Spatiotemporal evolution and influencing factors of China's economic development performance under carbon emission constraints.

BACKGROUND: China's high-quality economic development depends on achieving sustainable economic development, reaching peak carbon emissions, achieving carbon neutrality, and intensifying the development of an industrial and energy structure that saves resources and protects the environment. This study used the data envelopment analysis (DEA) model and the Malmquist productivity index to measure the economic development performance of mainland China under carbon emission constraints. Then, it described the spatiotemporal evolution of economic development performance and analyzed its influencing factors using the Tobit model. RESULTS: The results revealed that there were obvious differences in the trends of the static and dynamic performance of economic development. On the one hand, the static performance of economic development exhibited an upward trend from 2008 to 2020. Its distribution characteristics were dominant in the higher and high-level areas. On the other hand, the dynamic performance had a downward trend from 2008 to 2016 and then an upward trend from 2016 to 2020. In most provinces, the dynamic performance was no longer constrained by technological progress but rather by scale efficiency. It was found that the main factors influencing economic development performance were urbanization level, energy efficiency, vegetation coverage, and foreign investment, while other factors had no significant influence. CONCLUSIONS: This study suggests that China should improve its economic development performance by increasing the use of clean energy, promoting human-centered urbanization, increasing carbon absorption capacity, and absorbing more foreign capital in the future.

Carbon allowance allocation based on comprehensive performance of carbon emissions: Case of typical industries in Zhengzhou.

Studying the comprehensive performance of industrial carbon emission has profound significance for improving carbon allowance allocation scheme and achieving the carbon neutrality target. The paper selects 181 enterprises in Zhengzhou as the case, a comprehensive carbon emission performance indicator system and a carbon allowance allocation model were established, and compared with other allocation schemes (historical/baseline method). The results showed that the overall differences in the comprehensive performance evaluation indicator of carbon emissions in typical industries in Zhengzhou were obvious, and there was a correlation with the characteristics of industrial production activities. The overall emission reduction of Zhengzhou was 244.33×103t, and the emission reduction ratio was 7.94% by simulating carbon allowance allocation under the comprehensive performance. The carbon allowance allocation method based on the comprehensive performance has the strongest restraint on the "high emission, low performance" industry, which is more equitable and more conducive to carbon emission reduction. In the future, it will be recommended to give full play to the leading role of the government, implement industrial carbon allowance allocation based on the comprehensive performance evaluation of carbon emissions, to achieve multi-objectives of resource conservation, environmental pollution abatement, and carbon reduction.

A Review of Influencing Factors on Spatial Spread of COVID-19 Based on Geographical Perspective.

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.

Metformin upregulates E-cadherin and inhibits B16F10 cell motility, invasion and migration.

Malignant melanoma is a highly metastatic cancer, and has a poor prognosis once metastasis has occurred. E-cadherin downregulation is associated with a poorer prognosis in various types of cancer, including lung, ovarian, cervical and prostate. In the majority of cancer cell lines, E-cadherin upregulation inhibits cell motility, migration and invasiveness, and reduces tumor metastasis in in vivo models. In the present study, the inhibitory effects of metformin on the motility, invasion and migration of the B16F10 murine melanoma cell line, and the possible molecular mechanisms underlying this effect were investigated. B16F10 cells were treated with various concentrations of metformin for 24 h and their motility, migration and invasion were tested using a wound-healing assay, a migration assay and a matrigel invasion assay, respectively. Furthermore, the expression of E-cadherin was measured by immunocytochemistry, western blotting and reverse transcription-quantitative polymerase chain reaction. The results showed that metformin effectively upregulated the expression of E-cadherin, and inhibited B16F10 cell motility, migration and invasion, in a dose-dependent manner. This suggested that the inhibition of motility, migration and invasion of B16F10 cells by metformin may be associated with the upregulation of E-cadherin expression, indicating that metformin may have a role in the treatment of melanoma.