Decarbonization strategies aim at increasing renewable energy source (RES) capacity, including new photovoltaic (PV) systems. Utility-scale PV installations are often placed in agricultural areas, resulting in a reduction in agricultural land and affecting the environment. To balance agricultural and energy policies, PV development should not limit agricultural purposes, allowing sustainable exploitation under specific technological and environmental conditions, particularly in areas of actual or potential abandonment. Studying agricultural abandonment is complex due to its multifaceted nature, the lack of a clear definition, and challenges in acquiring cartographic data. This study introduces and compares two methodologies to identify abandoned agricultural areas, aiming to delineate macro-areas of potential abandonment and examine patterns for conversion to energy use, with a focus on Toscana, a region (NUTS-2) in central Italy, which has experienced cropland reduction unrelated to urbanization. The first, simplified approach analyses land cover changes from 2000 to 2018, while the second method provides a more detailed abandonment detection by means of medium spatial resolution satellite imagery from the Harmonized Landsat and Sentinel-2 dataset. A Random Forest classifier combined with Object-Based Image Analysis (OBIA) is applied to satellite data to map annual active/non-active croplands. Annual maps are then validated with a trajectory-based approach to detect agricultural land abandonment. This second methodology can help in providing spatially and timely meaning estimates of abandoned agricultural areas to be recovered for energy purposes and promote a sustainable growth of PV systems.

The growth of photovoltaic (PV) installations is essential for the global energy transition; however, comprehensive data regarding their spatial distribution are limited, which complicates effective energy planning. This research introduces a methodology for automatic recognition of ground-mounted PV systems in Italy, using semantic segmentation and Sentinel-2 RGB images with a resolution of 10 m. The objective of this methodology is to accurately identify both the locations and the sizes of these installations, estimate their capacity, and facilitate regular updates to maps, thereby supporting energy planning strategies. The segmentation model, which is founded on a U-Net architecture, is trained using a dataset from 2019 and evaluated on two separate cases that involve different dates and geographical areas. We propose a multi-temporal approach, applying the model to a sequence of images taken throughout the year and aggregating the results to create a PV detection probability map. Users have the flexibility to modify probability thresholds to enhance accuracy: lower thresholds increase producer accuracy, ensuring continuous area detection for capacity estimation, while higher thresholds boost user accuracy by reducing false positives. Additionally, post-processing techniques, such as filtering for plastic-covered greenhouses, assist minimizing detection errors. However, there is a need for improved model generalizability across various landscapes, necessitating retraining with images from a range of environmental contexts.

The decarbonization objectives set by the EU Green Deal to increase the renewable generation heavily rely on the contribution of wind energy, both onshore, through the installation of new plants and repowering of existing plants, and offshore. The issuance of the new "Fit for 55" package of measures will result in an increase in the objectives already identified for 2030 for Italy, which in all probability will be set at over 21 GW of installed capacity for onshore wind (i.e., doubling the currently operating power) and at least 3 GW for offshore wind. An informed energy planning of the territory is therefore paramount to efficiently maximize renewable penetration. In these regards, the development of informatic tools aimed at disentangling both resource availability and generation potential can effectively play a key role in supporting optimal technology displacement through space. RSE has worked on these themes since the end of the 1990s, when the first version of the Italian Wind Atlas (ATLAEOLICO) WebGIS was released, providing a support tool for adequate energy planning of the territory. Throughout the years, the Wind Atlas has represented a reference for various stakeholders (wind plants developers, authorities responsible for spatial planning and companies involved in the electricity grid development) who recognized its great utility in quickly identifying the most suitable Italian areas for wind energy exploitation in terms of long-term annual average wind speed and full load hours. With the purpose that this platform keeps providing tangible support for energy planning, we have worked on both renewing the anemological database and the WebGIS structure, which is the focus of this work. The new Italian Wind Atlas AEOLIAN provides for a new anemological database consisting in 30 years (1990-2019) of hourly wind data at 1.4 km horizontal resolution (WGS84 UTM32) covering the whole Italian territory and marine areas. Wind trajectories are estimated through the Weather Research and Forecasting (WRF) meteorological model combined with a statistical post-processing based on Analog Ensemble (AnEn). The renewed AEOLIAN WebGIS, developed through the open access framework TerriaJS, integrates standard functions for visualizing and querying data, data download functions and advanced tools to support local energy planning. It shows the spatial distribution of onshore and offshore wind speed [m/s] and full load hours [MWh/MW]. Each variable is computed as the 30 years annual average at the heights of 50, 75, 100, 125 e 150 m. a.s.l. Within AEOLIAN, users can download both variable maps and historical series of wind speed for more accurate evaluations. Besides maps, AEOLIAN also includes a tool for the technical and economical evaluation of a hypothetical wind farm at a local scale. This tool allows assessing the energy performances in terms of the net annual energy production and the average cost of the energy produced, considering local distribution of the wind resource, energy performances of the wind farm and investments and management costs.