(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 promotion of clean energy is critical to achieving sustainable development. This study investigates the factors that affect the exports, trade potential, and revealed comparative advantage (RCA) of clean energy products (CEPs) in the emerging Asian economies (EAEs). The export data of the CEPs was collected from UN Comtrade based on six‐digit HSN codes and for other variables, the world development indicators database was used for the period 2000–2020. The Augmented Gravity Model and Poisson Pseudo Maximum Likelihood Estimation (PPMLE) were applied for the analysis and the results show that exporting countries' economic growth is significantly promoting the exports of CEPs in all countries except Indonesia and Saudi Arabia. Surprisingly, it was found that distance is playing a positive role in export growth in most countries, a sign of a globalized world. However, these economies, especially India and Pakistan should resolve their border disputes and enhance regional cooperation to increase CEPs exports, which will help them achieve sustainable economic growth.

This study examines the impact of clean energy consumption on environmental quality, supporting the UN Sustainable Development Goal (SDG) 7, which aims to ensure access to clean, affordable energy for all. The Auto‐Regressive Distributed Lag (ARDL) model is used for Japan, and the Fourier ARDL for China and India, followed by a Granger Causality test. The ecological footprint is selected as a dependent variable because it provides a comprehensive picture of demand for natural resources, which, in turn, reflects environmental quality. Renewable energy consumption (REC) is treated as an independent variable representing clean energy use, which is the main focus of SDG 7. A few other relevant variables are selected that affect the environment's quality, such as gross domestic product (GDP), population density, gross fixed capital formation (GCF), and carbon dioxide (CO ₂ ) emissions. Data were collected from World Development Indicators and Our World in Data for the period 1970–2022. Asian powers China, India, and Japan have been selected for the study as they are Asia's three biggest economies and CO ₂ emitters. The results indicate that REC significantly lowers EFP in India, China, and Japan by 0.37%, 0.21%, and 1.51%, respectively. With higher economic growth, more investment should be in clean energy projects.

The current study examines the impact of economic growth, urbanization, trade openness, and energy consumption on climate change among four G7 countries: the United States of America (USA), the United Kingdom (UK), Japan, and Germany. For this, the Auto Regressive Distributed Lag (ARDL) model was used, and to assess the model's fitness, several machine learning algorithms were also employed, including Random Forest and Gradient Boost. Based on the coefficients of determination ( R ² ), the model is well‐fitted. The ARDL model's results show that in the long run, energy consumption increases CO ₂ emissions by 0.46%, 0.84%, 1.7%, and 0.7% in Japan, the UK, the US, and Germany, respectively. In the case of GDP, a negative relationship with CO ₂ emissions was observed in the UK and Germany, at rates of 0.60% and 1.06%, respectively. In contrast, a positive relationship was found in the USA, at a rate of 0.42%. Trade openness has shown a positive relation with CO ₂ emissions in both Japan and the USA. Urbanization has also demonstrated a positive association with CO ₂ emissions in the UK and the USA, while it has shown a negative association in Japan by 0.007%. All four countries need to invest more in clean energy to improve the current climate, or at the very least, stabilize it.