The article presents an exhaustive comparative analysis of NDVI, SAVI and NDWI spectral indices, extracted from satellite images of the Guangüiltagua Metropolitan Park in Quito. The relevance of remote sensing, using satellites such as Sentinel-2, in the monitoring of biodiversity and environmental conditions is highlighted. The research emphasizes the importance of vegetation indices, especially NDVI, to assess vegetation health and detect changes in vegetation cover. The study discusses in detail vegetation sensitivity, water identification, water stress response, soil effect compensation, and specific applications of NDVI, SAVI, and NDWI. The ability of these indices to provide crucial information on ecosystem health, water presence, and potential water stress conditions in Metropolitan Park is highlighted. The methodology applied involves the use of the Copernicus platform and the Sentinel-2 satellite, with radiometric and geometric correction procedures, as well as atmospheric correction. The results obtained reveal high NDVI values, indicating a dense vegetation cover, while NDWI suggests potential areas of water scarcity. The comparative analysis between the indices offers a deeper understanding of the relationship between vegetation and water in the park.

El artículo analiza el mercado creciente de arrendamiento de departamentos VIP y cómo la inteligencia artificial (IA) está transformando la experiencia en hoteles y alojamientos de Airbnb. La demanda de alojamientos de lujo ha aumentado, impulsada por viajeros que buscan experiencias exclusivas, destacándose características como ubicaciones privilegiadas, servicios personalizados y comodidades de alta gama. En hoteles, la implementación de IA incluye asistentes virtuales y chatbots para mejorar la atención al cliente, así como sistemas que personalizan la experiencia del huésped mediante el análisis de datos y preferencias. Además, la automatización de tareas administrativas y de mantenimiento incrementa la eficiencia operativa. En Airbnb, la IA optimiza la gestión de propiedades con herramientas que ajustan dinámicamente los precios y gestionan reservas, y aplicaciones que mejoran la seguridad y comodidad del huésped, como cerraduras inteligentes y sistemas de monitoreo. Los beneficios incluyen una mayor satisfacción del cliente, aumento de la lealtad, reducción de costos operativos y optimización de recursos. Sin embargo, también se enfrentan desafíos como la privacidad de los datos y la dependencia tecnológica. El artículo concluye que la integración de IA en el arrendamiento de departamentos VIP y en la industria de la hospitalidad mejora la experiencia del huésped y transforma el modelo operativo, anticipando una evolución continua y nuevas innovaciones.

This study introduces an innovative method for assessing the economic value of carbon stored by the urban park forest in Quito, Ecuador. The method uses Sentinel-2 remote sensing data and advanced deep learning techniques. A large forest study area was chosen, and detailed field measurements were taken to gather biomass and carbon data. Sentinel-2 satellite images were processed to correct for radiation and atmospheric conditions, and vegetation indices such as NDVI and EVI were calculated. To model forest biomass, a convolutional neural network (CNN) was created and trained using Sentinel-2 spectral bands and vegetation indices. The model was validated with independent field data and demonstrated high accuracy in estimating biomass and carbon, as indicated by evaluation metrics like RMSE and R². The findings include detailed maps showing the spatial distribution of biomass and carbon in the study area, which can be a valuable tool for forest management and the implementation of policies for valuing stored carbon. This approach combines the high resolution of Sentinel-2 data with the predictive power of neural networks, providing a robust and scalable method for estimating carbon across large forest areas. The study's conclusions emphasize the feasibility and accuracy of this approach and its potential for application in various forestry and geographical contexts.

This study explores how the Yaguarcocha community in Imbabura Province, Ecuador, can leverage its intangible heritage within the framework of the orange economy to promote inclusive growth and sustainable development. According to the Inter-American Development Bank, the orange economy comprises creative industries that drive economic growth while enriching cultural identity and social well-being. Through qualitative methods—including semi-structured interviews and thematic analysis—the research identifies key cultural assets such as artisanal craftsmanship, traditional gastronomy, and indigenous festivals. While Yaguarcocha is known for its kart racing and recreational tourism, its rich cultural heritage remains underutilized, limiting broader economic potential. The study underscores the need for targeted interventions, including cultural tourism promotion, heritage-focused education, and a distinctive artisanal brand. These strategies enhance community participation, stimulate innovation, and foster economic diversification. Ultimately, the findings highlight the pivotal role of education and public-private collaboration in revitalizing cultural identity. By positioning Yaguarcocha as a unique cultural destination, the research advocates for a development paradigm that integrates economic advancement with heritage preservation, offering a replicable model for sustainable community development in culturally rich regions of Ecuador and beyond.

Savings and Credit Cooperatives (COACs) within Ecuador’s Popular and Solidarity Economy (PSE) represent the principal source of financial intermediation for households and microenterprises with limited access to the formal banking system. This article examines the determinants of loan delinquency in Segment 4 and Segment 5 SCCs within Ecuador’s PSE framework, employing an unbalanced panel dataset of 368 institutions—220 in Segment 4 and 148 in Segment 5—and 5,056 quarterly observations spanning the period 2020Q1–2025Q4 (24 quarters). The econometric evidence obtained from a dynamic panel model estimated using the Arellano–Bond difference estimator (AB-2SLS), supplemented by seven robustness specifications, reveals a strong degree of persistence in loan delinquency, indicating that deterioration in portfolio quality is unlikely to be corrected spontaneously over time. The results further show that portfolio concentration in microcredit constitutes the most important structural risk factor, reflecting the greater vulnerability of micro-cooperatives to correlated defaults. Institutional size exhibits an asymmetric protective effect, a pattern consistent with the community relationship-banking hypothesis. Time dummy variables indicate that the decline in delinquency observed during 2020 was driven by regulatory payment-deferral measures rather than by a genuine improvement in members’ repayment capacity, whereas the increase observed after 2022 reflects the gradual exhaustion of those temporary policy interventions.