LAUSR

Abstract At present, China's carbon dioxide (CO 2 ) emissions ranked first in the world. Moreover, the CO 2 emissions in the manufacturing industry accounted for 55.0% of total CO 2 emissions. Thus, investigating the main driving forces of CO 2 emissions in this industry is crucial for reducing China's CO 2 emissions. The traditional estimation method can only get the “global” and “average” parameter estimation, but obscures the difference in the “local” parameter estimation across region. Geographically weighted regression embeds the latitude and longitude of the sample data into the regression parameters, and uses the local weighted least squares method to estimate the parameters point–by–point. To reveal the nonstationary spatial effects of driving forces, geographically weighted regression model is employed in this paper. The results show that economic growth has a positive impact on CO 2 emissions, and the impact continuously declines from the eastern region to the central and western regions. However, the impact of urbanization in the western region is higher than that in the eastern region and the central region. The impact of energy efficiency in the eastern and central regions is stronger than that in western region. The effect of industrialization also has a similar story. Therefore, in order to effectively achieve emission reduction, we need to take full account of spatial differences in different regions.