基于Lasso方法的我国能源消费影响因素分析

Analysis of Energy Consumption Influencing Factors in China Based on the Lasso Method

摘要: 随着经济发展的加快和资源需求量的加大，能源消费呈现出连年攀升的态势，能源消费影响因素的研究及能源消费需求的合理预测，对保证我国经济平稳持续健康发展是十分必要的。目前学者们分别用过简单线性回归法、主成分回归法及岭回归法对我国能源消费影响因素进行分析，但这些研究得到的模型可能太过精简而未能较为全面地找出能源消费的主要影响因素。而本文依据2000年~2012年我国能源消费总量的相关数据，针对变量偏多，观测数据少的特点选用了Lasso方法对我国能源消费影响因素建立了回归模型，得到了影响我国能源消费的主要因素有经济增长因素、人口增长因素、产业结构因素、技术进步因素、能源利用效率因素以及能源价格因素，因此，我们可主要从这些因素入手，对能源消费加以管理和控制。同时我们还用逐步回归法和岭回归法分别建立了回归模型，并将Lasso方法得到的结果与其进行比较，结果表明Lasso方法在能源消费影响因素的选择方面，比其他两种方法更为全面地找出能源消费的主要影响因素，在对2013年及2014年能源消费总量预测方面，Lasso方法比其他两种方法更为精确。

Abstract: With the acceleration of economic development and the increasing demand for resources, energy consumption shows a rising trend in recent years. To ensure the stable, sustainable and healthy development of China’s economy, it is necessary to study on consumption factors and to forecast energy consumption demand reasonably. As so far, scholars have used simple linear regression, principal component regression and ridge regression method for analyzing China’s energy consumption factors, but models achieved from these studies may be too lean to find more comprehensive energy consumption factors. While according to the related data of domestic energy consumption during 2000-2012, this paper chooses a new method—Lasso method to make regression model for domestic energy consumption, and then we get the main energy consumption effecting factors: economic development, demographic factor, industrial structure, technological progress, energy consumption efficiency and energy price factor, so we can control energy consumption through these main factors. Additionally, we use stepwise regression and ridge regression to make regression models, the results got from the Lasso, stepwise regression and ridge regression are compared, the study shows the Lasso method is better than the other methods in terms of variable selection, because it could find more comprehensive energy consumption factors; for predictions of 2013 and 2014, Lasso method is more accurate than the other two methods.