This study used fuzzy analytic hierarchy process (FAHP) to evaluate students’ performance in an environmental economics course under the mode of online and offline blended teaching (OOBT). OOBT was a new teaching mode combining traditional offline teaching with online teaching platform, which could improve students’ after-class learning efficiency and eliminate the limitations of traditional classroom teaching by using an online teaching platform. However, at present, the evaluation methods of OOBT students’ achievement have been scarce. Therefore, this article adopted FAHP to evaluate students’ performance under OOBT. The evaluated course was environmental economics. The results showed that FAHP was not completely dependent on students’ paper scores, and could evaluate students’ scores from group work, attendance, class discussions, and other aspects. The use of OOBT and FAHP led to overall improvement in students’ scores, the average score increased by 7.38, and 81.55% of students were in the middle grade. The case study showed that the integration of FAHP into OOBT could scientifically assign the weight of each evaluation index, make complex performance evaluation problems organized, and fully reflect the comprehensive quality of students. It could also understand students’ dynamics in time, and continuously optimize the course structure according to the feedback information.

Extreme random events will interfere with the inversion analysis of energy and environment systems (EES) and make the planning schemes unreliable. A Copula-based interval cost–benefit stochastic programming (CICS) is proposed to deal with extreme random uncertainties. Taking Yulin city as an example, there are nine constraint-violation scenarios and six coal-reduction scenarios are designed. The results disclose that (i) both system cost and pollutant emission would decrease as the industrial energy supply constraint-violation level increases; (ii) when the primary and secondary energy output increases by 9% and 13%, respectively, and industrial coal supply decreases by 40%, the coal-dependent index of the system would be the lowest, and the corresponding system profitability could reach [29.3, 53.0] %; (iii) compared with the traditional chance-constrained programming, Copula-based stochastic programming can reflect more uncertain information and achieve a higher marginal net present value rate. Overall, the CICS-EES model offers a novel approach to gain insight into the tradeoff between system reliability and profitability.

Student course performance evaluation serves as a critical pedagogical tool for diagnosing learning gaps and enhancing educational outcomes, yet conventional assessments often suffer from rigid single-metric scoring and ambiguous causality. This study proposes an integrated analytic framework addressing these limitations by synergizing pedagogical expertise with data-driven diagnostics through four key measure: (1) Interval Analytic Hierarchy Process (I-AHP) to derive criterion weights reflecting instructional priorities via expert judgment; (2) K-means clustering to objectively stratify students into performance cohorts based on multidimensional metrics; (3) Random Forest classification and SHAP value analysis to quantitatively identify key discriminators of cluster membership and interpret decision boundaries; and (4) attribution-guided interventions targeting cohort-specific deficiencies. Leveraging a dual-channel ecosystem across pre-class, in-class, and post-class phases, we established a hierarchical evaluation system where I-AHP weighted pedagogical sub-criteria to generate comprehensive student scores.