Probing Energy-Related CO₂ Emissions in the Beijing-Tianjin-Hebei Region Based on Ridge Regression Considering Population Factors

Abstract

The main driving force for huge energy consumption is population growth and economic development, and many studies have analyzed the factors that influence carbon dioxide emissions. But the influencing factors mainly refer to the economic and social fields. Few studies have looked at population factors, and the extended STIRPAT model and ridge regression method are used to pay attention to the impact of population factors on carbon dioxide emissions in the Beijing-Tianjin-Hebei region. The conclusions drawn are as follows: 1) For Beijing, the urbanization level, population density, per capita disposable income, education level, GDP and energy intensity have a positive impact on CO 2 emissions. However, age structure, family size and industrial structure play negative roles. The improvement of urbanization level has a distinctive positive influence on CO 2 emissions. 2) For Tianjin, most impact factors have a positive effect on CO 2 emissions, except family size and energy intensity. The decrease of family size is the first contributor to CO 2 emissions growth. 3) For Hebei, the urbanization level, population density, per capita disposable income, age structure, GDP and industrial structure, have a positive influence on CO 2 emissions. Education level, family size and energy intensity have a negative impact on CO 2 emissions, and population density is the most important factor.

The effects of nurturing pressure and unemployment on carbon emissions: cross-country evidence

“…These include population density, household size, gender structure, age structure, unemployment rate, etc. For example, Wen and Zhang ( 2020 ) insist that population density and household size have positive and negative effects on CO 2 emissions in the Beijing–Tianjin–Hebei region of China, respectively. An increase in the proportion of males can promote economic development in some regions of China, which can lead to an increase in carbon emissions (Li et al 2019a , b ).…”

Comparative Study on the Influencing Factors of the Greenhouse Gas Budget in Typical Cities: Case Studies of Beijing and Shenzhen

“…The growth rate of the registered population was 172.0%, and the registered population number was greater than the increase in the number of households, causing the household size in Shenzhen to increase continuously. It has been shown that an increase in the number of urban households and a decrease in the household size can cause increased emissions in terms of household energy and other aspects [70], and the household size in Beijing has been decreasing in recent years; thus, CO 2 emissions are expected to increase as the household size decreases. Significant increases in the number of households and the registered population of Shenzhen will bring more demands for energy and other resources, so an increase in the household size will increase Shenzhen's greenhouse gas emissions.…”

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

“…Aging population and educational levels are also factors in carbon emissions. Yang et al ( 2015 ) and Wen and Zhang ( 2020 ) showed that the change of population age structure in Beijing had a significant positive impact on carbon emissions, and the continuous expansion of aging population would continue to increase environmental pressure. Liddle and Lung ( 2010 ) found that people over 65 years old in developed countries had a positive impact on residential energy consumption.…”