Given the growing significance of the metaverse in research, it is crucial to understand its scope, relevance in the tourism industry, and the human-computer interaction it involves. The emerging field of metaverse tourism has a noticeable research gap, limiting a comprehensive understanding of the concept. This article addresses this gap by conducting a hybrid systematic review, including a variable-oriented literature review, to assess the extent and scope of metaverse tourism. A scrutiny on Scopus identified a reduced number of relevant documents. The analysis exposes theoretical and empirical gaps, along with promising opportunities in the metaverse and tourism intersection. These insights contribute to shaping a contemporary research agenda, emphasizing metaverse tourism. While this study offers an overview of current research in metaverse tourism, it is essential to recognize that this field is still in its early stages, marked by the convergence of technology and transformations in tourism. This exploration underscores the challenges and opportunities arising from the evolving narrative of metaverse tourism.

Payment for forest ecosystem services (PFES) policy is a prevalent strategy designed to establish a marketplace where users compensate providers for forest ecosystem services. This research endeavours to scrutinise the impact of PFES on households’ perceptions of forest values and their behaviour towards forest conservation, in conjunction with their socio-economic circumstances and their communal involvement in forest management. By incorporating the social-ecological system framework and the theory of human behaviours in environmental conservation, this study employs a structural equations model to analyse the factors influencing individuals’ perceptions and behaviours towards forest conservation. The findings indicate that the payment of PFES significantly increases forest protection behaviour at the household level and has achieved partial success in activating community mechanisms to guide human behaviour towards forest conservation. Furthermore, it has effectively leveraged the role of state-led social organisations to alter local individuals’ perceptions and behaviours towards forest protection.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

Cities are no longer viewed as creatures with a linear-climax-established cycle but as ecosystems with dynamic and complicated processes, with people as the primary component. Thus, we must understand urban ecology’s structure and function to create urban planning and appreciate the mechanisms, dynamics, and evolution that connect human and ecological processes. The ecological city (ecocity) is one of the city conceptions that has evolved with the perspective of urban ecology history. The concept of ecocity development within urban ecology systems pertains to recognizing cities as complex ecosystems primarily influenced by human activities. In this context, individuals actively engage in dynamic problem-solving approaches to address environmental challenges to ensure a sustainable and satisfactory quality of life for future generations. Therefore, it is necessary to study how ecocity has developed since it was initiated today and how it relates to the urban ecology perspective. This study aims to investigate the progression of scholarly publications on ecocity research from 1980 to 2023. Additionally, it intends to ascertain the trajectory of ecological city research trends, establish connections between scientific concepts, and construct an ecological city science network using keyword co-occurrence analysis from the urban ecology perspective. The present study used a descriptive bibliometric analysis and literature review methodology. The data was obtained by utilizing the Lens.org database, was conducted using the VOS (Visualization of Similarities) viewer software for data analysis. The urban ecology research area ecology of cities can be studied further from density visualization of ecosystem services and life cycle assessment. Finally, the challenges and future agenda of ecocity research include addressing humans by modeling functions or processes that connect humans with ecosystems (ecology of cities), urban design, ecological imperatives, integration research, and improving the contribution to environmental goals, spatial distribution, agriculture, natural resources, policy, economic development, and public health.