Understanding innovation ecosystems and the circular economy is crucial for systemic change in business and industry, fostering eco-innovation and advancing sustainable development. This study uses bibliometric analysis to uncover the research trends, patterns, and collaborations, revealing a significant gap in understanding the interactions between innovation ecosystems and the circular economy and offering potential avenues for future research that align with sustainable development. The analysis was carried out with the help of Biblioshiny and VOSviewer on the final selected documents of 2981 from the Scopus database through a search query and the SPAR-4-SLR stages of filtration. The key findings of the study are as follows: collaboration among countries involves accessing countries’ resources, knowledge, markets, and location. The study explores research trends in the innovation ecosystem and circular economy, focusing on five key clusters: the circular economy for eco-innovation and resource recovery, circular business models for fostering sustainable innovation in the bioeconomy, sustainability through renewable energy and sdgs, the sustainable business model for enhancing green innovation through entrepreneurship, and Artificial Intelligence (AI) and the circular business model in Industry 4.0. The bibliometric analysis identifies trends, research gaps, and collaboration patterns, revealing potential avenues for future exploration in industrial symbiosis, energy transition, and the system of innovation. The study analyzed only the documents available in Scopus. The exclusion of papers based on the search period, language, document type, and incomplete details are the limitations of this research and open the scope of potential future research. The findings will help existing researchers in this field as well as new researchers interested in this field by clearly understanding trends and further research scopes. The study also offers actional recommendations and practices to policymakers. Practices, and entrepreneurs for the attainment of global sustainability goals. The novelty and originality of this study rely on a thorough literature review that describes the state of the art in the innovation ecosystem and circular economy.

The study highlights the impact of cloud-native technology in entrepreneurship for innovative business models, scalability, and cost efficiency by combining the trends including microservices, serverless computing, and technology disruptions. The study attempts to examine the role of cloud-native technology affects the entrepreneurial landscape in creating and advancing innovative business models and approaches, and the difficulties while applying cloud-native technology. The systematic literature review based on PRISMA is used to explore cloud-native technology and its applications by analyzing selected 111 studies from Scopus, Web of Science, Dimensions, and ProQuest. The study identified that one of the main forces behind disruption is the quick development of new technologies including blockchain, 5G, IoT, quantum computing, and AI. Cloud-native technology supports innovative business models in entrepreneurship such as SaaS, serverless computing, managed Kubernetes, data analytics platforms, BaaS, and Edge Computing. The urgency to incorporate and develop new security features and tools for measurement is necessary. The study shows that startups can use cloud-native technologies for innovative business models that rely on serverless computing, subscription services, and on-demand cloud services which are providing opportunities. The findings of the study highlight the need for entrepreneurs to utilize emerging technologies to meet competition, and there is a requirement for continuous investment in data analytics, automation, cloud solutions, and emerging technologies. Collaboration is essential for the application of the technologies for early stage startups and enterprises. Incubators, accelerators, and venture capital firms support essential to the adoption of disruptive technologies. The policymaker's and enterprises' focus and actionable intervention are necessary for developing mechanisms to solve the barriers of regulatory compliance, lack of skilled manpower, data security, privacy issues, and upfront investment for entrepreneurial success.

Sustainable energy entrepreneurs promote sustainable development by focusing more on energy efficiency. This study examines the interdependence and driving–dependent relationships among the socioeconomic factors (SEFs) influencing sustainable energy enterprises (SEEs). A mixed-methods approach is used, beginning with a literature review and expert consensus, followed by total interpretive structural modeling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. Seven key SEFs are finalized through interviews with 12 experts. Data are then collected from 11 SEEs. The study reveals that the regulatory and institutional framework emerges as the primary driving factor influencing other SEFs, including financial accessibility, market demand, technological innovation, and infrastructure readiness. Social and cultural acceptance is identified as the most dependent factor. The study proposes future research directions by identifying the United Nations sustainable development goals (SDGs) related to the antecedents, decisions, and outcomes with theoretical linkages through the Antecedents–Decisions–Outcomes (ADO) framework. The major SDGs identified are SDG 4 (education), SDG 7 (energy), SDG 9 (industry), SDG 11 (communities), and SDG 13 (climate). The study highlights that regulatory support, funding access, skill development, and technology transfer are required areas for strategic focus. Understanding the hierarchy of SEs supports business model innovation, investment planning, and risk management.

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.