Carbon emissions research based on regional perspective is necessary and helpful for China to achieve its reduction targets. This research aims at analyzing the energy-related carbon emissions and finding out the most important driving forces for the carbon emissions increments in Guangdong province. LMDI (Logarithmic Mean Divisia Index) method based on the extended Kaya identity has been used to explore the main driving factors for energy-related carbon emissions in Guangdong province annually between 1990 and 2014. Research results show that the impacts and influences of various factors on carbon emissions are different in the different development stages. Economic growth effect and population size effect are the two most important driving factors for the increased carbon emissions. Energy intensity effect played the dominant role in curbing carbon emissions. Energy structure effect and technical progress effect had different but relatively minor effects on carbon emissions during the five different development stages.

In order to explore the main driving factors of housing prices in China, we used the data of housing prices in China's 116 cities, and analyzed the impact of 3 factors including housing demand, supply and cost on housing prices based on the method of Gray Relational Degree. The results show that: The land price has the most evident driving effect on the housing price differentiations in China's cities; the driving effects from population aggregation and residential land supply are also very remarkable; the supply-demand theory and cost theory could be adopted simultaneously in explaining the housing price differentiation phenomenon in China.