基于STIRPAT模型的中国能源压力分析——基于空间计量经济学模型的视角

21世纪的中国已成为世界第一大能源消费国,迅猛而粗放的经济发展对能源的需求和依赖倍增,这给能源生产和供给带来巨大压力。由于产业结构和区位的异同,各省的能耗呈现空间异质和空间趋同;部分能耗通过空间效应来解释,可以改正传统能耗模型的估计偏差。采用能源消费总量作为环境压力的衡量指标,以STIRPAT模型为基础,将能源消费的空间效应纳入到STIRPAT模型进行空间计量分析。结果发现,中国省域能源消费在空间上存在依赖性,人口、社会富裕度和第二产业比重与能源消费皆为正相关,随着人口、社会富裕度和第二产业比重的增加,对能源消费的弹性系数逐渐增加。适当地控制人口、社会倡导低碳生活以及节能降耗均能缓解能源压力,同时制定差异化的区域能源消费调控措施也很有必要。

China's Energy Stress Based on the STIRPAT Model: A Spatial Econometric Perspective

China is now claimed to be the largest energy consumer among all countries in the world, as it more than ever needs energy to sustain its consecutive two-digit annual GDP growth. The huge demand for energy has had a stronger impact on energy production and supply in the country. The distribution of energy consumption among provinces and cities may conceal significant spatial effects that each location has exerted onto its neighbors. When taken it into account in the analysis, these spatial effects may be used to rectify the estimation bias inherent in the traditional energy consumption model. This paper employed the total energy consumption as an index of environmental impacts to evaluate the spatial effect of energy consumption in China based on the STIRPAT model. The energy consumption data of all regions in China was first examined via the Moran's I index for the existence of spatial dependence. For calculating the index, a contiguity rule was employed to establish the spatial weight matrix for the regions. The exploratory analysis results in a value of 0.194 for Moran's I at the significant level of 0.05, which indicates a tendency of spatial clustering of similar consumption values. This warranted further analyses on a confirmatory nature. Two spatial econometric regression models based on spatial lag (SLM) and spatial error (SEM) respectively were then established to analyze the impact of several relevant factors on energy consumption. Results from these two models were compared on the basis of several statistical tests, and the SEM was selected to fit the data. The goodness of fit of the SEM reached 0.898, a 3% improvement over 0.871 of the adjusted R-squared resulting from the traditional OLS model. The results indicated that the average energy consumption between the years of 2006 and 2008 did present spatial interdependence to some degree among Chinese provinces, and the energy consumption behavior was collectively influenced by the internal factors of the province under investigation and its neighbors. There was a significant positive correlation between energy consumption and population, social affluence, and secondary industry. That is, the elastic coefficient of energy consumption increased gradually as these influential factors increased. A set of governmental countermeasures are necessary: moderate efforts should be made to revise birth control targets, civic investment should be increased to advocate a low-carbon lifestyle in China, and new and energy-saving technologies should be rapidly adopted in the Chinese industrial system. Above all, the strategic planning and policy making for the long-term reduction of energy consumption should consider the spatial interaction mechanism of energy consumption among different jurisdictions in the country.