ABSTRACT
The Chinese government has expressed great confidence in the role of green finance in fulfilling its carbon neutrality commitment. However, the effectiveness of green finance, especially under the impact of emergencies such as the COVID-19 pandemic scenario, requires further examination. Using data from 2000 to 2020 in China, the correlation between green finance and carbon dioxide (CO2) emissions has been analyzed, both in BaU scenario and the COVID-19 scenario. The following conclusions were drawn: (1) In BaU scenario, green finance can effectively reduce CO2 emissions, especially through government green expenditure and green credit; (2) In COVID-19 scenario, although there is no direct relationship between the pandemic and carbon emissions, the data shows that the pandemic has hindered the progress of green finance, weakened its ability to curb carbon emissions, and indirectly led to an increase in carbon emissions. This study not only clarifies the mechanism by which the COVID-19 pandemic affects carbon emissions through the green finance system but also addresses the common problem of data scarcity in green finance research.
Subject(s)
COVID-19ABSTRACT
Achieving the peak of carbon dioxide (CO2) 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 CO2 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 CO2 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 CO2 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 CO2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO2 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 CO2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance.
ABSTRACT
The Internet of Medical Things (IoMT) is a brand new technology of combining medical devices and other wireless devices to access to the healthcare management systems. This article has sought the possibilities of aiding the current Corona Virus Disease 2019 (COVID-19) pandemic by implementing machine learning algorithms while offering emotional treatment suggestion to the doctors and patients. The cognitive model with respect to IoMT is best suited to this pandemic as every person is to be connected and monitored through a cognitive network. However, this COVID-19 pandemic still remain some challenges about emotional solicitude for infants and young children, elderly, and mentally ill persons during pandemic. Confronting these challenges, this article proposes an emotion-aware and intelligent IoMT system, which contains information sharing, information supervision, patients tracking, data gathering and analysis, healthcare, etc. Intelligent IoMT devices are connected to collect multimodal data of patients in a surveillance environments. The latest data and inputs from official websites and reports are tested for further investigation and analysis of the emotion analysis. The proposed novel IoMT platform enables remote health monitoring and decision-making about the emotion, therefore greatly contribute convenient and continuous emotion-aware healthcare services during COVID-19 pandemic. Experimental results on some emotion data indicate that the proposed framework achieves significant advantage when compared with the some mainstream models. The proposed cognition-based dynamic technology is an effective solution way for accommodating a big number of devices and this COVID-19 pandemic application. The controversy and future development trend are also discussed.
ABSTRACT
Robots assist in many areas that are considered unsafe for humans to operate. For instance, in handling pandemic diseases such as the recent Covid-19 outbreak and other outbreaks like Ebola, robots can assist in reaching areas dangerous for humans and do simple tasks such as pick up the correct medicine (among a set of bottles prescribed) and deliver to patients. In such cases, it might not be good to rely on the fully autonomous operation of robots. Since many mobile robots are fully functional with low-level tasks such as grabbing and moving, we consider the mixed-initiative control where the user can guide the robot remotely to finish such tasks. For this mixed-initiative control, the user controlling the robot needs to visualize a 3D scene as seen by the robot and guide it. Mixed reality can virtualize reality and immerse users in the 3D scene that is reconstructed from the real-world environment. This technique provides the user more freedom such as choosing viewpoints at view time. In recent years, benefiting from the high-quality data from Light Detection and Ranging (LIDAR) and RGBD cameras, mixed reality is widely used to build networked platforms to improve the performance of robot teleoperations and robot-human collaboration, and enhanced feedback for mixed-initiative control. In this paper, we proposed a novel haptic-enabled mixed reality system, that provides haptic interfaces to interact with the virtualized environments and give remote guidance for mobile robots towards high-level tasks. The experimental results show the effectiveness and flexibility of the proposed haptic enabled mixed reality system.
Subject(s)
COVID-19ABSTRACT
Better understanding the carbon market can instruct further reforms to perfect its functionality, market efficiency is a key indicator to uncover its current performance. Previous studies have revealed foregone carbon market efficiency;however, given the dynamics of market, it merits the significance to track the up-to-date status. This paper specifically studies the Hubei pilot carbon market, which is quite representative considering its market scale as well as the COVID-19 pandemic context. Wild bootstrapping Variance Ratio test is implemented to detect the market efficiency with the most recent and abundant data. Results show that the market efficiency in the period of 2014 to 2020 is around 0.3951, less than 1, suggesting the weak form efficiency. Observing from sub-sample periods, the efficiency volatiles: climbed from 0.3621 to 0.4027, but drop to 0.3985 finally. Furthermore, the market efficiency soaring after the COVID-19, which echoes the smooth reopening and local supporting policies. To some extent, this study enlarged the impact study of COVID-19, which should be meaningful for further research. Unique contribution of this paper is providing latest evidence for the efficiency of China’s pilot carbon market, as well as proofs for soaring market efficiency after the pandemic.
ABSTRACT
We document a causal effect of conservative Fox News Channel in the United States on physical distancing during COVID-19 pandemic. We measure county-level mobility covering all U.S. states and District of Columbia produced by GPS pings to 15-17 million smartphones and zip-code-level mobility using Facebook location data. Then, using the historical position of Fox News Channel in the cable lineup as the source of exogenous variation, we show that increased exposure to Fox News led to a smaller reduction in distance traveled and smaller increase in the probability to stay home after the national emergency declaration in the United States. Our results show that slanted media can have a harmful effect on containment efforts during a pandemic by affecting people's behaviour.
Subject(s)
COVID-19ABSTRACT
As of April 26, 2020, more than 2,994,958 cases of COVID-19 infection have been confirmed globally, raising a challenging public health issue. A predictive model of the disease would help allocate medical resources and determine social distancing measures more efficiently. In this paper, we gathered case data from Jan 22, 2020 to April 14 for 6 countries to compare different models' proficiency in COVID-19 cases prediction. We assessed the performance of 3 machine learning models including hidden Markov chain model (HMM), hierarchical Bayes model, and long-short-term-memory model (LSTM) using the root-mean-square error (RMSE). The LSTM model had the consistently smallest prediction error rates for tracking the dynamics of incidents cases in 4 countries. In contrast, hierarchical Bayes model provided the most realistic prediction with the capability of identifying a plateau point in the incidents growth curve.
Subject(s)
COVID-19ABSTRACT
Since December 2019, an epidemic caused by novel coronavirus (2019-nCoV) infection has occurred unexpectedly in China. As of 8 pm, 31 January 2020, more than 20 pediatric cases have been reported in China. Of these cases, ten patients were identified in Zhejiang Province, with an age of onset ranging from 112 days to 17 years. Following the latest National recommendations for diagnosis and treatment of pneumonia caused by 2019-nCoV (the 4th edition) and current status of clinical practice in Zhejiang Province, recommendations for the diagnosis and treatment of respiratory infection caused by 2019-nCoV for children were drafted by the National Clinical Research Center for Child Health, the National Children's Regional Medical Center, Children's Hospital, Zhejiang University School of Medicine to further standardize the protocol for diagnosis and treatment of respiratory infection in children caused by 2019-nCoV.