传统工业为河南省的经济增长和城镇化进程做出了重要贡献，随着资源环境问题的日益突出，传统工业能源效率低、能源强度高等问题日益显现。报告利用Divisia因素分解法分析河南34个工业部门能源强度，利用BP神经网络模型对未来河南省高能耗类行业能源强度变化进行预测研究。研究结果表明：技术因素和结构因素对河南省工业部门能源强度降低起到推动作用，其中结构因素对工业部门能源强度降低的贡献度最高；从高、中、低耗能行业来看，高耗能行业对河南省工业能源强度降低贡献度最高，中、低耗能行业对河南省工业能源强度降低贡献度较小；高能耗行业在2020年前能源强度总体不断降低，但整体下降幅度较小；最后基于研究结论为河南省节能减排路径提出优化建议。
The traditional industrial industry has made great contributions to economic growth and urbanization of Henan Province. However，problems of low energy efficiency with high energy intensity are becoming more and more serious in industry sector. This paper is to analyze the energy intensity of 34 industries in Henan province by using the Divisia Factor Decomposition method. Then it makes a prediction of energy intensity of industries with high energy consumption，based on the BP neural network model. The results show that the technological factor and structural factor both play important roles in lowing the energy intensity in industry sector which the structural factor makes greater contribution. Meanwhile，the high-energy-consumed industries contribute most of the proportion of reducing energy intensity to all industries. What’s more，BP prediction suggests that there is an obvious descending trend of the energy intensity of high-energy-consumed industries before 2020，but the decline rate is small. Finally，this paper puts forward several suggestions for lowing energy intensity while increasing energy efficiency in Henan province.
