Practice Primacy: Revisiting the Knowledge–Action Gap in Pro-Environmental Behavior with eXplainable AI

Against the backdrop of an escalating global environmental crisis, bridging the “knowledge–action gap” in the pro-environmental behavior (PEB) of university students has become a key challenge for sustainable development education, aligning with SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). Traditional linear models often struggle to capture the complex non-linearities and interaction effects when explaining this gap. To overcome this limitation, this study introduces an integrated “prediction-plus-explanation” framework using eXplainable Artificial Intelligence (XAI). Based on survey data from 463 university students in China, we constructed a high-precision PEB prediction model (Accuracy = 93.55%) using the CatBoost algorithm and conducted an in-depth analysis of its internal decision-making mechanisms with the SHAP (SHapley Additive exPlanations) framework. The results reveal that a “Practice Primacy” model plays a dominant role in driving PEB: the formation of environmental habits, participation in environmental practices, and the investment of related resources are the overwhelmingly dominant factors in predicting individual behavior, with their cumulative contribution far exceeding that of traditional cognitive and attitudinal variables. Furthermore, heterogeneity analysis revealed significant group differences in these driving mechanisms: the behavioral decisions of male students tend to be more “value-driven,” while lower-division students are more susceptible to external educational interventions. By quantifying the non-linear effects and relative importance of each driver, this study offers a new “Action-to-Cognition” perspective for bridging the knowledge–action gap and provides robust, data-driven support for universities to design precise and differentiated intervention strategies, thus contributing to the achievement of SDGs.