Carbon price forecasting is used to assist emission-regulated firms in decision-making in trading. Based on the ABM method and trading rules of China's emission trading markets, in this study, we build a decision-making model for emission trading. The model takes into account factors such as risk preference and emission reduction costs for different types of emission-regulated firms. By setting initial values, adjustable parameters, and input variables, the model simulates emission trading decision-making strategies and carbon price trends in different scenarios. In this article, we selected 2,162 emission-regulated firms in China's national emission trading market for simulation. The results show that the proportion of sellers participating in the market and the proportion of free allowance allocation have a significant impact on the carbon price fluctuations; the introduction of the auction mechanism will cause short-term fluctuations in carbon prices. Through simulation of emission trading decision-making and carbon price forecasting, the model assists emission-regulated firms to minimize their emission regulation compliance costs, and prevent in advance the price and liquidity risks in emission trading markets.
This essay identifies two typical bottlenecks that occur when a vehicle cannot change lanes: car following and car stopping. The ideas of traffic field and traffic mass are presented in this work. When there are other vehicles in front of the target vehicle within a particular distance, a force is created that affects the target vehicle's driving speed. The characteristics of the driver and the vehicle collectively determine the traffic mass; the driving speed of the vehicle and external variables have no bearing on this. From physical level, this study examines the vehicle's bottleneck when following a car, identifies the outside factors that have an impact on how it drives, takes into account that the vehicle will transform kinetic energy into potential energy during deceleration, and builds a calculation model for traffic mass. The energy-time conversion coefficient is created from an economic standpoint utilizing the social average wage level and the average cost of motor fuel. Vissim simulation program measures the vehicle's deceleration distance and delay under the Wiedemann car following model. The difference between the measured value of deceleration delay acquired by simulation and the theoretical value calculated by the model is compared using the conversion calculation model of traffic mass and deceleration delay. The experimental data demonstrate that the model is reliable since the error rate between the theoretical calculation value of the deceleration delay obtained by the model and the measured value of simulation results is less than 10%. The article's conclusion is that the traffic field has an impact on moving cars on the road and that physical and socioeconomic factors should be taken into account while studying vehicle following behavior. The deceleration delay value of vehicle's driving and traffic mass have a socioeconomic relationship that can be utilized to calculate the energy-time conversion coefficient when dealing with the bottleneck of cars stopping and starting.
Based on the traffic analysis zones (TAZ) divided according to the type of land use, the trip generation volume of each TAZ can be calculated by using trip generation rates in land classification method, which urges us to find appropriate generation rates to ensure the reliability of the results. While OD matrix estimation method can acquire trip generation volume according to the volume of road sections with the drawback that this method requires to complete road traffic data and reasonable prior matrix. The paper combines the advantages of the two methods mentioned above. First, the trip generation volume of each TAZ is still calculated based on the generation rates, which can be used to calibrate the OD matrix estimation model. Once the OD matrix is estimated, the generation rates can be refined, and thus a new OD matrix can be computed. Then keep adjusting the data until it meets the accuracy demand. At last, a statistical test which can be used to judge the iteration will be presented.
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