(1) Background: Global warming is one of the most severe environmental problems humans are facing now. This study aims to assess the impacts of economic growth, trade openness, urbanization, and energy consumption on carbon emissions in India; (2) Methodology: In this longitudinal study, data have been collected from World Development Indicators and Our World in Data from 1980 to 2021. Two models have been used in this study, which are ARDL and the random forest model, which is a machine learning algorithm that uses the aggregated prediction for final prediction; (3) Results: The ARDL model revealed that the variables were cointegrated. In the short run, CO₂ emissions at previous lag, economic growth, and trade openness negatively correlated with CO₂ emissions, while energy consumption and urbanization exhibited a positive correlation. In the long run, energy consumption, urbanization, and trade openness positively correlated with CO₂ emissions, while economic growth and CO₂ emissions at previous lag demonstrated a negative correlation. The high value of the R² and low values of RMSE and M.A.E. in the Random Forest model shows the model’s fitness; (4) Conclusions: The study’s findings have been briefly discussed, and a few suggestions have been provided based on the results.

The Kumaraswamy alpha power Lomax model, a five-parameter sub-model of the Kumaraswamy alpha power transformed family, is explored in detail. It is of particular interest because there are a variety of possible symmetrical and asymmetrical forms for the density function of this distribution. The proposed distribution is loaded with several features. Maximum likelihood, least squares, weighted least squares, and Cramer-von Mises are the four techniques used to estimate the parameters of the new model. A simulation study has been conducted to assess its effectiveness. Actuarial measures like value at risk and tail value at risk are also derived. Compared to other recently introduced heavy-tailed distributions, the tail of the proposed distribution is heavier. Moreover, the model's usefulness is investigated using four real data sets from the fields of insurance, finance, and reliability. Compared to other well-known Lomax-based and competing distributions, the results demonstrate that the proposed distribution can fit the data better.