Analysis and Forecasting of Carbon Emission in SAARC Countries using Attention-based LSTM.

Climate change and global warming are urgent environmental issues demanding immediate action to safeguard future generations. The major contributor to the greenhouse effect, carbon dioxide $\left(\mathrm{CO}_{2}\right)$, primarily originates from industrial and transportation fossil fuel combustion. International agreements, like the Paris Agreement, call for a 30-35% reduction in CO 2 emissions compared to 2005 levels. This research aims to predict CO 2 emissions and raise awareness among SAARC nations and governments about the increasing trend. We introduce a novel predictive framework using Attention-based Long Short-Term Memory (A-LSTM) for CO 2 emissions analysis. The Attention mechanism assigns variable weights to input data, facilitating indirect connections between LSTM outputs and pertinent inputs. This enhances resource allocation in the A-LSTM model, overcoming computational constraints. We integrate input parameters encompassing CO 2 emissions from land-use changes, oil, natural gas, and coal combustion to forecast CO 2 emissions and correlate them with population and per capita GDP. Our comparative analysis conclusively demonstrates the superior performance of A-LSTM models over baseline LSTM models when applied to the CO 2 emission dataset sourced from Our World in Data (OWID) and World Bank Indicator database. Specifically, the LSTM model registers a MAPE of 24.968 and an RMSE of 0.34, whereas the Attention-based LSTM model showcases a marked improvement of 57% with a considerably lower MAPE of 10.5902 and an RMSE of 0.107.