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This paper analyzes the impact of COVID-19 on China's coal import trade. First , this paper makes a qualitative analysis of the status quo of China's coal import scale before and after COVID-19. Second, the impact of the pandemic on China's coal import was estimated by panel VAR model based on the confirmed global COVID-19 data and China's coal import data with Johns Hopkins University Center for Systems Science and Engineering database and Wind database from January 2019 to June 2021. The result shows that the pandemic has a significant impact on both the supply and demand of China's coal industry in short term. There is a rebound in coal demand due to manufacturing industries having resumed work and production under the successful pandemic control in China. Finally, this paper analyzes the measures of China's coal import process during the COVID-19 and suggests how to deal with the global public crisis in the future. [ABSTRACT FROM AUTHOR]

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O 3 concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone. [Display omitted] • Emergency response to COVID-19 (ER) had spill-over effect on air quality. • Empirical analysis was made with RDD to evaluate the impact of ER on air quality. • AQI and PM 2.5 , PM 10 , SO 2 and NO 2 concentrations decreased by approximately 10%. • Traffic restrictions and shutdown of industries during ER were important factors. • Socioeconomic resumption and ER termination did not affect air quality. [ABSTRACT FROM AUTHOR]

• This study applied a logistic growth model with parameters estimated by a non-linear least squares (NLS) method to daily new COVID-19 cases. • The model fitted time-series data exceedingly well for the whole of China, eleven selected Chinese provinces and municipalities, South Korea and Iran. • This study provided key estimates and a 95% confidence interval for parameters K , r, and P 0. • This study found that the growth rates of outbreaks differed between provinces in China and between South Korea and Iran. • As of March 13, 2020, this study's model suggested that countries such as the U.S.A., France, Italy, and Spain were still in the early stages of outbreaks. As the coronavirus disease 2019 (COVID-19) pandemic continues to proliferate globally, this paper shares the findings of modelling the outbreak in China at both provincial and national levels. This paper examines the applicability of the logistic growth model, with implications for the study of the COVID-19 pandemic and other infectious diseases. An NLS (Non-Linear Least Squares) method was employed to estimate the parameters of a differentiated logistic growth function using new daily COVID-19 cases in multiple regions in China and in other selected countries. The estimation was based upon training data from January 20, 2020 to March 13, 2020. A restriction test was subsequently implemented to examine whether a designated parameter was identical among regions or countries, and the diagnosis of residuals was also conducted. The model's goodness of fit was checked using testing data from March 14, 2020 to April 18, 2020. The model presented in this paper fitted time-series data exceedingly well for the whole of China, its eleven selected provinces and municipalities, and two other countries - South Korea and Iran - and provided estimates of key parameters. This study rejected the null hypothesis that the growth rates of outbreaks were the same among ten selected non-Hubei provinces in China, as well as between South Korea and Iran. The study found that the model did not provide reliable estimates for countries that were in the early stages of outbreaks. Furthermore, this study concured that the R 2 values might vary and mislead when compared between different portions of the same non-linear curve. In addition, the study identified the existence of heteroskedasticity and positive serial correlation within residuals in some provinces and countries. The findings suggest that there is potential for this model to contribute to better public health policy in combatting COVID-19. The model does so by providing a simple logistic framework for retrospectively analyzing outbreaks in regions that have already experienced a maximal proliferation in cases. Based upon statistical findings, this study also outlines certain challenges in modelling and their implications for the results. [ABSTRACT FROM AUTHOR]

Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ 2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. [ABSTRACT FROM AUTHOR]

The global outbreak of the COVID-19 pandemic has slowed down international investment and trade, and partially disrupted the global supply chain. This paper provides an overview of the findings in 80 scholarly articles from recent years, comprehensively summarises and analyzes the research on international capital flows and global value chain（GVC）reconstruction, and deeply reveals the impact of COVID-19 on OFDI, the change of OFDI performance before and after the pandemic and factors that may accelerate GVC reconstruction in the future. We identify research gaps in a combination of OFDI and GVC reconstruction at both the macro and micro level and suggest several future research topics such as political risk and bilateral relations factors, the role of China in OFDI and GVC dynamics, network and OFDI performance, network characteristics of GVC reconstruction, OFDI and GVC reconstruction. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive system for assessing contaminant emissions, including the dominant contaminant reduction effects, recessive contaminant reduction effects, and contaminant reduction efficiency indexes. The system is used to evaluate the linkage changes in contaminant emissions reduction across different sectors, the ease of regulation, and the returns on emissions reduction efforts. The study's results demonstrate that firstly, COVID-19 initially reduced contaminant emissions across almost all sectors, although this effect waned over time. Secondly, applying conventional regulatory measures to sectors with high reductions in contaminant emissions may not yield the expected results. Thirdly, while some sectors may be more difficult to regulate, they may offer a higher return on investment in terms of emissions reduction. Based on the above results, the paper advocates for timely measures to consolidate environmental gains, prevent rebounding of contaminant emissions, prioritize supervision and management of easily regulated sectors, prioritize upgrading of cleaner production in sectors with high reduction efficiency indices, and increase investment in environmental technology. • Created a new comprehensive system for assessing contaminant emission changes. • Quantified the unique linkage changes of contaminant emissions during COVID-19. • Developed new models to reveal contaminants' regulatory difficulty and return. • The innovative reduction effects of four key contaminant indicators were measured. • Applied WHEM firstly in research across multiple areas of contaminant emissions. [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 this paper, blunt slogans used in China's health campaign against coronavirus are closely examined and the public's conflicting comments on them are analyzed. These slogans, due to their extreme effectiveness in making the public comply with the health preventive measures suggested by the government, are called Yinghe or "hardcore" slogans by the Chinese people. Containing harsh and taboo language, they convey threats of death and disease, insults or negative evaluation, and harsh demands. Despite their impolite nature, "hardcore" slogans have received significantly more positive judgment than negative judgment, especially when they made their debut in rural areas in Henan, an agricultural province in China. Criticism towards these blunt slogans then gradually increased after their initial appearance. Plausible factors contributing to the change of judgment are analyzed. The public's conflicting judgment regarding the impoliteness of the slogans may be related to the public's different positioning across time and space. This study shows that impoliteness is a practice situated not only in discourse, genres, and institutions, but also in social, cultural, and political contexts. More attention should be paid to impoliteness in special social configurations (e.g., rural areas) and social emergencies, which not only contextualize a discourse event but also define it. • Impoliteness is situated culturally, politically, temporarily, and spatially. • Impoliteness can be a strategic and appropriate reaction to social emergencies. • Distinction between rural and urban settings is relevant to impoliteness studies. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of COVID-19 pandemic on Chinese systemic risk using a dynamic network model. We select 104 listed financial institutions to construct a dynamic tail-risk network during the epidemic. The tail-risk network is estimated by the Factor-Adjusted Regularized Model (FARM) selection approach. To analyze the impact of COVID-19 pandemic on systemic risk, we identify the role of financial institutions in the dynamic tail-risk network. Results show that COVID-19 pandemic increases the number of risk-drivers and decreases the number of risk-takers, meaning that it enhances systemic financial risk in China. Our evidence provides an effective method to regulate systemic risk in the financial market and is helpful to offer rescue policies once systemic risk events occur. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 epidemic spreads with strong transmission power in every part of China. Analyses of the trend is highly need when the Chinese government makes plans and policies on epidemic control. This paper provides an estimation process on the trend of COVID-19 outbreak using the provincial-level data of the confirmed cases. On the basis of the previous studies, we introduce an effective and practical method to compute accurate basic reproduction numbers (R 0 s) in each province-level division of China. The statistical results show a non-stop downward trend of the R 0 s in China, and confirm that China has made significant progress on the epidemic control by lowering the provincial R 0 s from 10 or above to 3.21 or less. In the inferential analysis, we introduce an effective AR(n) model for the trend forecasting. The inferential results imply that the nationwide epidemic risk will fall to a safe level by the end of April in China, which matches the actual situation. The results provide more accurate method and information about COVID-19. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 in 2020 has a severe impact on all countries. This paper first studies the impact of the epidemic on the macro-economy in China, which is one of the countries with early epidemic situation, in the rapid development stage of the epidemic situation, and the specific performance in macro-economy aspects. On this basis, study the development trend and macroeconomic performance of the United States as one of the most serious countries, and the relationship between the epidemic development and macro-economy in the United States. As for the differences in epidemic control measures between China and the US, China's indicators are significant in terms of sentiment factors, but the impact of COVID-19 on the sentiment factors in the US is different. The Chinese government has not adopted unlimited easing and maintained consistent policies. At present, the economic impact of COVID-19 on the US is far greater than that of China. [ABSTRACT FROM AUTHOR]

This paper examines the impact of female analysts' coverage on firm's philanthropic activities amidst the outbreak of the COVID-19 pandemic in China. Using a hand-collected dataset of corporate philanthropy, the paper provides robust evidence that firms covered by female analysts are more likely to contribute actively to the well-being of societies by increasing corporate donation. This positive relationship is more pronounced if the company is privately controlled or covered by female analysts with more working experience, or located in more infectious provinces. Overall, our findings call for more female analyst recruitment, yielding benefits of pressuring firms to engage in philanthropy. • Firms covered by more female analysts provide more donation during the pandemic. • This positive impact is more pronounced if analysts have more working experience. • The positive relationship is more pronounced if a firm is privately controlled. • The positive impact is more pronounced if a firm is in more infectious province. • Firm site visit is the channel that female analysts affect corporate donation. [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]

The new type of coronavirus, COVID 19, appeared in China at the end of 2019. It has become a pandemic that is spreading all over the world in a very short time. The detection of this disease, which has serious health and socio-economic damages, is of vital importance. COVID-19 detection is performed by applying PCR and serological tests. Additionally, COVID detection is possible using X-ray and computed tomography images. Disease detection has an important position in scientific researches that includes artificial intelligence methods. The combined models, which consist of different phases, are frequently used for classification problems. In this paper, a new combined approach is proposed to detect COVID-19 cases using deep features obtained from X-ray images. Two main variances of the approach can be presented as single layer-based (SLB) and feature fusion-based (FFB). SLB model consists of pre-processing, deep feature extraction, post-processing, and classification phases. On the other side, the FFB model consists of pre-processing, deep feature extraction, feature fusion, post-processing, and classification phases. Four different SLB and six different FFB models were developed according to the number and binary combination of layers used in the feature extraction phase. Each model is employed for binary and multi-class classification experiments. According to experimental results, the accuracy performance for COVID-19 and no-findings classification of the proposed FFB3 model is 99.52%, which is better than the best performance accuracy (of 98.08%) in the literature. Concurrently, for multi-class classification, the proposed FFB3 model has an accuracy performance of 87.64% outperforming the best existing work (which reported an 87.02% classification performance). Various metrics, including sensitivity, specificity, precision, and F1-score metrics are used for performance analysis. For all performance metrics, the FFB3 model recorded a higher success rate than existing work in the literature. To the best of our knowledge, these accuracy rates are the best in the literature for the dataset and data split type (five-fold cross-validation). Composite models (SLBs and FFBs), which are generated in this paper, are successful ways to detect COVID-19. Experimental results show that feature extraction, pre-processing, post-processing, and hyperparameter tuning are the steps are necessary to obtain a higher success. For prospective works, different types of pre-trained models and other hyperparameter tuning methods can be implemented. • Using pre-trained CNN models is an effective way to extract deep features • Feature fusion-based model is more successful to detect COVID-19 in X-Ray images • Data pre-processing and post-processing techniques increase the performance of the models • Hyperparameter tuned SVM is more successful than without one • To the best of our knowledge, obtained accuracy rates are the best in the literature [ABSTRACT FROM AUTHOR]

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major cities in China. The location information of 29 cities forms the topological structure of graph. The Spatial Convolution Layer proposed in this paper captures the spatial correlation features of the graph structure. The special Graph Data Fusion module distributes and fuses the two kinds of data according to different proportions to increase the trend of spatial correlation of the data. In order to reduce the complexity of the computational process, the Temporal Convolution Layer replaces the gated recursive mechanism of the traditional recurrent neural network with a multi-level residual structure. It uses the dilated convolution whose dilation factor changes according to convex function to control the dynamic change of the receptive field and uses causal convolution to fully mine the historical information of the data to optimize the ability of long-term prediction. The comparative experiments among DeepTRI and three baselines (traditional recurrent neural network, ordinary spatial–temporal model and graph spatial–temporal model) show the advantages of DeepTRI in the evaluation index and resolving two under-fitting problems (under-fitting of edge values and under-fitting of local peaks). [ABSTRACT FROM AUTHOR]

China was the first airline market in the world to be hit hard by the COVID-19 pandemic. It has been gradually recovering as the pandemic is largely contained domestically. However, with the global pandemic spread and great uncertainty, there has been a remarkable change in airline passengers' travel behavior. This paper collected air passenger-level data from TravelSky in the Chinese market. In addition to the analyses on aggregate passenger flow patterns, this paper explores changes in airline passenger travel behavior, such as ticket booking time, age distribution of passengers, refunds and ticket changes, and passenger arrival time at airports. This is one of the first studies to focus on micro-level changes in airline passenger travel behavior by using objective passenger-level data. The pandemic-induced psychological changes in air travelers are explored, providing useful managerial and policymaking implications for the normalization of the pandemic and the recovery of the airline market in the post-pandemic era. • There has been a remarkable change in airline passengers' travel behavior. • This paper collected air passenger-level data from TravelSky in the Chinese market. • We examine changes in airline passenger travel behavior. • Ticket booking time, age distribution, refunds and ticket changes, and passenger arrival time are explored. [ABSTRACT FROM AUTHOR]

Apart from the direct health and behavioral influence of the COVID-19 pandemic itself, COVID-19 rumors as an infodemic enormously amplified public anxiety and cause serious outcomes. Although factors influencing such rumors propagation have been widely studied by previous studies, the role of spatial factors (e.g., proximity to the pandemic) on individuals' response regarding COVID-19 rumors remain largely unexplored. Accordingly, this study, drawing on the stimulus-organism-response (SOR) framework, examined how proximity to the pandemic (stimulus) influences anxiety (organism), which in turn determines rumor beliefs and rumor outcomes (response). Further, the contingent role of social media usage and health self-efficacy were tested. The research model was tested using 1246 samples via an online survey during the COVID-19 pandemic in China. The results indicate that: (1)The proximity closer the public is to the pandemic, the higher their perceived anxiety; (2) Anxiety increases rumor beliefs, which is further positively associated rumor outcomes; (3) When the level of social media usage is high, the relationship between proximity to the pandemic and anxiety is strengthened; (4) When the level of health self-efficacy is high, the effect of anxiety on rumor beliefs is strengthened and the effect of rumor beliefs on rumor outcomes is also strengthened. This study provides a better understanding of the underlying mechanism of the propagation of COVID-19 rumors from a SOR perspective. Additionally, this paper is one of the first that proposes and empirically verifies the contingent role of social media usage and health self-efficacy on the SOR framework. The findings of study can assist the pandemic prevention department in to efficiently manage rumors with the aim of alleviating public anxiety and avoiding negative outcomes cause by rumors. [ABSTRACT FROM AUTHOR]

This paper discusses the situation of China's air cargo sector facing the COVID-19 pandemic. Based on the expectation that it can go out of recession more easily than China's air passenger sector, this paper analyzes four aspects that are favorable and unfavorable for its further development: (1) strengths (China's sustainable economic basis and proliferating cargo suppliers), (2) weaknesses (insufficient cargo capacity and less business internationalization), (3) opportunities (top authority support, rising e-commerce demand, and new technological momentum), and (4) challenges (uncertain trade environment and increasing profitability pressure). Then this paper suggests strategies for China's air cargo suppliers to adapt to the pandemic. • China's restrictive measures in air transport will persist for a long time. • China's air cargo sector has suffered a less severe depression than air passenger traffic. • The COVID-19 pandemic unveiled Chinese airlines' deficiency in cargo capacity. • The development of e-commerce promotes growing demand for air cargo in China. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2) is an important target for monitoring atmospheric quality. Deriving ground-level NO 2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO 2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO 2 concentrations over China based on machine learning models and the TROPOMI NO 2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO 2 concentrations. In addition to considering many influencing factors of the ground-level NO 2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO 2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO 2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO 2 concentrations. Taking four ground-level NO 2 concentrations hotspots as examples, the estimated ground-level NO 2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO 2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment. • The spatiotemporal information can enhance the estimated ability of the models. • ST-ET model has high accuracy in estimating ground NO 2 concentration with high R2 and low RMSE. • The estimated results can capture the spatial and temporal pattern especially during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO 2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO 2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO 2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO 2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO 2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO 2 emissions declines into firm climate action. • COVID-19 has negative effects on economy, but positive effects on CO 2 emissions. • A new model is used to measure economic and CO 2 emission effects during COVID-19. • Hubei had the biggest economic and CO 2 emission effects on China due to COVID-19. • Economic and CO 2 emission effects of COVID-19 are combined to select key sectors. [ABSTRACT FROM AUTHOR]

Achieving the peak of carbon dioxide (CO 2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO 2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO 2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO 2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025; (2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO 2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO 2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO 2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance. • Providing empirical studies regarding the mechanism of the inhibitory effect of green finance on CO 2 emissions. • Using systems dynamics model to address the missing data problem. • Making solid suggestions under COVID-19 Epidemic. • Building the relationship between the green finance system and CO 2 emissions and making forecasts for "3060" mission result. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

• Propose an innovative Bayesian model to control the risk of COVID-19 patients. • Propose two indicators to verify the feasibility and effectiveness of the model. • Provide the tool for risk assessment in the field of epidemics. In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics. [ABSTRACT FROM AUTHOR]

• A transfer learning framework and a CNN model are proposed for the black swan small-sample events. • (BEST-L). • Experiments show that the BEST-L outperforms traditional methods. • The transfer learning method can effectively use small samples to improve the model's generalization. • performance. • The BEST-L is tested for the load forecasting case with the COVID-19 scenario in Central China. The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 pandemic has a lasting and unprecedented negative impact on the global aviation industry. While countries such as China have successfully curbed the domestic outbreak of the virus with various restrictive and preventive measures, the challenge of avoiding imported cases remains. More importantly, it is still unclear to what extent these implemented aviation emergency responses have effectively mitigated the transmission risk of the virus. This paper provides an empirical assessment of aviation responses to the control of imported COVID-19 cases, with a focus on the following three strategies: the "circuit breaker" policy, the "negative Nucleic Acid testing (NAT)", and the "double negative tests" requirement. Non-recursive structural equation models (SEM) with latent variables were applied to detailed international flight data and individual epidemic survey data of Guangzhou, China, between May 1 and November 30, 2020. The results show that the "double negative tests" measure has a positive effect on eliminating the number of SARS-CoV-2 carriers, while the effects of single "circuit breaker" and its co-intervention with "negative NAT" are conterproductive. This study provides important implications to civil aviation agencies in regard to medium and long-term risk control of imported cases. Specifically, although the circuit breaker mechanism was designed to target on the risk control of imported COVID-19 cases, it may be more effective to carefully maintain a timely and reliable pre-boarding screening and testing to curb the number of imported cases. • We evaluated the aviation responses to the control of imported COVID-19 cases in China. • Detailed international flight data and individual epidemic survey data were used. • SEM method was used to evaluate the effects of three aviation response strategies. • The "double negative tests" measure has a positive effect on eliminating the imported cases. • The effects of "circuit breaker" with the"Negative Nucleic Acid testing" requirement are minimal. [ABSTRACT FROM AUTHOR]

[Display omitted] • Clean energy transitions based on the circular economy (CE) are analysed for a town in China. • This could reduce energy use, CO 2 emissions and PM 2.5 emissions compared to the BAU scenario. • COVID pandemic is a new opportunity to introduce circular economy measures. • Circular economy measures could accelerate clean energy transitions. This paper models the energy and emissions scenarios for a circular economy based clean energy transitions in a 140,000-population town in China, taking into account the new situation encountered by the COVID-19 pandemic. The modelled scenarios propose new clean energy transition roadmaps towards a sustainable urban system through the implementation of circular economy strategies. This is represented by the cascading use of industrial excess heat to form symbiosis between factories and to cover the growing building heat demand, as well as by the electrification of the transport sector and reusing the batteries for a second life as energy storage devices. The results show that for a circular economy scenario, during 2020–2040, an accumulated saving of 7.1 Mtoe final energy use (34%), a decline in 14.5 Mt CO 2 emissions (40%) and 592 t PM 2.5 emissions (43%) could be achieved compared with the business-as-usual scenario. The outcomes of the circular economy strategies are at least 7% better than the new policy scenario which simply has energy efficiency improvements. The outbreak of the COVID-19 tremendously impacts the socio-economic activities in the town. If taking the pandemic as an opportunity to enhance the circular economy, by 2040, compared with the scenario without introducing circular economy measures, the extra avoided final energy use, CO 2 emissions and PM 2.5 emissions could be 1.6 Mtoe (8%), 3.8 Mt (11%) and 229 t (17%) respectively. [ABSTRACT FROM AUTHOR]

With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models' prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events. [ABSTRACT FROM AUTHOR]

• A Maximum-Entropy Ecological Niche Model is used to estimate a global-scale probability distribution of COVID-19 high infection rate. • Environmental parameters (surface air temperature, precipitation, and elevation) and humanrelated parameters (CO 2 emission and population density) are used in the model. • The model is trained only with data of Italian provinces with high infection rate, but predicts known actual infection focuses, e.g. the Hubei province in China. • A risk index is proposed, which correctly classifies most World countries, which have reported high COVID-19 spread rate, as zones with high-risk of infection rate increase. • The methodology follows an Open-science approach where the model is published as a standardized Web service that maximises re-usability on new data and new diseases, and guarantees the transparency of the approach and the results. COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions. In this paper, infection rate of COVID-19 is modelled globally at a 0.5∘ resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO 2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment. The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results. [ABSTRACT FROM AUTHOR]