China's Carbon Emissions Peaking Pathway in the Post-COVID-19 Era (preprint)

Several countries have weakened the carbon emission objectives to immediately revive the economy in the post-COVID-19 era. Therefore, it is a challenge worth addressing to readjust the economic development and carbon emissions after the COVID-19 pandemic. From the perspective of China's carbon emissions, this study shapes a multi-objective dynamic optimization model based on the material capital input and R&D support aspects. The proposed model imitates China's economic development, energy consumption, and carbon dioxide (CO2) emissions. The model provides theoretical suggestion for the government to revive economic development and reduce carbon emissions. In addition, this research paper compares the evolutionary path of carbon peak under the two scenarios. The first scenario requires maintaining the pre-epidemic development state and pace of carbon emission reduction, referred to as the baseline scenario (BS). The second scenario is termed the optimal scenario (OS) based on the model calculation. The study findings exhibit that China is not able to accomplish the 2030 CO2 emission peak objective, under the BS. However, China under the OS shall expectedly accomplish the 2030 carbon peak objective ahead of schedule, while the peak CO2 emissions shall be around 11.28 billion tons. Reportedly, at least 788 million tons of CO2 reduction contrasted with the BS. Further, there is an 80.35% decline in energy intensity as compared to 2005. Consequently, the study results contribute theoretical guidance for the "green recovery" of China's economy and the adjustment of carbon emission reduction’s path after the COVID-19 epidemic. Consistent with this, the research method also contributes to the theoretical research on carbon emissions at the national level while extending a new research perspective for the economic- and environmental fields.

Modeling the Spread of Epidemics Based on Cellular Automata

Mathematical modeling is a powerful tool to study the process of the spread of infectious diseases. Among various mathematical methods for describing the spread of infectious diseases, the cellular automaton makes it possible to explicitly simulate both the spatial and temporal evolution of epidemics with intuitive local rules. In this paper, a model is proposed and realized on a cellular automata platform, which is applied to simulate the spread of coronavirus disease 2019 (COVID-19) for different administrative districts. A simplified social community is considered with varying parameters, e.g., sex ratio, age structure, population movement, incubation and treatment period, immunity, etc. COVID-19 confirmation data from New York City and Iowa are adopted for model validation purpose. It can be observed that the disease exhibits different spread patterns in different cities, which could be well accommodated by this model. Then, scenarios under different control strategies in the next 100 days in Iowa are simulated, which could provide a valuable reference for decision makers in identifying the critical factors for future infection control in Iowa.