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Carbon dioxide (CO 2 ) emissions have become a rising concern in China. Few studies have considered spatial correlation and agglomeration effect of CO 2 emissions for adjacent regions and provinces. This paper employs Geographical Weighted Regression (GWR) model to examine the impact of urbanization, industrial structure and energy intensity on CO 2 emissions and reveals the spatial correlation in different provinces in 2005, 2008, 2011 and 2015. The results indicate that there is an obvious spatial effect on CO 2 emissions of each province based on the GWR results. Urbanization is the most significant factor in the increase of CO 2 emissions for all provinces in each year. For the neighboring provinces, a coordinated low-carbon urban construction plan should be carried out based on the urbanization development level. Energy intensity has a positive effect on CO 2 emissions, but the effect on the emission reduction is relatively weak and unstable. It should strengthen exchanges and cooperation between provinces and regions by jointly exploring and promoting technologies to improve the efficiency of resource use and reduce CO 2 emissions. The influence of industrial structure on CO 2 emissions is positive, indicating that the industrial structure adjustment plays an important role in carbon emission reduction. [ABSTRACT FROM AUTHOR]

In an effort to address climate change, in 2013 China launched the world’s largest government-driven carbon emission reduction programme, the National Low Carbon Industrial Parks Pilot Programme (LCIPPP). This paper analyses this newly developed pilot program. To deepen our understanding of the causes and the impact of industrial park CO 2 emissions, we use the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and data from 20 pilot industrial parks involved in the LCIPPP for the period 2012–2016. This study quantitatively evaluates the effect of CO 2 emissions on output, energy structure, energy intensity, industrial structure, R&D intensity, and population change in different regions and nationally through an elasticity coefficient method. The results confirm that an increase in output and energy intensity is a dominant contributor to the growth of CO 2 emissions whereas an increase of the share of tertiary industry and R&D intensity has significant effects on reducing CO 2 emissions. The elasticity of energy intensity and renewable energy consumption on CO 2 emissions in the eastern region of China is the highest, indicating that using renewable energy to reduce CO 2 emissions for the industrial parks is more effective in the eastern region as compared to the central and western regions of the country. The elasticity of population is significantly negative in both the central and western areas while it is positive in eastern part of China, thereby illustrating that promoting labour intensive industries will be an effective way to reduce CO 2 emissions for the industrial parks in China’s central and western regions. Our study reveals that differentiated low carbon development pathways should be adopted. Concrete policy implications for reducing CO 2 emissions are also provided. [ABSTRACT FROM AUTHOR]