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.
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.
The realization of the “double carbon” goals and the development of green transportation require a focused approach to reducing carbon emissions from private cars. Starting from the perspective of social network analysis, this paper constructs the carbon emission network of private car cross-district mobility based on vehicle trajectory big data in Guangzhou and Foshan and analyzes its spatial network characteristics. Next, the MRQAP model is constructed to examine the impact of built environment factors on carbon emissions from private cars. Furthermore, the paper explores the moderating effect of private car mobility in the central urban area. The results indicate the following: (1) Private vehicle cross-district mobility in the Guangzhou and Foshan region are closely interconnected and exhibit a phenomenon of central clustering. (2) Both population density and the number of road intersections have a positive relationship with private car carbon emissions, and after a series of robustness tests, the results are still valid. (3) Private vehicle mobility in central urban areas contributes to an increase in carbon emissions, and the positive impact is reinforced by population density, while road intersections and private car mobility in central urban areas have a substitutive effect on private car carbon emissions.
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