Design of Carbon Emission Accounting and Monitoring System Based on Artificial Intelligence Algorithm

With the increasingly prominent problem of global warming, reducing carbon emissions has become the focus of global attention. The purpose of carbon emission accounting is to measure and analyze various influencing factors that may occur in the production process of enterprises, so as to provide effective and reliable reference information for decision makers, so as to achieve the goal of energy saving and emission reduction. To better calculate and monitor carbon emissions, AI algorithms have become an effective solution. This paper mainly applies the algorithm analysis and experimental design, optimizes the carbon emission accounting method through three different combined models, and designs the monitoring system for performance analysis. Experimental data show that the stability and accuracy of the particle swarm optimization algorithm reach 90% and 89%, respectively. Although the advantages of the particle swarm optimization algorithm are more obvious, there is still room for improvement in its performance. Artificial intelligence algorithms can effectively deal with a large amount of data and complex issues in carbon emission accounting and monitoring, and improve the accuracy and efficiency of carbon emissions. Through the application of artificial intelligence algorithms, the carbon emissions of enterprises, industries and even the whole region can be monitored and predicted in real time, which helps to develop more effective carbon emission reduction strategies. Artificial intelligence algorithms can identify and classify the contributions of different activities, processes and devices to carbon emissions, which helps to find key points and optimize solutions to reduce carbon emissions.