Global Variability in Carbon Footprint of Urea Production: A Statistical Analysis Using ANOVA and Clustering

The global carbon footprint of urea production exhibits substantial variability, hindering comparative assessments and decarbonization strategies in agricultural supply chains. This study identified and quantified the structural determinants driving this dispersion by synthesizing an international inventory (n = 60) combining Life Cycle Assessment databases, literature, and empirical industrial data. Methodologically, an extreme theoretical outlier (71,420 kg CO₂-eq/t urea) was isolated, and a refined dataset (n = 59) was evaluated using one-way ANOVA, Tukey's HSD test, and Ward's hierarchical clustering. Statistical analysis confirmed that a five-category technological typology—Coal, Mixed Systems, Average Gas, Efficient Gas, and Green Urea—is highly robust (F(4, 54) = 167.79; p < 0.001), with technology explaining 92.8% of global emission variance (η² = 0.9281). Mean impacts ranged from 2,735 kg CO₂-eq/t for coal to 334 kg CO₂-eq/t for green urea. Primary data from an Argentine plant (777.8 kg CO₂-eq/t cradle-to-gate) defined a practical lower bound for fossil systems, while commercial operations cluster within a baseline of 1,100–1,500 kg CO₂-eq/t. We conclude that urea carbon intensity is governed by feedstock technology and life-cycle accounting choices, providing an essential quantitative framework for inventory harmonization.