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

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.