Population Structure and Local Carbon Emission Reduction: Evidence from Guangdong, China

Abstract

Based on the data obtained on carbon emissions in Guangdong Province, China, from 1997 to 2019, this study focused on the relationship between energy consumption and population development in Guangdong Province. This study quantitatively analyzed the impact of different population structures and technological progress on carbon emissions in Guangdong Province by establishing an extended model of Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT). The results showed that the population size factor was not as good as the population structure factor for carbon emissions. In addition, different demographic factors affected carbon emissions differently with both positive and negative effects. Finally, relevant policy suggestions were proposed from the perspectives of encouraging the childbearing of appropriate-age residents, optimizing the population structure, reducing the separation of people and households, guiding residents to return to the traditional family model, guiding residents to live a low-carbon life, optimizing industrial institutions, and adjusting the energy consumption structure.

Spatiotemporal differences in and influencing effects of per-capita carbon emissions in China based on population-related factors

“…In light of the effects of population elements on per-capita carbon emissions, we advocate reverting to the traditional household structure and a large family. Previous research has noted that the decline in household size and the increase in the number of households will cause a rise in the demand for durable consumer items and basic household essentials, boosting carbon emissions 25 , and a similar finding was obtained in this investigation. We can therefore start by expanding household size and formulate policy proposals to indirectly reduce per-capita carbon emissions.…”

“…In addition, the link between population-related factors and carbon emissions has remained a considerable topic of interest. The STIRPAT model can be employed to quantify the effects of population-related factors on carbon emissions, and the findings revealed that various factors can produce unique effects, both positive and negative 25 . Therefore, more notably, considering population-related factors is necessary when examining carbon emissions based on population.…”

“…For instance, in China, the carbon emission features of the eastern, middle and western regions have been researched 51,52 . Other study regions in terms of carbon emission features include the Yellow River 53 , Yangtze River 52 , Beijing-Tianjin-Hebei region 54 and some provinces 25,55 . Among them, in terms of study regions, most investigations have concerned national and provincial strata, mainly based on geographical location, with just a few studies considering subregions based on population-related factors.…”

Research on the Performance Management of Carbon Reduction by Local Governments from a Game Perspective—The Case of the Zhejiang Power Restriction Incident

“…From an industrial perspective, formulating differentiated subsidy programs [29], improving reward and punishment mechanisms [30], and establishing governance mechanisms shared by stakeholders [31] can promote the green transformation of various industries and achieve carbon emission reduction policy goals. The literature above shows that the stakeholders in the government's administrative emission reduction are mainly the manufacturing industry, which emits carbon emissions, while ignoring a basic variable in social and economic activities-citizens [32]. From a macro perspective, demographic characteristics, such as the working-age population, family size, and educational level, have an increasingly significant impact on carbon emissions [33].…”

Realizing sustainable development goals in sub‐Saharan Africa: The role of industrialization on consumption‐based carbon emission

“…This is essential for understanding time-sensitive events and identifying interactions over time. The DCCE estimator will assist in addressing endogeneity and autocorrelation concerns in certain scenarios (Wen et al, 2023). The DCCE estimator was selected since it deals with the unobserved standard shocks using the CCE and controls the endogeneity issue (Edziah et al, 2022).…”