This paper presents a comprehensive holistic methodology implemented for sustainable lighting systems in educational institutions. The proposed methodology is aligned with the Sustainable Development Goals (SDGs), particularly with SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), and it follows international standards. The six-step process includes viability analysis, project design simulation using DIALux 4.13 software, the installation of LED lighting systems, and the redesign of some electrical circuits, followed by an analysis of return on investment and the monitorization of CO₂ and energy consumption. The proposed methodology results in significant return on investment (ROI), primarily achieved through energy savings and reduced maintenance costs. The implementation of LED tubes, combined with occupancy and natural light sensors, leads to a 66% reduction in energy consumption and a reduction of 15.63 tons (metric tons) of CO₂ annually, translating into a quick payback period of approximately 2.36 years. Additionally, the system includes Long-Term Monitoring, which ensures that energy consumption and lighting levels are continuously tracked.

The reliable operation of photovoltaic (PV) systems requires effective methods for distinguishing anomalies from normal environmental variations. This study presents an Artificial Intelligence of Things (AIoT) framework for real-time PV monitoring and anomaly diagnosis. The proposed system integrates custom data acquisition, MQTT communication, edge computing, an LSTM Autoencoder, and Random Forest classification. Experimental validation was conducted under real operating conditions at three scales: a 500 W microinverter-based system, a 3.9 kWp grid-connected installation, and a 62.5 kWp PV subsystem. The evaluated conditions included normal operation, environmental variations, inverter clipping, shading, dust accumulation, and localized hotspot conditions. The Random Forest classifier achieved 99.02% test accuracy for the classification of seven operating conditions, while the hybrid strategy subsequently identified localized and progressive anomalies through inter-string comparison and persistence-based rules. The results demonstrate the feasibility of low-latency PV anomaly diagnosis using edge intelligence with reduced dependence on continuous cloud processing.

Inadequate management of municipal solid waste represents a critical challenge for the sustainability of modern cities, characterized by low citizen participation rates due to the lack of direct incentives. Unlike existing approaches that isolate hardware classification or fleet monitoring, this article presents RENOVA as a socio-technical closed-loop system based on the Internet of Things (IoT) and artificial intelligence (AI). This system integrates an IoT-enabled smart bin, a gamified mobile application for citizens, and an administrative web panel for merchant redemption, all interconnected via a REST API. The system employs computer vision through the GPT-4o (OpenAI, San Francisco, CA, USA) multimodal model for the automatic classification of recyclable materials (PET plastic and Aluminum) and integrates a gamified rewards program to incentivize citizen participation. The methodology follows an applied technological development approach under the agile Scrum framework. Prototype validation demonstrated successful real-time communication between the IoT device and the cloud platform, achieving classification accuracy exceeding 95% under controlled conditions. A diagnostic survey applied to a convenience sample of 51 participants revealed that 94.1% accepted the proposed gamification model, while user experience evaluation (n = 74; consisting primarily of university-affiliated individuals aged 15–24) yielded a mean overall satisfaction score of 4.77/5.0 (SD = 0.48), with 79.7% of participants assigning the maximum rating. These findings reflect stated user acceptance and behavioral intention under prototype conditions rather than observed long-term behavioral change, and should not be generalized to broader urban populations without further validation. The proposed solution directly contributes to Sustainable Development Goals 11 (Sustainable Cities) and 12 (Responsible Consumption), suggesting a potentially scalable framework.