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Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

In order to deal with the increasing environmental challenges, the high carbon-emitting transport system keeps transitioning to achieve carbon peaking and carbon neutrality goals. This paper addresses the calculation of carbon emission estimation and identifying carbon reduction driving factors for the hinterland-based container intermodal network. Firstly, an analysis of the carbon emissions composition of the hinterland-based container intermodal networks is conducted. Furthermore, a carbon emission estimation model for the hinterland-based container intermodal network is developed. Subsequently, taking the Shanghai port and the Yangtze River Delta (YRD) hinterland as a case, we estimate the carbon emission of the hinterland-based container intermodal network under the Well-to-Wheel (WTW) and the Tank-to-Wheel (TTW) modes in the past ten years. Finally, the critical driving factors are identified by sensitivity analysis, and the variation of carbon emissions under different scenarios with carbon reduction policies are analyzed. The results indicate that the transportation structure and the fuel choices affect network emissions reductions significantly. In the current policy-based scenario, the WTW mode achieves the carbon peaking between 2030 and 2035, while the TTW mode peaks earlier. [ABSTRACT FROM AUTHOR]