Journal article 2 views
Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
Hongjun Zeng,
Mohammad Abedin 
,
Vineet Upreti
,
Vineet Upreti
Energy Economics, Start page: 108003
Swansea University Authors:
Mohammad Abedin , Vineet Upreti
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DOI (Published version): 10.1016/j.eneco.2024.108003
Abstract
This study presents the first analysis of the nexus between the Southern Oscillation Index (SOI), a measure of climate risk, and segmented clean energy indices (such as solar, renewable, and bioenergy). Our research findings indicate that (i) the Granger quantile causality significance of SOI on seg...
| Published in: | Energy Economics |
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| ISSN: | 0140-9883 |
| Published: |
Elsevier BV
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68156 |
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| Abstract: |
This study presents the first analysis of the nexus between the Southern Oscillation Index (SOI), a measure of climate risk, and segmented clean energy indices (such as solar, renewable, and bioenergy). Our research findings indicate that (i) the Granger quantile causality significance of SOI on segmented clean energy indices is asymmetric across different conditional quantiles. Significant predictability of SOI is observed only at the 0.25 and 0.75 quantile levels for all segmented clean energy indices, except for the WilderHill Clean Energy Index and NASDAQ OMX Fuel Cell Index. (ii) The clean energy market is significantly influenced by SOI under bullish market conditions. Impacts of SOI on all clean energy markets are nearly negligible when clean energy indices are at the median and lower quantile levels. (iii) The influence of strong La Niña episodes on segmented clean energy indices is more pronounced than during periods of intense El Niño phenomena. (iv) SOI exhibited a positive correlation at mid-term and long-term frequencies with segmented Clean Energy sectors, excluding bioenergy, for the majority of the sample period. Our conclusions provide deeper insights for investors managing clean energy investments in extreme climate conditions. Additionally, they offer useful information for policymakers to formulate viable economic policies addressing climate change, ensuring energy security, and facilitating a safer transition to clean energy. |
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| Keywords: |
Southern oscillation; Clean energy; Granger quantiles causality; Quantile on quantile regression; Wavelet |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Swansea University |
| Start Page: |
108003 |