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Background: Residents' adoption of preventive behaviours proved beneficial in preventing the large-scale transmission of the virus during the early stages of the COVID-19 outbreak. It is critical to investigate how social media triggers residents' preventive behaviour decisions during the COVID-19 outbreak. Methods: This paper selected online shopping as a specific preventive behaviour for empirical investigation. An online cross-sectional survey was conducted through the Sojump website from 1 to 15 March 2020, and a total of 1,289 valid questionnaires were collected from China. This paper uses multiple regression analysis to investigate the heterogeneous impacts of different information sources on residents' online shopping willingness and online shopping behaviour and the heterogeneous impacts of different information content in social media on the transformation of residents' online shopping willingness and online shopping behaviour. Results: The findings indicate that both official-media and self-media positively promote residents' online shopping willingness and behaviour, with official-media having a stronger promotional effect than self-media. Furthermore, official-media and self-media can collaboratively promote residents' online shopping willingness and online shopping behaviour. The ease-of-use and usefulness of information significantly promoted the transformation of residents' online shopping willingness. Conclusions: This study analyses the heterogeneous impacts of social media on residents' preventive behaviours from the perspectives of information source differentiation and information content differentiation, which enriches related studies and provides feasible paths for promoting residents' preventive behaviours. [ABSTRACT FROM AUTHOR]

Background: Human communities suffered a vast socioeconomic burden in dealing with the pandemic of coronavirus disease 2019 (COVID-19) globally. Real-word data about these burdens can inform governments about evidence-based resource allocation and prioritization. The aim of this scoping review was to map the cost-of-illness (CoI) studies associated with COVID-19. Methods: This scoping review was conducted from January 2019 to December 2021. We searched cost-of-illness papers published in English within Web of Sciences, PubMed, Google Scholar, Scopus, Science Direct and ProQuest. For each eligible study, extracted data included country, publication year, study period, study design, epidemiological approach, costing method, cost type, cost identification, sensitivity analysis, estimated unit cost and national burden. All of the analyses were applied in Excel software. Results: 2352 records were found after the search strategy application, finally 28 articles met the inclusion criteria and were included in the review. Most of the studies were done in the United States, Turkey, and China. The prevalence-based approach was the most common in the studies, and most of studies also used Hospital Information System data (HIS). There were noticeable differences in the costing methods and the cost identification. The average cost of hospitalization per patient per day ranged from 101$ in Turkey to 2,364$ in the United States. Among the studies, 82.1% estimated particularly direct medical costs, 3.6% only indirect costs, and 14.3% both direct and indirect costs. Conclusion: The economic burden of COVID-19 varies from country to country. The majority of CoI studies estimated direct medical costs associated with COVID-19 and there is a paucity of evidence for direct non-medical, indirect, and intangible costs, which we recommend for future studies. To create homogeneity in CoI studies, we suggest researchers follow a conceptual framework and critical appraisal checklist of cost-of-illness (CoI) studies. [ABSTRACT FROM AUTHOR]

Using the policy package pilot implemented in Hubei Province, China, in April 2020 as a natural experiment, we use the synthetic control (SC) and synthetic difference in differences (SDID) methods to estimate the impact of the Chinese government's support policy on the economic resilience and to analyze the mechanisms by which it impacts. This study finds that the policy package has contributed to the growth of economic resilience in the pilot provinces, with the policy package increasing the average economic resilience of the pilot provinces by 0.062 compared to their potential resilience. The validity and robustness of the above conclusions are objectively confirmed by multidimensional quantitative outcomes such as placebo tests, ranking tests, and replacements in calculating resilience. The mechanism analysis shows that the investment in real estate development, the stimulus for consumption, and the core industry development are virtual channels for the policy package to promote economic resilience growth in the pilot provinces. Moreover, traditional investment in transportation fixed assets plays a minor role. This paper quantitatively corroborates the academic idea that government governance capacity affects regional economic resilience (RER), and research can provide empirical support for regional economic recovery and policy support under a major crisis. [ABSTRACT FROM AUTHOR]

The impact investing literature largely focuses on private equity investing and overlooks the investments made through debt financing that actually dominate the market. To address this research gap, this paper investigates whether impact financing is associated with financial benefits. By using COVID-19 as an exogenous shock to China's stock market, this paper applies fixed effects panel data analysis with a difference-in-differences research design to provide robust empirical outcomes. The results reveal those financial institutions that better integrated environmental impacts into their financing process experienced positive stock return changes in response to the shock. This study answers the question of how well an impact scales. The findings suggest that impact financing is an effective model, as the impacts incorporated in the debt can be scaled up compared to impact investing funds with low volumes. Impact financing has enormous potential for financial institutions to engage in the green transition since they can derive pecuniary utility while delivering environmental impacts. The revelation of financial benefits also contributes to overcoming the lack of knowledge about impact financing and helps to remove the barriers that advance industry growth. • This paper studies whether impact financing is associated with financial benefits. • Financial firms that better integrate environmental impact in financing have positive stock return changes during COVID-19. • Financial firms can derive pecuniary utility from pursuing impact financing. • The findings suggest that impact financing can be an effective model to bring impact to a substantial scale. [ABSTRACT FROM AUTHOR]

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3–6% improvement in accuracy. [ABSTRACT FROM AUTHOR]

• A user persona oriented toward the enterprise is constructed to characterize power consumption in the context of COVID-19. • Abstract labels are extracted from enterprise power profiles based on industrial, regional, periodic power profiles. • Enterprises with similar electricity consumption characteristics or the same label are grouped to allow recommendations for customized electricity products, policy, and services. The COVID-19 epidemic has led to devastating consequences worldwide due to strict social distancing measures, travel bans, city lockdowns, and other activity restrictions. Numerous lessons have been accumulated by various industries or sectors in the process of coping with COVID-19. In our paper, the actual electricity consumption of 1.145 million enterprises in a province of China is investigated in the normal period, the breakout period, and the recovery period. An artificial intelligence based enterprise profiling model is established to characterize power consumption profile geographically, temporally, and industrially. The recovery rate of enterprise power consumption is proposed as a key indicator representing the status of production resumption. Under lockdown measures, enterprise in different regions or sectors exhibits diversified responses in power consumption, the unsupervised learning model and the correlated coefficient extract the abstract characteristics and labels from various enterprise power profile. Ultimately, enterprises with similar electricity consumption characteristics are grouped and labeled to provide customized power services. Accurate enterprise profiling can effectively aid in facing with the challenges of insufficient orders, tight capital chains, and rising costs caused by the epidemic. [ABSTRACT FROM AUTHOR]