Abstract: Inspired by the theoretical framework of "orderly human activities", this study introduces a novel multi-dimensional behavioral indicator framework integrating three dimensions — intention, knowledge, and capacity. Using panel data from eight representative countries spanning 1995–2022, we employ a hybrid analytical approach combining interpretable machine learning (Random Forest-SHAP) with multi-model prospective forecasting (STIRPAT, BPNN, MLP, GPR) to quantify behavioral driving mechanisms of carbon emissions and simulate multi-scenario emission trajectories. Key findings include: 1) Four core indicators within the intention-knowledge-capacity framework (renewable energy consumption, GDP per capita, researchers in R&D, and tertiary enrollment) collectively explain approximately 67% of cross-national variation in carbon emissions; 2) The marginal effects and moderating roles of education and R&D capacity indicators on carbon emissions show significant differentiation and synergy between developed countries and emerging economies, revealing the cross-border heterogeneity of soft drivers; 3) Scenario simulations show that the enterprising pathway not only yields earlier and lower emission peaks, but also substantially reduces predictive uncertainty through the synergistic strengthening of public engagement and institutional capacity. This study addresses the structural absence of the "human factor" in quantitative carbon emission research and offers a new theoretical framework and cross-national comparative evidence for integrating behavioral interventions into macro-climate policy.