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Geothermal energy has similar social acceptability issues as other renewable energy technologies. The territory of a geothermal project should be known in depth, understood and respected, including the public and its value, the energy issues and the entire socio-economic and political context as well. This knowledge can only be acquired with the tools provided by social sciences. It will be the key to build a project adapted to the territory, to communicate with and engage the public in a suitable way. Three tools helpful in fostering constructive interactions with the public have been examined: information sharing, creating local benefits, and public participation.

The paper addressed the issue of the emergence of renewable energy communities (RECs) in Italy, after the related law which entered into force in 2020 allowing the possibility of creating them. We especially focused on the process of creating alliances among different actors - professional, institutions, NGOs, citizens - highlighting possible trends or models for the future that need to be verified in further research. The approach proposed to carry out the analysis of this current process is the Actor-Network Theory, aiming at drawing the assemblages of human and non-human actants at a general level. In accordance with this analysis, we selected three case studies in order to show their different ways of organising, the relevance of the trust in establishing each REC and of local context in affecting the composition and features of the actor-networks. The proposed analysis aimed to show how it is possible to identify different networks in accordance with the different visions of the RECs and the socio-economic needs related to the various geographical areas of Italy. This kind of research and its rationale could be useful and replicable in relation to countries featuring considerable internal differences such as Italy.

The Hindu-Kush Karakoram Himalaya (HKKH) mountains and the Tibetan plateau are the world's largest snow and ice reservoir outside the polar regions and they are often referred to as the "Third Pole". These mountains feed the most important Asian river systems, and changes in snow and precipitation dynamics in this area could severely impact on water availability for downstream populations, agriculture and energy production, ecosystems and biodiversity. Despite their importance, precipitation and snowpack characteristics in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high elevation area. Global Climate Models (GCMs) still have too coarse spatial resolution to reproduce the small scale variability of precipitation and snow in orographically complex areas. Nevertheless, they may be effective in providing, even at a regional scale, a smooth but coherent picture of the large scale temporal and spatial patterns of these two variables in these areas. The quantification of the uncertainties in GCM simulations is essential to define the models skills in reproducing climate variability and to critically analyze future climate change projections. We investigate how the spatial and temporal variability of precipitation and snowpack in the HKKH region is represented in historical and future simulations of the state-of-the-art GCMs participating in the CMIP5 effort, and we investigate the role of elevation-dependent surface warming. The model outputs in the historical period are compared with the main, currently available observational datasets, including surface- and satellite-based observations and reanalysis data.

The enhancement of warming rates with elevation, the so-called elevation-dependent warming (EDW), is one of the clearest regional expressions of global warming. Real sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming rates in the recent decades, leading to serious impacts on mountain ecosystems and downstream societies, some of which are already occurring. In this study we use the historical and scenario simulations of one state-of-the-art global climate model, the EC-Earth GCM, run at five different spatial resolutions, from ~125 km to ~16 km, to explore the existence, characteristics and driving mechanisms of EDW in three different mountain regions of the world - the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau-Himalayas. The aim of this study is twofold: to investigate the impact (if any) of increasing model resolution on the representation of EDW and to highlight possible differences in this phenomenon and its driving mechanisms in different mountain regions of the northern hemisphere. Preliminary results indicate that autumn (September to November) is the only season in which EDW is simulated by the model in both the maximum and the minimum temperature, in all three regions and across all model resolutions. Regional differences emerge in the other seasons: for example, the Tibetan Plateau-Himalayas is the only area in which EDW is detected in winter. As for the analysis of EDW drivers, we identify albedo and downward longwave radiation as being the most important variables for EDW, in all three areas considered and in all seasons. Further these results are robust to changes in model resolution, even though a clearer signal is associated with finer resolutions. We finally use the highest resolution EC-Earth simulations available (~16 km) to identify what areas, within the three considered mountain ranges, are expected to undergo a significant reduction of snow or ice cover in the period 2039-2068 with respect to the period 1979-2008, using the EC-Earth projections under the RCP 8.5 concentration scenario.