Assessment of Human-Related Driving Forces for Reduced Carbon Uptake Using Neighborhood Analysis and Geographically Weighted Regression: A Case Study in the Grassland of Inner Mongolia, China

Spatio-temporal evolution characteristics of carbon emissions from road transportation in the mainland of China from 2006 to 2021

The leaping forward of the economy has promoted the rapid growth of road traffic demand, resulting in the carbon emissions of road traffic increasing significantly. It is well known that a one-size-fits-all emission reduction policy is not feasible. Therefore, conducting an investigation on the carbon emissions of all provincial-level regions within a country can assist the government in formulating carbon emission policies at a macro level tailored to different regions. In this study, the whole provincial-level administrative regions in the mainland of China were selected to quantify the carbon emissions of road traffic, and the carbon emissions from 2006 to 2021 were obtained by employing the top-down model. What's more, spatiotemporal characteristics of road transportation carbon emissions in those regions were explored based on Moran's I spatial autocorrelation method. In addition, the LMDI model was constructed based on five driving factors, namely energy intensity, energy consumption intensity, industrial scale, economic development, and population size, and the decomposition analysis of driving factors is carried out. The results show that carbon emissions from road traffic in all provincial regions showed an overall rising trend in the research period, with an average annual growth rate of 11.83 %. The distribution of road transportation carbon emissions exhibited an east-high, west-low distribution, with significantly higher emissions in the eastern and coastal regions compared to inland areas, additionally, China's seven geographical regions showed an initial rapid increase in carbon emissions followed by a stable growth trend. Secondly, five types of spatial clustering were identified of carbon emissions within provincial regions. Thirdly, the impacts of energy intensity and industrial scale were detrimental to road transportation carbon emissions, whereas economic development, energy consumption intensity, and population size had contrasting effects. Implications according to the above conclusions were put forward, aiming to provide guidance for the sustainable development of road transportation and expediting the achievement of the "carbon peaking and carbon neutrality" objective.