Wu Geng, Hu Yi-Chung, Chiu Yu-Jing, Tsao Shu-Ju
Department of Business Administration, Chung Yuan Christian University, 32023 Taoyuan, Taiwan.
Environ Dev Sustain. 2023;25(5):4173-4193. doi: 10.1007/s10668-022-02238-1. Epub 2022 Apr 5.
Predicting energy consumption is an essential part of energy planning and management. The reliable prediction of regional energy consumption is crucial for the authority in China to formulate policies by with respect to the dual control of its energy consumption and energy intensity. Given that energy consumption is affected by a number of factors, this study proposes a non-homogeneous, discrete, multivariate grey prediction model based on adjacent accumulation to predict the regional energy consumption in China. Interestingly regional GDP was selected by grey relational analysis as the independent variable in the proposed model. The results show that it can outperform the other multivariate grey models considered in terms of predicting regional energy consumption in China. Moreover, we found that economic development and energy consumption of each region in China remain closely related. In the post-COVID-19 period, regional economic development will continue to grow and increase energy consumption.
预测能源消耗是能源规划与管理的重要组成部分。可靠地预测区域能源消耗对于中国政府制定能源消耗和能源强度双控政策至关重要。鉴于能源消耗受多种因素影响，本研究提出一种基于相邻累加的非齐次、离散、多变量灰色预测模型，以预测中国的区域能源消耗。有趣的是，通过灰色关联分析，区域国内生产总值被选为所提模型中的自变量。结果表明，在预测中国区域能源消耗方面，该模型优于其他考虑的多变量灰色模型。此外，我们发现中国各地区的经济发展与能源消耗仍然密切相关。在新冠疫情后时期，区域经济发展将持续增长并增加能源消耗。
