Evaluating Effectiveness of Low-Carbon Transition Policy Mix Based on Urban Private Car Trajectory Data

Abstract

Under the vision of achieving carbon neutrality by 2060, it is urgent to introduce appropriate carbon reduction policy for city road traffic. This paper establishes a three-layer neural network model to predict the carbon emission from private cars based on urban private car trajectory data, simulates and analyzes the carbon emission from private cars, travel cost, personal income, and government revenue under the four policy perspectives, and evaluates and compares the emission reduction effects under four policy perspectives. Next, this paper evaluates the government revenue from the perspective of carbon tax and policy mix and compares the individual consumer utility of two-commodity and three-commodity mix, as well as the total social benefits under the four policy perspectives. The results show that the policy mix has better implementation effect on carbon emission reduction, personal income, and travel cost. The implementation effect of the single carbon tax policy is better in terms of government revenue. The implementation effect of the single carbon trading policy is better in terms of social benefit. In addition, as the carbon tax rate increases, the consumer utility tends to decline. Finally, this paper puts forward specific policy implementation proposals based on the above simulation analysis.