Decarbonizing electricity systems is essential for achieving global net-zero targets, yet the relative contributions of socioeconomic and environmental factors remain unclear. In this study, we developed a hybrid framework combining regression analysis and unsupervised principal component analysis (PCA) to assess the complex factors influencing power sector carbon intensity (g CO₂/kWh) across 170 countries. Fifteen indicators from social, environmental, and economic domains were selected and aligned based on their linear relationships with carbon intensity (β ±SE). Globally, the share of renewable electricity (β = −6.68 ±0.57, p <0.001) and low-carbon electricity (β = −6.86 ±0.49, p <0.001) showed the strongest negative associations with carbon intensity. In contrast, primary energy per unit of GDP (β=87.5 ±23.5, p=0.0003) was linked to higher emissions intensity. PCA indicated that environmental indicators collectively showed the strongest negative correlation with carbon intensity (r = −0.78, p <0.001), driven primarily by increases in low-carbon and renewables electricity (%) and decreases in CO₂ per energy unit (kg/kWh) and CO₂ per unit of GDP (kg/$), underscoring the central role of clean-electricity deployment and CO₂ productivity in decarbonization. Group-level analysis revealed stratification, with high-income economies averaging lower emissions (384 g CO₂/kWh) than lower-middle-income countries (428 gCO₂/kWh). Clustering showed that Europe and North America were low-emission outliers, whereas Central and Southern Asia had high emissions. Network and chord analyses suggest that social progress and economic capacity aid decarbonization but are not direct drivers, underscoring the need to transform environmental systems.