Benefit Analysis of Low-Carbon Policy Mix Innovation Based on Consumer Perspective in Smart City

Abstract

In the construction of smart city, the carbon emission reduction problem of road traffic needs to be solved urgently. It is of great significance to introduce reasonable low-carbon policies. Based on urban private cars trajectory data, this study, respectively, establishes the genetic algorithm-back propagation neural network model (GA-BP) and back propagation-adaptive boosting algorithm neural network model (BP-AdaBoost) to predict the carbon emissions of private cars. By comparing the two neural network models, the GA-BP neural network model has better prediction results. Next, this study establishes the cost-benefit model for consumers and compares consumers' participation willingness, emission reduction effect, and social benefits of consumers from the perspective of six kinds of low-carbon policies. The results show that the overall effect of the low-carbon policy mix of free quota is better than that of paid quota. In addition, different low-carbon policy mixes innovations have different policy implementation effects under different indicators. Overall, the low-carbon policy mix of carbon trading and emission reduction subsidy is better in the short term, and the low-carbon policy mix of carbon tax and emission reduction subsidy is better in the long term.