Examining the Application of Time Series Analysis in Forecasting Economic and Environmental Trends Over Time

Posted: Jun 22, 2023

• Jacob Garcia
• Olivia Thompson
• Samuel Lee

Abstract

The complex interplay between economic development and environmental sustainability represents one of the most pressing challenges of our time. Traditional forecasting approaches have largely treated these domains as separate systems, employing distinct methodological frameworks for economic forecasting and environmental modeling. This disciplinary segregation has limited our ability to understand and predict the dynamic feedback mechanisms that characterize coupled human-environment systems. Economic activities generate environmental externalities, while environmental changes subsequently constrain or enable economic opportunities, creating a complex web of interdependencies that evolve over time. Time series analysis provides a powerful framework for understanding temporal patterns in complex systems, yet its application to coupled economic-environmental forecasting remains underdeveloped. Most existing approaches either focus exclusively on economic indicators such as GDP growth, inflation, and employment rates, or environmental metrics like carbon emissions, temperature anomalies, and biodiversity indices. The few attempts at integration have typically employed simple regression techniques that fail to capture the nonlinear dynamics and time-lagged relationships that characterize these complex systems. This research addresses this methodological gap by developing a novel forecasting framework that explicitly models the bidirectional coupling between economic and environmental systems. Our approach builds upon seasonal autoregressive integrated moving average (SARIMA) models but introduces novel extensions to capture cross-system dependencies.