Decomposition and scenario analysis on carbon emissions of road transportation: case study of the rural areas of Tianjin, China

Road transportation, heavily reliant on fossil fuels, is one of the fastest-growing sources of carbon emissions within the transport sector in China, especially in rural areas. However, due to challenges in data availability, few studies have systematically analyzed carbon emissions from Rural Road Transportation (RRT). This paper utilizes a decomposition model to analyze the driving factors behind road transportation carbon emissions of rural Tianjin from 2011 to 2020. Additionally, an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is employed to explore potential pathways for achieving low-carbon development in transportation sector from 2021 to 2050. The findings reveal that: (i) The primary driver of carbon emissions in RRT of Tianjin has shifted from economic growth to technological level, while transportation intensity emerges as the primary factor contributing to the reduction of carbon emissions. (ii) The prediction results indicate that technology promotion policy has significant potential to reduce carbon emissions of RRT compared with development regulation policy. Under the Development Regulation Oriented (DRO) scenario, total carbon emissions are predicted to decrease by 19.73% between 2021 and 2050 compared to the Business as Usual (BAU) scenario. The Clean Promotion Oriented (CPO) scenario demonstrates an even greater reduction potential, with a decrease of 25.47%. In the Integrated Regulation Oriented (IRO) scenario, which integrates technology promotion and development regulation policies, the reduction in carbon emissions is even more substantial, amounting to a 39.41% decrease. Therefore, policies that reasonably regulate the pace of economic development while fostering the effective integration of clean technology innovations are most aligned with the future trajectory of low-carbon road transportation in rural Tianjin.