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Article type: Research Article
Authors: Wang, Jinyia | Yuan, Yongkea | Tian, Guangb; * | Zheng, Yuhaoc; * | Wang, Zhengzaoa
Affiliations: [a] Beijing University of Technology, Beijing University of Technology, Beijing, China | [b] State Grid Hebei Electric Power Co., Ltd., Hebei, China | [c] Norwich Business School, University of East Anglia, Norwich, NR4 7TJ, UK
Correspondence: [*] Corresponding authors: Guang Tian, State Grid Hebei Electric Power Co., Ltd., Hebei 071000, China. E-mail: soltian @126.com. Yuhao Zheng, Norwich Business School, University of East Anglia, Norwich, NR4 7TJ, UK. E-mail: 18002687766@163.com.
Abstract: Reasonably allocating the micro factor investment in the industry and trying to improve the energy efficiency are very important for the green development of industrial economy in Hebei Province, so as to the achievement of its double carbon target. This paper takes industrial energy consumption, assets and labor in Hebei Province as micro-input elements. Based on the panel data of 34 industrial industries in Hebei Province from 2005 to 2016, we identify the correlation of micro-input factors in different industries and conduct Granger causality test. The study shows that: (1) About half of the industry’s combined energy consumption is correlated with the number of employees and assets; About two-thirds of the industry’s asset workers are relevant; (2) There is a two-way causal relationship between the total energy consumption and assets of most industries and the annual average number of employees. (3) The rational allocation of capital, labor and energy consumption depends on the deepening reform of the factor market. The high energy consumption industry should actively improve and innovate the energy technology in this process to realize the coordinated promotion of energy saving and carbon reduction, and thus to promote the healthy and sustainable development of the industry steadily.
Keywords: Energy efficiency, micro-elements, Granger causality test
DOI: 10.3233/JCM-226079
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 22, no. 4, pp. 1069-1080, 2022
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