Mapping the landscape of energy markets research: A bibliometric analysis and predictive assessment using machine learning DOI
Thiago Christiano Silva,

Tércio Braz,

Benjamin Miranda Tabak

и другие.

Energy Economics, Год журнала: 2024, Номер 136, С. 107698 - 107698

Опубликована: Июнь 24, 2024

Язык: Английский

Evolution analysis of the decoupling state and drivers between economic growth and CO2 emissions in RCEP member countries DOI Creative Commons
Xichun Luo, Honghao Zhao

Energy Strategy Reviews, Год журнала: 2025, Номер 59, С. 101709 - 101709

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0
What is the global causality among geopolitical risk, financial development, and energy transition in the OECD countries? New insights from causality and heterogeneity DOI
Zhenhua Zhang, Mingcheng Zhao, Xinyu Zhang

и другие.

International Review of Financial Analysis, Год журнала: 2025, Номер unknown, С. 104288 - 104288

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
The relationship between artificial intelligence, geopolitical risk, and green growth: Exploring the moderating role of green finance and energy technology DOI
Xiyue Yang, Hui Chen, Bofeng Li

и другие.

Technological Forecasting and Social Change, Год журнала: 2025, Номер 217, С. 124135 - 124135

Опубликована: Апрель 28, 2025

Язык: Английский

Процитировано

0
Exploring the Interrelationship Between Energy, Geopolitical Risk, and Bitcoin Based Green Business Strategies DOI Creative Commons
Pooja Kumari, Amit Shankar, Rsha Alghafes

и другие.

Business Strategy and the Environment, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

ABSTRACT This study examined how Bitcoin, energy prices, and geopolitical risk interact by examining the first four moments (mean, variance, skewness, kurtosis) of their return distributions using wavelet analysis. The findings reveal that co‐movement patterns index, Bitcoin prices are time frequency sensitive. During turbulent period 2020–2024, significant cross effect was observed at medium‐ long‐term scales in relationship between index index. Similarly, case cross‐effects were detected short‐term scales. From 2021 onwards, a strong coherence is high medium frequencies for all moment pairs among risk. In terms Bitcoin‐energy relationship, mean volatility noted throughout most sample across different bands. Moreover, cross‐skewness cross‐kurtosis connections more prominent short‐ medium‐term horizons, especially during covid pandemic. These insights valuable investors policymakers management.

Язык: Английский

Процитировано

0
Mapping the landscape of energy markets research: A bibliometric analysis and predictive assessment using machine learning DOI
Thiago Christiano Silva,

Tércio Braz,

Benjamin Miranda Tabak

и другие.

Energy Economics, Год журнала: 2024, Номер 136, С. 107698 - 107698

Опубликована: Июнь 24, 2024

Язык: Английский

Процитировано

3