Rao Jiwen, He Yong
School of Management, Guangdong University of Technology, Guangzhou, 510520, China.
Environ Sci Pollut Res Int. 2023 Apr;30(16):46669-46684. doi: 10.1007/s11356-023-25511-w. Epub 2023 Feb 1.
Analysis of industrial energy intensity is greatly significant in China specifically from the perspective of sector heterogeneity due to considerably different levels of energy utilization in various industrial sub-sectors. This study proposes a new methodology to forecast energy intensity in industrial sub-sectors, considering the complexity of the socioeconomic system. This research collects the data of 36 industrial sub-sectors in China and combines fuzzy C-means clustering (FCM), rough set (RS) and support vector machine (SVM) to predict the energy intensity of industrial sub-sectors in 2030. First, this method classifies all the industrial sub-sectors according to energy intensity level and identifies the main factors that affect the energy consumption of the industrial sub-sectors. Second, the resulting classification paves the way for specifying models to forecast energy consumption. Finally, scenario analysis predicts the energy intensity of each industrial sub-sector in 2030. This exploration has the following results. (1) Energy intensity has significantly different trends in various industrial sub-sectors. For example, industrial sub-sectors with low energy intensity mainly belong to the manufacturing industry (S06-S33). In contrast, the medium- and high-energy intensity categories mainly belong to the mining industry (S01-S05) and energy extraction and supply industry (S34-S36). (2) The critical factors affecting industrial energy consumption are fixed assets, R&D investment, and labor investment. (3) By 2030, the energy intensity has a downward trend in various industrial sub-sectors in China. The scenario analysis implies that China's energy intensity would reach the current world average level under the low-speed development scenario. Also, China's energy intensity would reach the current world advanced level under the medium-speed or high-speed development scenario.
从行业异质性角度来看，分析工业能源强度在中国具有重大意义，因为各工业子行业的能源利用水平差异很大。本研究提出了一种新方法来预测工业子行业的能源强度，同时考虑到社会经济系统的复杂性。本研究收集了中国36个工业子行业的数据，并结合模糊C均值聚类（FCM）、粗糙集（RS）和支持向量机（SVM）来预测2030年工业子行业的能源强度。首先，该方法根据能源强度水平对所有工业子行业进行分类，并确定影响工业子行业能源消耗的主要因素。其次，所得分类为指定预测能源消耗的模型铺平了道路。最后，情景分析预测了2030年各工业子行业的能源强度。本探索有以下结果。（1）各工业子行业的能源强度趋势差异显著。例如，低能源强度的工业子行业主要属于制造业（S06-S33）。相比之下，中高能源强度类别主要属于采矿业（S01-S05）和能源开采与供应业（S34-S36）。（2）影响工业能源消耗的关键因素是固定资产、研发投资和劳动力投资。（3）到2030年，中国各工业子行业的能源强度呈下降趋势。情景分析表明，在低速发展情景下，中国的能源强度将达到当前世界平均水平。此外，在中速或高速发展情景下，中国的能源强度将达到当前世界先进水平。
