Under the “new normal”, China is facing more severe carbon emissions reduction targets. This paper estimates the carbon emission data of various provinces in China from 2008 to 2014, constructs a revised gravity model, and analyzes the network structure and effects of carbon emissions in various provinces by using social network analysis (SNA) and quadratic assignment procedure (QAP) analysis methods. The conclusions show that there are obvious spatial correlations between China’s provinces and regions in terms of carbon emissions: Tianjin, Shanghai, Zhejiang, Jiangsu and Guangdong are in the center of the carbon emission network, and play the role of “bridges”. Carbon emissions can be divided into four blocks: “bidirectional spillover block”, “net beneficial block”, “net spillover block” and “broker block”. The differences in the energy consumption, economic level and geographical location of the provinces have a significant impact on the spatial correlation relationship of carbon emissions. Finally, the improvement of the robustness of the overall network structure and the promotion of individual network centrality can significantly reduce the intensity of carbon emissions.

Urbanization is an important factor in the growth of carbon emissions, as the city is a dense area of carbon emissions. This paper estimates the carbon emissions at the provincial, municipal, and county spatial scales in the Yangtze River Delta region during 2008–2015. On this basis, this paper makes a comprehensive analysis of the pathway and difference of the urbanization to the carbon emission by using the scale variance decomposition method, the space correlation analysis method, the mediation effect test method, and the space panel data model. The results show that the urbanization of the Yangtze River Delta has a significant positive impact on carbon emissions; The pathway from urbanization to industrial structure has a significant impact on carbon emissions. Although the pathway from industrial structure to urbanization to carbon emissions is insignificant, the industrial structure directly affects carbon emissions. There is a significant path from urbanization to the level of economic development to carbon emissions, but there is no mechanism for the economic development level to adversely affect the level of urbanization and thus affect carbon emissions; the chain action pathway from the urbanization level to the employment level to the economic development level to carbon emissions is not significant. Finally, based on the research conclusions, the corresponding policy recommendations are submitted.

Population mobility accelerates urbanization convergence and mitigates the negative impact of the spatial agglomeration effect on urbanization convergence, which is the most important conclusion in this paper. Taking 38 cities in China’s three urban agglomerations (the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region) from 2005 to 2016 as research subjects, the study first shows that there is a large gap in the level of urbanization between the three major urban agglomerations, but the gap has been constantly narrowed and presents a trend of absolute convergence and conditional convergence. Furthermore, without adding a population mobility variable, the combination of the diffusion effect of high-urbanization cities and the high growth rate of low-urbanization cities causes the inter-regional urbanization level to be continuously convergent in the Yangtze River Delta region; however, the combination of the agglomeration effect of high-urbanization cities and the high growth rate of low-urbanization cities causes the inter-regional urbanization to be divergent in the Pearl River Delta and the Beijing–Tianjin–Hebei region. Under the influence of population mobility, the “catch-up” effect in low-urbanization regions is greater than the agglomeration effect in high-urbanization regions, which promotes the continuous convergence of inter-regional urbanization.