China’s transportation sector suffers from energy over-consumption and CO₂ over-emission, resulting in increasing pressure to improve energy and environmental efficiency. Current measurement techniques cannot produce precise CO₂ emission data, and this uncertainty makes previous approaches problematic for analyzing energy and environmental efficiency. This study combines stochastic multicriteria acceptability analysis (SMAA-2) with data envelopment analysis (DEA) to evaluate the energy and environmental efficiency of Chinese transportation sectors in the presence of uncertain CO₂ emission data. The improved SMAA-DEA approach effectively handles CO₂ data uncertainty and also considers all possible input and output weights, thus providing meaningful information (such as maximum efficiency, average efficiency, and rank acceptability index) to guide the development of effective policies to improve efficiency. This study’s empirical findings show that the energy and environmental efficiency of transportation sectors in 30 provincial regions is poor, great efficiency disparities exist between regions, and uneven development has occurred in China.