This paper examines the load capacity curve hypothesis by the tourism and renewable energy from top tourism economies in the World. We employ the data from 2000 to 2020 and applied the panel GMM and panel quantile regression to arrive at our empirical findings. The results of the two models demonstrate the non-validity of the Load Capability Curve (LCC) hypothesis and the significant role of touristic arrival (TRA) and greener energy consumption (GEC) on the load capacity factor (LCP) by contrasting the ecological footprint per capita and bio-capacity. Furthermore, renewable and clean energy is recommended to address air pollution and climatic vulnerability. Thus, the empirical results of the current study provide acumens for policymakers of top tourism economies to consume green innovation technologies to counterbalance the environmental and socio-economic issues induced by the tourism sector without halting economic growth and sustainable tourism development. The study discusses policy-related implications for sustainable development.

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.