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This article summarizes the international scientific research output of global forest product models, infers future research trends and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

In 1999, Joseph Buongiorno, a scholar at the University of Wisconsin in the United States of America, proposed the global forest products model (GFPM), which was first applied to research in the global forestry sector. GFPM is a recursive dynamic model based on five assumptions: macroeconomics, local equilibrium, dynamic equilibrium, forest product conversion flow and trade inertia. Using a certain year from 1992 to present as the base period, it simulates and predicts changes in prices, production and import and export trade indicators of 14 forest products in 180 countries (regions) through computer programs. Its advantages lie in covering a wide range of countries and a wide variety of forest products. The data mainly include forest resource data, forest product trade data, and other economic data required by the model, sourced from the Food and Agriculture Organization (FAO) of the United Nations and the World Bank, respectively.

Compared to international quantitative and modeling research in the field of forest product production and trade, China's related research is not comprehensive and in-depth, and there is not much quantitative and mathematical modeling research, resulting in a significant gap. This article summarizes the international scientific research output of global forest product models, infers future research trends, and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

On the basis of summarizing and analyzing the international scientific research output of GFPM, sorting out the current research status and progress at home and abroad, this article discusses potential research expansion directions in 10 aspects, including the types, yield and quality of domestic forest product production, international trade of forest products, and external impacts on the forestry system, in order to provide new ideas for global forest product model research in China.

Sacred forests are biocultural landscapes deeply rooted in centuries-old traditions of spiritual veneration. These sacred sites, including shrines, temple forests churches and graveyards, have historically been significant reservoirs of traditional resource management practices underpinned by spiritual reverence. However, despite their cultural and ecological importance, the cultural ecosystem services inherent to these sacred forests remain unexplored, particularly in urban settings.

This study focused on six sacred sites within Kanazawa City, Japan, using a meticulous face-to-face survey with 342 participants. We collected data on the extent of forest utilisation, the breadth of activities engaged in by visitors and their holistic appraisal of the rendered cultural ecosystem services. The findings illustrate the multifaceted benefits of urban sacred forests, encompassing participation in religious ceremonies, cultural events and festivals, complemented by educational programming that elucidates the historical and traditional underpinnings of the shrines and their surrounding communities.

It has been observed that urban forests have a crucial role in providing spiritual and communal connectivity, preserving traditional heritage, offering vital aesthetic values as green spaces and making visitors connected with nature while they are in the urban landscape. However, a concerning trend has emerged, as the younger demographic appears to lack interest in participating in the stewardship and cultural activities associated with these biocultural landscapes. Community engagement strategies must be strengthened, conservation measures should be implemented and cultural awareness programs need to be established to ensure the perpetuation and appreciation of these valuable urban sacred forests.

This study provides original perspectives on the measurable cultural ecosystem services and intangible values associated with urban sacred forests using the sacred forests in Kanazawa City, Japan. Our research illuminates the various advantages that visitors derive by examining the intersection of spiritual traditions, resource management practices and cultural significance, which has been relatively unexplored. The present study provides a significant basis for establishing initiatives that seek to promote the cultivation of respect and responsibility towards urban sacred forests.

One of the crucial elements of addressing global climate challenges through urban tourism is the continuing existence of urban forests. The reasoning is that the ecological attraction of urban forests can impact visitors’ intention to conduct pro-environmental behavior, including low-carbon actions. Thus, more visitors to urban forests will positively affect enhancing the quality of the urban environment. However, the extent to which ecological attraction can influence pro-environmental behavior warrants further investigation due to the complexity of psychosocial factors that impact behavioral intention. The main objective of this research is to examine the effects of the ecological attractiveness of urban forests on the pro-environmental behavior of visitors by exploring motivation, ecological experience, perceived value and knowledge as mediators. Moreover, whether the nature of the urban forest and facilities attract visitors simultaneously is also studied.

Data were collected from 615 respondents who visited three urban forests in Bandung, the second-most populous city in Indonesia, by five-point Likert questionnaires. As an analytical tool, SEM PLS was applied to establish the effect of the ecological performance of the urban forest on the increase in environmentally conscious behavior among urban forest visitors.

The findings demonstrate that the attractiveness of an urban forest affects the growth of environmentally responsible behaviors. Nonetheless, the attractiveness of urban forests is dictated more by their infrastructure than their ecological function. On the contrary, the visitors’ knowledge level can improve their motivation, environmental experience and perceived environmental value. These findings show the significance of developing educational programs with an emphasis on the experience of the visitors so that their ecological performance can contribute to improved low-carbon behavior. In conclusion, this work contributes to the management of sustainable urban tourism.

This work also has some limitations. First, the medium R-square on intention behavior to low-carbon action suggests investigating other influential factors to produce a more robust conscious behavior. Mkono and Hughes (2020) mention that many complex factors that cause positive intention do not necessarily lead to environmental action. Thus, many psychosocial variables need to be explored in different models. Second, the convenient sampling used here does not represent the whole population, making generalization difficult. Thus, further work needs to apply more rigorous sampling techniques to validate the findings. Further investigations may also need to be conducted in other urban forests in another Asian country with a similar and different social context for benchmarking, as this study found that the type of attractive urban forest design is a more dense forest, which differs from other studies based in Europe. Exploring more influencing behavioral factors of pro-environmental action in the model is also suggested. Thus, we could contribute more to support recreational activities in urban forests.

As an implication for planning an urban forest to increase its recreational function, the authors illustrate the importance of producing educational programs. Although the improved knowledge of visitors has been shown to strengthen their commitment to perform pro-environmental actions, the mediating role of motivation, experience and perceived value reveals that some activities are required to achieve visitor motivation to actual behavior. Consequently, designing an urban forest requires not only the enhancement of eco-attractions and artificial elements for the convenience of visitors but also the development of an environmental education program that can improve visitors’ environmental experience and perception of ecological value. The designed educational program may use an experiential education approach incorporating objective knowledge of Earth’s current state. The urban forest education program must encourage visitors’ connection and participation with nature. Moreover, knowledge and information about Earth’s environmental quality can increase visitors’ perceived value, ensuring that their activities in the urban forest contribute to improved health, environmental quality and social environment. Thus, with well-managed and provided education, they are encouraged to adopt low-carbon action because it complements their contribution to a better quality environment.

The theoretical contribution of this research is generated through the role of urban forest attractiveness in the intention to conduct low-carbon action, which influences solutions to existing urban environmental problems. This work exhibits that both ecological attractiveness and attractiveness of artificial elements in urban forests can attract visitors and subsequently boost their outdoor recreation motivation, ecological experience and perceived value and then turn them to boost their intention to conduct low-carbon action. The physical characteristics of a site are behavioral stimuli that can increase a person’s motivation, experience and perception of the value of the environment, thereby increasing their intention to engage in low-carbon actions. This environment behavioral construction is fundamental in understanding that urban forests offer ecological benefits and influence the social quality of urban communities. Nevertheless, without visitor activity, urban forests are merely physical entities that become increasingly demanding to maintain. Due to this, an urban forest that is socially active and has an influence on promoting environmentally conscious behavior is needed, and its presence is becoming ever more crucial. This work shows the significance of integrating psychosocial approaches into managing tourism in urban forests.

This study was conducted to analyse the socio-ecological problems faced by the Suhelwa Wildlife Sanctuary and understand its potential and challenges for developing ecotourism following Triple Bottom Line (TBL) principles. The study also benchmarked best ecotourism practices across the globe to create an ecotourism plan that would provide alternative livelihood and help in sustainable management of the area by reducing poverty, dependency on forests and biodiversity protection.

Suhelwa Wildlife Sanctuary was chosen because this area has several socio-ecological crises with limited livelihood options, and there is an urgent need for alternative livelihood opportunities in the form of ecotourism. The study followed an ethnographic approach through observation, participant observation, and semi-structured interviews. Content and thematic analysis was conducted through Atlas Ti9.0 software for data analysis. Subsequently, benchmarking best ecotourism practices through a literature review was done to develop an ecotourism action plan.

The First finding was related to the study area divided into three themes: problems, potential for ecotourism development, and challenges for ecotourism development. The second finding was related to benchmarking best practices and suggesting an action plan.

This work studied an area not sufficiently acknowledged by academicians and policymakers concerning ecotourism development. The work also benchmarks the best practices for ecotourism and proposes a sight-specific ecotourism action plan in accordance with TBL.

This study aims to investigate the intricate relationships between a community energy system, water resources and biodiversity conservation, with a specific focus on augmenting community energy resilience in Bondo. The primary objective is to gain an in-depth understanding of how community members perceive and experience the challenges related to balancing the often-conflicting demands of energy, water and biodiversity conservation within this context.

The research uses a qualitative approach to unravel the multifaceted dynamics of community energy systems, water resources and biodiversity conservation in Bondo. Data were collected through focus groups and direct observations, enabling a nuanced exploration of community perspectives and lived experiences. The subsequent analysis of this qualitative data follows established thematic analysis procedures.

The study's findings shed light on the formidable barriers that impede rural communities in Malawi from accessing electricity effectively. Even in communities fortunate enough to have electricity connections, the lack of knowledge regarding productive electricity use results in community energy systems operating at significantly reduced load factors. Furthermore, the intricate challenge of managing a biodiversity hotspot persists, exacerbated by the densely populated peripheral communities' continued reliance on forest, land and water resources. These activities, in turn, contribute to ecosystem degradation.

In a context where government-led management of forest reserves and game reserves has not yielded the expected results due to a multitude of factors, there arises a compelling need for innovative approaches. One such innovation involves fostering partnerships between the government and experienced trusts as lead organisations, providing a fresh perspective on addressing the complex interplay between community energy systems, water resources and biodiversity conservation. This novel approach opens doors to explore alternative pathways for achieving the delicate balance between human energy needs and the preservation of vital ecosystems.

The purpose of our study is to study the impact of chief executive officer (CEO) optimism and narcissism on the company's capital structure.

(1) The authors opt for regression, machine learning and text analysis to explore the impact of narcissism and optimism on the capital structure. (2) We analyze CEO interviews and employ three methods to evaluate narcissism: the dictionary proposed by Anglin, which enabled us to assess the following components: authority, superiority, vanity and exhibitionism; count of first-person singular and plural pronouns and count of CEO photos displayed. Following this approach, we were able to make a more thorough assessment of corporate narcissism. (3) Latent Dirichlet allocation (LDA) technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs and to find differences between the topics of interviews and letters provided by narcissistic and non-narcissistic CEOs.

Our research demonstrates that narcissism has a slight and nonlinear impact on capital structure. However, our findings suggest that there is an impact of pessimism and uncertainty under pandemic conditions when managers predicted doom and completely changed their strategies. We applied various approaches to estimate the gender distribution of CEOs and found that the median values of optimism and narcissism do not depend on sex. Using LDA, we examined the content and key topics of CEO interviews, defined as positive and negative. There are some differences in the topics: narcissistic CEOs are more likely to speak about long-term goals, projects and problems; they often talk about their brand and business processes.

First, we examine the COVID-19 pandemic period and evaluate how CEO optimism and pessimism affect their financial decisions under specific external conditions. The pandemic forced companies to shift the way they worked: either to switch to the remote work model or to interrupt operations; to lose or, on the contrary, attract clients. In addition, during this period, corporate management can have a different outlook on their company’s financial performance and goals. The LDA technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs. Second, we use three methods to evaluate narcissism. Third, the research is based on a set of advanced methods: machine learning techniques (random forest to reveal a nonlinear impact of CEO optimism and narcissism on capital structure).

The goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.

To estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.

For USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.

The research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.

Initiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.

We compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.

By analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.

Our findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.

The purpose of this study is to analyze the horizontal expansion of Burayu Town between 1990 and 2020. The study typically acts as a baseline for integrated spatial planning in small- and medium-sized towns, which will help to plan sustainable utilization of land.

Landsat5-TM, Landsat7 ETM+, Landsat5 TM and Landsat8 OLI were used in the study, along with other auxiliary data. The LULC map classifications were generated using the Random Forest Package from the Comprehensive R Archive Network. Post-classification, spatial metrics, and per capita land consumption rate were used to understand the manner and rate of expansion of Burayu Town. Focus group discussions and key informant interviews were also used to validate land use classes through triangulation.

The study found that the built-up area was the most dynamic LULC category (85.1%) as it increased by over 4,000 ha between 1990 and 2020. Furthermore, population increase did not result in density increase as per capita land consumption increased from 0.024 to 0.040 during the same period.

As a result of financial limitations, there were no high-resolution satellite images available, making it challenging to pinpoint the truth as it is on the ground. Including senior citizens in the study region allowed this study to overcome these restrictions and detect every type of land use and cover.

Data on urban growth are useful for planning land uses, estimating growth rates and advising the government on how best to use land. This can be achieved by monitoring and reviewing development plans using satellite imaging data and GIS tools.

The use of Random Forest for image classification and the employment of local knowledge to validate the accuracy of land cover classification is a novel approach to properly customize remote sensing applications.

This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.

Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.

The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.