AI-Driven Prediction of Ecological Footprint Using an Optimized Extreme Learning Machine Framework

Ibrahim Alrmah, Ahmad Alzubi, Oluwatayomi Rereloluwa Adegboye

Accurate forecasting of the ecological footprint (EF) is critical for advancing the Sustainable Development Goals, particularly those related to climate action, responsible consumption and production, and sustainable cities. To address the limitations of conventional machine learning models, such as instability due to random weight initialization and poor generalization, this study proposes a novel hybrid model that integrates the Chinese Pangolin Optimizer (CPO) with the Extreme Learning Machine (ELM). Inspired by the foraging behavior of pangolins, the CPO efficiently optimizes the ELM’s input weights and biases, significantly enhancing prediction accuracy and robustness. Using comprehensive monthly United States data from 1991 to 2020, the model forecasts EF based on key socioeconomic and environmental indicators, including GDP per capita, human capital, financial development, urbanization, globalization, and foreign direct investment. The CPO–ELM model outperforms benchmark models, achieving an R2 of 0.9880 and the lowest error metrics across multiple validation schemes. Furthermore, SHAP (Shapley Additive Explanations) analysis reveals that GDP per capita, human capital, and financial development are the most influential drivers of EF, offering policymakers actionable insights. This study demonstrates how interpretable AI-driven forecasting can support evidence-based environmental governance and contribute directly to sustainability targets under the SDG framework.