus is environmentally unsustainable and economically inferior to most green recovery schemes. © 2022 The Author(s)
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Risk and return are two fundamentals that have an impact on an investor's or hedger's investing choices. Based on the proposed synchronous movement intensity index, this paper aims to improve the hedging performance by adjusting the model-driven hedge ratio and realize the trade-off between return and risk in futures hedging. First, without loss of generality, we forecast crude oil spot and futures volatility using 10 GARCH-type models, including three linear models and seven nonlinear models, to obtain the ex-ante hedging ratio under the minimum variance framework. Then, we develop a novel and tractable method to identify the market state based on the index of consistency intensity, in which the index portrays the synchronous degree of stock price movements in the energy sector. Last but not least, we propose the hedge ratio adjustment criteria based on the identified state, and adjust the ratio driven by GARCH-type models of futures in accordance with the market state. Empirical results of crude oil futures markets indicate that the proposed state-dependent hedging model is superior to the commonly used models in terms of three criteria including mean of returns, variance, and ratio of mean to variance of returns for measuring hedging effect. We apply the DM test to make a statistical inference and discover that while the mean and the ratio of mean to variance of returns are increasing, the variance and hedging effectiveness of the hedged portfolio based on the modified methods are not significantly affected. Furthermore, the superiority of the proposed method is robust to different market conditions, including significant rising or falling trends, large basis, and COVID-19 pandemic. We also test the robustness of the proposed method with respect to the baseline model, quantile, and evaluation window. Overall, this paper provides a more realistic approach for crude oil risk managers to hedge crude oil price risk, some corresponding implications are also concluded. © 2022 Elsevier Ltd
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The online depression community (ODC) has become a popular resource for people with depression to manage their mental health during the COVID-19 pandemic. This study proposed a novel perspective based on response style theory to investigate whether depression individuals' distractive and ruminative behaviors in ODC were related to social support received and co-rumination. Furthermore, we explored the influences of social support and co-rumination on suicidal behaviors using panel data set. We collected text data from 22,286 depressed users of a large ODC in China from March 2020 to July 2021, and conducted text mining and econometrics analyses to test our research questions. The results showed that depression users' online ruminative behaviors had a positive relationship with the co-rumination and had a negative relationship with social support received. Besides, constructive distractive behaviors (i.e., providing social support to others) increased the support users received from others but had a negative relationship with co-rumination. Depression users' future suicidal behaviors are influenced by past received social support and co-rumination. The received social supports and co-rumination have a negative and positive influence on depression users' future suicidal behaviors, respectively. Our results enrich the application of response style theory in online medicine. They provide meaningful insights into behaviors that influence the acquisition of online social support and the incidence of online co-rumination in ODCs. This study helps relevant institutions to conduct more targeted online suicide interventions for depression patients. © 2022 Elsevier Ltd
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Sustainable development is central to the current societal functioning, whose complexity demands consideration on a regional scale. However, there are disparate methods to express sustainable development, many of which use qualitative analysis cumbersome for policy-makers. Previous studies focused on environmental, economic, and social impacts without fully considering the regulation mechanisms of the plethora of administrative bodies. To fill this research gap, this research establishes an integrated assessment framework involving four pillars: environment and ecology, society and culture, economics, and governance and policy. Further, indicator systems and quantitative analysis give comparable and objective results. The current study applied them to one of the most economically significant and developed Chinese regions, the Yangtze River Delta. The result shows a dynamic variation in regional sustainability from 2010 to 2019, indicating an annual increase. Although economic and societal development has been increasing steadily, environmental development has stagnated in the past two years, and the influencing policy has fluctuated dramatically. Our analysis was done in Shanghai, Jiangsu, Zhejiang, and Anhui. Even though all regions showed increasing sustainability, we observed an imbalance in regional sustainable development. Achieving a regional approach and enhanced regional coordination in the Yangtze River Delta is imperative and cannot be ignored by local, regional, and national policy-makers. More importantly, this study created a model capable of predicting the impact of the COVID-19 epidemic on regional sustainable development. The model showed that, compared with predicted values, a 6.65% decrease in the integrated sustainability index ensued, attributed to the pandemic in Zhejiang province. © 2022 Elsevier Ltd
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Purpose: The recent COVID-19 has obliged governments to enact large-scale policies to contain it. A topic of economic debate is the quantification of the impact that these policies can create in the economy, with the aim of activating regulatory mechanisms to minimize this impact. In this vein, this study aims to propose a quantification of the effects of the Italian government policy that blocks nonessential production activities. Design/methodology/approach: The authors use a multisectoral extended inoperability model on the social accounting matrix of Italy. The analysis identifies the pandemic's impact on outputs, endogenous demands, value-added and disposable incomes of institutional sectors. Findings: The construction and real estate sectors revealed a significant contraction followed by the retail trade and hotel and catering services sectors. The output contraction further impacts the value-added generation, disposable income and final demand components. Originality/value: The current pandemic is alleged to have a greater impact than the epidemics of the past century, considering the present dimension of the world economy and the increasing interconnections between industries and institutions. In this scenario, it is challenging to safeguard not only human health and life but also the economy. Hence, there is a need to establish a trade-off between health and economics;and in this regard, the current study empirically quantifies the impact of health measures on the economy. The findings of this study help identify the sectors that are more prone to disaster effects and also present the structure of income circular flow in the Italian economy. © 2021, Emerald Publishing Limited.
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Although switching from non-renewable to renewable energy is believed to stimulate low-carbon economic growth, the means to establishing this energy transition have largely remained unexplored in the extant literature. Against this backdrop, this study focuses on evaluating how scaling public investment in renewable energy-related research and development projects impacts the carbon productivity levels in the top-10 renewable energy-investing countries. The estimation strategy comprised econometric methods that can handle cross-sectional dependency and slope heterogeneity related concerns in the data. Regarding the key findings, higher public research and development-related investments in renewable energy are observed to boost carbon productivity levels in the concerned countries, while natural resource consumption and net exports are found to reduce carbon productivity. Besides, the results endorsed that public research and development investment for renewable energy development exhibits a moderating role by jointly boosting carbon productivity with higher natural resource consumption and net exports. Moreover, it is also seen to inflict a mediating effect by jointly boosting carbon productivity with urbanization. In line with these findings, the concerned governments are recommended to scale such investment in order to stimulate technological innovation so that renewable energy transition can take place to establish low carbon economic growth. © 2023 Elsevier Ltd
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The prevalence of inadequate SARS-COV-2 (COVID-19) responses may indicate a lack of trust in forecasts and risk communication. However, no work has empirically tested how multiple forecast visualization choices impact trust and task-based performance. The three studies presented in this paper ($N=1299$) examine how visualization choices impact trust in COVID-19 mortality forecasts and how they influence performance in a trend prediction task. These studies focus on line charts populated with real-time COVID-19 data that varied the number and color encoding of the forecasts and the presence of best/worst-case forecasts. The studies reveal that trust in COVID-19 forecast visualizations initially increases with the number of forecasts and then plateaus after 6-9 forecasts. However, participants were most trusting of visualizations that showed less visual information, including a 95% confidence interval, single forecast, and grayscale encoded forecasts. Participants maintained high trust in intervals labeled with 50% and 25% and did not proportionally scale their trust to the indicated interval size. Despite the high trust, the 95% CI condition was the most likely to evoke predictions that did not correspond with the actual COVID-19 trend. Qualitative analysis of participants' strategies confirmed that many participants trusted both the simplistic visualizations and those with numerous forecasts. This work provides practical guides for how COVID-19 forecast visualizations influence trust, including recommendations for identifying the range where forecasts balance trade-offs between trust and task-based performance. © 2022 IEEE.
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Social influence characterizes the change of an individual's stances in a complex social environment towards a topic. Two factors often govern the influence of stances in an online social network: endogenous influences driven by an individual's innate beliefs through the agent's past stances and exogenous influences formed by social network influence between users. Both endogenous and exogenous influences offer important cues to user susceptibility, thereby enhancing the predictive performance on stance changes or flipping. In this work, we propose a stance flipping prediction problem to identify Twitter agents that are susceptible to stance flipping towards the coronavirus vaccine (i.e., from pro-vaccine to anti-vaccine). Specifically, we design a social influence model where each agent has some fixed innate stance and a conviction of the stance that reflects the resistance to change;agents influence each other through the social network structure. From data collected between April 2020 to May 2021, our model achieves 86% accuracy in predicting agents that flip stances. Further analysis identifies that agents that flip stances have significantly more neighbors engaging in collective expression of the opposite stance, and 53.7% of the agents that flip stances are bots and bot agents require lesser social influence to flip stances. © 2013 IEEE.
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Bus operators have to make trade-offs between transporting more passengers and maintaining social distancing to reduce ridership congregation amid Corona Virus Disease 2019 (COVID-19) outbreak. The traditional bus boarding mode could easily lead passengers fully occupy the bus available capacity at one stop, and it would prevent subsequent passengers from boarding. It is crucial to establish a new operating mode and strategy to ensure all passengers have opportunities to ride and to collaboratively optimise the bus timetable. In this paper, the boarding limit strategy that considers the fairness of passenger boarding probability is proposed to address the inequitable problem with minimise the passenger travel time and the number of stranded passengers. The coupling relationship between bus dwell time and passenger flow is used to collaboratively optimise the bus timetable. Case studies are conducted to illustrate the performance of the boarding limit strategy in improving passenger boarding equity. © 2023 Hong Kong Society for Transportation Studies Limited.