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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.

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) ##

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).

Within an overall project to assess the ability of the agricultural sector to contribute to bioenergy production, we set out here to examine the economic and technological viability of a bioenergy facility in an uncertain economic context, using the stochastic viability approach. We consider two viability constraints: the facility demand for lignocellulosic feedstock has to be satisfied each year and the associated supply cost has to be lower than de profitability threshold of the facility. We assess the viability probability of various supplying strategies consisting in contracting a given share of the feedstock demand with perennial dedicated crops at the initial time and then in making up each year with annual dedicated crops or wood. The demand constraints and agricultural prices scenarios over the time horizon are introduced in an agricultural and forest biomass supply model, which in turns determines the supply cost per MWh and computes the viability probabilities of the various contract strategies. A sensibility analysis to agricultural prices at initial time is performed. Results show that when they are around or under the median (of the 1993–2007 prices), the strategy consisting in contracting 100% of the feedstock supply with perennial dedicated crops is the best one.

This work deals with an integrated solar PV/T hybrid air collector suitable for applications such as heating and drying of hay and industrial products. The main purpose of this study is to evaluate and optimize the thermal and electrical performances of this PV component and its integration system. A 2D mathematical model describing the thermal behaviour of the component is presented. The simulation values are compared to the measured data obtained under steady conditions on a solar PV/T air collector. Then, a description is provided regarding the in situ experimental studies which are carried out on three various models of the same component, in order to validate the thermal model previously developed. As further steps, various simulations will be performed on a drying system under a full building integration setup. 25th European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion, 6-10 September 2010, Valencia, Spain; 5150-5152

The present paper tries to address up to what extent corporate social responsibility (CSR) might advance socio-ecological sustainability of the products with a geographical indication (GI), mainly in developing countries. To this end it would be addressed how agricultural firms became financialized in recent decades. It would be then presented how CSR practices have recently emerged to address this issue and whether they have been successful or not. Finally, it would be highlighted the potential of GI certification practices as part of CSR practices that could potentially boost sustainability for agricultural firms in developing countries.

D'ici 2050, l'agriculture devra répondre à une augmentation de la demande alimentaire, due à une augmentation de la population qui devrait atteindre alors les 9 milliards. A l'échelle nationale, cela représente des enjeux de compétitivité et de croissance économique. Par ailleurs, afin d'assurer cette augmentation de la production, il y a deux solutions : augmenter la proportion des terres agricoles au détriment des écosystèmes naturels; et augmenter la productivité agricole. A travers une revue d'articles agronomiques et économiques, nous montrons l'importance de considérer la qualité du sol dans la productivité et la durabilité des exploitations agricoles. Cependant les pratiques agricoles préservant la qualité du sol ne sont pas largement adoptées, ce particulièrement en France. Une analyse économique de ces enjeux permet d'appréhender le processus de décision des agriculteurs, et de déterminer quelles sont les stratégies optimales permettant de répondre à ces enjeux. Nous proposons un modèle de contrôle optimal qui illustre les liens entre les pratiques agricoles et la qualité du sol, quand la qualité du sol est considérée comme un facteur de production endogène. L'intérêt et l'originalité de cet article est d'associer différentes disciplines pour étudier le rôle de la qualité du sol dans la durabilité et la rentabilité des exploitations. Agriculture is facing an expected increase in food production demand, caused by an increased global population of 9 billion people by the middle of this century. At national scale, competitiveness and economic growth issues are at stake. To insure this increase in production, there are two solutions: extend the proportion of agricultural lands at the expense of natural ecosystems; and increase agricultural productivity. Through a review of agronomic and economic articles, we show the importance of considering soil quality in the productivity and sustainability of farms. However, farming practices preserving soil quality are not widely adopted, particularly in France. An economic analysis of these issues provide an understanding of farmers' decision making process, and indicate what the optimal strategies can be to cope with these challenges. We propose an optimal control model that illustrates the links between farming practices and soil quality when soil quality is considered as an endogenous production factor. The interest and originality of this article is to associate different disciplines to investigate the role of soil quality in the sustainability and profitability of farms.

In the proposed paper, data and expertise were collected to provide information and arguments about several issues: about the main criteria of good bread in terms of environmental, nutritional, sensory and sanitary aspects; then, about ways to achieve them in the wheat-to-bread chain, while maintaining affordable prices. Computation for determining the best compromise between the various desired criteria is a challenge, since not all of them are compatible. Several scenarios are explored to highlight tensions between desired criteria, problem formulation and solution analysis such as best- and worst-case scenarios. Hence the approach demonstrates the interest of information systems and computational methods for decision support in agricultural and food policies.