Despite significant business-related research publications, extant studies are yet to show how well business research directly maps to 17 Sustainable Development Goals (SDGs). Our study uses a qual-quant approach combining the PRISMA framework, performance analysis, and content analysis to find answers to this research gap. Using the Australia Business Deans Council list of 2500+ journals as a proxy for business research during 2016–2022, our study shows that only about 25% of business research is directly mapping to SDGs with SDG 3 (Good Health and Well Being) and SDG 7 (Affordable and Clean Energy) being the most mapped. The most cited journals are the Journal of Cleaner Production, Applied Energy, and the European Journal of Public, which largely focus on SDG 7, SDG 13 (Climate Action), and SDG 3. Top authors focus their research mainly on SDG 7, SDG 13, and SDG 12 (Responsible Consumption and Production), with few publications dedicated to SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), and SDG 5 (Gender Equality). Applied Economics has emerged as the dominant field for SDGs. Further, our study is the first to incorporate social network analysis to understand the interconnected nature of SDGs in the context of business research. This research provides substantial policy insight, particularly insight relevant to the formulation of regulations that promote the alignment of business researchers with SDGs.

Sustainable energy systems (SESs) support intelligent modeling, automation, and governance that enable energy access, infrastructure innovation, and climate resilience. Despite their potential, their integration with large language models (LLMs) raises concerns regarding energy intensity, transparency, equity, and regulation. This study adopts a mixed-methods review combining a BERTopic-based thematic analysis and case-based synthesis to examine applications of LLMs in energy modeling, optimization, etc., and to assess their alignment with the United Nations Sustainable Development Goals. These applications support SDG 7 (Affordable and Clean Energy) by improving access to energy knowledge and decision support, SDG 9 (Industry, Innovation and Infrastructure) through intelligent and scalable digital infrastructure, and SDG 13 (Climate Action) by climate-responsive planning and operational efficiency. The findings reveal that modular, agent-based LLM workflows enhance energy modeling and regulatory compliance. However, sustainability trade-offs necessitate responsible Artificial Intelligence (AI) governance emphasizing transparency, ethical design, and inclusivity. This review informs policy and practice by suggesting that LLMs offer potential value for sustainable energy application deployment within responsible AI governance frameworks that emphasize ethical design, accountability, and equitable access. The study provides future research directions using the ADO (antecedents–decisions–outcomes) framework, emphasizing regulatory readiness, ethical design, and inclusive governance aligned with SDGs 7, 9, and 13, among others.

Agentic artificial intelligence (AAI) systems—capable of autonomously executing multi-step instructional tasks with minimal human oversight—are reshaping higher education while exposing the limitations of adoption- and continuance-centred models that treat value and risk as independent predictors. In an environment of widespread AI availability, the more meaningful question concerns how deeply educators integrate delegation-capable systems into teaching, assessment, and instructional workflows. Drawing on affordance theory, institutional theory, and automation risk perspectives, this study develops and tests a dual-pathway framework that conceptualises pedagogical integration as a dynamic value–risk trade-off embedded within institutional and contextual conditions. Survey data from 338 higher-education educators across Global North and South contexts, all of whom underwent a structured screening process to verify exposure to agentic rather than conventional generative AI systems, were analysed using PLS-SEM with robustness and endogeneity checks. The model explains substantial variance in pedagogical integration (R² = 0.619), pedagogical value (R² = 0.621), and autonomy risk (R² = 0.615). Institutional AI capability is positively associated with pedagogical value and negatively associated with autonomy risk, whereas perceived AI complexity shows the opposite pattern. Higher pedagogical value is also associated with lower perceived autonomy risk (β = −0.445, p < 0.001), suggesting that educators may interpret system autonomy as a manageable pedagogical feature rather than solely as a source of concern. Regulatory clarity weakens the complexity–risk relationship, while AI legitimacy strengthens the value–integration relationship. Given the cross-sectional design, findings should be interpreted as associative rather than causal.