期刊
ENERGY
卷 243, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.123066
关键词
Driving factors; Carbon intensity; The heavy industry; Semi-parametric regression model
资金
- National Natural Science Foun-dation of China [71974085]
This article investigates the main influencing factors of carbon intensity in China's heavy industry. The results show that spatial agglomeration and technological progress have non-linear impacts on carbon intensity, while energy consumption structure, environmental regulations, and industrial structure have linear relationships with carbon intensity.
China has promised that by 2030, its carbon intensity will be cut down by about 60%. The purpose of this article is to investigate the main influencing factors of the heavy industry's carbon intensity. Different from the existing related literature, this article uses a semiparametric model to empirically analyze the carbon intensity of China's heavy industry. The main results are as follows: (1) the relationship between spatial agglomeration and carbon intensity shows a non-linear pattern, mainly caused by the cross regional flow of production factors. (2) The impact of technological progress on carbon intensity also displays significant non-linear characteristics, mainly due to the staged differences in research and development investment. The following three factors are linearly related to carbon intensity. (1) Energy consumption structure has a more significant linear impact on the carbon intensity in the central and eastern regions, due to the heavy coal consumption of the heavy industry in these two regions. (2) The linear impact of environmental regulations on the carbon intensity in the eastern region is more significant, owing to the region's considerable investments in environmental governance. (3) The industrial structure has the most significant linear impact on the carbon intensity in the eastern region, since the scale of the tertiary industry in this region is larger. The conclusions can provide empirical support for local government managers to formulate differentiated energy policies.(c) 2022 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据