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This current review examines the scientific literature report on non-timber forest products (NTFPs) commercialisation and forest conservation in different jurisdictions.

A systematic review using Scopus-indexed articles on NTFP commercialisation and forest conservation was done using the PRISMA framework.

The review categorised the factors influencing the commercialisation of NTFPs and forest conservation into five broad factors and sub-factors: socioeconomic, market-based, ecosystem, cultural and institutional factors. The scholarly publications on NTFP commercialisation and forest conservation have been undulating, with two years recording no publication on the subject matter under review. Besides, China and India in Asia are leading in the number of publications on NTFPs’ commercialisation. The review revealed ambivalence and symbiotic relationship among the factors influencing the commercialisation of NTFPs and forest conservation. Specifically, tenure arrangement, strict regulations to forest entry, market information asymmetry, bureaucracy in certification acquisition, seasonality and distance were identified as barriers to NTFPs’ commercialisation. While market demands for NTFPs increased, NTFPs’ prices and unsustainable harvesting activities were threats to forest conservation. Policymakers should focus on safeguarding customary property rights and indigenous knowledge in forest conservation, designing workable capacity-building schemes for NTFP entrepreneurs and reducing the cost and processes in certification acquisition.

There are reviews on NTFPs’ commercialisation and livelihoods, but a synergy between NTFPs’ commercialisation and forest conservation for forest policy direction is yet to be done in the literature. Also, while earlier studies systematically reviewed literature on NTFPs’ commercialisation, they did not relate the studies to forest conservation.

This study aims to investigate the problems small-scale oil palm plantations in Indonesia’s forest areas face and the government policies addressing them.

Survey and data collection were used to determine the socioeconomic, environmental, legal and governance problems related to the development of smallholder plantations. Information was obtained from the respondents via a rapid rural appraisal approach.

The potential land for potential participants in the community oil palm rejuvenation programme is a forest area of 1,628,749.60 ha. Owing to its legal dimensions and unsustainable land management, the rejuvenation regulatory programme has not reached independent farmers.

The use of plantation space beyond its designation hinders the government’s goal of accelerating the rejuvenation programme. The problems regarding the accumulation of forest area result in low achievement of the annual rejuvenation target in Riau Province (21%–25%). The authors present solutions to resolve land ownership conflicts and implement strategic policies to ensure the sustainable development of such plantations.

The authors introduce a conflict–resolution model for small-scale smallholder oil palm plantations to resolve the problems of forest area claims unaddressed in the Indonesian Job Creation Law. Land conflict resolution is categorised into five typologies: oil palm plantations with business permits; those without a forestry permit and subject to administrative sanctions; business activities in forest areas without forestry permits; resolving non-conformities in the progress of land or management controlled and used in forest areas prior to their designation by removing land parcels through modifying the forest area boundaries; and the settlement for farmers without cultivation registration certificates but have established plantations and whose land tenure can be proven.

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.

Empirical examinations of initial public offering (IPO) initial returns often rely heavily on linear regression models. However, these models can prove inefficient owing to their susceptibility to outliers, a common occurrence in IPO data. This study introduces a machine learning method, known as random forest, to address issues that linear regression may struggle to resolve.

The study’s sample comprises 352 fixed-priced IPOs from the year 2004 until 2021. A unique aspect of this research is its application of the random forest method. The accuracy of random forest in comparison to other methods is evaluated. The findings indicate that the random forest model significantly outperforms other methods in all of the evaluated aspects.

The variable importance measure indicates that investors’ demand, divergence of opinion among investors and offer price are the most crucial predictors of IPO initial returns. These determinants hold particular significance due to the widespread use of the fixed-price method in Malaysia, as this method amplifies the information asymmetry in the IPO market.

To the best of the authors’ knowledge, this study is among the pioneering works in Malaysian literature to apply the random forest method to address the constraints of conventional linear regression models. This is achieved by considering a more extensive array of factors and acknowledging the influence of outliers. Additionally, this study adds value to Malaysian literature by ranking and identifying the ex-ante information that best signals the issuing firm’s quality. This contribution facilitates prospective investors’ decision-making processes and provides issuing firms with effective means to communicate their value and quality to the IPO market.

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

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 research aims to investigate post-forest fire perceptions of businesses towards the environment as a stakeholder. Through interviews with affected businesses, the authors aim to understand whether disasters prompt sustainability prioritisation beyond legal or market demands, shedding light on potential shifts in environmental attitudes and decision-making processes.

The authors used qualitative methods to investigate post-disaster shifts in environmental perceptions. Using site visits, preparatory meetings and semi-structured interviews between October 2017 and April 2021, the authors gained insights into destruction, recovery efforts and stakeholder perspectives. Content analysis provided valuable decision-making insights, particularly in understanding the landscape dominated by SMEs reliant on short-term strategies.

Interviews revealed varied perspectives on stakeholder recognition, especially concerning the natural environment. Although some managers promptly acknowledged stakeholder groups, the recognition of the natural environment as one varied. Concerning the natural environment as a stakeholder, responses ranged from ecological acknowledgment to denying its stakeholder status. Despite differing views, many agreed on the forest's importance, especially for resource-reliant industries. The findings suggest that although many decision makers verbally acknowledge the natural environment as a stakeholder, their actions reveal the opposite.

The limitations are the COVID-19 pandemic in the data research phase. The methodology applied (qualitative) can be a limitation in itself and the authors recommend further research, applying mixed or quantitative methods. The research covers one event in one country. It is relevant to test our questions and conclusions in other countries/after other natural disasters. Incorporating other stakeholders' views and exploring alternative theories could enhance understanding and challenge existing results.

This study holds practical implications for understanding the relationship between organisations and the natural environment, particularly in recognising it as a stakeholder. By acknowledging the environment as a stakeholder, organisations can mitigate the effects of future natural disasters, as well as reducing their environmental footprints. Implementing these insights can lead to more informed decision-making processes and contribute to more effective resources and stakeholder management.

Recognizing the environment as a stakeholder fosters environmental consciousness and community engagement. Addressing the natural environment as such enhances the ownership and responsibility of the surrounding natural environment.

The study's originality lies in its exploration of organisational responses to natural disasters, particularly in recognizing the environment as a stakeholder. It offers unique insights into decision-making processes and attitudes towards environmental responsibility, contributing to advancing understanding and informing strategies for sustainable disaster management on a global scale.

Mangrove forests are one of the most bio-diverse and productive wetland environments on earth. However, these unique tropical forest environments that occupy coastal areas are among the most threatened habitats globally. These threats include logging, conversion of land for agriculture and mariculture and degradation due to pollution over the past 50 years. The large population of resilient mangroves occupying the Turneffe Atoll area in Belize faces growing anthropogenic threats such as permanent clearing of land for housing, infrastructural development and pollution and natural factors (climate change). Given the few formal studies done to evaluate mangrove resilience at Turneffe Atoll, the purpose of this study was to evaluate mangrove resilience and nursery functions in the Turneffe Atoll Marine Reserve (TAMR).

Mangrove fish abundance and forest structure was assessed by means of a visual census and the point-centred quarter method (PCQM) for 11 sites that span across conservation and general use zones.

This study found that the more resilient mangroves (lower vulnerability ranks, higher standing biomass and higher fish biomass and abundance) exist in general use zones and warrant the need for improved mangrove conservation measures for these areas by Turneffe Atoll Sustainability Association (TASA).

Limitations of the methods for data collection included accessibility within mangrove forests stands when establishing PCQM, observer bias among data collectors, sites without surrounding mangroves were not captured to serve as a true control group and poor visibility underwater affected the estimation of fish species and size. The timeline for this research was only three months based on available funding, and no follow-up study was done to make a true comparison.

The findings of this research have a guiding role in the formulation of conservation measures such as better waste management, a robust framework for mangrove management, a communication strategy to guide public awareness and long-term monitoring surveys.

This study aimed to understand the agri-entrepreneurial traits of undergraduate university students using machine learning (ML) algorithms.

This study used a conceptual framework of individual-level determinants of entrepreneurship and ML. The Google Survey instrument was prepared on a 5-point scale and administered to 656 students in different sections of the same class during regular virtual classrooms in 2021. The datasets were analyzed and compared using ML.

Entrepreneurial traits existed among students before attending undergraduate entrepreneurship courses. Establishing strong partnerships (0.359), learning (0.347) and people-organizing ability (0.341) were promising correlated entrepreneurial traits. Female students exhibited fewer entrepreneurial traits than male students. The random forest model exhibited 60% accuracy in trait prediction against gradient boosting (58.4%), linear regression (56.8%), ridge (56.7%) and lasso regression (56.0%). Thus, the ML model appeared to be unsuitable to predict entrepreneurial traits. Quality data are important for accurate trait predictions.

Further studies can validate K-nearest neighbors (KNN) and support vector machine (SVM) models against random forest to support the statement that the ML model cannot be used for entrepreneurial trait prediction.

This research is unique because ML models, such as random forest, gradient boosting and lasso regression, are used for entrepreneurial trait prediction by agricultural domain students.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.