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This deliverable includes the methodology in EnergyPROSPECTS for an in-depth study of energy citizenship. It features the criteria used for selecting the cases for indepth study, the list of cases selected for in-depth study as well as key research foci and empirical research questions.

The rapidly changing climate presents unprecedented challenges, especially in permafrost regions, where its thawing directly affects houses, mines, roads, pipelines, airport runways, and rail tracks. Monitoring the dynamics of ground ice in permafrost landscapes is essential for assessing geohazards and adapting infrastructure to changing conditions. This abstract presents a new initiative utilizing cosmic neutrons metrology for permafrost studies, specifically focusing on ground-ice content monitoring. Traditional methods for assessing ground-ice content in permafrost regions involve labor-intensive surveys and analysis of geomorphological features. However, these approaches are often limited in spatial coverage and may not capture the full extent of ground-ice distribution. In response to these challenges, we propose leveraging cosmic neutrons as a non-invasive and scalable method for assessing ground-ice content. Cosmic neutrons naturally penetrate the Earth's surface and interact with hydrogen atoms in the soil, including those bound in water molecules. By measuring the cosmic neutron albedo, which is the flux of epithermal neutrons that are partially thermalized in the ground, we can infer the presence and distribution of ground ice. This approach builds upon established techniques used for soil moisture monitoring, adapting them for permafrost studies. In that regard, we conducted Monte Carlo simulations of cosmic neutron transport and interaction with modeled ground to investigate the feasibility of using cosmic neutrons for ground-ice content monitoring. Our simulations considered various ground compositions and ice concentrations to assess the sensitivity of cosmic neutron measurements to ground-ice content. The results of our simulations provide promising proof-of-concept for the proposed method, demonstrating its potential for accurately estimating ground-ice content in permafrost landscapes. Additionally, we are developing at DOSEO (CEA – Saclay, France) thermal over fast neutron reference fields that could be used to test, characterize, and calibrate detectors specifically for the presented application. The proposed method offers several advantages. Firstly, it provides measurements on a metric scale (~10m wide on ~1m depth), effectively balancing local detail with regional coverage, thus making it well-suited for mapping geohazards on a kilometric scale. Additionally, cosmic neutrons albedo measurements is non-destructive and non-invasive and can be applied across diverse permafrost landscapes, offering valuable insights into ground-ice dynamics. Moreover, mapping ground-ice content is feasible through the flexible deployment of neutron detectors. These detectors can either be moved around the study area to gather spatially distributed measurements or installed as fixed monitoring stations, enabling continuous observation of ground-ice dynamics across different seasons and over time. Integration of data from cosmic neutrons albedo measurements into Geographic Information System (GIS) applications enhances the ability to map geohazards and predict future changes in permafrost landscape stability. By combining information on ground-ice content with elevation, drainage patterns, periglacial features, and infrastructure, a better understanding of the interactions between climate change and geohazards in permafrost regions is achievable.In conclusion, cosmic neutrons metrology offers a promising approach for monitoring ground-ice content in permafrost landscapes. By advancing our understanding of permafrost dynamics, this initiative contributes to the broader goals of cryosphere monitoring and sustainable development.

Considering recent environmental regulations, the need to adapt domestic biomass combustion systems to models that generate less emissions has gained relative importance. The present research proposes an analysis of the bed cooling effects on emission patterns, specifically focusing on the concentration, typology and morphological aspects of the released particles. The study was carried out by comparing the behaviour of a small-scale pilot plant with air stratification, with and without bed cooling. The results revealed an optimal behaviour of the facility with distributions of 30% primary-70% secondary air, accompanied by a significant decrease in emissions due to the reduction in the operating temperatures. More than 75% of the particles were retained in the bed on the cooled surfaces due to the effect of the prominent temperature gradient that was produced. Among the types of emitted particles (mostly with sizes below 0.1 μm), the presence of partial biomass degradation remnants was observed, representing three-quarters of the total collected matter. To a lesser extent, the presence of carbonaceous agglomerates was detected and usually in very compact clusters; however, in cases of high primary air supply, large amounts of immature soot were observed. Agencia Estatal de Investigación | Ref. RTI2018-100765-B-I00

Abstract In this paper, a general mathematical model for the dynamic simulation of a single-effect LiBr/water absorption chiller is presented. The model is based on mass and energy balances applied to the internal components of the machine, and it accounts for the non-steady behaviour due to thermal and mass storage in the components. The validation of the mathematical model is performed through experimental data collected on a commercial small-capacity water-cooled unit. Due to the peculiar technology adopted in the real chiller, a special effort was made to identify the appropriate values of the main physical parameters. The validation of the model is based on the values of the water temperature at the outlet of the machine, as no measurement inside the machine was possible; anyway, a consistency analysis applied to the internal parameters is also presented. The agreement between experimental and simulated results is very good, both on a daily and on a seasonal basis.

AbstractEuropean climate is associated with variability and changes in the mid-latitude atmospheric circulation. In this study, we aim to investigate potential future change in circulation over Europe by using the EURO-CORDEX regional climate projections at 0.11° grid mesh. In particular, we analyze future change in 500-hPa geopotential height (Gph), 500-hPa wind speed and mean sea level pressure (MSLP) addressing different warming levels of 1 °C, 2 °C and 3 °C, respectively. Simple scaling with the global mean temperature change is applied to the regional climate projections for monthly mean 500-hPa Gph and 500-hPa wind speed. Results from the ensemble mean of individual models show a robust increase in 500-hPa Gph and MSLP in winter over Mediterranean and Central Europe, indicating an intensification of anticyclonic circulation. This circulation change emerges robustly in most simulations within the coming decade. There are also enhanced westerlies which transport warm and moist air to the Mediterranean and Central Europe in winter and spring. It is also clear that, models showing different responses to circulation depend very much on the global climate model ensemble member in which they are nested. For all seasons, particularly autumn, the ensemble mean is much more correlated with the end of the century than most of the individual models. In general, the emergence of a scaled pattern appears rather quickly.

Les conséquences du changement climatique sont particulièrement aiguës au Kenya, où 80 % des terres sont arides ou semi-arides. Quelle perspective critique et constructive le concept d’habitabilité peut-il offrir dans ce contexte ? Fondé sur un travail de terrain réalisé au Kenya en 2022 et 2023, cet article entend affiner une définition in situ de l’habitabilité, en utilisant exclusivement des données qualitatives issues de deux phases de recherche, complétées par des exercices de vidéo-cartographie. Loin de proposer une lecture positiviste de l’habitabilité, portée par une conception exclusivement mécanique et linéaire des dynamiques de mobilité, cette étude propose de mieux relier l’habitabilité aux pratiques quotidiennes, aux habitudes, aux solidarités, voire aux résistances qui constituent la vie ordinaire des habitants des communautés et des personnes déplacées. L’habitabilité se situerait alors au cœur de l’intrication entre socialité et spatialité, entre social et biologique, qui définit – selon Stanley Cavell – la forme de vie humaine. The consequences of climate change are particularly acute in Kenya, where 80% of the land is arid or semi-arid. What critical and constructive perspective can the concept of habitability offer in this context? Based on fieldwork carried out in Kenya in 2022 and 2023, this article aims to refine an in-situ definition of habitability, using exclusively qualitative data from two research phases, supplemented by video-mapping exercises. Far from proposing a positivist reading of habitability, driven by an exclusively mechanical and linear conception of mobility dynamics, this study proposes to better link habitability to the daily practices, habits, solidarities and even resistances that make up the ordinary lives of community dwellers and displaced persons. Habitability would then lie at the heart of the intricacy between sociality and spatiality, between the social and the biological, which defines – according to Stanley Cavell – “the human form of life.”

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.