Exploration of the technologies used to understand and manage complex systems — including artificial intelligence, data analytics, modeling, and monitoring tools that support sustainability and governance.
Raspberry Pi environmental monitoring projects show how low-cost computing platforms can support climate observation, water quality analysis, biodiversity monitoring, renewable […]
Explore 10 Arduino projects aligned with the Sustainable Development Goals, from irrigation and energy tracking to pollution sensing, recycling, and biodiversity monitoring.
This technical deep dive explores low power embedded systems across ARM microcontrollers, embedded Linux platforms, and FPGA architectures, examining how energy-efficient design supports sustainable computing and large-scale digital infrastructure.
FPGA environmental monitoring enables real-time sensing and control for smart agriculture. This article explores how reconfigurable hardware can process environmental data at the edge to support more adaptive and resilient farming systems.
Wide-area IoT protocols such as LoRaWAN, NB-IoT, and LTE-M enable resilient communication networks for disaster recovery in remote regions, supporting environmental monitoring, early warning systems, and situational awareness when conventional infrastructure fails.
Precision farming uses IoT sensors, connected devices, and agricultural drones to transform fields into data-driven systems, helping farmers monitor soil conditions, irrigation needs, and crop health in real time.
China’s green energy transition is reshaping the global clean technology landscape. This article examines how coordinated policy, large-scale investment, and ambitious climate targets are accelerating China’s shift toward a low-carbon energy system.
Urban farming infrastructure reframes local food production as a systems challenge. This article explores how distributed food systems can improve resilience in cities facing climate volatility, supply chain disruption, and growing population pressures.
Research suggests coastal flood risk may be underestimated because many hazard models rely on simplified sea level assumptions rather than direct coastal measurements, raising questions about how accurately communities are assessing flood exposure.
