## 1 Introduction  
Against the backdrop of increasingly severe global warming and climate change, the estimation and management of corporate carbon emissions have become key issues in scientific research and policy-making [1]. As the primary tools for assessing and predicting corporate carbon emissions, carbon emission estimation models play a crucial role in formulating emission reduction strategies and achieving carbon neutrality goals [2; 3]. As the world's largest carbon market, China requires efficient and accurate carbon emission data collection for the success of large-scale carbon trading. An accurate carbon emission estimation is fundamental for establishing and maintaining a carbon trading system [4; 5]. Accurate carbon emission estimation not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals [6]. In general, the emission factor method and online monitoring method are the two main methods for monitoring carbon emissions. The emission factor method estimates COemissions indirectly by analyzing operational data and emission coefficients of companies or facilities. The online monitoring method calculates the actual CO2 emissions directly, and relies on continuous emission monitoring systems (CEMSs) to track the concentration of CO2 in gas emissions and the velocity of flue gas in real time. In practical applications in China, emission monitoring environments are complex and diverse, meaning CEMSs cannot be applied to applications in China [7]. Currently, the emission factor method is the primary method used to estimate carbon emissions by third-party verification organizations in China [8]; however, this method is inefficient and lags behind because enterprises generally submit their carbon emissions data on a monthly or yearly basis. At present, coal-fired power plants are one of the largest sources of carbon emissions in China. Coal composition, which is usually accompanied by mixed combustion, is different and diverse in China's coal-fired power plants [9]. There is an urgent need to find a way to estimate carbon emissions in coal-fired power plants of China. Therefore, a novel carbon emission prediction method is proposed for power generation companies in this paper. Based on the production data and carbon emission data from 25 coal-fired power plants, the Optuna-LightGBM-XGBoost model, with a parallel processing framework, is proposed in this paper. The model aims to use artificial intelligence technology to establish the relationship between electricity consumption and carbon emissions. It uses real-time power data to measure carbon emissions from electricity, solving the problem of the reliance of the current emission factor method on manual post-examination verification, and thereby effectively improving the efficiency of carbon emission estimation and reducing costs. The MAE, MSE, MAPE, and R2 of the model are 0.652, 0.939, 0.136, and 0.994, respectively. Compared with the optimal MAE, MSE, and MAPE of the traditional machine learning model, they are further reduced by 26.8%, 47%, and 2.9%, and R2 is increased by approximately 0.6 percentage points. In summary, the main contributions of this study are encapsulated in the development and implementation of the Optuna-LightGBM-XGBoost model, which is a novel artificial intelligence framework that substantially enhances the accuracy and efficiency of carbon emission estimations. This model uniquely combines the LightGBM and XGBoost algorithms, which are optimized by the Optuna hyperparameter tuning framework. It represents a significant departure from traditional methods that primarily rely on post-analysis of emission factors, by utilizing real-time electricity consumption data to directly estimate emissions.