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.

This study evaluates the influence of quality certificates on sustainable food production in Poland, considering economic, social, and environmental dimensions. Analyzing 25 different certificates, the research explores their criteria, procedures, and costs across various food product categories, including meat, fish, and plant-based products. The study provides a detailed review of certification processes, from initiation to audits and inspections. It identifies both commonalities and differences among certificates, each addressing unique aspects such as environmental impact, worker rights, and product origins. Despite the diversity in standards and procedures, the study underscores the need for standardized international criteria to improve transparency and meet consumer expectations, highlighting the significant role of quality certificates in advancing sustainable food production.

The project of returning farmland to forest is a new project of increasing farmers' income, ecological efficiency and

benefiting the country. The key to the success of returning farmland to forest project is to strictly control the key technologies such as regional planning, forest species selection, tree species selection, good seedling, structural configuration, meticulous soil preparation, serious planting, tending and management. According to the actual

situation of Yuanling County, suitable for the tree, choose the market prospects, fast-growing tree species afforestation,

reasonable adjustment of forest structure, ecological benefits and economic benefits simultaneously, take high-

quality high-yield and efficient forestry development. Returning farmland to forest project has played huge ecological benefits, economic and social benefits.

In the era of rapid information technology development, artificial intelligence (AI) and virtual reality (VR) technologies have gradually infiltrated the field of university English teaching, brought significant applications and impacted to English language learning in listening, speaking, writing, translation, and personalized learning. AI plays a vital role as an auxiliary teaching method in university English instruction, and the integration of VR technology further enhances teaching efficiency. This research will propose relevant recommendations to provide theoretical references for university English education in the age of AI, while also offering insights and guidance to educators in the education industry during the informatization reform of education.

In order to strengthen the study of soil-landscape relationships in mountain areas, a digital soil mapping approach based on fuzzy set theory was applied. Initially, soil properties were estimated with the regression kriging (RK) method, combining soil data and auxiliary information derived from a digital elevation model (DEM) and satellite images. Subsequently, the grouping of soil properties in raster format was performed with the fuzzy c-means (FCM) algorithm, whose final product resulted in a fuzzy soil class variation model at a semi-detailed scale. The validation of the model showed an overall reliability of 88% and a Kappa index of 84%, which shows the usefulness of fuzzy clustering in the evaluation of soil-landscape relationships and in the correlation with soil taxonomic categories.