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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 distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades: - European offshore wind farms - tidal energy converter deployements in Europe - wave energy converter deployements in Europe

In the recent decade, Machine Learning techniques have been widely deployed in solar systems due their high accuracy in predicting the performances without going through the physical modelling. In this work, the Artificial Neural Network (ANN) method is adopted to forecast the electrical and thermal efficiencies of a photovoltaic/thermal (PVT) air collector system. Indeed, two accurate modelling techniques have been used to generate the output results for training and validation. Both deployed electrical and thermal models have been validated experimentally and demonstrated high accuracy. Then, real climatic samples of one year with a 10 minute step of the Jordan valley location have been adopted to generate the electrical and thermal efficiencies. These latter are used in the training and validation of the developed ANN model under various combinations of the weather variables. The solar irradiance and the module temperature are the most important variables to consider as input in a NN-based model respectively. The developed ANN model shows MAE of 0.0078% and 3.3607% in predicting the electrical and thermal efficiency respectively. The electrical efficiency can be predicted with higher accuracy than the thermal efficiency. Further, the results demonstrate that the ANN outperforms the LS-SVM in forecasting the PVT air collector performances.

Recent calls to do climate policy research with, rather than for, stakeholders have been answered in non-modelling science. Notwithstanding progress in modelling literature, however, very little of the scenario space traces back to what stakeholders are ultimately concerned about. With a suite of eleven integrated assessment, energy system and sectoral models, we carry out a model inter-comparison for the EU, the scenario logic and research questions of which have been formulated based on stakeholders' concerns. The output of this process is a scenario framework exploring where the region is headed rather than how to achieve its goals, extrapolating its current policy efforts into the future. We find that Europe is currently on track to overperforming its pre-2020 40% target yet far from its newest ambition of 55% emissions cuts by 2030, as well as looking at a 1.0-2.35 GtCO2 emissions range in 2050. Aside from the importance of transport electrification, deployment levels of carbon capture and storage are found intertwined with deeper emissions cuts and with hydrogen diffusion, with most hydrogen produced post-2040 being blue. Finally, the multi-model exercise has highlighted benefits from deeper decarbonisation in terms of energy security and jobs, and moderate to high renewables-dominated investment needs.

Although there are clear economic and environmental incentives for producing. energy from solar and wind power, there can be local opposition to their installation due to their impact upon the landscape. To date, no international guidelines exist to guide quantitative visual impact assessment of these facilities, making the planning process somewhat subjective. In this paper we demonstrate the development of a method and an Open Source GIS tool to quantitatively assess the visual impact of these facilities using line-of-site techniques. The methods here build upon previous studies by (i) more accurately representing the shape of energy producing facilities, (ii) taking into account the distortion of the perceived shape and size of facilities caused by the location of the observer, (iii) calculating the possible obscuring of facilities caused by terrain morphology and (iv) allowing the combination of various facilities to more accurately represent the landscape. The tool has been applied to real and synthetic case studies and compared to recently published results from other models, and demonstrates an improvement in accuracy of the calculated visual impact of facilities. The tool is named r.wind.sun and is freely available from GRASS GIS AddOns. (C) 2014 Elsevier Inc All rights reserved.

AbstractQuantification of CO2 fluxes at the Earth’s surface is required to evaluate the causes and drivers of observed increases in atmospheric CO2 concentrations. Atmospheric inversion models disaggregate observed variations in atmospheric CO2 concentration to variability in CO2 emissions and sinks. They require prior constraints fossil CO2 emissions. Here we describe GCP-GridFED (version 2019.1), a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019.1 provides monthly fossil CO2 emissions estimates for the period 1959–2018 at a spatial resolution of 0.1°. Estimates are provided separately for oil, coal and natural gas, for mixed international bunker fuels, and for the calcination of limestone during cement production. GCP-GridFED also includes gridded estimates of O2 uptake based on oxidative ratios for oil, coal and natural gas. It will be updated annually and made available for atmospheric inversions contributing to GCP global carbon budget assessments, thus aligning the prior constraints on top-down fossil CO2 emissions with the bottom-up estimates compiled by the GCP.

<span lang="EN-CA">Estimating wind speed characteristics plays an essential role in designing a wind power plant at a selected location. In this study, the Weibull, gamma, and exponential distribution models were proposed to estimate and analyze the wind speed parameters and distribution functions. Real measured data were collected from Medan City, Indonesia. The scale and shape factors of wind distribution for three years data were calculated. The observed cumulative probability of the three models was compared to predicted wind characteristics. The probability density function (PDF) and the cumulative density function (CDF) of wind speed were also analyzed. The results showed that the Weibull model was the best model to determine PDF, while the exponential model was the best model to determine CDF for the Medan City wind site.</span>