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The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

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.

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).

Cyber-physical attacks are the most significant threat facing the utilisation and development of the various smart grid technologies. Among these attacks, false data injection attacks (FDIAs) represent a major category, with a wide variety of types and effects. There has been extensive reporting on FDIAs recently. Several detection algorithms have been developed over the past few years to address this threat. In Chapter 2, this thesis starts by providing a deep analysis of the literature on these algorithms. The concluding remarks of this chapter present the main criteria that should be considered in developing future detection algorithms for FDIAs in different systems of smart grids. Following that, this dissertation proposes FDIA detection algorithms in the major systems in smart grids that are the most susceptible and vulnerable towards FDIAs. In wide-area monitoring systems, being able to promptly differentiate FDIA from normal grid contingencies is crucial for a grid operator to decide the correct response and reduce FDIA false alarms. In Chapter 3, two FDIA characterisation algorithms are developed to address this issue. The automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. FDIAs are particularly difficult to detect and represent a major threat to AGC systems. Chapter 4 proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC systems. It then utilises this unsupervised learned model in detecting FDIA affecting the AGC system. The utilisation of distributed generation units in power distribution systems has increased the complexity of system monitoring and operation. Numerous information and communication technologies have been adopted recently to overcome the associated challenges, but they have created wide opportunities for energy theft and other types of cyber-physical attacks. Chapter 5 utilises the developed spatio-temporal learning algorithm in Chapter 4 in detecting the various possibilities of FDIA affecting the distribution systems by evaluating the reconstruction error of each measurement sample. The proposed algorithm is data-driven, which makes it resilient against distribution systems’ uncertainties and nonlinearities. The collected results indicate a superior detection performance of the proposed detection algorithms compared to those in the literature.

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

{"references": ["Kim H., Tadesse Y., Priya S., 2009, Energy Harvesting Technologies,\np3-4", "Curz Joao, 2008, Ocean Wave Energy, p1-4", "Zhu D., Beeby S., 2011, Energy Harvesting Systems, p1-3", "OECD, 2006, Energy Technology perspectives 2006: scenarios &\nstrategies to 2050, Organisation of Economic Cooperation &\nDevelopment, page 229-230.", "Khaligh A. and Onar Omer C., 2008, Energy Harvesting Solar, Wind, and\nOcean Energy Conversion System, pp223-230, pp250.", "Briney A., 2012, Waves - Ocean Waves, viewed at 10th April 2012,\n.", "Berteaux H. O., 1976, Buoy Engineering, The University of Michigan,\nUSA.", "Falnes, J 2007, \u00d4\u00c7\u00ffA review of wave-energy extraction-, ScienceDirect, vol.\n20, pp. 185-201", "Alaska Sea Grant, viewed at 16th April 2012,\n.\n[10] Robinson M. C., 2006, Renewable Energy Technologies for Use on the\nOuter Continental Shelf, National Renewable Energy Laboratory USA,\nviewed at 10th April 2012,\n.\n[11] Behrens, S, Heyward, J, Hemer, M, Osman, P 2011, \u00d4\u00c7\u00ffAssessing the wave\nenergy converter potential for Australian coastal regions-, Renewable\nEnergy, vol. 43, pp. 210-217.\n[12] Herbich, J 2000, Handbook of coastal engineering, Mcgraw-Hill\nprofessional.\n[13] Jefferys ER, 1980, Device characterization. In: Count BM (ed) Power\nfrom sea waves. Academic Press, pp 413-438."]} This paper presents an overview of the Ocean wave kinetic energy harvesting system. Energy harvesting is a concept by which energy is captured, stored, and utilized using various sources by employing interfaces, storage devices, and other units. Ocean wave energy harvesting in which the kinetic and potential energy contained in the natural oscillations of Ocean waves are converted into electric power. The kinetic energy harvesting system could be used for a number of areas. The main applications that we have discussed in this paper are to how generate the energy from Ocean wave energy (kinetic energy) to electric energy that is to eliminate the requirement for continual battery replacement.

The aim of this work was to gain insight and knowledge with the basic concepts of the Bitcoinnetwork and its relation to energy consumption. Precisely, the ambition of this literature-based research was to identify key determinants of the network ́s energy intensiveness. After an extensive review of the relevant literature on the topic, key principles of Bitcoin ́s electricity consumption were derived.With regards to environmental sustainability concerns, it was found that the network ́sproperties, in theory, allow for improvements of the unit economics of renewable energy production facilities and renewable intensive energy grids. Such applications, however, are dependent on the unforecastable market dynamics of the Bitcoin price.In a contextualization approach it was attempted to categorize the electricity consumption levels of the Bitcoin network based on the services offered with similar but not comparable entities.This approach finds that energy consumption does not provide a conclusive and instructive comparative parameter to determine whether energy consumption levels of the Bitcoin network.The paper concludes with advocating for location dependent policy approaches that encourage the strategic deployment of Bitcoin mining hardware to minimize environmental and economic opportunity cost.