In recent years, investors, corporations, and enterprises have shown great interest in the Bitcoin network; thus, promoting its products and services is crucial. This study utilizes an empirical analysis for financial time series and machine learning to perform prediction of bitcoin price and Garman-Klass (GK) volatility using Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Facebook prophet models. The performance findings show that the LTSM boost has a noticeable improvement compared to SARIMA and Facebook Prophet in terms of MSE (Mean Squared Error) and MAE (Mean Average Error). Unlike Long Short-Term Memory (LSTM), a component of Deep Learning (DL), the finding explains why the bitcoin and its volatility forecasting difficulty has been partially met by traditional time series forecasting (SARIMA) and auto-machine-learning technique (Fb-Prophet). Furthermore, the finding confirmed that Bitcoin values are extremely seasonally volatile and random and are frequently influenced by external variables (or news) such as cryptocurrency laws, investments, or social media rumors. Additionally, results show a robust optimistic trend, and the days when most people commute are Monday and Saturday and an annual seasonality. The trend of the price and volatility of bitcoin using SARIMA and FB-Prophet is more predictable. The Fb-Prophet cannot easily fit within the Russian-Ukrainian conflict period, and in some COVID-19 periods, its performance will suffer during the turbulent era. Moreover, Garman-Klass (GK) forecasting seems more effective than the squared returns price measure, which has implications for investors and fund managers. The research presents innovative insights pertaining to forthcoming cryptocurrency regulations, stock market dynamics, and global resource allocation.

The study focuses on understanding how the use of generative Artificial Intelligence (AI) can beneficially result in circular supply chain transformation while embedding design intelligence, ethical intelligence, and predictive intelligence within socio-technical systems. This study proposes and validates a model that integrates generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness, which collectively affect circular supply chain resilience and socio-environmental value realization, mediated by Sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To test the hypothesis, data were collected from 264 professionals in supply chain and technology-related industries in the USA. As the findings suggest, generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness significantly enhance sustainable process reconfiguration capability, which drives AI-enabled stakeholder co-creation. A serial mediation model indicates that Generative AI capabilities affect circular supply chain resilience and socio-environmental value realization via sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To our surprise, the regenerative policy ambidexterity negatively moderates the relationship between AI-enabled stakeholder co-creation and the realization of socio-environmental value. The results provide actionable advice for managers implementing generative AI in sustainable supply chains. Instead of focusing solely on algorithmic efficiency, if an organization can develop reconfiguration capability and engage stakeholders, it would generate systemic sustainability benefits.

Given the significant surge in greenhouse gas emissions over the past several decades, the demand for low-carbon energy products has increased globally. However, geopolitical risks and tensions have also been escalating, which can reshape the trade of low-carbon energy products. Despite growing work on geopolitical risk and energy transition, no study has yet examined how geopolitical tensions reshape countries' revealed comparative advantage in low-carbon energy trade. This study therefore aims to fill this research gap by providing an understanding of how geopolitical risk affects comparative advantage in low-carbon energy trade across 27 countries worldwide. Taking the data period from 2000 to 2021, the study implements several panel regression models to account for endogeneity as well as cross-country heterogeneity. The results reveal that geopolitical risk undermines a country's comparative advantage in international trade of low-carbon energy products, regardless of the model specification. Domestically adopted low-carbon energy innovation suggests a positive outcome for enhancing comparative advantage in this category, while low-carbon energy policy has no significant impact. These results imply that governments and firms aiming to build durable comparative advantage in low-carbon energy trade should complement innovation-support policies with strategies that reduce exposure to geopolitical disruptions in green value chains.