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Pumping elephantThe COVID-19 pandemic has adversely affected the lives of people around the world in millions of ways . Due to this severe epidemic, all countries in the world have been affected by all aspects, mainly economic. It is widely discussed that the COVID-19 outbreak has affected the world economy. When considering this dimension, this study aims to examine the impact of the COVID-19 pandemic on the world economy, socio-economics, and sustainability. In addition, the research focuses on multiple aspects of social well-being during the pandemic, such as employment, poverty, the status of women, food security, and global trade. To this end, the study used time series and cross-sectional analysis of the data. The second-hand data used in this study comes from the websites of major international organizations. From the analysis of secondary data, the conclusion of this article is that the impact of the pandemic is huge. The main finding of the thesis is that the social economy is affected by the pandemic, causing huge losses in terms of economic well-being and social capital.

PM(2.5) pollution has produced adverse effects all over the world, especially in fast-developing China. PM(2.5) pollution in China is widespread and serious, which has aroused widespread concern of the government, the public and scholars. This paper evaluates the evolution trend and spatial pattern of PM(2.5) pollution in China based on the data of 281 prefecture-level cities in China from 2007 to 2017, and reveals the pollution situation of PM(2.5) and its relationship with industrial restructuring and technological progress by using spatial dynamic panel model. The results show that China's PM(2.5) pollution has significant path dependence and spatial correlation, and the industrial restructuring and technological progress have significant positive effects on alleviating PM(2.5) pollution. As a decomposition item of technological progress, technical change effectively alleviates PM(2.5) pollution. Another important discovery is that the interaction between industrial restructuring and technological progress will aggravate PM(2.5) pollution. Finally, in order to effectively improve China's air quality, while advocating the Chinese government to pursue high-quality development, this paper puts forward a regional joint prevention mechanism.

AbstractMega‐city regions (MCRs) have emerged as the main spatial form of China's new urbanization strategy and become the basic spatial units participating in global and regional competition. However, MCRs are not equally capable of boosting regional economic development due to their different levels of development. Therefore, this paper adopts the concept of competitiveness as both a theoretical framework and an empirical model to evaluate the development status of China's MCRs. Based on a review of the existing literature, this paper proposes a multi‐tier evaluation system to calculate the competitiveness of 13 MCRs. The chosen indicators come from the six perspectives of economic development, human resources, infrastructural accessibility, integration into the global economy, capacity for scientific and technological innovation, and sustainable development. The results show that there are great disparities and regional inequalities in competitiveness across different MCRs. The Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Tangshan MCRs are the first‐tier MCRs with the highest levels of development and have significant global influence as well. Chengdu–Chongqing, Shandong Peninsula, South‐central Liaoning, and Wuhan belong to the second‐tier of MCRs that show partial advantages and have significant regional influence. The remaining regions belong to the third‐ or fourth‐tier of MCRs that have relatively weak competitiveness. The competitiveness of MCRs largely depends on the concentration of core elements in core cities.

Against the backdrop of innovation fault-tolerance, government compensation for failed generic technological innovation projects is beneficial to stimulate re-innovation behaviour. However, considering the information asymmetry, a collusion tendency exists between the compensated party and the evaluator during the process of compensation. To prompt the government to build collusion-proof mechanisms to reduce collusion loss, the evolutionary game method was used to build replicated dynamic equations and a Jacobian matrix of both sides based on the information topology between the conspirators and regulator. Through the evolutionary equilibrium analysis and numerical simulation, evolutionary stability strategies (ESS) under different topological relationship information (non-intersect, partial intersect, and inclusive type) were found. Results show that the collusion behaviour can be effectively restrained when the government is unaware of collusion information, the net defense income is positive, and the penalty threshold is the product of the net collusive income and the ratio of the collusive and regulatory information. With the increasing amount of collusive information available to the government, the conspirators tend to adhere to moral principles subject to strict regulation. In addition, the moderating effect of penalty factor is positively correlated with the private information possessed by both sides. The conclusion is beneficial to provide theoretical support for optimizing the government-led compensation mechanism for failed generic technological innovation projects.

Existing studies find that exploratory innovation requires both access to heterogeneous resources for the establishment of novel knowledge combinations and high-quality cooperation to strengthen the absorption of heterogeneous knowledge. Therefore, structural holes and prominent positions exert critical effects on exploratory innovation. Few existing studies have investigated the influences of the two positions on exploratory innovation simultaneously. This study aims to identify the influence of network positions on exploratory innovation and the contingency mechanism. Basing on joint patent filing relationships identification belong to IPC Green Inventory, R&D collaboration networks in China consisted of 215 firms in the technological field of low-carbon energy were constructed. Negative binomial regression was used to analyse the influences of networks positions on exploratory innovation and the moderating effect of network density. Results reveal that network prominence and structural holes have inverted U-shaped effects on exploratory innovation. The simultaneous occupation of prominent positions and structural holes hinders exploratory innovation. Network density exerts positive and negative effects on the relationships between network prominence and exploratory innovation and between structural holes and exploratory innovation, respectively. The conclusions drawn in this study provide a reference for R&D network structural optimization toward exploratory innovation.

This paper examines the effect of energy costs on industry export competitiveness. Most studies in the literature use direct energy consumption (energy consumption at the final stage of production) and domestic energy prices to compute energy costs faced by domestic industries. Using multi-country input-output information, this study measures the effect of aggregate energy costs on export performance, where aggregate energy costs include not only direct energy costs, but also indirect energy costs passed on through the upstream supply chain. This study develops a theoretical trade model that incorporates tradable intermediate goods to inform its empirical strategy. It then estimates a reduced-form model using a panel data for 10 manufacturing sectors in 43 countries from 1991 to 2012. The analysis finds that ignoring input-output relationships can lead to significant over- or underestimates of the effect of energy price shocks on exports, depending on intermediate factor intensities and trade relationships. Using estimated trade elasticities, the study simulates the economic consequences of energy cross-subsidies and carbon taxes. The results show that energy cross-subsidies that raise energy tariffs on industry to support lower rates for households and farmers in India could reduce the country's net manufacturing exports by $6.1 billion a year. Similarly, a carbon tax that unilaterally increases energy prices by 10 percent in the European Union could reduce European Union-wide net manufacturing exports by 1.9 percent annually.