The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.

The Sustainable Development Goals and circular economy are two critical aspects of the 2030 Agenda for Sustainable Development. They both seek to reduce the waste of natural resources and enhance society’s social, economic, and environmental goals. This study aims to identify, develop, test, and verify the significant antecedents that affect the adoption of supply chain analytics and its consequences for achieving the circular economy. We have divided the conceptual framework into two parts. In the first part, the relationship among data integration and scalability, organizational readiness, and policies and regulations as Technological–Organizational–Environmental factors as antecedents in adopting supply chain analytics. In the second part, the dynamic capabilities view grounded the relationship among supply chain analytics, supply chain integration, and sustainable supply chain flexibility effect directly and indirectly on the circular economy. Data have been collected using the survey method from 231 respondents from the manufacturing industry in Pakistan. Data have been analyzed using (i) partial least square structure equation modeling (ii) and artificial neural network approaches. The empirical findings proved that antecedents (data integrity and scalability, organizational readiness, and policy and regulation) and consequences (supply chain integration and sustainable supply chain flexibility) of supply chain analytics adoption would improve the circular economy performance. Additionally, artificial neural networks have supported these relationships. The adoption of supply chain analytics will enable organizations to supply chain integration. Additionally, organizations with more integration and analytics in their operations tend to have more flexibility and a circular economy. Moreover, organizations and society will obtain social, economic, and environmental benefits and reduce wastage and negative environmental impacts.

Objective: The global challenge of climate change and the need for energy conservation have prompted a reevaluation of energy sources and policies worldwide. This study aims to investigate the societal acceptability of solar photovoltaic (PV) systems among citizens of smart cities, an aspect crucial yet underexplored in the context of renewable energy technologies. Methods/Analysis: A comprehensive survey was conducted involving 560 respondents to assess public perceptions, attitudes, and behaviors toward solar PV systems. The study also examined the moderating effects of area (urban/rural), gender, trust, and duration of use (experience) on societal acceptability. Findings: The results show that both independent and moderating variables significantly influence the social acceptability of solar innovations in smart cities. Key factors identified include the user-friendly design of solar systems, effective awareness campaigns highlighting their benefits, and compatibility with existing technologies. These elements are crucial in fostering positive attitudes and intentions towards the adoption of solar energy. Novelty/Improvement:This research provides valuable insights for policymakers, energy planners, and researchers, emphasizing the importance of considering demographic and experiential factors in policy-making. The findings suggest that societal acceptance of solar PV systems can be enhanced by targeting area-specific needs, leveraging trust, and promoting the benefits of prolonged usage experience. Doi: 10.28991/CEJ-2025-011-02-016 Full Text: PDF

Sustainable path-planning algorithms are essential for executing complex user-defined missions by mobile robots. Addressing various scenarios with a unified criterion during the design phase is often impractical due to the potential for unforeseen situations. Therefore, it is important to incorporate the concept of adaptive autonomy for path planning. This approach allows the system to autonomously select the best path-planning strategy. The technique utilizes dynamic programming with an adaptive memory size, leveraging a cellular decomposition technique to divide the map into convex cells. The path is divided into three segments: the first segment connects the starting point to the center of the starting cell, the second segment connects the center of the goal cell to the goal point, and the third segment connects the center of the starting cell to the center of the goal cell. Since each cell is convex, internal path planning simply requires a straight line between two points within a cell. Path planning uses an improved $A^{\ast }$ (I-$A^{\ast }$) algorithm, which evaluates the feasibility of a direct path to the goal from the current position during execution. When a direct path is discovered, the algorithm promptly returns and saves it in memory. The memory size is proportional to the square of the total number of cells, and it stores paths between the centers of cells. By storing and reusing previously calculated paths, this method significantly reduces redundant computation and supports long-term sustainability in mobile robot deployments. The final phase of the path-planning process involves pruning, which eliminates unnecessary waypoints. This approach obviates the need for repetitive path planning across different scenarios thanks to its compact memory size. As a result, paths can be swiftly retrieved from memory when needed, enabling efficient and prompt navigation. Simulation results indicate that this algorithm consistently outperforms other algorithms in finding the shortest path quickly.

The developed and developing countries have ambitious plans to generate solar power for a better environment. Thus, adopting solar innovations is integral to creating a green world. This study proposes a framework integrating the factors that affect consumers' decisions to adopt or reject solar innovations. For this purpose, we used a systematic literature review to gather potential technology acceptance theories and factors investigated in these studies. Out of 1531 initially searched articles, twenty-four (24) were found relevant and screened fully. The findings revealed that the Diffusion of Innovation (DOI) theory was dominant in evaluating the solar innovation decision by the consumers, followed by the Theory of Planned Behaviour (TPB). Moreover, the DOI's innovation characteristics (relative advantage, complexity, observability, compatibility, and trialability) were the most widely used factors for assessing solar innovations' decisions by consumers. The public and private institutions should consider technological characteristics and the households' characteristics to formulate policies for scaling up the adoption of solar innovations.