Factors and prediction of carbon emissions based on PSO-BP neural network model under the development of digital economy

The Yellow River Economic Belt, where the degree of digital economy development is uneven, is the first research object used in this study. It then suggests a way to measure the degree of digital economy development and carbon emissions in order to address the problem of effectively controlling carbon emissions in the rapidly developing digital economy. Finally, a genetic method is presented to further enhance the backpropagation neural network model’s update process, which was improved utilizing the particle swarm optimization technique. According to the findings, this research identified three primary elements: digital industrialization, digital finance, and digital ecological environment. According to the findings, this research identified three primary elements: digital industrialization, digital finance, and digital ecological environment. With the use of digital technology, the digital ecological environment fosters a peaceful coexistence between people and the natural world. In addition to encouraging the advancement of digital technology, it may also help to integrate digital transformation and green development. The use of digital technology in ecological environment governance can assist accomplish sustainable development goals, improve resource allocation, and encourage intelligent and green production and life. In order to change conventional financial service models, the financial sector known as “digital finance” makes use of digital technologies and data components. It has the potential to be very important in encouraging industrial upgrading and propelling the growth of new industries. Additionally, the whole credit structure of the industrial chain may be improved by digital credit and risk management, which will support the economic structure’s optimization. The use of digital technology to a variety of sectors, encouraging their digital transformation and modernization, is known as digital industrialization. It is a key component of a contemporary industrial system that may drive new industries and formats, support the intelligent and information-based transformation of established industries, and improve the economic structure. At the same time, the associated carbon emissions dropped by 0.0439 units for every unit rise in the study area’s digital economy’s degree of growth. The region’s overall population, energy consumption, sophisticated industrial structure, and industrial structure rationalization all positively promote carbon emissions, whilst other variables have the opposite impact. The final study approach had the highest predictive performance, with a high goodness of fit of 0.9936 and an average absolute error of 16.971. The aforementioned study results demonstrate that the methodology can effectively evaluate the level of carbon emissions and the development of the digital economy across different regions and provide targeted solutions to lower carbon emissions in line with local conditions, thus fostering the vibrancy of the digital economy.