In this era of the industrial revolution, environmental and technological advancements profoundly impact carbon emissions, which negatively and positively affect global climate change, raising global warming and mitigating carbon emissions, respectively. The emission of carbon dioxide and greenhouse gases sets an imbalance in the environment, creating natural disasters or reducing the capacity of carbon storage. Technology advancements also increase energy use, driving up emissions. To effectively understand the complex relationship between environmental and technological issues with carbon emissions, this study applies statistical analysis in order to validate many hypotheses based on carbon emissions. The statistical result shows the p-value of all the hypotheses was below 0.05, which claims that all the null hypotheses are rejected, which suggests that renewable energy supply and development in technologies are decreasing the CO \(_2\) emissions, indicating a negative correlation between them. Furthermore, some machine learning models (linear regression, AdaBoost, support vector regression, and KNN) were trained for the determination of carbon emission assumptions, where linear regression achieved 99% accuracy and AdaBoost and support vector regression both scored 95% accuracy. On the other hand, explainable AI techniques (SHAP) have been used to clearly understand the effectiveness of linear regression models and the essential features that influence model prediction and learning.