## 2 Related Work
Electricity consumption information is not only easily accessible in available real-time datasets, but can also be tracked in real time, which significantly improves the efficiency and timeliness of data collection [21]. Electricity data are considered more reliable due to their higher accuracy and stability. Numerous research results have confirmed that it is feasible to use electricity consumption to estimate CO\({}_{2}\) emissions. As an example, [PERSON] et al. [22] proposed a carbon emission prediction model for the flat glass industry based on electricity consumption. By processing and analyzing the electricity data of China's flat glass industry, the study built an electricity-carbon model using support vector regression (SVR), and experimentally verified the validity and accuracy of the model, which proved that it is effective to use electricity data for carbon emission modeling. [PERSON] et al. [23] proposed an innovative carbon emission estimation method based on the correlation between electricity and carbon emissions and non-intrusive load monitoring (NILM), which is dedicated to improving the accuracy and interpretability of carbon emission estimation in the field of electricity production. The core of the methodology is to decompose the total power consumption of the enterprise, specify the power consumption of each piece of key equipment, and calculate the carbon dioxide emissions accordingly. In order to strengthen the accuracy of the analysis, the study adopts a two-stage learning structure optimized by a deep learning algorithm, which is validated using actual data from a power plant in China. Recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) networks were applied in the construction of the electricity-carbon model, demonstrating the potential of the method in accurately estimating carbon emissions. [PERSON] et al. [24] proposed a new method for monitoring carbon emissions of cement enterprises based on electricity data using machine learning algorithms. By building single-sample daily and multi-sample annual electricity-carbon monitoring models and comparing nine regression methods, they found that the Lasso model performs the best, and effectively reveals the key influence of the electricity emission factor of clinker production and the percentage of electricity consumption in clinker section on carbon emissions. [PERSON] et al. [25] proposed a method to monitor corporate carbon emissions using big data on electricity, and verified the effectiveness of the method through a practical case study of 810,000 enterprises in Beijing. [PERSON] et al. [26] proposed an analysis method based on automated machine learning (AutoML) for predicting indirect carbon emissions caused by electricity consumption in multiple wastewater treatment plants. Overall, although the current research on exploring the relationship between electricity consumption and carbon emissions using AI techniques is still in the preliminary stage, its great potential and broad application prospects have attracted extensive attention from both academia and industry. By accurately analyzing the complex relationship between electricity use and carbon emissions, these studies can not only help identify the key links between high energy consumption and high emissions, and then guide the implementation of energy efficiency and emission reduction strategies, but also provide a scientific basis and technical support to respond to the challenges of global climate change, and promote the realization of the goal of carbon neutrality.