Reducing emissions from fluorinated gases (F-gases) is considered one of the key conditions for the European Union (EU) to be able to meet the requirements of the European Green Deal, primary objectives of which are the reduction in greenhouse gas emissions by at least 55% by 2030 compared to 1990 and the gradual transition to net climate neutrality by 2050. To this end, a quota system for hydrofluorocarbons (HFCs) is already in place, introduced through the updated F-Gas Regulation (517/2014). The aim of this paper is to review an innovative scheme currently under development that strongly promotes the Recovery, Recycling and Reuse (3R) of F-gases extracted from the heating, ventilation, air conditioning and refrigeration (HVAC-R) equipment of the building sector, thus offering a sustainable alternative source for refrigerant supplies. Therefore, the basic operating principles of the so-called “3R ECOSYSTEM” are outlined and especially its three main components, which are: the self-certification/self-declaration platform, the F-gas identification and recycling units (prototypes) and the Retradeables marketplace. In addition, an overview of both the demonstration phase and the scheduled replication phase of the 3R ECOSYSTEM is provided, including the estimated impact on carbon dioxide (CO₂) savings due to the progressive uptake of the circular use of existing F-gases across Europe.

Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios.