In recent years, Europe has found itself facing the problem of continuous urbanization and excessive energy consumption, which is approaching the exhaustion of available energy resources. In view of this, the notions of sustainability and resilience have become fundamental in resource management and policy formulation. In such a context, renewable energies play a key role in the global energy pool. Among these, wind energy production accounts for almost half (43%) of global generating capacity [1]. However, despite its obvious merits, the “Big Wind sector” presents a number of potential shortcomings related largely to the short lifespan of these components and the lack of efficient operation and maintenance (Ο&Μ) planning schemes. The latter can in fact increase up to 25-30% of the total levelized cost per kWh produced over the life of a wind turbine (WT) or 75-90% of the investment costs [2].The useful life of a turbine wind power (WT) is set at around 20 years and very few turbines have already reached their life expectancy. This relative infancy translates into a lack of experience on proper maintenance and optimal operational planning of these facilities. However, unlike conventional engineering systems, wind turbines are subject to significant levels of continuous cyclic loading, making the system prone to fatigue. Due to the harsh conditions they are subjected to, wind turbine components are designed to sustain a rather short lifespan of 20 years, ensuring that the system is not left idle due to early failure of a sub-component. In reality, there still remains the important question of when component repair or replacement might be preferable to complete replacement... mid-paper... yes, the remaining challenge ahead is to demonstrate the benefits of long-term monitoring of such systems. The second pillar of the suggested framework is a methodology to translate the value of monitoring information into quantifiable terms, providing a reliability framework for LCA of WTs, capable of addressing the following issues, as identified in [4]:1. Automate inspection and maintenance work.2. Increase safety by minimizing the risk of accidents, many of which have been reported as fatal.3. Reduce excessive O&M costs, which are increasing disproportionately towards the late life stage of a WT (minimizing downtime, decreasing the frequency of sudden failures and associated logistics and maintenance costs, providing reliable energy). Figure 2 illustrates the proposed scheme, inspired by the work of Frangopol et al. (2001) [9].