The ecological footprint (EFP) is an important measure reflecting the interaction between humans and the environment. It provides the requirements to absorb the waste and emissions generated by humans in terms of pressure on natural resources. Therefore, an accurate prediction of EFP is vital to develop an understanding of sustainable development, the ecosystem, environmental protection, and resource utilization, especially in India, which has one of the highest total ecological deficits. This study applies various machine learning (ML) models to predict EFP in India based on 11 potential predictors covering trade openness (TO), urban population (UP) and renewable and fossil-fuel energy consumption over the period 1980–2017. The results show that the Random Forest (RF) model generates the lowest errors for prediction among the considered models and that five variables, namely inflation, renewable energy consumption (REC), role of primary sector in the economy, UP and human capital (HC), are the most crucial predictors of EFP.
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