Clean energy consumption and economic growth in China: a time-varying analysis

IF 0.7 4区 经济学 Q3 ECONOMICS
Pejman Bahramian, Andisheh Saliminezhad, Sami Fethi
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引用次数: 0

Abstract

Abstract Assessing the causal relationships between clean energy consumption and economic growth in China, a central actor in the world’s climate future, have received considerable attention among scholars. However, due to the lack of methodological rigour in the causality analysis, available literature failed to provide solid inferences on the links between the variables. Therefore, this study aims to re-examine the variables’ dynamic linkages with a more well-established approach from 1965 to 2020. We use a time-varying framework that relaxes the assumption of parameter stability, a remarkable feature that distinguishes our paper from the previous studies. Utilizing the conventional Granger causality test, we fail to detect causation between the variables. However, the evidence of substantial time variation in the causal relationships implies that the standard framework’s inference is unreliable. The findings of our time-varying analysis indicate different forms of causality flows in various subperiods. This can be a dependable reason for China to follow its enhanced carbon neutrality target safely. The results of our study also emphasize the significance of considering time-varying causality tests to avoid the risk of misleading inferences.
中国清洁能源消费与经济增长的时变分析
摘要作为世界气候未来的核心参与者,评估中国清洁能源消费与经济增长之间的因果关系受到了学者们的极大关注。然而,由于因果关系分析缺乏方法上的严谨性,现有文献未能对变量之间的联系提供可靠的推断。因此,本研究旨在从1965年到2020年,用一种更成熟的方法重新审视变量的动态联系。我们使用了一个时变框架,该框架放松了参数稳定性的假设,这是我们的论文与先前研究的显著区别。利用传统的格兰杰因果关系检验,我们无法检测变量之间的因果关系。然而,因果关系中大量时间变化的证据表明,标准框架的推断是不可靠的。我们的时变分析结果表明,在不同的子周期中存在不同形式的因果关系流。这可能是中国安全实现碳中和目标的可靠理由。我们的研究结果还强调了考虑时变因果关系检验的重要性,以避免误导性推断的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.40
自引率
12.50%
发文量
34
期刊介绍: Studies in Nonlinear Dynamics & Econometrics (SNDE) recognizes that advances in statistics and dynamical systems theory may increase our understanding of economic and financial markets. The journal seeks both theoretical and applied papers that characterize and motivate nonlinear phenomena. Researchers are required to assist replication of empirical results by providing copies of data and programs online. Algorithms and rapid communications are also published.
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