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Data and additional plots for the Scientific Reports paper titled, "Modelling photovoltaic soiling losses through optical characterization". This includes the spectral transmittance data and all of the optical microscopy. The manuscript presents the results of an international collaboration that investigated the spectral effects of soiling and dust naturally deposited on PV glass exposed outside at seven locations worldwide. These places were chosen to represent a wide variety of climates and environmental conditions. They are also relevant to the continued growth of installed PV systems. We report on one of the largest pools of naturally deposited soiling on glass. While most of the published soiling work focuses on a single site, there are few comparative studies of soiling from multiple locations. The glass coupons were analyzed after an 8-week outdoor exposure, but were not meant to reflect a complete picture of soiling at a given site. Our focus was on the fundamental aspects of soiling from the perspective of both the spectral shape of the transmittance and the corresponding particle size distribution. The optical transmittance and the particle size distribution of the soiling were compared in order to find correlations that could be universally valid, and that could open possibilities to modelling PV soiling losses. Together with this, we present the first effort where empirical models from other disciplines are applied in order to describe both the spectral characteristics of the soiled samples and their corresponding particle size distributions. The spectral hemispherical transmittance of the soiled coupons was taken using a Cary 500 spectrophotometer. The transmittance was fit to a modified form of the Ångström turbidity equation. Microscope images were captured at 100x and 500x magnification for the coupons soiled at each of the seven sites. Micrographs for each glass coupon were taken using a Keyence VHX-5000 microscope at a resolution of 1200 pixels x 800 pixels. The micrographs were analyzed with ImageJ to obtain the particle size distribution.

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future). More specifically, it contains: - in `other_pop_data` folder files from World Bank, the International Database from the US Census, and from IHME - in the `SSP` folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from IIASA and as in the version 3.0 downloaded from IIASA workspace - in the `UN` folder, the demographic projections from UN - `IAMstat.xlsx`, an overview file of the metadata accompanying the scenarios present in the IPCC databases - `RFF.csv`, an overview file containing the population projections obtained by Resources For the Future '- the remaining `.csv` files with names `AR6#`, `AR5#`, `IAMC15#` contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6 This data in intended to be downloaded for use together with the package downloadable here. The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).

OSeEM-SN is a tool constructed using Oemof Tabular to apply cross-sectoral approaches for analyzing 100% renewable and sector-coupled sub-national energy systems. The model is validated using the case study of Schleswig-Holstein, Germany. To run the scripts, you need to install Oemof Tabular using the following command- pip install oemof.tabular For details on Oemof Tabular please go through the documentation- https://oemof-tabular.readthedocs.io/ The model uses Oemof-Solph, a model generator for energy system modelling and optimisation. The oemof.solph package is part of the Open energy modelling framework (Oemof). This an organisational framework to bundle tools for energy system modelling. Details on Oemof-Solph is described here- https://github.com/oemof/oemof-solph The article based on the OSeEM-SN based analysis of Schleswig-Holstein is available at- https://www.mdpi.com/2071-1050/13/7/3852 The complete model, including all datasets, scripts, and results are available at: Details are also available at: https://github.com/znes/OSeEM-SN If you have any questions about the model, please contact- mnimaruf@gmail.com

To reduce greenhouse gas (GHG) emissions, the European Union (EU) has targets for utilizing energy from renewable sources. By 2030, a minimum of 3.5% of energy in the EU���s transport sector should come from renewable biological sources, such as crop residues. This paper analyzed EU���s ���advanced bioethanol��� potential from wheat straw and maize stover and evaluated its environmental (land, water, and carbon) footprint. We differentiated between gross and net bioethanol output, the latter by subtracting the energy inputs in production. Results suggest that the annual amount of the sustainably harvestable wheat straw and maize stover is 81.9 Megatonnes (Mt) at field moisture weight (65.3 Mt as dry weight), yielding 470 PJ as gross (404 PJ as net) advanced bioethanol output. Calculated net advanced bioethanol can replace 2.95% of EU transport sector���s energy consumption. EU���s advanced bioethanol has a land footprint of 0.28 m2 MJ���1 for wheat straw and 0.18 m2 MJ���1 for maize stover. The average water footprint of advanced bioethanol is 173 L MJ���1 for wheat straw and 113 L MJ���1 for maize stover. The average carbon footprint per unit of advanced bioethanol is 19.4 and 19.6 g CO2eq MJ���1 for wheat straw and maize stover, respectively. Using advanced bioethanol can lead to emission savings, but EU���s advanced bioethanol production potential is insufficient to achieve EU���s target of a minimum share of 3.5% of advanced biofuels in the transport sector by 2030, and the associated water and land footprints are not smaller than footprints of conventional bioethanol.

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Temporal extent The map is representative for ca. 2018. Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. The dataset features area and mass for different street types area and mass for different rail types area and mass for other infrastructure area, volume and mass for different building types Masses are reported as total values, and per material category. Units area in m² height in m volume in m³ mass in t for infrastructure and buildings Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

The questionnaire is provided in pdf "GreenSoul_Questionnaire.pdf" The coded data from 303 interviewees is provided in "all_code_final_zenodo.xlsx" The dataset is composed of several factors which are all explained in different TXT files. * socio-economic_description_not_dependent_of_work.txt - Contains all the information from participants which is irrespective of their current workplace. This file contains typical socio-demographic and cultural attributes from respondents. * socio-economic_description_dependent_of_work.txt - Contains socio-economic and cultural information from participants which is relevant to the workplace in relation to energy efficient practices in such an environment. * actions-at-work.txt - Are a set of attributes which describe certain practices of employees in relation to energy efficiency. * persuasive_strategies.txt - Explain the ratings from 1 to 5 that participants attributed to a set of persuasive strategies. These strategies are framed within Psychological Persuasive principles which are also explained in the file. * all_attributes_together.txt - All the variables together without distinction of the environment where they are applicable. Finally, plots from every construct or attribute are provided in the files uploaded in "PNG" extension

Abstract. Design of an optimal wind farm topology and wind farm control scheduling depends on the chosen metric. The objective of this paper is to investigate the influence of optimal wind farm control on the optimal wind farm layout in terms of power production. A successful fulfilment of this goal requires: 1) an accurate and fast flow model; 2) selection of the minimum set of design parameters that rules the problem; and 3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is a priori limited to wind turbine derating. A high-speed linearized CFD RANS solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disk method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space spanned by these (2N + Nd Ns N) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed in two subsets, which in turn define a nested set of optimization problems to achieve the fastest possible optimization procedure. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control in the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

Quantitative reliability, availability, and maintainability (RAM) assessments are of fundamental importance at the early design stages, as well as planning and operation of marine renewable energy systems. This paper presents an RAM framework adaptable to different offshore renewable technologies, conceived to provide support in the choice of the device components and subsequent planning of the O&M strategies. A case study, characterizing a pilot farm of oscillating water column (OWC) wave energy converters (WECs), is illustrated together with the method used to obtain reliable estimate of its key performance indicators (KPIs). Based on a fixed feed-in-tariff for the project, economic figures are estimated, showing a direct relationship with the availability of the farm and the cost of maintenance interventions. Consequently, the probability distributions of the most relevant output variables are presented, and the mutual correlations between them investigated using principal components analysis (PCA) with the aim of discovering the relationships influencing the performance of the offshore farm. In this way, the contributions of the individual factors on the profitability of the project are quantified, and generic guidelines to support the decision-making process are derived. It is shown how this type of analysis provides important insights not only to ocean energy farm operators after the deployment of the devices, but also to device developers at the early design stage of wave energy concepts.