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Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective

Hongjun Zeng, Mohammad Abedin Orcid Logo, Vineet Upreti Orcid Logo

Energy Economics, Start page: 108003

Swansea University Authors: Mohammad Abedin Orcid Logo, Vineet Upreti Orcid Logo

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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...

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Published in: Energy Economics
ISSN: 0140-9883
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68156
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spelling v2 68156 2024-11-04 Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 8f0fcae811cfbfabf93901185944c055 0000-0002-9803-7551 Vineet Upreti Vineet Upreti true false 2024-11-04 CBAE 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. Journal Article Energy Economics 0 108003 Elsevier BV 0140-9883 Southern oscillation; Clean energy; Granger quantiles causality; Quantile on quantile regression; Wavelet 3 11 2024 2024-11-03 10.1016/j.eneco.2024.108003 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-11-04T10:16:31.1508909 2024-11-04T09:44:57.6785901 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Hongjun Zeng 1 Mohammad Abedin 0000-0002-4688-0619 2 Vineet Upreti 0000-0002-9803-7551 3
title Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
spellingShingle Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
Mohammad Abedin
Vineet Upreti
title_short Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
title_full Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
title_fullStr Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
title_full_unstemmed Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
title_sort Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
8f0fcae811cfbfabf93901185944c055
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
8f0fcae811cfbfabf93901185944c055_***_Vineet Upreti
author Mohammad Abedin
Vineet Upreti
author2 Hongjun Zeng
Mohammad Abedin
Vineet Upreti
format Journal article
container_title Energy Economics
container_volume 0
container_start_page 108003
publishDate 2024
institution Swansea University
issn 0140-9883
doi_str_mv 10.1016/j.eneco.2024.108003
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 0
active_str 0
description 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.
published_date 2024-11-03T10:16:30Z
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score 11.036815

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