How does population aging affect carbon emissions?-analysis based on the multiple mediation effect model.

This paper explores the effect mechanism of population aging on carbon dioxide emissions using provincial panel data in China from 2005 to 2019 and examines how aging affects carbon dioxide emissions through stepwise regression test and Sobel test respectively by constructing a multiple mediating effect model. The following conclusions have been drawn: (1) the results of the baseline regression show that population aging has a significant inhibitory effect on carbon emissions. (2) The results of intermediary effect test show that the intermediary effect of population aging through technological progress, industrial structure, consumption level, and consumption structure is significant. (3) The results of heterogeneity test show that the mediating effect of technological progress is not significant in the eastern region, central region, and western region, and the mediating effect of industrial structure and consumption structure is significant in the eastern region and central region, and the mediating effect of consumption level is significant in the eastern region and western region. (4) The results of the robustness test also verify the mediating effect conclusion once again and prove the robustness of the results. Overall, as an aging society country, China should take the impact of aging into consideration more in the process of achieving emission peak and carbon neutrality.

Forecasting the Number of New Coronavirus Infections Using an Improved Grey Prediction Model.

BACKGROUND: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. METHODS: The number of new coronavirus infections was characterized by "small data, poor information" in the short term. The grey prediction model provides an effective method to study the prediction problem of "small data, poor information". Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). RESULTS: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. CONCLUSION: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts forward specific suggestions.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.