基于脉冲响应函数的中国钢铁产业能源效率及其影响因素的动态分析

Dynamic Analysis of the Relationship between TFEE and Its Influential Factors in China’s Steel Sector Based on the Impulse Reaction Function

基于我国钢铁产业2002年-2008年间的21个省市面板数据,对钢铁产业技术进步,产业平均规模,产业集中度,产业产权结构,产业对外贸易程度与钢铁产业的全要素能源效率长期均衡关系进行了估算;在此基础上,使用向量误差修正模型,利用脉冲响应函数和方差分解模型就钢铁产业全要素能源效率与其影响因素之间的短期动态关系作进一步分析。结果表明:在长期均衡关系中,技术进步对我国钢铁产业能源效率贡献最大,其次为产业集中度和产权结构,对外贸易因素对钢铁产业能源效率贡献较小;从短期动态均衡关系来看,钢铁产业平均规模和产权结构对钢铁产业全要素能源效率的贡献度最大。

China’s steel sector is energy intensive, accounting for approximately 16% of China’s total energy consumption and 26% of industrial energy consumption. Energy efficiency in China’s steel sector has increased over time. Energy consumption per unit of GDP declined from 39.8 to 21.4 tons of coal equivalent between 2002 and 2008. However, China’s steel sector still has one of the lowest levels of energy efficiency among major steel producers, particularly compared with developed countries. The average energy consumption per unit of steel is about 20% higher than developed countries. In order to investigate factors affecting energy efficiency in China’s steel sector, this paper employs a unique set of steel panel data of 21 provinces in China during the period 2002-2008. Based on the provincial panel data, a long cointegration model was designed to estimate total factor energy efficiency （TFEE） and its factors, including technological change, average scale, degree of concentration, state-owned controlling degree, and trade degree. Results reveal that three variables have a significant impact on TFEE in the long run, i.e., technological change, and ownership, as well as industrial average scale, with showing influence coefficients of 0.96, 0.1, and 0.07 respectively. To estimate the short-term influence of the 5 factors imposed on TFEE, a short dynamic model was used to estimate TFEE and its factors by the impulse reaction function and variance decomposition model, based on the vector auto regression model （VAR）. It could be concluded that the average scale and ownership yield a significant impact on TFEE in the short term. Technological change impulse on TFEE is not statistically significant in the short VAR model. Its impulse responses on TFEE are merged by the TFEE impulse on TFEE in the short term. Three variables, i.e., average scale, degree of concentration, and state-owned controlling degree, exert a continuous impact on TFEE in the short run. It could also be concluded that technological change exerts a dominant effect on steel energy efficiency. The Lorenz curve of provincial energy consumption in steel sector implies that the government should promote technological advancement in provincial steel sectors. To elevate the average scale and degree of concentration in China’s steel sector, the government should also continue eliminating backward production capacity of the steel sector, and encouraging consolidation and reorganization of large iron and steel enterprises. The export structure should be continuously optimized to reinforce its contribution to steel energy efficiency