The integration of complex system concepts and sustainability in chemical engineering education is often limited to elective or separate courses rather than their integration into the core curriculum. This pedagogical gap can lead to graduates who lack a holistic understanding of the intricate interplay between industrial processes and the Earth’s ecological limits, and the feedback loops required to address complex global challenges. This paper presents a transformative approach to close this gap by embedding the Planetary Boundaries framework and system thinking across core chemical engineering courses, such as Material and Energy Balances, Reaction Engineering, and Process Design, and extending this integration to capstone projects. The framework treats the curriculum itself as an interconnected learning system in which key systems concepts are revisited and deepened through contextualized examples and digital modeling tools, including process simulators and life-cycle assessment. We map each boundary to illustrative process examples and learning activities and discuss practical implementation issues such as curriculum crowding, educator readiness, and data availability. This approach aligns with outcome-based education goals by making system thinking and absolute sustainability explicit learning outcomes, preparing future chemical engineers to design processes that respect planetary limits while balancing technical performance, economic feasibility, and societal needs.

Existing research often regards a family business’s choice of auditor as a simple and isolated decision. In contrast, this paper presents a complex adaptive system in a broader definition of family businesses, and shows audit supervision emerging dynamically from the interplay of digital transformation, governance, and family generational succession. This paper finds that a larger extent of digital transformation makes family businesses more likely to adopt industry specialist audits. This relationship happens through agency problems within the system. Crucially, the involvement of a digitally literate second generation rebalances these forces by alleviating agency issues, thereby strengthening the push toward industry specialist audits. Our study provides a perspective, revealing that audit governance is not a discrete choice but a system-level outcome of internal tensions and adaptations. This framework offers clear guidance: (1) for family owners, fostering digital literacy in the next generation is key to improving governance during technological change; (2) for auditors, it highlights the evolving risk and demand landscape in digitalizing family firms; (3) for policymakers, it encourages the importance of initiatives that support both digital transformation and governance development in family businesses.

Employee satisfaction, as a critical form of organisational social capital, represents a significant interdisciplinary topic in management and finance. A key question is whether it can be transformed into sustainable innovation momentum for corporates amid extreme crisis shocks. This study examines Chinese A-share listed corporates, utilising large-scale anonymous employee evaluation data from the Chinese employer review platform ‘KanZhun.com’, to construct corporate-level employee satisfaction indicators. Through econometric modelling, it investigates the impact of employee satisfaction on corporate innovation output during major crises and its underlying mechanisms. Findings reveal that during crises, employee satisfaction significantly enhances overall corporate innovation levels, with a particularly pronounced effect on green innovation. Mechanism analysis indicates that high employee satisfaction primarily drives innovation, especially green innovation, through two channels. These channels include reducing internal governance costs and alleviating external financing constraints. Heterogeneity tests further reveal that this effect is particularly pronounced in high-tech industries, technology-intensive sectors, non-state-owned corporates, and corporates under strong external institutional constraints or with relatively weak innovation capabilities. This study expands the theoretical boundaries of employee satisfaction’s economic value from an innovation perspective. It further provides Chinese empirical evidence for corporates seeking to enhance innovation resilience in complex environments via employee feedback and quality labour relations.

The rapid advancement of autonomous vehicle systems (AVS) has introduced complex challenges to road safety. While some studies have investigated the contribution of factors influencing AV-involved crashes, few have focused on the impact of vehicle-specific factors within AVS on crash outcomes, a focus that gains importance due to the absence of a human driver. To address this gap, the advanced machine learning algorithm, LightGBM (v4.4.0), is employed to quantify the potential effects of vehicle factors on crash severity and collision types based on the Autonomous Vehicle Operation Incident Dataset (AVOID). The joint effects of different vehicle factors and the interactive effects of vehicle factors and environmental factors are studied. Compared with other frequently utilized machine learning techniques, LightGBM demonstrates superior performance. Furthermore, the SHapley Additive exPlanation (SHAP) approach is employed to interpret the results of LightGBM. The analysis of crash severity revealed the importance of investigating the vehicle characteristics of AVs. Operator type is the most predictive factor. For road types, highways and streets show a positive association with the model’s prediction of serious crashes. Crashes involving vulnerable road users can be attributed to different factors. The road type is the most significant factor, followed by precrash speed and mileage. This study identifies key predictive associations for the development of safer AV systems and provides data-driven insights to support regulatory strategies for autonomous driving technologies.

Promoting social participation is a core objective of digital inclusive development. Drawing on rural household survey data from five provinces in China and the Digital Governance Index developed by Peking University, this study systematically examines the impact of digital governance on rural households’ social participation deprivation. The benchmark regression results show that the effect of digital governance on rural households’ social participation deprivation follows an inverted U-shape, characterized by an initial increase followed by a subsequent decline. A series of robustness and endogeneity tests confirms the stability of these findings. Further heterogeneity analyses reveal pronounced regional differences. In the western region, the impact of digital governance on farmers’ social participation deprivation follows a U-shaped pattern, with deprivation initially decreasing and then increasing as digital governance deepens. By contrast, in the central and eastern regions, the inflection point of the inverted U-shaped relationship shifts further to the right relative to the full sample. Furthermore, digital governance exerts a significantly stronger mitigating effect on social participation deprivation among households experiencing higher levels of deprivation. Mechanism analysis shows that digital governance reduces farmers’ social participation deprivation by enhancing their perceived access to public services and improving their psychological well-being. However, moderation analysis shows that household-level digital exclusion and relative poverty significantly weaken these beneficial effects.

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze and calibrate the key policy levers influencing owner participation timing (T*). The model, resolved using finite difference methods, captures the system’s non-linear threshold effects by simulating critical system parameters, including system instability (price volatility, σp), internal friction (management fee, wggt), and demand signals (transaction ratio, Q). Simulations reveal extreme non-linear system responses: a 100% increase in system instability (σp) delays participation by 325.5%. More critically, a 100% surge in internal friction (management fees) delays T* by 492% and triggers a 95% revenue collapse—demonstrating the risk of systemic collapse. Conversely, a 20% rise in the demand signal (Q) advances T* by 100% (immediate participation), indicating the system can be rapidly shifted to a new equilibrium by activating positive feedback loops. These findings support a sequenced calibration strategy: regulators must first manage instability via price stabilization, then counteract high friction with subsidies (e.g., 60%), and amplify demand loops. The LCC framework provides a novel dynamic decision support system for calibrating complex urban transportation systems, offering policymakers a tool for scenario testing to accelerate policy adoption and alleviate urban congestion.

Accurate credit risk assessment for small firms and farmers is crucial for financial stability and inclusion; however, many models still rely on binary default labels, overlooking the continuum of borrower vulnerability. To address this, we propose Transformer–LightGBM–Stacked Logistic Regression (TL-StackLR), a hybrid stacking framework for multi-class loan default prediction. The framework combines three learners: a Feature Tokenizer Transformer (FT-Transformer) for feature interactions, LightGBM for non-linear pattern recognition, and a stacked LR meta-learner for calibrated probability fusion. We transform binary labels into three risk tiers, Low, Medium, and High, based on quantile-based stratification of default probabilities, aligning the model with real-world risk management. Evaluated on datasets from 3045 firms and 2044 farmers in China, TL-StackLR achieves state-of-the-art ROC-AUC scores of 0.986 (firms) and 0.972 (farmers), with superior calibration and discrimination across all risk classes, outperforming all standalone and partial-hybrid benchmarks. The framework provides SHapley Additive exPlanations (SHAP) interpretability, showing how key risk drivers, such as income, industry experience, and mortgage score for firms and loan purpose, Engel coefficient, and income for farmers, influence risk tiers. This transparency transforms TL-StackLR into a decision-support tool, enabling targeted interventions for inclusive lending, thus offering a practical foundation for equitable credit risk management.

Modernizing public security risk governance demands a paradigm shift from reactive response to proactive, systems-oriented prevention. Prevailing governance models, with their focus on institutions and technology, often neglect the micro-foundational mechanisms of risk generation: the internal psychological processes of individuals. To address this gap, this study develops a novel theoretical model—the F(T, P, C, R) framework—which integrates self-organization theory with a psychological gaming perspective. We conceptualize an individual’s behavioral choice (F_behavior) as an emergent outcome of the dynamic interplay among four constitutive factors: the situational context of Time (T) and Place (P), and the cognitive assessments of perceived Risk Control power (C) and perceived Risk Destructive power (R). Employing automotive driving behavior—specifically decisions regarding safe distance maintenance and the adoption of autonomous driving technologies—as our primary analytical scenario, we derive a dynamic risk-decision matrix. This matrix categorizes behavioral outcomes into four distinct quadrants (Confirm, Tend-to-Confirm, Tend-to-Deny, Deny) based on the subjective calculus between C and R, thereby elucidating the internal logic of risk-related choices. The study’s main contribution is constituted by this novel micro-behavioral analytical framework that integrates cognitive science with systems-based governance principles. It offers theoretical insights for behavioral public policy and provides a structured toolkit for diagnosing and designing targeted interventions, ultimately aiming to enhance proactive risk management and systemic resilience.

Mixed traffic flow composed of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) represents a core characteristic of intelligent transportation systems. However, its operational efficiency is significantly constrained by lane management strategies and CAV cooperative driving behaviors. To investigate this, a cellular automata-based simulation model is developed that integrates multiple car-following rules, a lane-changing strategy, and a platoon coordination mechanism. Through a systematic comparison of 13 lane management strategies in one-way two-lane and three-lane configurations, this study analyzes the influence mechanisms of lane allocation and cooperative driving on traffic flow, considering fundamental diagram characteristics, operating speed, CAV degradation behavior, and maximum platoon size. The results indicate that the performance of different strategies exhibits phased evolution with increasing CAV penetration rates. At low penetration rates, providing relatively independent space for HDVs effectively suppresses random disturbances and improves throughput. At medium to high penetration rates, dedicated CAV lanes—especially those with spatial continuity—enable cooperative platoons to fully leverage their advantages, leading to significant improvements in traffic capacity and operational stability. These findings demonstrate an optimal alignment between cooperative driving mechanisms and lane configurations, offering theoretical support for highway lane management in mixed traffic environments.

The widening digital divide within the Global Critical Mineral Resource Supply Chain (GCMRS) 4.0 creates significant barriers to cross-border governance and operational efficiency. To quantify and address this disparity, this study identifies 20 Critical Success Factors (CSFs) through expert interviews with 15 industry specialists in South Korea. A hybrid multi-criteria decision-making framework integrating Fuzzy DEMATEL, Analytic Network Process (ANP), and the Choquet integral is developed to map causal relationships and determine factor weights. The empirical results reveal a distinct ‘technology-first’ dependency. Specifically, Scalable Technical Solutions and Cloud Computing Access emerge as the primary driving forces with the highest global weights, while Digital Investment Subsidies serve as the central hub for resource allocation. Unlike generic governance models, this study provides a quantifiable decision-making basis for policymakers. It demonstrates that bridging the hard infrastructure gap is a prerequisite for the effectiveness of soft collaborative mechanisms in the critical mineral sector.