This research aims to evaluate the accuracy of the long-term relationship between renewable and sustainable energy sectors and emerging technologies, including quantum computing, artificial intelligence (AI), and big data. Using a novel methodology that integrates the Time-Varying Parameter Vector Autoregressive (TVP-VAR) frequency connectedness approach with Long Short-Term Memory (LSTM) neural networks, the study examines the long-term interconnectedness, considering the dynamic nature of coefficients and covariance structures. The analysis spans from May 14, 2018, to September 6, 2023. It focuses on six critical clusters within the sustainable and renewable energy sectors: clean energy, green energy, solar energy, the water industry, wind energy, and the low-carbon industry. Additionally, the study explores two contemporary technology domains, AI and big data, alongside quantum computing. The findings reveal that AI and its associated technologies generally exhibit weaker connections to the renewable and sustainable energy sectors. However, specific pairs, such as those involving business intelligence and AI, show notable interconnectedness. Overall, quantum computing entities demonstrate lower levels of connectedness than the AI/significant data sector, with Microsoft standing out for its solid and broad connections to renewable and sustainable industries. Further analysis identifies distinct patterns, with AI and related technologies showing strong long-term memory connections with renewables and green energies. At the same time, platforms centered on business intelligence and AI display comparatively weaker long-term ties. Among the quantum computing companies, IBM and Google have shown superior performance through specific subsectors. Finally, this study offers valuable insights into the evolving dynamics and interconnectedness at the intersection of renewable and sustainable energies, quantum computing, and the AI/big data industries. The findings support strategic decision-making in sustainable energy transitions and underscore the significance of industry-specific factors in shaping long-term collaborations.

中文翻译：

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量子计算、人工智能和大数据股票在可再生和可持续能源上的动态连接


本研究旨在评估可再生能源和可持续能源部门与新兴技术（包括量子计算、人工智能 （AI） 和大数据）之间长期关系的准确性。该研究使用一种将时变参数向量自回归 （TVP-VAR） 频率连通性方法与长短期记忆 （LSTM） 神经网络相结合的新方法，考虑了系数和协方差结构的动态性质，检查了长期互连性。分析时间为 2018 年 5 月 14 日至 2023 年 9 月 6 日。它侧重于可持续和可再生能源领域的六个关键集群：清洁能源、绿色能源、太阳能、水工业、风能和低碳工业。此外，该研究还探讨了人工智能和大数据以及量子计算这两个当代技术领域。研究结果表明，人工智能及其相关技术通常与可再生能源和可持续能源领域的联系较弱。但是，特定对（例如涉及商业智能和 AI 的对）显示出明显的互连性。总体而言，量子计算实体的连通性水平低于 AI/重要数据领域，其中 Microsoft 因其与可再生和可持续行业的坚实而广泛的联系而脱颖而出。进一步的分析确定了不同的模式，人工智能和相关技术显示出与可再生能源和绿色能源的强烈长期记忆联系。与此同时，以商业智能和 AI 为中心的平台显示出相对较弱的长期联系。在量子计算公司中，IBM 和 Google 在特定子领域表现出卓越的表现。 最后，本研究为可再生能源和可持续能源、量子计算和 AI/大数据行业交叉领域不断发展的动态和相互联系提供了有价值的见解。这些发现支持可持续能源转型的战略决策，并强调了行业特定因素在形成长期合作中的重要性。