1. Introduction
As the world’s second-largest economy and the world’s largest developing country, China is facing a significant challenge in controlling carbon emissions while maintaining steady economic growth [
1]. The report of the 19th National Congress of the Community Party of China pointed out that it is necessary to establish and improve the economic system of green and low-carbon circular development, build a clean and low-carbon energy system [
2], advocate a green bottom single lifestyle, and determine the path for China to achieve green and low-carbon economic development [
3], which is the core topic of China’s future green and low-carbon development research. China proposed that it “will strive to reach its peak carbon by 2030 and strive to achieve carbon neutrality by 2060”; this is known as the double carbon goal [
4]. China must propose corresponding action routes according to its national conditions and development stages to achieve the double carbon goal. As one of the basic variables of social and economic activities, the population is an important factor influencing climate change, and it overlaps with other social, economic, and environmental factors and affects regional carbon emissions [
5].
With the improvement of residents’ income and living standards, ecology, a clean environment, and healthy food have become necessities in people’s daily lives. The development mode of earning GDP at the cost of exhausting resources and sacrificing the environment can no longer meet China’s future development needs [
6]. As a major economic province in China, Guangdong Province has been at the forefront of China’s reform and opening up since 1978. As Guangdong Province is one of the leading provinces of China’s economic development, its carbon emissions over the years also rank among the top in the country. Since the reform and opening up of China, Guangdong Province has been a province with a large population inflow in China [
7]. According to the seventh national census, the inflow population exceeded 20 million, accounting for more than 20% of the total population of Guangdong Province, ranking first in the country. As a result, changes in Guangdong’s population, carbon emissions, and the relationship between them have become significant [
8]. Existing studies on carbon emissions mainly focused on analyzing traditional factors such as population size, per capita GDP, per capita income, and energy efficiency. However, systematic research on population structure factors is lacking. This study presents an analysis from the population perspective, includes five influential factors, and conducts model analysis and empirical research on the factors of population structure from different levels. Taking various statistical data of the population in Guangdong Province from 2000 to 2020 as an example, the impact of the change of population structure on the carbon emission of Guangdong Province in recent years is studied under the background of the national population growth rate slowing down. This study investigates the relationship between population factors and local carbon emissions, so as to provide a reference for the next step of China’s population policy making to achieve the “double carbon goal”.
4. Results
The adverse effect of multicollinearity is that it produces a large variance. Appropriately reducing the variance of the parameter estimator cannot completely eliminate the problem of multicollinearity; however, it can significantly remove the impact caused by multicollinearity. Ridge regression is a method to reduce the variance of parameter estimators at the cost of introducing errors. This is an improvement of the least squares estimation. When there are multiple correlations in the independent variables, the ridge regression algorithm artificially adds a factor K to the main diagonal of the independent variable standardization matrix, which makes regression coefficient estimation biased but can significantly improve the stability of the estimation. The standard deviation of the regression coefficient is smaller than that of the least two-way estimation.
4.1. Secondary Industry Has the Highest Impact on Carbon Emissions, and Primary Industry Has the Lowest
First, based on Model 4, an extended STIRPAT model was established to observe the impact of the number of employed people in the primary, secondary, and tertiary industries on carbon emissions as demographic variables.
was the dependent variable, and
were the independent variables. SPSS software was used to carry out ridge regression fitting, and the changes in the ridge trace map and determinable coefficient
are shown in
Figure 4.
When k = 0.08, the change in the regression coefficient of each variable tends to be stable and the resulting ridge regression equation is as follows:
The corresponding standardized ridge regression equation is as follows:
The decisive coefficient of the model was 0.9212, the F-test value was 240.55, and P was 0.0000. The test results are significant. The variance expansion factor, VIF, of the standard regression coefficients of each variable was less than 10, and the maximum value was only 2.17. The overall fitting effect of the model satisfied these regression requirements. The results of ridge regression are shown in
Table 3.
According to the ridge regression results of Model 4, there is a positive correlation between the number of people employed in the primary industry, the secondary industry, and the tertiary industry and the carbon dioxide emissions in Guangdong Province. Still, there is a significant difference in the degree of impact. The secondary industry has the highest degree of impact on carbon emissions, while the primary industry has the lowest degree. This is because the increase in employment in the secondary industry means that more energy is used in daily industrial production and the manufacture of products, leading to the growth of carbon emissions.
4.2. The Population of All Ages Promotes the Growth of Carbon Emissions, and Newborns Can Inhibit the Growth of Carbon Emissions
Second, based on Model 5, an extended STIRPAT model was established to observe the impact of the total population and population age structure as demographic variables on carbon emissions, where
was the dependent variable,
were the independent variables, and SPSS software was used to carry out ridge regression fitting. The changes in the ridge trace map and resolution coefficient
are shown in
Figure 5.
When k = 0.08, the change in the regression coefficient of each variable tends to be stable and the resulting ridge regression equation is as follows:
The corresponding standardized ridge regression equation is as follows:
The decisive coefficient of the model was 0.9210, the F-test value was 109.9047, and P was 0.0000. The test results are significant. The variance expansion factor, VIF, of the standard regression coefficients of each variable was less than 10, and the maximum value was only 1.8685. The overall fitting effect of the model satisfied these requirements. The results of the ridge regression are shown in
Table 4.
According to the ridge regression results of Model 5, the total population, the proportion of young people, and the proportion of older adults in Guangdong Province are positively correlated with the carbon dioxide emissions in Guangdong Province. Still, the natural population growth in Guangdong Province is negatively associated with the carbon emissions. Thus, newborns can inhibit the growth of carbon emissions.
4.3. Rural Population and Low Educated Population Restrain the Growth of Carbon Emissions, While Urban Population and Highly Educated Population Promote It
Third, based on Model 6, an extended STIRPAT model was established to observe the impact of urban and rural structure and residents’ education level in Guangdong Province as population variables on carbon emissions, where
was the dependent variable and
were the independent variables. SPSS software was used for ridge regression fitting. Changes in the ridge trace map and determination coefficient
are shown in
Figure 6.
When k = 0.08, the change in the regression coefficient of each variable tends to be stable and the resulting ridge regression equation is as follows:
The corresponding standardized ridge regression equation is as follows:
The decisive coefficient of the model was 0.9295, the F-test value was 170.3324, and P was 0.0000. The test results were significant. The variance expansion factor, VIF, of the standard regression coefficients of each variable was less than 10, and the maximum value was only 1.6042. The overall fitting effect of the model satisfied these requirements. The results of the ridge regression are shown in
Table 5.
According to model 6 ridge regression results, the urban population and the proportion of people over 15 years old with a college education or above positively correlate with carbon dioxide emissions in Guangdong Province. In comparison, the rural population and the proportion of people over 15 years old with junior high school education or below are negatively correlated with carbon emissions in Guangdong Province. Accelerated urbanization and extensive economic growth will lead to excessive energy consumption and a lot of carbon emissions.
4.4. Married and Multi-Person Families Can Curb the Growth of Carbon Emissions
Fourth, based on Model 7, an extended STIRPAT model was established to observe the impact of household structure and marital status as demographic variables on carbon emissions, where
was the dependent variable and
were the independent variables. SPSS software was used for ridge regression fitting. Changes in the ridge trace map and determination coefficient
are shown in
Figure 7.
When k = 0.1, the change in the regression coefficient of each variable tends to be stable and the resulting ridge regression equation is as follows:
The corresponding standardized ridge regression equation is as follows:
The decisive coefficient of the model was 0.9165, the F-test value was 83.6852, and P was 0.0000. The test results were significant. The variance expansion factor, VIF, of the standard regression coefficients of each variable was less than 10, and the maximum value was only 1.6353. The overall fitting effect of the model satisfied these requirements. The results of ridge regression are presented in
Table 6.
According to the results of Model 7 ridge regression, the total population of Guangdong Province and the proportion of one person/family in Guangdong Province are positively correlated with carbon dioxide emissions in Guangdong Province, while the proportion of more than four people/family in Guangdong Province and the proportion of the married population in Guangdong Province are negatively correlated with carbon emissions.
4.5. Local Settlement of Population Can Inhibit the Growth of Carbon Emissions
Finally, based on Model 8, an extended STIRPAT model was established to observe the impact of household separation and the education level of the employed population as demographic variables on carbon emissions, where
was the dependent variable and
were the independent variables. SPSS software was used to carry out ridge regression fitting. Changes in the ridge trace map and resolvable coefficient
are shown in
Figure 8.
When k = 0.08, the change in the regression coefficient of each variable tends to be stable and the resulting ridge regression equation is as follows:
The corresponding standardized ridge regression equation is as follows:
The decisive coefficient of the model was 0.8655, the F-test value was 73.2744, and P was 0.0000. The test results were significant. The variance expansion factor, VIF, of the standard regression coefficients of each variable was less than 10, and the maximum value was only 1.9961. The overall fitting effect of the model satisfied these requirements. The results of ridge regression are presented in
Table 7.
As can be seen from the ridge regression results of Model 8, the proportion of employees with a college education or above and the proportion of employees with a junior high school education or below are positively correlated with CO2 emissions in Guangdong Province. In contrast, the proportion of the population with local households negatively correlates with carbon emissions. Therefore, local household registration can inhibit the growth of carbon emissions.
6. Conclusions
With the continuous increase in population size, the population in Guangdong Province is aging, the proportion of the working population is slowly decreasing, and the level of population urbanization is increasing. The size of the household population continues to shrink, and the proportion of large families with more than four people per household continues to decline. In addition, the total carbon emissions and per capita carbon dioxide emissions in Guangdong Province are rising. Although the energy consumption structure continues to be optimized, crude oil and coal still account for most fossil energy consumption. The improvement in efficiency has not significantly inhibited the increase in total carbon emissions. The impact of changes in population structure on carbon emissions in Guangdong Province is greater than that of population size, but different characteristics of population structure have different effects.
First, from the perspective of the urban–rural structure of the population, the improvement in the urbanization rate of the population played a role in promoting the growth of carbon emissions. As Guangdong Province is a province with a continuous population inflow, the growth of its carbon emissions has increased due to population growth.
Second, the working-age population had a greater role in promoting the growth of carbon emissions than the aging population, and the newborn population had an inhibitory effect on the growth of carbon emissions.
Third, small and large families showed opposite impacts on carbon emissions, with small families promoting the growth of carbon emissions and large families having a certain inhibitory effect.
Fourth, the proportion of local household registration showed inhibitory effects on the growth of carbon emissions. With the gradual liberalization of the registered residence system, Guangdong Province showed advantages in attracting talent, which had various inhibitory effects on the growth of carbon emissions.
Fifth, from the perspective of the education level of the population, the impact of the population over the age of 15 years on carbon emissions presents different situations. Low education levels inhibited the growth of carbon emissions, whereas high education levels promoted it. The education level of employed workers has a significant role in promoting carbon emissions, with a high education level having a smaller impact, indicating that among the urban employed population, the population with a high education level has a lower carbon production and life mode than the population with a low education level.
Sixth, from the perspective of the employment structure of different industries of the population, the populations of those employed in the primary, secondary, and tertiary industries positively affected carbon emissions, but the proportions of their impact differed. Employment in tertiary industries had a lower impact on carbon emissions.
Seventh, the level of population consumption had a high positive effect in different models, and the change in population consumption patterns may become a new growth point for carbon emissions in Guangdong Province.
Finally, from the perspective of technological progress, the reduction in carbon emission intensity caused by the optimization of the energy structure and the improvement of energy efficiency had a significant inhibitory effect on carbon emissions in Guangdong Province. However, this was far from offsetting the impact of other factors on the growth of carbon emissions. Nevertheless, technological progress indicators significantly inhibited carbon emissions in the five models, showing that Guangdong Province has great potential to improve carbon emissions through technological progress.