A neural network approach to carbon emission prediction in industrial and power sectors
Syed Azeem Inam, Syed Javaid Zaidi, Abdullah Ayub Khan, Sajid Ullah
Abstract
This study analyzes global contamination trends, emphasizing the contributions of industrial and electricity sectors to environmental degradation. Using the analysis of the temporal series, the study identifies the sectoral and regional disparities in pollution levels, highlighting the urgent need for specific mitigation strategies. A feedforward neural network (FFNN) model predicts pollution levels over the next 3 years, revealing the most affected countries and allowing proactive policy interventions. The model proposed in the present study achieves a Mean Squared Error (MSE) of 79.6%, a Root Mean Squared Error (RMSE) of 89.2%, and a Mean Absolute Error (MAE) of 75.8%, ensuring a reliable prognosis accuracy. The results provide information based on multi-country sectoral data of fifty nations by applying an FFNN-powered global emission model, combining time series. The framework enables political leaders and industry leaders to implement sustainable practices, optimize emission controls, and develop specific regulations through predictive scenario analysis, providing a reliable basis for decision-making in the field of pollution control and climate change mitigation.