Research on the Development of New Energy Vehicles and the Prediction of Carbon Emissions in a Region of Asia

This study examines the development level and influencing factors of the new energy vehicle industry cluster in a certain country in Asia, aiming to promote coordinated industrial development and accelerate industrial upgrading, thus contributing to the enhancement of the country's economic development. Based on the development trend of new energy vehicles in the country, this paper utilizes methods such as short-term time series analysis, entropy weight analysis, and the BP neural network model to make reasonable predictions about future market dynamics. With a large amount of data on vehicle ownership, the data is analyzed by considering the proportion of new energy vehicles in the specific country's region compared to other regions within the country, as well as the emergence of new energy vehicle enterprises in the region over the past two years. The market shares of various brands are also compared to understand the significant role played by the development of new energy vehicles in the specific country's region and its leading position nationwide. Data classification and integration operations are implemented in Excel, and an ARIMA model is built in Python. By utilizing the advantages of the ARIMA model and available data, the market ownership of new energy vehicles in the specific country's region is predicted for the next three years. The entropy weight analysis method is then used to construct an index matrix, and combining the above models with the BP neural network model, the ownership of vehicles three years later is predicted. The trend chart of market ownership obtained in the previous step is analyzed to identify the changing patterns over time. Carbon emission data is collected, and the correlation coefficient between new energy vehicles and carbon emissions is used to estimate the reliability of the linear regression prediction model. A comparison between carbon emissions from new energy vehicles and traditional fuel vehicles is evaluated to assess their impact on carbon reduction. Finally, carbon emission models are established in the fields of energy, transportation, construction, etc., considering factors such as policies, through multiple linear regression predictions. The carbon emissions in each sector are determined.