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In recent years, the continuous spread of the COVID-19 epidemic has impacted the supply chain of enterprises. Mitigating the supply chain’s vulnerability has great significance for the survival and development of enterprises. Optimizing the business environment and building a digital government will help improve the external environment for enterprise development. However, its impact on the vulnerability of the enterprise supply chain has yet to be studied. Taking the impact of COVID-19 as an example, this paper uses the survey data of nearly 40,000 enterprises of the National Federation of Industry and Commerce in 2020 and “10,000 private enterprises evaluating the business environment”, to conduct systematic empirical research and fill the research gap in this area. The study indicates that the business environment and digital government can significantly mitigate the impact of COVID-19 on the supply chain. This conclusion is still valid after a series of robustness tests. Mechanism analysis demonstrates that the business environment and digital government can prompt the government to introduce effective mitigation measures promptly, better guarantee production factors and logistics, and thus improve the vulnerability of the enterprise supply chain. This study deepens our understanding of the economic outcome of the business environment and digital government and also sheds new light on supply chain management.

PurposeThe purpose of this paper is to explore the internal interaction mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth from a systematic perspective, based on which this work forecasts their future development trends.Design/methodology/approachIn this study, a multivariate grey model is applied to the prediction of marine scientific research and education, industrial structure upgrading and marine economic growth. Considering the impact of the COVID-19 on marine development, this paper introduces the weakening buffer operator into MGM(1,m) and constructs the AWBO-MGM(1,m) model. To verify the validity and accuracy of the new model, this paper uses AWBO-MGM(1,m), MGM(1,m), GM(1,N), GM(1,1), back propagation neural network and linear regression models for simulation and prediction based on the data from 2010 to 2021, respectively.FindingsFrom the theoretical perspective, the development of marine scientific research and education can accelerate industrial upgrading and promote marine economic growth by providing high-quality talents, promoting marine science and technology progress and reducing transaction costs; while the upgrading of marine industrial structure and marine economic growth can promote the development of marine scientific research and education by guiding social capital, enhancing talent demand and stimulating market vitality. From the empirical analysis, the AWBO-MGM(1,m) model can effectively deal with epidemic shocks and has higher fitting and prediction accuracy than the other five comparative models.Practical implicationsThe government should pay attention to the construction of marine scientific research and education, so as to provide high-quality talents and advanced scientific research results for the high-quality development of marine economy. On the basis of using science and technology to firmly build the primary and secondary marine industries, the government should actively guide the labor, capital and other factors of production to the tertiary industry, thereby promoting the optimization and upgrading of marine industrial structure.Originality/valueOn the one hand, the interplay mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth is analyzed from a systematic perspective; on the other hand, the enhanced AWBO-MGM(1,m) possesses higher forecasting performance and is applicable to the systemic multivariate forecasting problem in the presence of outstanding external shocks.

While economic growth has been the main goal of countries around the world, environmental problems such as air pollution have also arisen. Since the increase in economic uncertainty is limiting production capacity and consumers’ marginal propensity to consume, which reduces CO2 emissions, economic policy uncertainty has become one of the most important factors affecting CO2 emissions. COVID-19 has demonstrated that economic policy uncertainty reduces the enthusiasm of market participants, which, in turn, reduces energy demand and CO2 emissions. In order to further study the impact of economic policy uncertainty on air pollution, this study uses a panel model to empirically test the data for a sample of 15 countries covering the period from 1997 to 2019. According to the empirical results, we find that the economic policy uncertainty has a significant negative impact on per capita CO2 emissions. That is, the higher the uncertainty of economic policy, the lower the per capita CO2 emissions of countries. What’s more, this negative effect is larger in emerging market countries than in advanced countries.

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.

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.

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

This study aims to find the nexuses among energy efficiency, renewable energy consumption, foreign direct investment, logistics industry, manufacturing industry and global trade during the COVID-19 pandemic and their impact on global supply chains in exporting nations of the world. The data for this study has been extracted from the World Development Indicators and Statista 2021 for 13 years ranging from 2007-to 2020 for nine top exporting countries. The fixed effect panel estimation technique was implied to examine and analyze the data. The results of our study revealed that highly risky diseases significantly impact supply chain operations globally. Global supply chains, logistics and manufacturing industries significantly influence global trade operations. Our results implicate that the overall international trade and logistics can be enhanced by improving the manufacturing and logistics industries by coping with the risk of pandemic diseases. Moreover, by utilizing cost-effective, renewable and efficient energy resources companies address sustainability issues of global trade and operations. By exerting further attention to the proficiency of the levies approval process, competence and quality of logistics services, and ease of assembling competitively priced shipments, the governments can significantly enhance the export from the logistics industry. Also, increasing manufacturing and agricultural value-added healthier consequences might be acquired in global supply chain operations from the manufacturing industry.

The Boeing 737 MAX crisis and COVID-19 pandemic have seriously influenced the development of China’s aircraft leasing industry in the past two years. This paper applies system dynamics theory to explore the sustainable development of China’s aircraft leasing industry. It analyses the dynamic mechanism and constructs a system dynamics model. Based on China’s macroeconomic data and historical data from the financial, aviation, and leasing industries, it aims to stimulate the development of China’s aircraft leasing industry in the next five years. Through sensitivity analysis, this research finds that changes in GDP growth have the most obvious impact on the sustainable development of China’s aircraft leasing industry. Reducing the average financing cost and the income tax rate of aircraft leasing companies, increasing their investment in talent, and controlling risk will increase the market share of China’s aircraft leasing companies and promote the development of the industry. However, increasing the number of aircraft leasing companies has little effect on market share. On this basis, this paper proposes policy recommendations to promote the sustainable development of China’s aircraft leasing industry.