Deep Learning and Econometric Analysis of CO2 Emissions in Bangladesh: A Transition Towards Renewable Energy and Sustainable Practice

Abstract

Environmental sustainability achievement is an increasingly significant issue in current society. Globally, unimpeded greenhouse gas emissions threaten environmental sustainability. As a developing economy, Bangladesh is extremely reliant on energy consumption, which results in greenhouse gas emissions and raises a significant threat to environmental sustainability. In our comprehensive analysis to study the energy consumption patterns of Bangladesh and their environmental implications, we employed a suite of advanced tests, including the auto-regressive distributed lag model. The Augmented-Dicky Fuller test and the Bound test, respectively, confirm the unit root and the co-integration status of the study variables. These methodologies illuminated the intricate relationship between fossil fuel consumption, industrial growth, and the consequent CO2 emissions in the region. Despite the evident challenges posed by non-renewable energy sources, there's a discernible shift towards renewable energy and sustainable practices, especially in industrial sectors. This transition is further evidenced by the Long Short-Term Memory forecasting model, which projects a promising decline in CO2 emissions over the next six years, plummeting from 70 metric tons to a mere 15 metric tons annually. While these findings highlight the strides Bangladesh is making towards sustainability, they also underscore the importance of continued emphasis on green technology and eco-friendly policies to ensure a sustainable future.