Tropical peat swamp is an essential ecosystem experiencing increased degradation over the past few decades. Therefore, this study used the social-ecological system (SES) perspective to explain the complex relationship between humans and nature in the Sumatran Peatlands Biosphere Reserve. The peat swamp forest has experienced a significant decline, followed by a significant increase in oil palm and forest plantations in areas designated for peat protection. Human systems have evolved to become complex and hierarchical, constituting individuals, groups, organizations, and institutions. Studies on SES conducted in the tropical peatlands of Asia have yet to address the co-evolutionary processes occurring in this region, which could illustrate the dynamic relationship between humans and nature. This study highlights the co-evolutionary processes occurring in the tropical peatland biosphere reserve and provides insights into their sustainability trajectory. Moreover, the coevolution process shows that biosphere reserve is shifting toward an unsustainable path. This is indicated by ongoing degradation in three zones and a lack of a comprehensive framework for landscape-scale water management. Implementing landscape-scale water management is essential to sustain the capacity of peatlands social-ecological systems facing disturbances, and it is important to maintain biodiversity. In addition, exploring alternative development pathways can help alter these trajectories toward sustainability.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

Overwhelming studies unanimously agreed that preservation of the environment is a central climax in the discourse of green banking. There is a growing interest in exploring green banking practices for fostering financial inclusion, economic growth and sustainable development as part of Vision 2030 in Saudi Arabia. There are insufficient studies that examine this in the context of Saudi Arabia. This study aims at exploring the potential of green banking in order to attain sustainable banking and financial inclusion in achieving vision 2030in the country. Qualitative content analysis is used as a methodology of the study. Data were gathered through different sources such as: Web of Science (WOS), related journals, newspapers, published references, research papers, library sources and environmental organizations reports. It is indicated that green banking initiatives can be instrumental in fostering sustainable economic and environmental development in the Kingdom. The paper highlighted various activities of green banking such as: renewable and clean energy, financing green agriculture/food security, high-quality infrastructure among others. Nonetheless, some impediments to the green banking practices such as: risks facing green banks, poor quality of financial services among others are also mentioned in this paper. The paper proffers solutions to the challenges impeding green banking practices. In conclusion, the financial and banking industries in Saudi Arabia has been proving reform of the sector through greening economy. It is there suggested that the stakeholders and policymakers should provide efficient and effective technical, operational legal frameworks for enhancing green economy in achieving Vision 2030 in the country.

This study explores the impact of environmental degradation on public debt in the largest Southeast Asian (ASEAN-5) countries. Prior research has not examined environmental degradation as a possible determinant of public debt in the ASEAN region. As such, the primary objective is to examine key determinants of public debt, notably economic growth, trade openness, investment, and environmental degradation. Utilizing the Fully Modified Ordinary Least Squares (FMOLS) method and data from 1996 to 2021, the study reveals a negative correlation between investment and public debt. Conversely, a positive relationship exists between economic growth, environmental degradation, and public debt levels. These findings hold significant implications for policymakers seeking to craft effective economic and environmental strategies to ensure sustainable development in the ASEAN-5 region. Stronger economic growth can drive up public debt. Importantly, the study highlights the importance of tailored approaches, considering each country’s unique fiscal and developmental characteristics. Applying the Two-Gap Model enhances the understanding of these complex dynamics in shaping public debt and its relationship with environmental factors.