关键词: 减污降碳, 协同效益, 碳税, 框架效应, 系统动力学

Abstract: For the target of carbon peak and carbon neutrality, the research on the mechanism of urban traffic pollutants and CO₂ reduction (referred to as “pollution control and carbon reduction”) has become an urgent task. Transportation accounts for 24% of global CO₂ emissions, while road transportation accounts for about 18%. However, transportation sector accounts for a significant proportion of energy consumption, with road transportation emissions bearing the brunt. The greenhouse gases and atmospheric pollutants generated by vehicle emissions have a relatively adverse impact on climate, economy, and people’s physical and mental health. Therefore, research on the synergistic effects of urban traffic pollution control and carbon reduction (TPCCR) has important practical significance.
In this article, the system dynamics and behavioral economics theory are combined, and a frame-effect-based-motor-vehicle-trip-psychological-decision-making algorithm is constructed, considering the carbon tax policy, which is applied to the management of TPCCR. Through dynamic simulation and comparative analysis, the impact of the baseline scenario, green low-carbon and psychological linkage scenarios on the synergistic effect of TPCCR is revealed. The optimization scenarios have also been formulated. The data mainly comes from the “China Mobile Source Environmental Management Annual Report”, “Beijing Transport Development Annual Report”, “Beijing Statistical Yearbook”, “China Statistical Yearbook”, and some existing literature.
The results show that: the single carbon tax policy can reduce motor vehicle pollutants and CO₂ emissions to a certain extent, with synergistic effects, but the emission reduction effect on PM_{2. 5} is still limited. The pollutants and CO₂ emissions reduction of passenger and trucks vary significantly, especially during the epidemic period. The improved model (low-carbon and psychological linkage scenarios) can achieve the goal of carbon reduction and has certain pollution reduction performance. Compared with the original model, the low-carbon and psychological linkage scenarios show a decrease of approximately 5.8% and 23. 3% in the amount of CO₂, and a decrease of approximately 5.9% and 23. 6% in the amount of PM_{2. 5}. Therefore, both types of improved models have synergistic effects on reducing pollution and carbon emissions, but the effect of the low-carbon scenario is relatively limited. The psychological linkage model that emphasizes the strength of carbon tax policies and framework effects has a more significant effect. Based on the above findings, the following suggestions are proposed from the perspectives of optimizing economic regulation models, emphasizing differences in passenger and freight emissions reduction, and strengthening psychological guidance.
Some governments have tried to take measures in response to the call for low carbon, but the CO₂ and PM_{2. 5} content of motor vehicles still has risen rapidly year by year in the current situation, which does harm to the economy and environment. The single carbon tax policy can reduce the total emissions of motor vehicle pollutants (including CO₂ and PM_{2. 5}), and has a synergistic effect on reducing pollution and carbon emissions of motor vehicles. In the carbon tax policy, the psychological guidance generated by the framework effect is taken into account to make a relatively significant change in the synergistic emission reduction effect. The reasons may include two aspects. The first is that the framework effect enhances the intensity of the carbon tax policy and reduces the number of motor vehicle trips to a certain extent. The second is to add psychological guidance to the carbon tax policy, which makes enterprises vulnerable to the impact of the positive framework. In the face of uncertain operating income and the tax relief that can be obtained by reaching the emission reduction target, enterprises choose the tax relief with certainty, and the logistics and passenger flow controlled by enterprises can be reduced, thus reducing the emissions of CO₂ and PM_{2. 5} from motor vehicles. Therefore, the two-factor drive has the synergistic effect of “1+1>2” for reducing pollution and CO₂ of motor vehicles.

Key words: pollution control and carbon reduction, synergistic effect, carbon tax, framing effect, system dynamics