In response to the current practical demand of timely and high-frequency data updates of energy consumption and carbon emissions in relevant government departments and enterprises, based on power data as the core and integrating multiple sources of data, a ridge⁃multivariable linear regression model for energy consumption calculation is proposed, by comparing and analyzing the performance of various models under various fitting methodologies. Based on this, a model for carbon emission monitoring is proposed. The superposition principle of linear models is applied to achieve the functions of annual data modeling and monthly data prediction, solving the key difficulty of modelling with inconsistent frequency of statistical data and avoiding the errors introduced by traditional data splitting methods at the data source. The application of the ridge regression weighting method solves the multicollinearity problem of the input data. The proposed model has a wide range of applicability and realizes the functions of monitoring and predicting energy and carbon emissions at different spatial scales such as regions and industries and at different time scales such as annual, quarterly and monthly. It has the effect of "solving complexity with simplicity" and fills the gap of high-frequency statistics data in energy and carbon emissions and provides theoretical basis and data support for relevant government departments, industry associations, etc. to monitor energy consumption and carbon emissions and formulate relevant policies.