https://www.selleckchem.com/JAK.html Tremendous energy consumption appears as rapid economic development, leading to large amount of CO2 emissions. Although plentiful studies have been made into the driving factors of CO2 emissions, the existing literatures that take the spatial differences and temporal changes into consideration are few. Therefore, this study first analyzes the variations of total CO2 emissions' spatial distribution from 2008 to 2017 and present the changes of driving factors, finding significant spatial autocorrelation in CO2 emissions at the province level, and that urbanization rate, per capita GDP and per capita CO2 emissions increased significantly, but energy consumption structure and trade openness decreased. We then compared the effects of different factors affecting CO2 emissions, using classic linear regression model, panel data model, and the geographically weighted regression (GWR) model, and the three models roughly agree on the effects of factors. The GWR model considering spatial heterogeneity provides more detai N-shape Kuznets curve, and the underdeveloped regions are in the rising stage between the two inflection points, while developed regions are at the end of the rising stage and about to reach the second inflection point.The booming development of e-commerce has brought about rapid growth in the express delivery industry in China. However, urban express distribution is increasingly difficult and seriously affecting the traffic, safety, and environmental conditions of cities due to small, scattered end points, unreasonable allocation of resources, and seriously repeated resource waste. Therefore, there is an urgent need to solve the problems associated with the unreasonable resource allocation of express distribution. In the context of green logistics, a new mode of collaborative distribution based on intelligent end service station (IESS) is proposed. Following the measurement models of carbon emissions, before and after colla