# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 1 Introduction  
In recent years, the European Union has been very committed to increasing the amount of energy generated from renewable sources. The first target, set with the Renewable Energy Directive of 2009 [1], aimed at increasing the percentage of energy from renewable sources to 20% by 2020; the Renewable Energy Directive II of 2018 [2] set this percentage to 32%, to be reached by 2030. The REPowerEU directive from May 2022 [3] proposed to increase this target to 45% by 2030 in order for the European Union to become increasingly autonomous from importing fossil fuels. In this paper, we deal with the generation of electricity from solar radiation by means of photovoltaic panels, which is particularly widespread in Italy (in 2021, it met 8.7% of the national electricity demand, and 21.8% of the production from renewable sources [4]). The installation of photovoltaic systems has been promoted by the Italian government, e.g., by way of incentives such as \"Superbonus 110%\" [5, 6]. Building rooftops offer significant potential for the deployment of photovoltaic (PV) systems, and enable better geographic correlation between supply and demand [7]. The installation of photovoltaic modules has low impact from the point of view of urban planning, since the panels are mounted onto existing roofs. Energy produced by photovoltaic systems can play a crucial role in reducing greenhouse gas emissions [8]; the contribution of rooftop photovoltaic systems is essential in smart cities [9], and the estimation of the production potential is a prerequisite for the transformation of cities and energy communities [10, 11] into net zero energy districts [12]. The estimation of the photovoltaic potential is, therefore, required in various fields of study and research. The determination of the photovoltaic potential in urban areasconsists of four basic parts [13; 14]: physical potential, geographic potential, technical potential, and economic potential. (i) Physical potential consists of assessing the solar irradiation over the region of interest; (ii) geographic potential consists of identifying and characterizing the surfaces suitable for photovoltaic installations; (iii) technical potential studies the transformation of the solar energy harvested by the panels into electric energy; (iv) economic potential evaluates the return on the investment incurred in the installation of photovoltaic systems. According to the literature, different techniques are used according to the scale of the analysis to be conducted [15]. Studies on a continental [7] or global [16] scale have a lower level of detail, with analyses primarily using statistical methods and machine learning techniques. [PERSON] et al. carried out a global-scale estimation of the technical potential of rooftop PV systems [16]. Earth's surface is divided into sectors of 10 km\({}^{2}\), and the total area of building roofs is estimated for each with a machine-learning approach on the basis of population density, and geographic information system (GIS) datasets. The result is the technical and economic potentials calculated for all countries and for each month of the average year. [PERSON] et al., using satellite and statistical data, and machine-learning techniques, estimated the photovoltaic potential on EU rooftops at 100 m spatial resolution [7]. Statistical methods are still used in national- [17] and regional [18]-scale studies, often being supplemented with GIS data [19]. The use of machine-learning algorithms coupled with high-resolution GIS data and physical models allowed for [PERSON] et al. to estimate with a high level of detail the large scale rooftop photovoltaic potential in Switzerland [19]. An evaluation of the geographic potential is realized by combining the estimated average monthly irradiance obtained from satellite data with available rooftop area, shading effects and rooftop geometry. Lastly, the use of physical models allows for the estimation of the technical potential and its uncertainty. The work of [PERSON] et al.