Abstract
This study integrates weather forecasting and machine learning to propose a new method for corporate carbon emission accounting and management. First, set up a weather data acquisition system to obtain real-time and future 15-day weather forecast data; Then use the carbon emission calculation method to monitor and calculate the energy consumption and carbon dioxide emissions in the production process of the enterprise in real time; Then, with the help of machine learning algorithms such as neural networks, a prediction model is built based on historical data to analyze and predict the impact of different factors on carbon emissions. Finally, through data analysis and cost forecasting, we provide reliable carbon cost prediction and management services for enterprises, help enterprises adjust production plans, improve production efficiency, and formulate scientific and effective emission reduction strategies. This method significantly improves the accuracy and stability of carbon emission forecasting, and has important practical significance for enterprise carbon cost accounting and management, which can help enterprises reduce environmental risks and costs, and provide new ideas and methods for enterprise carbon emission management and sustainable development.