A machine learning approach to carbon emissions prediction of the top eleven emitters by 2030 and their prospects for meeting Paris agreement targets - Arju Manara Begum; Mahadee Al Mobin (Scientific Reports)
Abstract The continued rise in global carbon dioxide ( $$CO_2$$ ) emissions challenges international climate policy, particularly the goals of the Paris Agreement. This study forecasts $$CO_2$$ emissions through 2030 for the eleven highest-emitting nations–China, the United States, India, Russia, Japan, Iran, Indonesia, Saudi Arabia, Canada, South Korea, and Germany–while assessing their progress toward Nationally Determined Contributions (NDCs). Using data from 1990 to 2023, we apply a robust data pipeline comprised of six machine learning models and sequential squeeze feature selection incorporating eleven economic, industrial, and energy consumption variables. We have modelled the scenario with an average prediction accuracy of 96.21%. Results indicate that Russia is on track to exceed its reduction targets, while Germany and the United States will fall slightly short. China, India, Japan, Canada, South Korea, and Indonesia are projected to miss their commitments by significant margins. At the same time, Iran and Saudi Arabia are expected to increase emissions rather than reduce them. These findings highlight the need for strengthened energy efficiency policies, expanded renewable energy adoption, enhanced carbon pricing mechanisms, and stricter regulatory enforcement. Emerging economies require international collaboration and investment to support low-carbon transitions. This study provides a data-driven assessment of emission trajectories, emphasizing the urgency of coordinated global action, technological innovation, and adaptive policy measures to align emissions with the 1.5 $$^{° }{\hbox {C}}$$ warming threshold. This work represents a novel integration of multivariate machine learning modelling, data-driven feature selection, and policy-oriented emission forecasts, establishing new methodological and empirical benchmarks in climate analytics.