The emergence of new transformational technology, known as the fourth industrial revolution, has crucially opened a new window to green economic growth. The transition to low carbon, green economy, and green sustainability has gained momentum simultaneously in developed and developing countries. The greening policy echoes the pending climate change and its entrenching disruptions. Financial technology, or FinTech seems to be a promising direction in unlocking the green dilemma; to be concrete, FinTech and the green economy are separately documented in the literature. Against this background, the current study investigates the intersection between green economic growth and FinTech by conducting a systematic-cum-bibliometric analysis of published papers in the Scopus database with the goal of first examining the role and opportunities of implementing green FinTech as a stimulus for transition towards green economic growth in African countries and, second, identifying knowledge gaps and future policy and research directions by developing an integrated framework to help African countries in the transition to green economic growth and green FinTech. The results illustrate an increasing trend in research attention towards the green FinTech concept and its relationship with green economic growth, climate change, and greening rules and standards. A deep inspection of the mined papers indicates that future research trajectories are oriented into five different mainstreams: technology and instruments in digital finance; regulation, policies, and green FinTech; climate risk mitigation through FinTech; FinTech and environmental quality; green finance and climate change mitigation. Based on these research directions, an integrated framework was conceptualised that aims to deliver green economic growth using FinTech as a vehicle of transition for African countries.

Tourism is one of the main sources of wealth for the Moroccan regions, since, in 2019, it contributed 7.1% to the total GDP. However, it is considered to be one of the sectors most vulnerable to exogenous shocks (political and social stability, currency change, natural disasters, pandemics, etc.). To control this, policymakers tend to use various techniques to forecast tourism demand for making crucial decisions. In this study, we aimed to forecast the number of tourist arrivals to the Marrakech-Safi region using annual data for the period from 1999 to 2018 by using three conventional approaches (ARIMA, AR, and linear regression), and then we compared the results with three artificial intelligence-based techniques (SVR, XGBoost, and LSTM). Then, we developed hybrid models by combining both the conventional and AI-based models, using the technique of ensemble learning. The findings indicated that the hybrid models outperformed both conventional and AI-based techniques. It is clear from the results that using hybrid models can overcome the limitations of each method individually.