Forecasting Drivers of Green Economy in the United States: Role of FDI and Information Technology Using Machine Learning Approach

This research examines how growing gross domestic product (GDP), information and communications technology, foreign direct investment (FDI) inflows, renewable energy use, and urbanization affect ecosystem conditions in the United States, using time series data between 1990 and 2022. By employing advanced econometric tools, including autoregressive distributed lag (ARDL), dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegrating regression (CCR), in addition to unit root tests, such as ADF, P-P, and DF-GLS, the study tests the degree of integration of the explanatory factors. The ARDL bounds test results highlight the substantial implication of information and communication technology (ICT) adoption and sustainable energy usage on environmental sustainability. The results indicate that long-term and short-term effects of ecological footprint (EF) will go higher due to a 1% increase in GDP, urbanization, and FDI by 0.281%, 0.229%, and 0.267%, and 0.341%, 0.231%, and 0.145%, respectively. These indicate that environmental degradation in the United States is a result of higher economic growth, FDI, and urbanization. Furthermore, the current article applies a machine learning approach to predict the values of EF from 1990 to 2040. In response, the study calls for policy reforms to recalibrate the nation’s energy portfolio by prioritizing green energy and boosting ICT usage to minimize dependency on fossil fuels and reach net-zero emissions. The article underlines the relevance of eco-friendly urban structure and the availability of access to clean energy to promote not only monetary expansion but also biodiversity conservation, opening a long-term and balanced picture of natural health.