Multi-Province Collaborative Carbon Emission Forecasting and Scenario Analysis Based on the Spatio-Temporal Attention Mechanism—Empowering the Green and Low-Carbon Transition of the Transportation Sector Through Technological Innovation

As one of the primary contributors to carbon emissions in China, the transportation sector plays a pivotal role in achieving green and low-carbon development. Considering the spatio-temporal dependency characteristics of transportation carbon emissions driven by economic interactions and population mobility among provinces, this study proposes a predictive framework for transportation carbon emissions based on a spatio-temporal attention mechanism from the perspective of multi-province spatio-temporal synergy. First, the study conducts transportation carbon emission accounting by considering both transportation fuel consumption and electricity usage, followed by feature selection using an enhanced STIRPAT model. Second, it integrates the spatio-temporal attention mechanism with graph convolutional neural networks to construct a multi-province transportation carbon emission collaborative prediction model. Comparative experiments highlight the superior performance of deep learning methods and spatio-temporal correlation modeling in multi-province transportation carbon emission collaborative prediction. Finally, three future development scenarios are designed to analyze the evolution paths of transportation carbon emissions. The results indicate that technological innovation can significantly improve the efficiency of transportation emission reduction. Moreover, given that the eastern region and the central and western regions are at distinct stages of development, it is essential to develop differentiated emission reduction strategies tailored to local conditions to facilitate a green and low-carbon transformation in the transportation sector.