scholarly journals How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3319
Author(s):  
Chulin Pan ◽  
Huayi Wang ◽  
Hongpeng Guo ◽  
Hong Pan

This study focuses on the impact of population structure changes on carbon emissions in China from 1995 to 2018. This paper constructs the multiple regression model and uses the ridge regression to analyze the relationship between population structure changes and carbon emissions from four aspects: population size, population age structure, population consumption structure, and population employment structure. The results showed that these four variables all had a significant impact on carbon emissions in China. The ridge regression analysis confirmed that the population size, population age structure, and population employment structure promoted the increase in carbon emissions, and their contribution ratios were 3.316%, 2.468%, 1.280%, respectively. However, the influence of population consumption structure (−0.667%) on carbon emissions was negative. The results showed that the population size had the greatest impact on carbon emissions, which was the main driving factor of carbon emissions in China. Chinese population will bring huge pressure on the environment and resources in the future. Therefore, based on the comprehensive analysis, implementing the one-child policy will help slow down China’s population growth, control the number of populations, optimize the population structure, so as to reduce carbon emissions. In terms of employment structure and consumption structure, we should strengthen policy guidance and market incentives, raising people’s low-carbon awareness, optimizing energy-consumption structure, improving energy efficiency, so as to effectively control China’s carbon emissions.

2005 ◽  
Vol 86 (1) ◽  
pp. 41-51 ◽  
Author(s):  
SYLVAIN GLÉMIN

The fate of lethal alleles in populations is of interest in evolutionary and conservation biology for several reasons. For instance, lethals may contribute substantially to inbreeding depression. The frequency of lethal alleles depends on population size, but it is not clear how it is affected by population structure. By analysing the case of the infinite island model by numerical approaches and analytical approximations it is shown that, like population size, population structure affects the fate of lethal alleles if dominance levels are low. Inbreeding depression caused by such alleles is also affected by the population structure, whereas the mutation load is only weakly affected. Heterosis also depends on population structure, but it always remains low, of the order of the mutation rate or less. These patterns are compared with those caused by mildly deleterious mutations to give a general picture of the effect of population structure on inbreeding depression, heterosis, and the mutation load.


1977 ◽  
Vol 14 (4) ◽  
pp. 586-591 ◽  
Author(s):  
Vijay Mahajan ◽  
Arun K. Jain ◽  
Michel Bergier

In the presence of multicollinearity in data, the estimation of parameters or regression coefficients in marketing models by means of ordinary least squares may give inflated estimates with a high variance and wrong signs. The authors demonstrate the potential usefulness of the ridge regression analysis to handle multicollinearity in marketing data.


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