###### Abstract
[21] introduced an adaptive traffic signal priority control framework based on multi-objective DRL, which allocated weights based on vehicle type and passenger load to balance competing objectives within a public transport signal priority strategy. Results indicated that, under mixed traffic conditions, this framework performed best at a 75% Connected and Automated Vehicle (CAV) penetration rate, with the highest efficiency and safety achieved for buses, cars, and CAVs. The reward functions used in DRL-based intersection signal timing models typically consider traffic parameters such as queue length, waiting time, accumulated delay, vehicle speed, vehicle count, number of stops, and sudden braking events [22; 23; 24; 25; 26; 27]. These reward functions are computed by harmonically weighting some or all of these parameters to achieve optimal trade-offs between traffic efficiency and safety. As pollution and emissions have increasingly become a global focus, road transport systems are now recognized as one of the major sources of fuel waste and air pollution. Frequent acceleration and deceleration, as well as prolonged idling at intersections, contribute to significant traffic disruptions, leading to inefficiencies and energy wastage. More and more researchers are considering the reduction in pollutant emissions as one of the key objectives in intersection optimization. [PERSON] et al. [28] found that, in addition to speed-limiting effects, intersection signal control can also lead to increased emissions. [PERSON] et al. [29] calibrated emission factors based on a vehicle power-to-weight ratio model and optimized signal control with the goal of minimizing both vehicle delays and emissions. [PERSON] et al. [30] proposed a traffic signal control optimization method based on a coupled model that integrates macro-level traffic analysis and macro-level emission estimation. By focusing on vehicle emissions as the optimization target, they employed a genetic algorithm to optimize signals, effectively reducing vehicle travel time and emissions. [PERSON] et al. [31] and [PERSON] et al. [32] incorporated exhaust emissions into optimization objectives, constructing a multi-objective timing optimization model that considered intersection delay time, stop frequency, queue length, and exhaust emissions. Their model demonstrated significant improvements in both traffic and environmental benefits. [PERSON] et al. [33] proposed a bus priority pre-signal model aimed at reducing fuel consumption and carbon emissions. Under the premise of bus priority, the \"red first, then green\" principle for pre-signals was introduced. Results indicated that the model significantly reduced carbon emissions at intersections, as well as delays and stop frequencies for buses. [PERSON] et al. [34] introduced an adaptive Meta-DQN Traffic Signal Control (MMD-TSC) method, which incorporated a dynamic weight adaptation mechanism, simultaneously optimizing traffic efficiency and energy savings while considering per capita carbon emissions as an energy metric. Compared to fixed-time TSC, the MMD-TSC model improved energy utilization efficiency by 35%. [PERSON] et al. [35] proposed an intersection signal timing optimization method based on the D3 QN model in mixed CAV (Connected and Automated Vehicle) and HV (Human-Driven Vehicle) scenarios. Using the \(VSP\) power-to-weight algorithm to calculate dynamic vehicle emissions, this method significantly reduced both vehicle carbon emissions and waiting times. Scholars have increasingly prioritized reducing energy consumption and emissions as key objectives for signal optimization and have demonstrated the effectiveness of DRL-based intersection signal optimization models in achieving carbon reduction [36; 37; 38].