Impact of Environmental Policy Mix on Carbon Emission Reduction and Social Welfare: Scenario Simulation Based on Private Vehicle Trajectory Big Data

Abstract

Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict carbon emissions from private cars in various provinces and cities in China. Secondly, by simulating different scenarios of carbon tax, carbon trading, and their policy mix, the propensity score matching model is constructed to explore the effects of the policy mix on carbon emission reduction from private cars and social welfare while conducting regional heterogeneity analysis. Finally, policy proposals are proposed to promote carbon emission reduction from private cars and enhance social welfare in China. The results indicate that the environmental policy mix has a significant positive impact on carbon emission reduction from private cars and social welfare. Furthermore, in the regional heterogeneity analysis, the implementation of the policy mix in eastern regions has a significant positive effect on both carbon emission reduction from private cars and social welfare, while in central and western regions, it shows a significant positive impact on social welfare but has no significant effect on carbon emission reduction in the private car sector.

Research on a Carbon Emission Prediction Method for Oil Field Transfer Stations Based on an Improved Genetic Algorithm—The Decision Tree Algorithm

“…The carbon emission factors of each energy source are extrapolated based on data from research reports such as the 2005 China Greenhouse Gas Inventory Study and the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The carbon emissions accounting formula is as follows [3]: CO 2 emissions from fossil fuel combustion are based on the amount of fuel burned by each combustion facility within the boundary, multiplied by the corresponding fuel carbon content and carbon oxidation rate. The carbon emissions of the furnace formula can be obtained as follows:…”

“…Faruque et al [2] established a prediction model for the impact of carbon dioxide emissions on electric consumption and gross domestic product (GDP) and compared and analyzed the prediction accuracy of four deep learning methods: convolutional neural network (CNN), convolutional neural network-long short-term memory (CNN-LSTM), LSTM and dense neural network (DNN). Chen et al [3], based on private car trajectory data, using inverse geocoding and an artificial neural network to predict the carbon emissions of private cars in various regions, also evaluated the emissions reduction potential of various regions from the perspectives of efficiency, effect, and fairness, which provided a reference for formulating emission reduction strategies in China's road transportation field. The second aspect is to conduct scenario analysis or establish a carbon peaking prediction model to predict the carbon peaking situation of various industries.…”