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.
Investing in human capital can assist in achieving technological innovations, while the spatial spillover effects of human capital on urban innovation in urban agglomeration are largely ignored. Using the panel data of 108 cities in China’s Yangtze River Economic Belt (YREB) during 2011–2020, this paper explores the interactions between human capital and urban innovation with a two-way fixed effects Spatial Durbin Model framework, which incorporates the interpretation of spatial spillover effects. The results show that urban innovation in the YREB has spatial heterogeneity in the structure, which is reflected in its diffusion from the downstream cities on the eastern coast to the upstream cities in the western region. Then, the low-level human capital inhibits the development of local innovation, while intermediate and high-level human capital improves local innovation. Furthermore, the spatial spillover effect shows an opposite trend. The impact of human capital on urban innovation is not significant in the downstream cities, such as Shanghai and Nanjing. Finally, three policy directions are proposed to optimize the human capital structure of the YREB, which are, strengthening investment in human capital and technological innovation, enhancing the talent spillover effect, and improving basic education.
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