The COVID-19 outbreak is a manifestation of the contradiction between man and land. Geography plays an important role in epidemic prevention and control with its cross-sectional characteristics and spatial perspective. Based on a systematic review of previous studies, this paper summarizes the research progress on factors influencing the spatial spread of COVID-19 from the research content and method and proposes the main development direction of geography in epidemic prevention and control research in the future. Overall, current studies have explored the factors influencing the epidemic spread on different scales, including global, national, regional and urban. Research methods are mainly composed of quantitative analysis. In addition to the traditional regression analysis and correlation analysis, the spatial lag model, the spatial error model, the geographically weighted regression model and the geographic detector have been widely used. The impact of natural environment and economic and social factors on the epidemic spread is mainly reflected in temperature, humidity, wind speed, air pollutants, population movement, economic development level and medical and health facilities. In the future, new technologies, new methods and new means should be used to reveal the driving mechanism of the epidemic spread in a specific geographical space, which is refined, multi-scale and systematic, with emphasis on exploring the factors influencing the epidemic spread from the perspective of spatial and behavioral interaction, and establish a spatial database platform that combines the information of residents’ cases, the natural environment and economic society. This is of great significance to further play the role of geography in epidemic prevention and control.
Background: Guided by the green transformation of economic development, incorporating carbon peaking and carbon neutrality into ecological progress, and accelerating the formation of an industrial and energy structure that saves resources and protects the environment are the intrinsic requirements for China's high-quality economic development. The paper uses the DEA model and Malmquist productivity index to measure the economic development performance of 30 provincial units in mainland China, and summarizes the spatial-temporal evolution characteristics. Then, the Tobit model is used to analyze the influencing factors.
Results: Our results show that: (1) The static performance of economic development generally showed an upward trend from 2008 to 2020, except for Beijing, Jilin, Heilongjiang, Guangdong, Hainan, Ningxia and Xinjiang, most of provinces had different degrees of input redundancy and output insufficiency. (2) The spatial distribution pattern of the static performance under carbon emissions constraint is dominated by the higher and high-level areas. As time goes by, the number of provincial units in different level areas tends to be stable, and the spatial distribution shows staggered layout characteristics. (3) The dynamic performance of economic development shows a downward trend from 2008 to 2016, and an upward trend from 2016 to 2020. The dynamic performance in most of provincial units has realized the transformation from the constraint of technological progress to the constraint of scale efficiency. (4) The urbanization level, economic development level, energy efficiency and the vegetation coverage are the main factors affecting economic development performance, while other factors such as industrialization level, environmental regulation, motorization level, openness and government intervention have no significant effect.
Conclusions: This study suggests that China should adopt industrial structure transformation and upgrading, strengthen environmental regulation, promote new energy vehicles and introduce high-tech industries to improve economic development performance in the future.
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