This paper aims to investigate local communities’ participation in eco-tourism projects by using the community of Situ Cisanti located in Tarumajaya Village, West Java as a case. Data were gathered through observation, in-depth interviews, and documentation analysis. Observations and in-depth interviews were conducted simultaneously for two months, from September to October 2021. In-depth interviews were conducted with 15 informants from the elements; village government officials of Tarumajaya, Perhutani, and local communities who participated in the Situ Cisanti eco-tourism project, which was completed through a documents analysis. According to the findings, local community participation in Situ Cisanti eco-tourism consists of conservation and economic participation. Conservation participation is demonstrated by their participation in restoration and greening activities such as reforestation, etc. in Situ Cisanti and its surroundings, whereas economic participation is demonstrated by the establishment of stalls, culinary, coffee, souvenir, and homestay businesses as a result of Situ Cisanti eco-tourism. Furthermore, the existence of this eco-tourism has empowered women because new business opportunities that arise are not only run by men but also by women. As a result, this study implies that the participation of local eco-tourism communities not only has an impact on empowering conservation knowledge and economics, but it can also imply women empowerment.

Building cooling load depends on heat gains from the outside environment. Appropriate orientation and masonry materials play vital roles in the reduction of overall thermal loads buildings. A net-zero energy building performance has been analyzed in order to ascertain the optimum orientation and wall material properties, under the climatic conditions of Owerri, Nigeria. Standard cooling load estimation techniques were employed for the determination of the diurnal interior load variations in a building incorporating renewable energy as the major energy source, and compared with the situation in a conventionally powered building. The results show a 19.28% reduction in the building’s cooling load when brick masonry was used for the wall construction. It was observed that a higher heat gain occurred when the building faced the East-West direction than when it was oriented in the North-South direction. Significant diurnal cooling loads variation as a result of radiation through the windows was also observed, with the east facing windows contributing significantly higher loads during the morning hours while the west facing windows contributed higher amounts in the evening. The economic analysis of the net-zero energy building showed an 11.63% reduction in energy cost compared to the conventional building, with a 7-year payback period for the use of Solar PV systems. Therefore, the concept of net-zero energy building will not only help in energy conservation, but also in cost savings, and the reduction of carbon footprint in the built environment.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

Although much bibliometric research has been conducted to analyze publications on energy policy, a systematic investigation of the sustainability of nuclear energy use after the Fukushima nuclear accident is still lacking. Therefore, this study conducted a comprehensive bibliometric review of the sustainability of nuclear energy policy (NEP). This study discusses NEPs, highlighting their disadvantages; emerging research themes; and networks of the most productive authors, countries, journals, and institutions over the last 20 years (2002–2022). This timeframe was selected because of the Fukushima nuclear accident, which has been one of the largest environmental disasters in recent years. Bibliometric analysis was carried out by reviewing 1146 documents from the Scopus database using the keywords “energy policy” and “nuclear energy.” The OpenRefine software was used to deep-clean keywords with the same meaning, and VOSviewer was used to visualize them. The results show that over the past two decades, future research themes and trends in the study of NEP have focused on nuclear fuel, the Fukushima nuclear accident, risk perception, energy transition, and renewable energy. Bibliometric analysis has positively affected the development of NEP in countries that do not yet have nuclear power plants, such as Indonesia.