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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.

In the agricultural sector, climate change (CC) affects multiple weather variables at different stages of crop cycles. CC may influence the mean level or affect the distribution of events (e.g., rainfall, temperature). This work evaluates the economic impact of CC-related changes in multiple climatic components, and the resulting uncertainty. For this purpose, a three-stage discrete stochastic programming model is used to represents farm sector of an irrigated area of Italy and to examine the influence of CC on rainfall and on maximum temperature. These variables affect the availability of water for agriculture and the water requirements of irrigated crops. The states of nature, and their change, are defined more broadly than in previous analyses; this allows examining the changes of more climatic variables and crops cultivation. The effect of CC is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future. The results show that the agricultural sector would seek to lower costs by modifying patterns of land use, farming practices and increasing the use groundwater. The overall economic impact of these changes is small and due primarily to the reduced availability of water in the future. The temperature increase is, in fact, largely offset by the effects of the increase in CO2 levels, which boosts the yield of main crops of the irrigated zone. Therefore, availability and water management becomes a crucial factor to offset the increase of evapotranspiration and of water stress resulting from the increase of temperature. However, the costs of CC are very high for some types of farming, which suffer a large reduction in income.

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) ##

These data sets provide the WRF [1] calculated wind data for Pritzwalk (onshore) and FINO3 (offshore) as Python dictionaries. Additionally, the files contain k-means cluster objects derived from these profiles. These data sets were used for power assessment and design exploration of Airborne Wind Energy Systems using the awebox [2] optimization toolbox. WRF setups are described in detail and used in publication [3,4,5]. Wind data are interpolated to fixed heights of: [10, 28, 50, 70, 90, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700, 800, 1000, 1200] meters above ground. Onshore wind data: Location lat: 53° 10.78' N; long: 12° 11.35' E Time: 1 September 2015 - 31 August 2016 Timestep: 10 min Offshore wind data: Location lat: 55° 11.7' N, long: 7° 9.5' E Time: 1 September 2013 - 31 August 2014 Timestep: 10 min The clusters are derived from both horizontal wind velocity components using the scikit-learn’s k-means clustering algorithm [6]. For our purposes, wind vectors were rotated such that the main wind speed always points in the same direction (u_main,u_deviation). [1]: Weather Research and Forecasting Model [2]: awebox [3]: Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems [4]: Offshore and onshore ground-generation airborne wind energy power curve characterization [5]:Ground-generation airborne wind energy design space exploration [6]: sklearn.cluster.KMeans

This repository contains the raw data of the inputs and results presented in the paper "Electrification of light-duty vehicle fleet alone will not meet mitigation targets" published in Nature Climate Change (2020) by Alexandre Milovanoff, I. Daniel Posen, and Heather L. MacLean (Department of Civil & Mineral Engineering, University of Toronto).

Global transition towards renewable energy production has increased the demand for new and more flexible hydropower operations. Before management and stakeholders can make informed choices on potential mitigations, it is essential to understand how the hydropower reservoir ecosystems respond to water level regulation (WLR) impacts that are likely modified by the reservoirs' abiotic and biotic characteristics. Yet, most reservoir studies have been case-specific, which hampers large-scale planning, evaluation and mitigation actions across various reservoir ecosystems. Here, we investigated how the effect of the magnitude, frequency and duration of WLR on fish populations varies along environmental gradients. We used biomass, density, size, condition and maturation of brown trout (Salmo trutta L.) in Norwegian hydropower reservoirs as a measure of ecosystem response, and tested for interacting effects of WLR and lake morphometry, climatic conditions and fish community structure. Our results showed that environmental drivers modified the responses of brown trout populations to different WLR patterns. Specifically, brown trout biomass and density increased with WLR magnitude particularly in large and complex-shaped reservoirs, but the positive relationships were only evident in reservoirs with no other fish species. Moreover, increasing WLR frequency was associated with increased brown trout density but decreased condition of individuals within the populations. WLR duration had no significant impacts on brown trout, and the mean weight and maturation length of brown trout showed no significant response to any WLR metrics. Our study demonstrates that local environmental characteristics and the biotic community strongly modify the hydropower-induced WLR impacts on reservoir fishes and ecosystems, and that there are no one-size-fits-all solutions to mitigate environmental impacts. This knowledge is vital for sustainable planning, management and mitigation of hydropower operations that need to meet the increasing worldwide demand for both renewable energy and ecosystem services delivered by freshwaters. Data of environmental characteristics and brown trout populations in 102 Norwegian hydropower reservoirsThe data contains field-collected data of brown trout populations in 102 Norwegian reservoirs with variable environmental characteristics. The brown trout data (i.e. response variables) include estimates of: "Biomass" (grams of fish per 100m2 net per night); "Density" (number of fish per 100m2 net per night); "Mean weight" (mean wet mass in grams); "Mean condition" (mean Fulton's condition factor); and "Mean maturity length" (mean total length of mature females in millimeters). All abbreviations for different variables (columns) are explained in the paper. Many reservoirs ("Lake") have various names, some including Norwegian letters (æ, ø & å). Hence, we recommend to use coordinate data (EPSG:4326; "decimalLongitude" and "decimalLatitude") and Norwegian national lake ID numbers ("Lake_nr"; managed by the Norwegian Water Resources and Energy Directorate; www.nve.no) to locate the reservoirs. The variables "Year", "Month" and "Day" refer to times when survey fishing was conducted. Lake morphometry data ("A"=surface area, "SD"=shoreline development) is obtained from NVE database. The lake climatic and catchment data ("T"=mean July air temperature, "NDVI"= Normalized Difference Vegetation Index, and "SL"=terrain slope) is obtained and measured as described by Finstad et al. (2014; DOI: 10.1111/ele.12201). Other abbreviations include: "FC"=presence of other fish species (1=absent, 2=present); "GS"=gillnet series (1=Nordic, 2=Jensen); and "ST"=brown trout stocking (0=no stocking, 1=stocking). The water level regulation (WLR) metrics include: ): "WLR_magnitude"= maximum regulation amplitude; "WLR_frequency"=relative proportion of weeks with a sudden rise or drop in water level; and "WLR_duration"=the relative proportion of weeks with exceptionally low water levels.Data-in_doi.org-10.1016-j.scitotenv.2017.10.268.xlsx