针对当前相关政府部门、企业对能源消费和碳排放等数据时效性和高频次更新的现实需求，以电力数据为核心，多源数据相融合，通过对比分析多种模型在多种拟合方法下的表现，确定了岭回归赋权下的多元线性回归（ridge-multivariable linear regression，R-MLR）能耗测算模型，并在此基础上建立了碳排放监测模型。应用线性模型的可叠加性原理实现了年度数据建模、月度数据预测的功能，解决了统计数据频率不一造成的建模困难的关键问题，同时避免了传统数据拆分方法在数据源头引入的误差。岭回归赋权方法的应用解决了模型输入数据的多重共线性问题。所提模型适用范围广，在区域、行业等不同空间尺度和年度、季度、月度等不同时间尺度实现了能源和碳排放监测与预测的功能，具有“以简应繁”的效果，填补了当前能源和碳排放数据高频统计数据上的空白，可为相关政府部门、行业协会等进行能源消费和碳排放的监测以及制定相关政策提供理论依据和数据支撑。

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.