The impact of renewable energy on inflation in G7 economies: Evidence from artificial neural networks and machine learning methods

被引：1
|
作者：
Zhang, Long [1 ]
Padhan, Hemachandra [2 ]
Singh, Sanjay Kumar [3 ]
Gupta, Monika [4 ]
机构：
[1] Peking Univ, HSBC Business Sch, Shenzhen, Peoples R China
[2] Indian Inst Management Sambalpur, Sambalpur, India
[3] Indian Inst Management Lucknow, Lucknow, India
[4] SP Jain Inst Management & Res SPJIMR, Mumbai, India
来源：
ENERGY ECONOMICS | 2024年 / 136卷
关键词：
Inflation rates; Energy prices; Renewable energy; Energy transition; Artificial neural network; Machine learning methods; EXCHANGE-RATE; GROWTH; CONSUMPTION; NEXUS;
D O I：
10.1016/j.eneco.2024.107718
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
This paper examines the impact of cleaner energy adoption (i.e., renewable energy consumption and generation) on inflation rates in G7 economies from 1997 to 2021. The Principal Component Analysis is used to construct the renewable energy consumption and generation indices. Then, the paper runs various artificial neural networks and machine learning methods to test the validity of the cleaner energy-led inflationary economy hypothesis. It is observed that renewable energy consumption and production significantly predict inflation rates along with macroeconomic variables. The effects of renewable energy consumption and production on inflation rates are positive. Related policy implications are also discussed.
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收藏
页数：12
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