Resolving the Ozone Dilemma: An Integration of Game Theory and Time Series Forecasting

Abstract

The main cause of the ozone layer's depletion, which is a serious environmental problem, is human activity such as the emission of chemicals that deplete the ozone layer, like Chloro-Fluro Carbons. The combination of machine learning (ML) and game theory methods appears to be a novel and promising way to better anticipate and address ozone layer depletion. The interactions between different stakeholders, such as nations or industries, that affect the dynamics of the ozone layer can be modeled using a framework provided by game theory. In the meantime, large-scale dataset analysis made possible by Time Series Forecasting along with correlation allows for more precise forecasts and well-informed decision-making. This study's main goal is to improve the accuracy of ozone layer depletion predictions by utilizing ARIMA Time Series forecasting, correlation with the Air Quality Index along with the science of strategy for better decision-making via Game Theory. The proposed methodology has proposed a way to create a more realistic and comprehensive model by taking into account the strategic interactions among various entities that contribute to the depletion of the ozone layer. By using an interdisciplinary approach, we hope to aid in the creation of practical plans for environmental sustainability and ozone layer protection. ARIMA predicted the values for the upcoming years, with a Root Mean Squared Value of 5.04. The Game Theory approach generates a report tailored to the needs of the user suggesting the protocols to be followed. Finally, the authors also correlated the Air Quality Index with the Ozone Layer Depletion with an accuracy of 82% with Gradient Boosting.