A Digital Twin is a virtual representation of a physical system, process, or product that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help decision-making. In the context of environmental monitoring and optimization, Digital Twins monitor the environment in near real-time and provide alerts if detected incidents require immediate and dedicated action. They support management of local and long-term environmental goals and can enhance environmental efficiency by enabling optimization of resource consumption and emissions levels. Digital Twins rely on information collected from remote and personal sensing enabled by the Internet of Things (IoT), and the resulting big data generated by the ecosystem is handled and analyzed by means of big data analytics. The Digital Twin concept, enabled by ICT developments, can be defined as “a virtual representation of a physical system that is updated through the system’s life cycle in near real-time by means of available physical-virtually fused data to elucidate hidden states and changes, optimize control strategies, and enable operational forecasts, analyses, and decision-making.” Such models find natural application in the context of environmental monitoring and optimization, where they can monitor the environment in near real-time and provide alerts if detected incidents require immediate and dedicated action. They can also support the management of local and long-term environmental goals, and enhance environmental efficiency by stimulating the optimization of resource consumption and emissions levels. The corresponding information is collected from remote and personal sensing enabled by the IoT, while the resulting big data generated by the ecosystem is handled and analyzed by means of big data analytics.