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L'empreinte carbone des technologies numériques est une préoccupation depuis plusieurs années. Cela concerne principalement la consommation électrique des datacenters; beaucoup de fournisseurs dans le domaine du cloud s'engagent à n'utiliser que des sources d'énergie renouvelables. Cependant, cette approche néglige la phase de fabrication des composants des infrastructures numériques. Nous considérons dans ce travail de recherche la question du dimensionnement des énergies renouvelables pour une infrastructure de type cloud géographiquement distribuée autour de la planète, considérant l'impact carbone à la fois de l'électricité issue du réseau électrique local en fonction de la location de sa production, et de la fabrication des panneaux photovoltaïques et des batteries pour la part renouvelable de l'alimentation des ressources. Nous avons modélisé ce problème de minimisation de l'impact carbone d'une telle infrastructure cloud sous la forme d'un programme linéaire. La solution est le dimensionnement optimal d'une fédération de cloud sur une année complète en fonction des localisations des datacenters, des traces réelles des travaux à exécuter et valeurs d'irradiation solaire heure par heure. Nos résultats montrent une réduction de l'impact carbone de 30% comparés à la même architecture cloud totalement alimentée par des énergies renouvelables et 85% comparés à un modèle qui n'utiliserait qu'une alimentation via le réseau local d'électricité. The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model. Données computationnelles ou de simulation: En tenant compte des données en entrée (description de la fédération de centres de données, fichiers de configuration appropriés, conditions météorologiques, etc.), le logiciel est capable de proposer un dimensionnement optimal pour la fédération des datacenters à faible émission de carbone distribuée à l'échelle mondiale : surface des panneaux photovoltaïques et capacité des batteries pour chaque datacenter de la fédération. Des scripts sont disponibles pour mettre en forme les solutions proposées. Simulation or computational data: Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration. Audience: Research, Policy maker UpdatePeriodicity: as needed

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

The goal of WP3 is to develop a design framework for novel ground (slinky/earth basket) type shallow heat exchangers. This design framework, based on developing theoretical models of heat transfer and on experimental data, will be implemented in a design- and engineering calculation tool to support the implementation of these new technologies in the market. The design framework defines the goals of the (thermal and hydraulic) design (especially sizing) of the ground source heat exchanger, as a function of different boundary conditions (building energy demand, soil thermal parameters, required system performance etc.). Moreover, an engineering tool it is aimed at the overall system design and will support the engineer in the choices of heat exchanger technology (vertical, horizontal or earth basket/slinky) and other design parameterizations. This deliverable describes the overall design process and provides information and procedures for data collection and evaluation. The detailed description of the design process for different types of Ground Heat Exchangers is based on the design of the actual GHEX systems implemented in the demo sites of the Geofit project and includes vertical borehole heat exchangers, shallow slinky heat exchangers and earth basket type heat exchangers. This deliverable is suited to be implemented in a design handbook or procedure that can be part of an integrated quality control system.

The EMB3Rs Unified Modelling Platform is a tool to assist on modelling the recovery of excess heat and its reuse to meet final energy demand within and beyond the boundaries of industrial sites. The tool consists of a knowledge base and several simulation modules. This database comprises the research data generated in the course of the EMB3Rs project by using the EMB3Rs platform. The pdf file contains the detailed description of the database content It has been deposited at Zenodo’s open data repository with DOI 10.5281/zenodo.7994255.

These data are part of a data portal that accompanies the special issue ‘Climate change adaptation needs a science of culture,’ published in Philosophical Transactions of the Royal Society B in 2023. To access the data portal, please visit: https://doi.org/10.5061/dryad.bnzs7h4h4. This code represents a computational model investigating the dynamics of coupled and decoupled resource use and efficiency gains. It can be used to simulate the effects of exploration-exploitation strategies on efficiency, consumption and sustainability, considering different levels of direct and indirect rebound effects. The model simulates a population of agents who make decisions on whether to explore or exploit a natural resource. These agents become more efficient over time based on their chosen strategy, affecting resource consumption. Different scenarios are considered, including various rebound effects, which influence how efficiency gains impact resource use. The key elements of the model include agents' uncertainty about the efficiency of their actions, the operationalization of efficiency as a reward, and the calculation of resource consumption based on efficiency gains and rebound effects. The model provides insights into how agents' decisions and resource use evolve over time under different conditions. This computational framework offers a valuable tool for exploring the complex dynamics of resource consumption and management in the face of environmental challenges. It can be applied to gain a deeper understanding of the Jevons Paradox and its implications for sustainable resource use. This computational model simulates the dynamics of exploration and exploitation strategies within a population of agents. These agents make decisions on whether to explore new solutions or exploit existing ones, with a focus on maximizing efficiency. The model employs a N-armed bandit problem approach, where agents select actions to maximize efficiency gains. Efficiency is operationalized as a reward, and agents use sample means to estimate expected efficiency. A balance between exploration and exploitation is maintained through a probability-based algorithm. The code also encompasses resource domains, representing different resources and their dynamics, along with computations of resource consumption, existing resources, and sustainability indices. The simulations consider various parameter combinations to examine the model's behavior. Overall, the code serves as a tool for studying the interplay between exploration, exploitation, efficiency, and resource consumption within a population of agents across different scenarios, making it valuable for investigating the effects of rebound effects on resource consumption and sustainability. The simulations run a comprehensive set of parameter combinations to explore the model's behavior thoroughly. # Code from: Efficiency traps beyond the climate crisis: Exploration-exploitation tradeoffs and rebound effects. Python scripts to run the model, as described in: Segovia-Martin J, Creutzig F, Winters J. 2023 Efficiency traps beyond the climate crisis: exploration–exploitation tradeoffs and rebound effects. Phil. Trans. R. Soc. B 378: 20220405. https://doi.org/10.1098/rstb.2022.0405 The code and supplementary materials are all freely accessible at the following link: [https://github.com/School-of-Collective-Intelligence/Jevons-Paradox-and-Cultural-Evolution](https://github.com/School-of-Collective-Intelligence/Jevons-Paradox-and-Cultural-Evolution) The simulator can be accessed via the following links: [https://jevons-collectiveintelligence.pythonanywhere.com/](https://jevons-collectiveintelligence.pythonanywhere.com/) or [https://jsegoviamartin.pythonanywhere.com/](https://jsegoviamartin.pythonanywhere.com/) The DOI of this Dryad repository: [https://doi.org/10.5061/dryad.qjq2bvqnk](https://doi.org/10.5061/dryad.qjq2bvqnk) ##

This dataset contains the underlying data for the following publication: Song, L., Lieu, J., Nikas, A., Arsenopoulos, A., Vasileiou, G., & Doukas, H. (2020). Contested energy futures, conflicted rewards? Examining low-carbon transition risks and governance dynamics in China's built environment. Energy Research & Social Science, 59, 101306., https://doi.org/10.1016/j.erss.2019.101306. Full details of methods used to create the dataset and provided within this publication.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).