Small and medium enterprises (SMEs) are vital contributors and significant drivers of any manufacturing sector. The Industry 4.0 (I 4.0) revolution has made the global economy highly competitive and automated, requiring Indian SMEs to adapt more quickly. Therefore, this study aimed to identify the barriers to implementing I 4.0, simplifying the complex interrelationship among such barriers with the help of a suitable model, categorizing them as independent and dependent ones, and, ultimately, leveling the same drivers, autonomous linkages, and dependent forces. The present investigation thoroughly examined the existing literature and summarized the list of barriers into fifteen significant barriers to the smooth establishment of Industry 4.0 in India. The identified barriers were analyzed with the help of Interpretive Structural Modeling (ISM) Diagraph and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. This study was able to explore the interrelationship among these barriers. The study has found found a lack of support from stakeholders, and insufficient managerial support emerged as a major factor neglected by Indian SMEs. However, uncertainty in the predicted demand for products, the lack of an alternate solution to the technological breakdown, and doubt about the sustainability of Industry 4.0 (relating to its potential to lead to unemployment in society, etc.) are significant contingent barriers. These barriers can impact the other strategic choices related to the successful implementation of Industry 4.0. This study’s observations can help decision-makers make strategic decisions to manage the barriers affecting Industry 4.0 in Indian SMEs. This research revealed a scope that can be extended to other South Asian and developing nations. The results of the present work can be further studied with structural equation modeling (SEM) and multiple regression analysis (MRA).

The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.

Data-driven prioritization of photovoltaic (PV), battery, and converter technologies is crucial for achieving sustainability, efficiency, and cost-effectiveness in the increasingly complex domain of hybrid renewable energy systems (HRES). Conducting an in-depth and systematic ranking of these components for solar-based HRESs necessitates a comprehensive multi-criteria decision-making (MCDM) framework. This study develops as the most recent and integrated approach available in the literature. To ensure balanced and objective weighting, five quantitative weighting techniques, Entropy, Standard Deviation, CRITIC, MEREC, and CILOS, were aggregated through the Bonferroni operator, thereby minimizing subjective bias while preserving robustness. The final ranking was executed using the measurement of alternatives and ranking according to compromise solution method (MARCOS). Subsequently, comparative validation was conducted across eight additional MCDM methods, supplemented by correlation and sensitivity analysis to evaluate the consistency and reliability of the obtained results. The results revealed that thin-film PV modules (0.7108), hybrid supercapacitor batteries (0.6990), and modular converters (1.1812) emerged as the top-performing technologies, reflecting optimal trade-offs among technical, economic, and environmental performance criteria. Correlation analysis (ρ > 0.9 across nine MCDM methods) confirmed the stability of the rankings. The results establish a reproducible decision-support framework for designing sustainable hybrid systems. These technologies demonstrated superior thermal stability, cycling endurance, and system scalability, respectively, thus laying a foundation for more sustainable and resilient hybrid energy system deployments. The proposed framework provides a reproducible, transparent, and resilient decision-support tool designed to assist engineers, researchers, and policy-makers in developing reliable low-carbon components for the realization of future carbon-neutral energy infrastructures.

In the burgeoning nexus between Industry 4.0 and entrepreneurial ecosystems, a transformative and disruptive dynamic has been observed. Defined by the integration of digital technologies into traditional sectors, Industry 4.0 has been found to alter the way in which entrepreneurs conceptualize, function, and collaborate. This review elucidates salient outcomes and implications of such intersections. Paramount among the findings are the proliferation of companies operating within the Industry 4.0 paradigm, the emergence of hubs centred on collaborative innovation, and the rising prominence of industry-specific ecosystems. Startups have been identified as pivotal in effecting supply chain transformations, embedding sustainability, and fostering digital talent. For practitioners navigating this terrain, it is imperative to champion digital metamorphosis, forge strategic partnerships, underscore the primacy of sustainable practices, and nurture digital expertise. Corporate alliances, ecosystem synergies, and opportunities in the global marketplace have been underscored as potent avenues for expansion and ingenuity. Furthermore, a marked influence of Industry 4.0 on the resilience and adaptability of entrepreneurial ecosystems has been discerned. The confluence of such technologies and ecosystems has been posited as offering an unprecedented juncture for both nascent and established businesses to spur economic progress, tackle pressing global challenges, and cultivate a robust culture of innovation and entrepreneurship.

The transition to renewable energy sources (RES) for electricity generation has gained significant momentum due to environmental and sustainability concerns. However, the high initial costs associated with RES implementation remain a critical barrier, particularly for micro, small, and medium enterprises (MSMEs). To address this challenge, a cost-effective optimization framework for the hybrid renewable energy system (HRES) was proposed, integrating advanced decision-making methodologies. The study focused on a case study of an MSME in a rural village in Ludhiana, Punjab, where the feasibility of various HRES configurations was evaluated using HOMER Pro software. The optimization process aims to minimize key financial metrics, including net present cost (NPC), operation and maintenance (O&M) costs, and the levelized cost of energy (LCOE), while simultaneously reducing carbon emissions. Sensitivity analyses were conducted to assess the impact of critical parameters such as diesel prices, inflation rates, and system constraints. To rank the HRES configurations, a multicriteria decision-making (MCDM) approach is employed, combining the Method based on the Removal Effects of Criteria (MEREC) for weight determination and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) for system ranking. The results demonstrate that the proposed framework effectively identifies the most cost-effective and environmentally sustainable HRES configuration, providing a robust decision-making tool for MSMEs. This study not only contributes to the growing body of knowledge on RES optimization but also offers practical insights for policymakers and stakeholders aiming to promote renewable energy adoption in small-scale industrial settings.