For the purpose of diminishing the growing impact of energy use on the environment and providing policy focus in China, this study decomposes impact factors of energy-related CO₂ emissions into nine parts using various economic methods, typically using the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to incorporate necessary factors and ridge regression to eliminate multicollinearity. Results indicate the positive and conversely inhibitory impact factors, which we sort by influencing degrees as: total population, industrialization level, service level, energy consumption structure, urbanization level, GDP per capita, capital asserts investment, foreign trade degree, and technology level. Factors excluding technology level and energy consumption structure are main positive determinants of accelerated CO₂ emissions. Above all, total population has the greatest interpretative ability. Given these regression results, policy proposals concerning key impact factor regulations are provided to maintain carbon emission abatement and sustainability.