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Riesgos y Oportunidades en el Mercado de Instrumentos Financieros Sostenibles: Préstamos Vinculados a la Sostenibilidad
No full textLos préstamos vinculados a la sostenibilidad (Sustainability-Linked Loans, SLL) son instrumentos financieros que ajustan las tasas de interés según el cumplimiento de objetivos Ambientales, Sociales y de Gobernanza (ASG). Regulados por los Principios de Préstamos Vinculados a la Sostenibilidad (SLLP), estos instrumentos han crecido significativamente, alcanzando los 735 mil millones de dólares en 2021, debido a su flexibilidad y atractivo para inversores. En España, la financiación sostenible creció un 1% en 2023, destacando los SLL con un incremento del 11%. Sin embargo, desafíos como la dificultad para establecer metas ambiciosas y el impacto limitado de los ajustes de tasas plantean interrogantes sobre su efectividad. A pesar de ello, los SLL mejoran la reputación de las instituciones financieras y fomentan el compromiso con la sostenibilidad. El éxito de los SLL depende de la transparencia y ambición de sus métricas (KPI y SPT), así como de revisiones externas rigurosas. Aunque el mercado muestra signos de estabilización, estos instrumentos representan una oportunidad clave para alinear objetivos financieros y sostenibles, si se gestionan adecuadamente los riesgos
Quantifying the Potential Economic Impact of Catalan Independence: A General Equilibrium
No full textCom afrontar el repte de la nova legislació CSDDD (Diligència Deguda) i la seva aplicació en la cadena de subministrament de les organitzacions _ Full Paper
Get PDFCap a una economia més digital i sostenible
Get PDFCap a una economia més digital i sostenible. Eix sostenibilitat mediambiental. La indústria davant el repte mediambiental
Quina és la solució més sostenible des del punt de vista ambiental per fer front a la sequera?
Get PDFLa sequera persistent a Catalunya, agreujada pel canvi climàtic, exigeix solucions sostenibles pel que fa a la gestió de l’aigua que equilibrin impactes econòmics i ambientals. Aquest article examina les diferents alternatives que el Govern de la Generalitat d’ERC va proposar per a reduir la vulnerabilitat de les Conques Internes de Catalunya (CIC) davant l'escassetat d'aigua, incloent-hi la dessalinització, la regeneració, les millores en la potabilització, el transvasament d’aigua i el transport d’aigua en vaixell. S'analitzen els costos econòmics associats i les petjades de carboni i hídrica de cada tecnologia, amb una comparació específica per disposar de informació sobre la seva viabilitat tècnica, econòmica i ambiental. Els resultats ofereixen una guia per a la presa de decisions en el disseny de polítiques públiques per una gestió hídrica més resilient
Ductile-Fragile Transition: A Novel Comparison Between Fracture Mechanics of Materials and Framed Structures_Conference Presentation
No full textTechnical Due Diligence Report – Anderson Dam & Hydropower Plant (California, USA)
No full textThis study represents the technical due diligence of the hydroelectric plant consisting of the
Anderson Dam (Leroy Anderson) and its power station, located in California, USA.
This technical contribution is structured analyzing the hydrogeological aspects of the dam site's
geographical context, including its structural behavior in the event of significant seismic events, and
then going more in detail on the energy assessments that take into account the temporal distribution
of rainfall and flow rates, as well as the efficiency of the hydraulic machinery installed
F-Q-LiTO: A Federated Q-Learning-Based Lightweight Intelligent Task Orchestrator for Multi-Tenant Container Clusters
No full textThe demand for intelligent, scalable, and energy-conscious container orchestration has increased due to the growth of microservice-based designs and multi-tenant workloads. A novel federated reinforcement learning framework for adaptive task scheduling in heterogeneous container clusters, F-Q-LiTO (Federated Q-Learning-Based Lightweight Intelligent Task Orchestrator), is proposed in this research. In contrast to traditional orchestrators, F-Q-LiTO uses federated Q-learning to decentralise decision-making, guaranteeing convergence across dispersed nodes while maintaining data locality and minimising synchronisation overhead. The system has several lightweight components, including energy-conscious placement penalties, XOR filters for secure container fingerprinting, Count-Min Sketches (CMS) for constant-space resource estimation, and workload forecasting based on the Long Short-Term Model (LSTM) for proactive migration. In comparison to DeepPlace, F-Q-LiTO reduced task deadline misses by around 34% and achieved an average SLA satisfaction of 96.8% when tested on simulated multitenant workloads with over 1000 tasks. Ablation studies confirm that federated coordination and predictive migration materially improve performance. Global Q-values converged within six episodes, and SHAPbased explanations identify CPU forecast, SLA urgency, and node energy state as dominant decision factors. F-Q-LiTO demonstrates practical, interpretable, and low-latency orchestration suitable for dynamic edge– cloud deployments.OPEN ACCESS Received: 01/08/2025 Accepted: 09/10/2025 Published: 27/11/202
AI-Enhanced and Other Load Modelling in Modern Power Systems: A Comprehensive Review of Advances, Challenges, and Future Directions
No full textLoad modelling is a crucial element of power system study that significantly affects the field’s planning, operation, and control methods. With the increasing penetration of renewable energy sources, electric vehicles, demand-side management, and distributed generation (DG), the traditional static and dynamic load model approaches are being replaced. This paper reviews extensively the existing load modelling techniques, namely, component-based load modelling, measurement-based load modelling, and hybrid methods. In addition, advancements tuned by artificial intelligence (AI) and machine learning (ML) are critically reviewed, emphasizing improving the accuracy, flexibility, and real-time adaptability of load models. For instance, Long Short-Term Memory (LSTM) networks have demonstrated significant improvements in forecasting accuracy, while Reinforcement Learning (RL) techniques enable adaptive and real-time control of load dynamics. Special focus is laid on load modelling in conditions of imbalance, dynamic parameter identification, and integration with smart grids and active distribution networks (ADNs). The review also discusses the importance of uncertainty embedded in probabilistic and data-driven models, customer behaviour, and the stochastic nature of distributed energy resources (DERs). The areas of future study emphasized AI-assisted adaptive architectures, hybrid frameworks, and digital twin applications for resilient and intelligent load modelling.OPEN ACCESS Received: 01/08/2025 Accepted: 26/09/2025 Published: 27/11/202