FORECASTING THE WORLD'S QUARTERLY GREENHOUSE GAS EMISSIONS VIA MULTI-LAYER PERCEPTRON

This study investigates the dynamics of environmental and economic indicators by examining time series data to understand their impact on sustainable development and environmental management. This study analyzes the data obtained from different sectors by determining the trends of the main variables such as Agriculture, Forestry and Fishing (AFF), Construction (C), Electricity, Gas, Steam and Air Conditioning Supply (EGSACS), Manufacturing (MAN), Mining (MIN), Other Services Industries (OSI), Total Households (TH), Total Industry and Households (TIH), Transportation and Storage (TS), Water supply; sewerage, waste management and remediation activities (WSSWMRA) and uses Artificial Neural Networks for data analysis. The results show that; it can be said that the artificial neural network produces results very close to the real values.