Data-Driven System Dynamics and Chauvenet-Based Outlier Detection for Assessing Global CO₂ Emissions under Fossil Energy and Electric Vehicle Transition Scenarios

Understanding the structural drivers of global CO₂ emissions requires integrated analysis of fossil fuel production, total energy consumption, and electric vehicle (EV) deployment trends. This study presents a data-driven modeling framework combining system-dynamics formulation with statistical outlier detection implemented in Python to evaluate emission trajectories over a ten-year historical period. The methodology incorporates historical datasets of global CO₂ emissions, primary energy consumption, fossil fuel production, and EV manufacturing volumes. A computational routine developed in Python applies the criterion proposed by William Chauvenet to identify statistically inconsistent observations within the dataset, ensuring robustness prior to regression and correlation analyses. Carbon intensity (CO₂ per unit of energy) is calculated to assess decoupling behavior, while correlation matrices and elasticity indicators quantify the relative influence of fossil production and EV penetration on emissions. The dynamic structure expresses CO₂ emissions as a function of fossil energy share, total energy demand growth, and electrification rate. Sensitivity analysis evaluates the responsiveness of emissions to variations in these parameters. Results indicate that emission reductions are strongly dependent on carbon intensity evolution rather than EV growth alone. Outlier detection enhances model reliability by preventing anomalous years from biasing trend interpretation. The proposed framework provides a transparent and computationally efficient tool for emission diagnostics, transition scenario evaluation, and policy-oriented forecasting within the context of sustainable mobility and global energy transformation.