Political authorities, energy operators and other stakeholders have the responsibility to implement energy transition pathways by increasing decentralised renewable energy generation. As the main stakeholder, authorities often lack the appropriate tools to frame and encourage the transition and to monitor the impact of energy transition policies. On the other hand, network operators need appropriate frameworks and guidelines to implement the transition with a sustainable business perspective. This report capitalize on previous work which uses a data driven approach based on geographical information system (GIS) and machine learning techniques to generate investment pathways towards decentralised energy generation. This report takes a closer look at the economic, environmental, technical and security impact of the development of decentralized energy systems. Firstly, Chapter 1 is looking back at the definition of performance indicators, leading to a better understanding of (i) the logic behind the selection of local system boundaries to evaluate energy efficiencies, (ii) the growing importance of grey energy and (iii) the necessary shift to hourly time scale for proper life cycle assessment of the future smart grid, while on the same time, (iii) the need to consider horizon greater than 20 years for investment planning. Secondly, Chapter 2 shows how long-term investment planning methodology, which is targeting big energy consumers, can favor the emergence of future decentralized energy infrastructure, while harvesting energy from the local environment. Chapter 3 demonstrates, by optimising solar panel layout in the RE-Demo test case, that grid-aware district scale approaches are required to identify the best investment strategies. Finally, Chapter 4 presents upcoming opportunities for investments in the digitalization of future smart grids.