## 1 Introduction  
Carbon emissions have become an important topic of discussion in recent years, and transportation carbon emissions, as an important component of carbon emissions, have also received widespread attention [1]. Along with the innovation in driving automation systems, the innovation and development of automobile industry technology are of great significance for reducing energy consumption, relieving traffic congestion, and improving travel safety and mobility [2, 3]. As the first scenario to apply and popularize high-level unmanned autonomous driving, such as highly and fully autonomous driving, freeways are key scenarios where heterogeneous traffic flows occur [4]. In particular, the frequent acceleration, deceleration, and lane change of motor vehicles in the impact zone of off-ramps not only lead to a complex driving environment in the impact zone but also trigger a sudden increase in vehicle carbon emissions [5]; therefore, the impact zone of off-ramps is a key part of highway carbon reduction and carbon sequestration. The factors affecting the carbon emissions of traffic within off-ramp areas are multifaceted, and their complex and variable traffic characteristics indicate that appropriate traffic flow operation models and more refined carbon emission models are required to better reveal their characteristics and laws. The off-ramp diversion area on a highway is an intersection section where the main road and off-ramp meet, including the off-ramp, deceleration lane, and sections of the main road within the affected range. Prior to vehicles exiting the main road, lane-changing vehicles perform pre-lane-changing operations, resulting in the deceleration of neighboring vehicles, yielding, and engaging in coordinated actions. As vehicles exit the main road, their deceleration and lane-changing behaviors can cause delays and fluctuations in upstream traffic. The speed, acceleration, and other characteristics of vehicles after they leave the main road are influenced by the road alignment, traffic conditions, and road conditions [6; 7; 8; 9]. However, when intelligently connected vehicles are introduced into traffic flow, the average vehicle energy consumption of the traffic flow changes. Information exchange between vehicles and road infrastructure enhances traffic flow stability, resulting in corresponding changes in road operational efficiency [5; 10; 11; 12; 13; 14]. Additionally, when intelligently connected vehicles form platoons and adopt intelligent platooning control, they can effectively reduce road congestion and improve the operational efficiency in the off-ramp area [15; 16; 17]. Regarding traffic carbon emission models for conventional fuel vehicles, mainstream energy consumption and emission models are mainly developed by countries in Europe and the United States. The existing emission models can be categorized into three types based on their principles: average speed-based, driving cycle-based, and specific power-based (VSP-based) models [18]. See Table 1.  
For electric vehicles, the driving range under the same State of Charge (SOC) varies, and the energy consumption per unit distance differs under different driving conditions. Many researchers have made improvements to carbon emission models for conventional fuel vehicles and specifically analyzed the impact of battery SOC on vehicle energy consumption and proposed improved models [21; 22]. Some scholars have analyzed vehicle forces and energy conversions to develop similar instantaneous energy consumption estimation models for electric vehicles from the perspective of vehicle dynamics [23; 24; 25].