Climate change is one of the most critical global challenges, driven primarily by the rapid increase in greenhouse gas concentrations. Carbon sequestration, the process by which ecosystems capture and store carbon, plays a key role in mitigating climate change. This study investigates the factors influencing carbon sequestration in subtropical planted forest ecosystems. Field data were collected from 100 randomly sampled plots of varying sizes (20 m² × 20 m² for trees, 5 m² × 5 m² for shrubs, and 1 m² × 1 m² for herbs) between February and April 2022. A total of 3,440 plants representing 36 species were recorded, with Prosopis juliflora and Prosopis cineraria as the dominant tree species and Desmostachya bipinnata as the dominant herb. Regression analysis, Pearson correlation, and structural equation modeling were performed using R software to explore relationships between carbon sequestration and various biotic and abiotic factors. Biotic factors such as diameter at breast height (DBH; R=0.94), tree height (R=0.83), and crown area (R=0.98) showed strong positive correlations with carbon sequestration. Abiotic factors like litter (R=0.37), humus depth (R=0.43), and electrical conductivity (E.C; R=0.11) also positively influenced carbon storage. Conversely, pH (R=-0.058), total dissolved solids (TDS; R=-0.067), organic matter (R=-0.1), and nitrogen (R=-0.096) negatively impacted carbon sequestration. The findings highlight that both biotic and abiotic factors significantly influence carbon sequestration in planted forests. To enhance carbon storage and mitigate climate change, efforts such as afforestation, reforestation, and conservation of subtropical forest ecosystems are essential.

This study aims to determine the effect of Human Capital Management (HCM) and work ethics on the performance of life insurance agents mediated by Organizational Citizenship Behavior-Organization (OCB-O) and Organizational Citizenship Behavior-Individual (OCB-I). The data was collected from 103 respondents who had entered the category of having won the Top Agent Awards (TAA) using a survey approach with questionnaires. The population consisted of life insurance agents who had won the TAA/MDRT, a 5 Likert scale questionnaire, and analyses using the SEM-AMOS-21 program. The results prove HCM has a positive significant effect on work ethics; HCM does not have a substantial impact on OCB-O and OCB-I; Work Ethics have a considerable effect on OCB-I and OCB-O; OCB-O and OCB-I have no significant impact on performance; HCM does not have a substantial effect on performance; Work Ethics does not have a considerable impact on performance, however, if OCB-I mediates HCM it will strengthening agent Performance, likewise, Work Ethics if mediated by OCB-I, will strengthening Performance. The findings of this study are that for insurance agents to perform well, companies can treat agents as HCM and work ethics, and it is essential to pay attention to OCB-I as mediation in improving agent performance.

The Ecuadorian electricity sector encompasses generation, transmission, distribution and sales. Since the change of the Constitution in Ecuador in 2008, the sector has opted to employ a centralized model. The present research aims to measure the efficiency level of the Ecuadorian electricity sector during the period 2012–2021, using a DEA-NETWORK methodology, which allows examining and integrating each of the phases defined above through intermediate inputs, which are inputs in subsequent phases and outputs of some other phases. These intermediate inputs are essential for analyzing efficiency from a global view of the system. For research purposes, the Ecuadorian electricity sector was divided into 9 planning zones. The results revealed that the efficiency of zones 6 and 8 had the greatest impact on the overall efficiency of the Ecuadorian electricity sector during the period 2012–2015. On the other hand, the distribution phase is the most efficient with an index of 0.9605, followed by sales with an index of 0.6251. It is also concluded that the most inefficient phases are generation and transmission, thus verifying the problems caused by the use of a centralized model.

In an effort to bridge the gap of economic and social inequality among the community, rural areas in Indonesia are encouraged to be self-sufficient in generating income. This makes the central government create various policies so that the regional government maximizes the management of its potential as an economic resource for the well-being of its people. One of the ways to manage this potential is to encourage rural areas to create tourism products that can be sold to the public. The Indonesian governments openly use the tourism sector as a tool for the development in many rural areas. Next, efforts to achieve successful development of the district will be closely related to the strategic planning and long-term cooperation of each local government with stakeholders in its implementation. These two points are the basic elements of the new regionalism theory. This theory states that the role of local governments is very important in initiating and making policies for new economic activities for a significant improvement in the quality of their population. Therefore, this study tries to explore how the new theory of regionalism can include rural development from a tourism perspective as a way to stimulate the fading economy in rural area of Indonesia. The study found that the new theory of regionalism needs support from various aspects such as social-cultural, community participation, the three pillars of sustainable development namely economic, social, and environmental as well as basic aspects to shape sustainable rural development through tourism.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

Soil erosion is characterized by the wearing away or loss of the uppermost layer of soil, driven by water, wind, and human activities. This process constitutes a significant environmental issue, with adverse effects on water quality, soil health, and the overall stability of ecosystems across the globe. This study focuses on the Anuppur district of Madhya Pradesh, India, employing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) tools to estimate and spatially analyze soil erosion and fertility risk. The various factors of the model, like rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), conservation practices (P), and cover management factor (C), have been computed to measure annual soil loss in the district. Each factor was derived using geospatial datasets, including rainfall records, soil characteristics, a Digital Elevation Model (DEM), land use/land cover (LULC) data, and information on conservation practices. GIS methods are used to map the geographical variation of soil erosion, providing important information on the area's most susceptible to erosion. The outcome of the study reveals that 3371.23 km2, which constitutes 91% of the district's total area, is identified as having mild soil erosion; in contrast, 154 km2, or 4%, is classified as moderate soil erosion, while 92 km2, representing 2.5%, falls under the high soil erosion category. Additionally, 50 km2, or 1.35%, is categorized as very high soil erosion and around 30 km2 of the study area is classified as experiencing severe soil erosion. The analysis further discovers that the annual soil loss in the district varies between 0 and 151 tons per hectare per year. This study indicates that most of the district is classified under low soil erosion; only a tiny fraction of the area is categorized as experiencing high and very high soil erosion. The study provides significant insights into soil erosion for policymakers and human society to bring their attention to the need for sustainable soil conservation practices in the undulating terrain/topography and agriculturally dominated district of Anuppur.