At present, states and entire regions that possess significant reserves of sought-after minerals have great potential to maintain and even improve their socio-economic position in the foreseeable future. Since the beginning of 2000, the increase in mining volumes of minerals has been more than 50%; however, more than half of all extracted raw materials fall to only five leading countries: China, the USA, the Russian Federation, Australia, and India. This article presents the results of the analysis of the global structure of mineral production by type and geographic region. The article provides an in-depth analysis of the world’s leading mining companies, identifying the key players in the industry. A comprehensive overview of each company’s performance, including key financial indicators and production statistics, is presented. The main environmental risks as a result of the continued increase in the global scale of mining have been identified. The prospects for the development of the mining sector are shown. The results of the study can be used by the scientific community as an information source.

Biomass production (BIO) and its anomalies were modeled using MODIS satellite images and gridded weather data to test an environmental monitoring system in the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, an agricultural growing region bordered by the states of Sergipe (SE), Alagoas (AL), and Bahia (BA), Northeast Brazil. Spatial and temporal variations on BIO between these biomes were strongly identified, with the annual long-term averages (2007–2023) for AF and CT of 78 ± 22 and 58 ± 17 kg ha−1 d−1, respectively. BIO anomalies were detected through its standardized indexes—STD (BIOSTD), comparing the results for the years from 2020 to 2023 with the long-term rates from 2007 to each of these years. The highest negative BIOSTD values were in 2023, but concentrated in CT, indicating periods with the lowest vegetation growth, regarding the long-term conditions from 2007 to 2023. The largest positive BIOSTD values were for the AF biome in 2022, evidencing the highest vegetative vigor in comparison with the long-term period 2007–2022. The proposed BIO monitoring system is important for environmental policies as they picture suitable periods and places for agricultural and forestry explorations, allowing sustainable managements under climate and land-use changes conditions, with possibilities for replication of the methods in other environmental conditions.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

This study explores the spatial distribution pattern of educational infrastructure development across districts and cities in North Sumatra, identifying significant disparities between urban and rural areas. The study aims to: (1) determine the distribution of educational development across districts and cities, (2) analyze global spatial autocorrelation, and (3) identify priority locations for educational development policies in North Sumatra Province. The methodology includes quantile analysis, Moran’s Global Index, and Local Indicators of Spatial Autocorrelation (LISA) using GeoDa software to address spatial autocorrelation. The results indicate that there are nine areas with a low School Participation Rate Index (SPRI), eleven areas with a low School Facilities and Infrastructure Index (SFII), and eleven areas with a low Regional Education Index (REI). Spatial autocorrelation analysis reveals that SFII shows positive spatial autocorrelation, while SPRI and REI exhibit negative spatial autocorrelation, indicating a high level of inequality between regions. Labuhan Batu Selatan and Labuhan Batu are identified as priorities for the provincial government in overseeing educational development policies.

To achieve the Paris Agreement's temperature goal, greenhouse gas emissions should be reduced as soon as, and by as much, as possible. By mid-century, CO₂ emissions would need to be cut to zero, and total greenhouse gases would need to be net zero just after mid-century. Achieving carbon neutrality is impossible without carbon dioxide removal from the atmosphere through afforestation/reforestation. It is necessary to ensure carbon storage for a period of 100 years or more. The study focuses on the theoretical feasibility of an integrated climate project involving carbon storage, emissions reduction and sequestration through the systemic implementation of plantation forestry of fast-growing eucalyptus species in Brazil, the production of long-life wood building materials and their deposition. The project defines two performance indicators: a) emission reduction units; and b) financial costs. We identified the baseline scenarios for each stage of the potential climate project and developed different trajectory options for the project scenario. Possible negative environmental and reputational effects as well as leakages outside of the project design were considered. Over 7 years of the plantation life cycle, the total CO₂ sequestration is expected to reach 403 tCO₂∙ha⁻¹. As a part of the project, we proposed to recycle or deposit for a long term the most part of the unused wood residues that account for 30% of total phytomass. The full project cycle can ensure that up to 95% of the carbon emissions from the grown wood will be sustainably avoided.