East and south coastal China is made up of a cluster of six developed provinces whose CO2 emissions account for one third of the total CO2 emissions in China. As such, it is meaningful to predict carbon emissions in this region to assess whether China can achieve emission reduction targets. This paper employed STIRPAT to analyze the factors impacting the carbon emissions of this area from 2000 to 2015, including population (POP), urbanization (UR), GDP per capita (GDP), energy intensity (EI), and industrial structure (IS). The results showed that GDP was mainly responsible for increasing carbon emissions while EI played a significant role in reducing it. Considering the importance of GDP, EI, and IS obtained from regression analysis, basic, highest-rate, middle, and advanced scenarios were set to predict carbon emissions according to different change rates. In the basic scenario, carbon intensity was reduced by 48.5% in 2020 compared to 2005, which was slightly higher than the national target of 40-45%, and was reduced by 59.7% in 2030, which was close to a 60-65% reduction. Nevertheless, in the advanced scenario, carbon intensity was reduced by 51.7% in 2020 and 69.1% in 2030 compared to 2005, which were higher than the national targets. Therefore, improving energy efficiency, optimizing energy structure, and adjusting industrial structure were suggested to be major strategies for carbon intensity mitigation. © 2018 by the authors.