• Energy Research
• CN close
• CA close
• COVID-19 close

Owing to the pandemic of Coronavirus disease 2019 (COVID-19), the demands on ultracold-chain logistics have rapidly increased for the storage and transport of mRNA vaccines. Herein, we report a soluble luminescent thermometer based on thermally activated dual-emissions of Mn 2+ -alloyed 2D perovskite quantum wells (QWs). Owing to the Mn 2+ alloying, the binding energy of perovskite QW exciton is reduced from 291 to 100 meV. It facilitates the dissociation of excitons into free charge carriers, which are then transferred and trapped on Mn 2+ . The temperature-dependent charge transfer efficiency can be tuned from 8.8% (−93 °C) to 30.6% (25 °C), leading to continuous ratiometrical modulation from exciton-dominated violet emission to Mn 2+ -dominated orange emission. The highest sensitivity (1.44% per K) is approximately twice that of the Mn 2+ -doped chalcogenide quantum dots. Taking advantage of highly reversible color switching, Mn 2+ -alloyed QWs provide an economical solution to monitor the ultracold-chain logistics of the COVID-19 vaccine.

This paper proposes the dynamic inoperability input–output model (DIIM) to analyze the economic impact of COVID-19 in Shanghai in the first quarter of 2022. Based on the input–output model, the DIIM model introduces the sector elasticity coefficient, assesses the economic loss of the system and the influence of disturbances on other sectors through sectoral dependence, and simulates the inoperability and economic loss changes through time series. A multi-evaluation examination of the results reveals that the degree of inoperability of sub-sectors is inconsistent with the ranking of economic losses and that it is hard to quantify the impact of each sector directly. Different from the traditional DIIM model that only considers the negative part of the disaster, the innovation of this paper is that the negative value of the inoperability degree is used to measure the indirect positive growth of sectors under the impact of the Shanghai pandemic shock. At the same time, policymakers need to consider multi-objective optimization when making risk management decisions. This study uses surrogate worth trade-off to construct a multi-objective risk management framework to expand the DIIM model to enable policymakers to quantify the trade-off between economic benefit and investment costs when making risk management decisions.

This paper investigates the multidimensional risk spillovers among crude oil, the US and Chinese stock markets during the COVID-19 epidemic through a GARCHSK-Mixed Copula-CoVaR-Network method. Firstly, we find that during the COVID-19 period, the oil-stock risk spillovers are obviously stronger than those during the normal period. And there are significant risk spillovers from the US and Chinese stock markets to the oil markets. It is also discovered that the oil markets are greatly influenced by the second board stock markets, also known as the growth enterprise markets, especially during the COVID-19 outbreak. Furthermore, the bidirectional China-oil risk spillovers during the COVID-19 pandemic have rapidly increased. Besides, it is reported that the relationships across oil futures, main board and second board stock markets in the US and China are stable under different TSI levels and extreme events. Finally, the GARCHSK-Mixed Copula-CoVaR-Network outperforms the control groups in terms of marginal distribution and dependence structure. Our study not only offers new method and insight into the oil-stock relationship, but also has economic implications for investors and policymakers.

Macroeconomic stability is the core concept of sustainable development. However, the coronavirus disease (COVID-19) pandemic has caused government debt problems worldwide. In this context, it is of practical significance to study the impact of government debt on economic growth and fluctuations. Based on panel data of 30 provinces in China from 2012 to 2019, we used the Mann–Kendall method and Kernel Density estimation to analyze the temporal and spatial evolution of China’s provincial government debt ratio and adopted a panel model and HP filtering method to study the impact of provincial government debt on economic growth and fluctuation. Our findings indicate that, during the sample period, China’s provincial government debt promoted economic growth and the regression coefficient (0.024) was significant. From different regional perspectives, the promotion effect of the central region (0.027) is higher than that of the eastern (0.020) and western regions (0.023). There is a nonlinear relationship between China’s provincial government debt and economic growth, showing an inverted “U-shaped” curve. Fluctuations in government debt aggravate economic volatility, with a coefficient of 0.009; tax burden fluctuation and population growth rate aggravate economic changes. In contrast, the optimization of the province’s industrial structure and the improvement of the opening level of provinces slow down economic fluctuations.

The fluctuation of iron ore prices is one of the most important factors affecting policy. Therefore, the accurate prediction of iron ore prices has significant value in analysis and judgment regarding future changes in policies. In this study, we propose a correlation analysis to extract eight influencing factors of iron ore prices and introduce multiple linear regression analysis to the prediction. With historical data, we establish a model to forecast iron ore prices from 2020 to 2024. Taking prices in 2018 and 2019 as samples to test the applicability of the model, we obtain an acceptable level of error between the predicted iron ore prices and the actual prices. The prediction model based on multiple linear regression has high prediction accuracy. Iron ore prices will show a relatively stable upward trend over the next five years without the effects of COVID-19.

While past experiences show that a health system financing mechanism can support resilience to shocks, the impact on the sustainability of the financing system is exceptionally important considering the magnitude of the COVID-19 pandemic. The role of Social Health Insurance (SHI) in responding to the pandemic brings about an influence on insurance system sustainability. This study investigates the impact of China's COVID-19 treatment policy on the sustainability of its SHI system, explores influences of the policy on Wuhan's system, and discusses the effects of an assumed equivalent emergency on SHI funds for five other provincial capital cities in China.The study was conducted using pay-as-you-go actuarial models of Urban Employee Basic Medical Insurance (UEBMI) and Urban and Rural Residents Basic Medical Insurance (URRBMI) funds, which constitute China's basic health insurance system. Current and accumulated balances of the funds in 2020 are predicted and utilized to measure the sustainability of health insurance funds during emergencies.The findings suggest a disparity in the capacities of insurance schemes and localities. If the surplus before 2018 is not considered, it is likely that the URRBMI fund of Wuhan would suffer a deficit, whereas the UEBMI would retain a considerable surplus. To maintain the current actuarial balance of the URRBMI fund, coverage for ordinary inpatient and outpatient expenses would have to be significantly reduced in Wuhan, potentially affecting enrollees' wellbeing. A similar situation may occur in three other cities, some with underdeveloped economies and lower per capita income are likely to be encountered with worse situation than Wuhan.Concerning fragmentation of China's SHI system, to strengthen longer-term preparedness to manage future emergencies, this study suggests the integration of insurance schemes and provincial pooling, fund balance adjusting and an emergency safety net are also advised. All options call for more public health investments.

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

The COVID-19 pandemic disrupted global supply chains, posing significant challenges to the textile and clothing industry. However, amidst these threats, there is a unique opportunity to rebuild a more sustainable and resilient fashion industry. This paper explores the shift from pre-COVID-19 business models and consumption values towards a renaissance characterized by real sustainability. By examining the transition from eco-efficiency to eco-effectiveness and from cradle-to-grave to cradle-to-cradle approaches, this study emphasizes the need for the active involvement of key stakeholders in co-creating value for all. Through a comprehensive analysis of the post-COVID era, this study aims to understand textile and clothing industry professionals’ perceptions and attitudes towards sustainable design practices and identify challenges in implementing such strategies. The findings contribute to the knowledge of sustainability in the post-COVID era and provide insights and strategies for textile and clothing industry players to thrive in a sustainable and transformed landscape.