Carbon trading and COVID-19: a hybrid machine learning approach for international carbon price forecasting.

Accurate carbon price forecasting can better allocate carbon emissions and thus ensure a balance between economic development and potential climate impacts. In this paper, we propose a new two-stage framework based on processes of decomposition and re-estimation to forecast prices across international carbon markets. We focus on the Emissions Trading System (ETS) in the EU, as well as the five main pilot schemes in China, spanning the period from May 2014 to January 2022. In this way, the raw carbon prices are first separated into multiple sub-factors and then reconstructed into factors of 'trend' and 'period' with the use of Singular Spectrum Analysis (SSA). Once the subsequences have been thus decomposed, we further apply six machine learning and deep learning methods, allowing the data to be assembled and thus facilitating the prediction of the final carbon price values. We find that from amongst these machine learning models, the Support vector regression (SSA-SVR) and Least squares support vector regression (SSA-LSSVR) stand out in terms of performance for the prediction of carbon prices in both the European ETS and equivalent models in China. Another interesting finding to come out of our experiments is that the sophisticated algorithms are far from being the best performing models in the prediction of carbon prices. Even after accounting for the impacts of the COVID-19 pandemic and other macro-economic variables, as well as the prices of other energy sources, our framework still works effectively.

Latent profiles of psychological status among populations cumulatively exposed to a flood and the recurrence of the COVID-19 pandemic in China.

Henan Province in Central China was hit by unprecedented, rain-triggered floods in July 2021 and experienced a recurrence of the COVID-19 pandemic. The current study aims to identify the latent profiles of psychological status and acceptance of change among Henan residents who have been cumulatively exposed to these floods and the COVID-19 pandemic. A total of 977 participants were recruited. Latent profile analysis (LPA) was used to explore underlying patterns of psychological status (i.e., perceived risk of the COVID-19 pandemic, post-traumatic stress symptoms, anxiety and rumination) and acceptance of change. The predictors were evaluated with multinomial logistic regression. LPA identified four patterns of psychological status and acceptance of change: high distress/high acceptance (5.1%), moderate distress/moderate acceptance (20.1%), mild distress/mild acceptance (45.5%), and resilience (29.3%). The additive impact of the floods and COVID-19 pandemic and negative emotion during the floods were the risk factors, while flood coping efficacy, trust, and a closer psychological distance change were the protective factors. The present study therefore provides novel evidence on psychological status after both a natural disaster and a major public health event. The cumulative effects of the floods and the COVID-19 pandemic may have heightened the risk of post-disaster maladaptation. A complex relationship between psychological outcomes and acceptance of change was also found. The findings of this study thus provide a foundation for both disaster management and psychological assistance for particular groups.

An observation approach in evaluation of ozone production to precursor changes during the COVID-19 lockdown.

The increase of surface ozone during the Corona Virus Disease 2019 (COVID-19) lockdown in China has aroused great concern. In this study, we combine 1.5 years of measurements for ozone, volatile organic compounds (VOCs), and nitrogen oxide (NOX) at four sites to investigate the effect of COVID-19 lockdown on surface ozone in Dongguan, an industrial city in southern China. We show that the average concentrations of NOX and VOCs decreased by 70%-77% and 54%-68% during the lockdown compared to pre-lockdown, respectively. Based on the source apportionment of VOCs, the contribution of industrial solvent use reduced significantly (86%-94%) during the lockdown, and climbed back slowly along with the re-opening of the industry after lockdown. A slight increase in mean ozone concentration (3%-14%) was observed during the lockdown. The rise of ozone was the combined effect of substantial increase at night (58%-91%) and small reduction in the daytime (1%-17%). These conflicting observations in ozone response between day and night to emission change call for a more detailed approach to diagnostic ozone production response with precursor changes, rather than directly comparing absolute concentrations. We propose that the ratio of daily Ox (i.e. ozone + NO2) enhancement to solar radiation can provide a diagnostic parameter for ozone production response during the lockdown period. Smaller ratio of daily OX (ozone + NO2) enhancement to solar radiation during the lockdown were observed from the long-term measurements in Dongguan, suggesting significantly weakened photochemistry during the lockdown successfully reduces local ozone production. Our proposed approach can provide an evaluation of ozone production response to precursor changes from restrictions of social activities during COVID-19 epidemic and also other regional air quality abatement measures (e.g. public mega-events) around the globe.

A scientificity and feasibility evaluation of COVID-19 clinical studies registered in China.

BACKGROUND: An outbreak of novel coronavirus infection in Wuhan, China, in early 2020 has now developed into a worldwide pandemic. Researchers in China and around the world have conducted many clinical studies on the scientific response to infectious diseases. Here, we review and summarize the registration protocols for clinical research of the novel coronavirus disease (COVID-19). METHODS: We searched all the registered studies in all platforms under the International Clinical Trials Registry Platform (ICTRP) of the World Health Organization (WHO) before March 11, 2020, screened the registration scheme for novel coronavirus, extracted the basic research information, research quality, feasibility information, and described the scientificity and feasibility of interventional research. RESULTS: From January 23, 2020 to March 11, 2020, 379 clinical studies were registered, 260 were therapeutic studies among them, and 96.8% studies were conducted in China by Chinese researchers. Some studies were registered 2 weeks just after the virus was identified, and up to 22 studies registered for one day. The number of interventional studies was greater than observational studies (263 vs. 116). Among the interventional studies, nearly 50% studies were funded, 87.7% were randomized, and 79.4% of the primary outcome indicators were objective. However, the sample size of the studies ranged from 60 to 200, with the total sample size accounting for 40% of the confirmed infected population. More than 60% studies might face the problem of insufficient sample size. CONCLUSIONS: COVID-19 clinical study registration has been considerable, rapid, and high quality in study design. However, the feasibility of these studies may still be problematic, especially in such as insufficient sample size, poor coordination among multidisciplinary teams, and weak quality control in the research process.