The nexus between clean energy market risk and US business environment: evidence from wavelet coherence and variance analysis

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-09-09 DOI:10.1007/s00477-024-02810-3

Ming Li, Cem Işık, Jiale Yan, Ran Wu

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Abstract

Given the critical role of the clean energy market in the global economy and environmental sustainability, this paper investigates the impact of the U.S. Business Conditions Index (ADS) on the risk of segmented clean energy markets across different time scales and market conditions, as well as its spillover mechanisms. By using wavelet coherence and wavelet quantile analysis, we examine how the Aruoba–Diebold–Scotti (ADS) Business Conditions Index affects the risk levels of segmented clean energy indices under varying market conditions. To further understand this impact mechanism, we also employ the quantile Granger causality test to analyze the spillover effects of ADS on the clean energy market. The results show that the ADS index significantly influences the risk levels of segmented clean energy markets, with notable differences across various time scales and market conditions. The contributions of this study include: (1) segmenting the measurement of clean energy market risk into the Solar Index (SOLAR), Renewable Energy Index (RE), Biomass Index (BIO), Wind Energy Index (WIND), and Clean Energy Index (WILDER); (2) providing new evidence on the impact of the ADS Business Conditions Index on segmented clean energy market risk; and (3) offering new perspectives for different clean energy market participants to better navigate complex business environments and develop effective risk management strategies.

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清洁能源市场风险与美国商业环境之间的联系：小波一致性和方差分析的证据

鉴于清洁能源市场在全球经济和环境可持续发展中的关键作用，本文研究了美国商业条件指数（ADS）在不同时间尺度和市场条件下对细分清洁能源市场风险的影响及其溢出机制。通过使用小波相干性和小波量化分析，我们研究了在不同市场条件下，Aruoba-Diebold-Scotti（ADS）商业条件指数如何影响细分清洁能源指数的风险水平。为了进一步理解这一影响机制，我们还采用了量子格兰杰因果检验来分析 ADS 对清洁能源市场的溢出效应。结果表明，ADS 指数显著影响了细分清洁能源市场的风险水平，并且在不同时间尺度和市场条件下存在明显差异。本研究的贡献包括(1) 将清洁能源市场风险的测量细分为太阳能指数 (SOLAR)、可再生能源指数 (RE)、生物质能指数 (BIO)、风能指数 (WIND) 和清洁能源指数 (WILDER)；(2) 就 ADS 商业条件指数对细分清洁能源市场风险的影响提供了新的证据；(3) 为不同的清洁能源市场参与者更好地驾驭复杂的商业环境和制定有效的风险管理策略提供了新的视角。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.