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The use of renewable energy has become necessary because of the harmful effects of current energy sources on the environment, limited availability and financial crisis. Transparent solar panels have emerged as a promising technology for integrating renewable energy generation into building structures. Therefore, this paper aims to explore the feasibility of transparent solar panels for high-rise building façades in Sri Lanka.

The research apprehended a qualitative approach, including two expert interview rounds adhering to the Delphi technique with 17 and 15 experts each per round. Manual content analysis was incorporated to analyse the collected data.

Regarding operation and maintenance, the study emphasizes the importance of regular inspection, cleaning and repair of transparent solar panels to ensure optimal performance and longevity. These activities contribute to maximizing energy generation and maintaining the aesthetic appeal of the building. The benefits of implementing transparent solar panels on building façades are manifold. They include renewable energy generation, reduced greenhouse gas emissions, improved energy efficiency and enhanced architectural aesthetics. Furthermore, the research findings underscore the potential of transparent solar panels to contribute to Sri Lanka’s sustainable development goals and address the country’s increasing energy demand. However, the study also identifies challenges that need to be addressed for successful implementation.

This study contributes to understanding the feasibility of transparent solar panels for high-rise building façades in Sri Lanka. The research findings offer valuable insights into the operation and maintenance aspects, benefits, challenges and strategies for implementing transparent solar panels effectively. This knowledge can guide policymakers, architects and developers in making informed decisions regarding the integration of transparent solar panels, thereby promoting sustainable and energy-efficient building practices in Sri Lanka.

Organizations use innovations and respond to external pressures, creating a transition to the sociotechnical system. In their transitions, they interact with the environment and undergo adaptation-selection. The extant literature used a multilevel perspective (MLP) with a structural view and examined dynamics and transitions (phenomena) in a noninterventionistic setting. This study aims to examine the dynamics and phenomena with a microstructural or functional view and expand the MLP; this paper uses neo-institutionalism and human values as part of the functional view. Moreover, when the authors examine the phenomena in an interventionistic setting, they can obtain certain unique dynamics and their influence on the phenomena.

The authors need to examine the phenomena in its setting, so this paper selected a case study, Indian electricity generation. For diverse heuristic and analytic views, it selected two Indian states.

The findings from the functional view showed that organizations exhibit certain traits of neo-institutionalism and human values, which mediate their responses (behavior) to external pressures. Additionally, due to the interventionist state, their dynamics use shaping instead of selection logic for innovations, which decides the transition pathway selection (technology adoption). It further decided the extent to which innovations cumulate as stable designs. As a result, the responses and the transition provide benefits in the short term while invariably failing in the long term.

By selecting cases with higher investments in renewable energies and combustible fuels, the authors can expand the functional view to include user typologies such as producers, intermediaries and citizen groups and obtain further insights into transitions.

The study highlights the generation dynamics specific to Indian electricity generation and its transition pathways. The study’s outcome provides insights to researchers and practitioners in formulating policy changes and transforming electricity generation.

The study uses a functional view comprising neo-institutionalism and human values and expands the sociotechnical transition theory. In addition, selecting an interventionist setting provided insights into dynamics specific to organizational behavior and associated services. Finally, the obtained insights offer suggestions for technology development to better manage transitions with adaptation-selection.

This paper aims to propose, the multi-objective method for optimal planning and operation of distributed generators (DGs) on distribution system (DS) using hybrid technique is proposed.

The proposed hybrid technique denotes hybrid wrapper of black widow optimization algorithm (BWOA) and bear smell search algorithm (BSSA). BWOA accelerates the convergence speed with combination of the search strategy of BSSA; hence, it is named as improved black widow-bear smell search algorithm (IBWBSA) technique.

The multiple-objective operation denotes reducing generation cost, power loss, voltage deviation with optimally planning and operating the DS. For setting up the DG units on DS, IBWBSA technique is equipped to simultaneously reconfigure and find the optimal areas.

In this planning model, the constraints are power balance, obvious power flow limit, bus voltage, distribution substation’s capacity and cost. Then, proposed multiple-objective hybrid method to plan electrical distribution scheme is executed in the MATLAB/Simulink work site.

Blockchain for energy trading is a trending research area in the current context. However, a noticeable gap exists in the review articles focussing on solar energy trading with blockchain technology. Thus, this study aims to systematically examine and synthesise the existing research on implementing blockchain technology in sustainable solar energy trading.

The study pursued a systematic literature review to achieve its aim. The data extraction process focussed on the Scopus and Web of Science (WoS) databases, yielding an initial set of 129 articles. Subsequent screening and removal of duplicates led to 87 articles for bibliometric analysis, utilising VOSviewer software to discern evolutionary progress in the field. Following the establishment of inclusion and exclusion criteria, a manual content analysis was conducted on a subset of 19 articles.

The results indicated a rising interest in publications on solar energy trading with blockchain technology. Some studies are exploring the integration of new technologies like machine learning and artificial intelligence in this domain. However, challenges and limitations were identified, such as the absence of real-world solar energy trading projects.

This study offers a distinctive approach by integrating bibliometric and manual content analyses, a methodology seldom explored. It provides valuable recommendations for academia and industry, influencing future research and industry practices. Insights include integrating blockchain into solar energy trading and addressing knowledge gaps. These findings advance societal goals, such as transitioning to renewable energy sources (RES) and mitigating carbon emissions, fostering a sustainable future.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

The purpose of this paper is to track the maximal power of wind energy conversion system (WECS) and enhance the search capability for WECS maximum power point tracking (MPPT).

The hybrid technique is the combination of tunicate swarm algorithm (TSA) and radial basis function neural network.

TSA gets input parameters from the rectifier outputs such as rectifier direct current (DC) voltage, DC current and time. From the input parameters, it enhances the reduced fault power of rectifier and generates training data set based on the MPPT conditions. The training data set is used in radial basis function. During the execution time, it produces the rectifier reference DC side voltage that is converted to control pulses of inverter switches.

Finally, the proposed method is executed in MATLAB/Simulink site, and the performance is compared with different existing methods like particle swarm optimization algorithm and hill climb searching technique. Then the output illustrates the performance of the proposed method and confirms its capability to solve issues.

The demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss minimization.

In this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization techniques.

The proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid algorithms.

The main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.

The purpose of this study is to analyze the dynamic changes of the arms trade network not only from the network structure but also the influence mechanism from the aspects of the economy, politics, security, strategy and transaction costs.

The study employs the Temporal Exponential Random Graph Model and the Separable Temporal Exponential Random Graph Model to analyze the endogenous network structure effect, the attribute effect and the exogenous network effect of 47 major arms trading countries from 2015 to 2020.

The results show that the international arms trade market is unevenly distributed, and there are great differences in military technology. There is a fixed hierarchical structure in the arms trade, but the rise of emerging countries is expected to break this situation. In international arms trade relations, economic forces dominate, followed by political, security and strategic factors.

Economic and political factors play an important role in the arms trade. Therefore, countries should strive to improve their economic strength and military technology. Also, countries should increase political mutual trust and gain a foothold in the industrial chain of arms production to enhance their military power.

The contribution of this paper is to analyze the special trade area of arms trade from a dynamic network perspective by incorporating economic, political, security, strategic and transaction cost factors together into the TERGM and STERGM models.

The emergence of distributed ledger technology (DLT) has transformed the way construction industries approach data management, ushering in an era of increased transparency, security and efficiency. The purpose of this study is to investigate the strategies to promote the adoption of DLT in the Nigerian construction sector. This was done to address the challenges that hinder the widespread adoption of DLT within the Nigerian construction sector.

A comprehensive literature review informed the design of a structured questionnaire for data collection. The questionnaire was distributed among diverse construction professionals to explore their perceptions of potential strategies. The collected data were analyzed using the Shapiro–Wilk test for data distribution, while mean values and standard deviations were used to facilitate the ranking of strategies. The Kruskal–Wallis H-test was used to assess opinion differences, and exploratory factor analysis was applied to uncover underlying dimensions.

The findings revealed the top five strategies for DLT adoption as conducting workshops and seminars to educate professionals, collaborating with universities for DLT courses, encouraging joint projects for shared insights, forming consortia for DLT standards and allocating funding for DLT research in construction. Through factor analysis, the strategies identified were categorized into four principal clusters: awareness and education advancements, government support and incentives, industry collaboration and standards and pilot projects and demonstrations.

While prior studies have identified barriers to DLT adoption and offering recommendations, this research advances the field by empirically investigating and assessing several of these strategies proposed in various studies. This approach provides valuable insights that go beyond existing research, offering a deeper understanding of the practical and contextual dynamics influencing DLT adoption in the construction sector.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.