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17 results for “stocks and flows”
The MAT_STOCKS database: economy-wide material flows and material stock dynamics around the world
<p>Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.</p> <p>This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (<em>MISO2) </em>(Wiedenhofer et al. 2024)<em>. </em>MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository. </p> <p><strong>The MAT_STOCKS database version 1.0 </strong>provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:</p> <ul> <li>Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)</li> <li>Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)</li> <li>Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)</li> <li>177 countries</li> <li>1900 to 2016 </li> </ul> <p>All units in kilotons. Paramter names are in accordance with the system definition given in the publication.</p> <p>Additionally, this repository includes all data presented in the figures of the related journal article.</p> <p><strong>Further information</strong></p> <p>This dataset complements the following scientific article:</p> <p>Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, <a href="https://doi.org/10.1111/jiec.13575">https://doi.org/10.1111/jiec.13575</a></p> <p>The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <em>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</em></p> <p><strong>Funding</strong></p> <p>This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.<br><br></p>
Dataset: Amplify Cash Flow High Income ETF (HCOW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Amplify Cash Flow Dividend Leaders ETF (COWS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Victoryshares Free Cash Flow ETF (VFLO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Themes US Cash Flow Champions ETF (USCF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Themes US Small Cap Cash Flow Champions ETF (SMCF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Victoryshares Small Cap Free Cash Flow ETF (SFLO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Invesco Nasdaq Free Cash Flow Achievers ETF (QOWZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Yale stocks and flows database (YSTAFDB)
<p>This Zenodo upload accompanies a Data Descriptor in <em>Scientific Data</em>, titled "YSTAFDB, a unified database of material stocks and flows for sustainability science". We refer to this database as the Yale stocks and flows database (YSTAFDB).</p> <p>Here, we provide core and supplementary ('hierarchy') tables for YSTAFDB in comma separated value file format. We additionally provide an example of a template that we used to manually prepare data for entry into YSTAFDB.</p> <p>A complete description of YSTAFDB can be found in the Data Descriptor mentioned above. Key properties of YSTAFDB include:</p> <ul> <li>100,000+ material cycles, criticality, and recycling data records</li> <li>quantitative stocks and flows data for 62 elemental cycles and various engineering material cycles</li> <li>data records describing stocks and flows at spatial scales from cities to global, and from the early 1800s to ca. 2013</li> </ul>
FIGURE 2 in Different stocks of Prochilodus lineatus along the Cinzas River basin: contributions from a free-flowing tributary in the Capivara Reservoir region, Paranapanema River basin (upper Paraná River)
FIGURE 2 | Results of Bayesian clustering analysis (STRUCTURE) forProchilodus lineatus samples obtained along in Laranjinha River (L1–L4), Cinzas River main channel (CI), Capivara Reservoir (CA), Tibagi River (TI) and a restocking event (ST). Estimates of the number of K groups based on mean likelihood Ln(K) (A) and ∆K statistic (B). Graphical representation based on K = 3 (C). Each column represents a different individual and the colors represent the probability membership coefficient of that individual for each genetic cluster.
FIGURE 1 in Different stocks of Prochilodus lineatus along the Cinzas River basin: contributions from a free-flowing tributary in the Capivara Reservoir region, Paranapanema River basin (upper Paraná River)
FIGURE 1 | Sampling locations for Prochilodus lineatus along the Capivara Reservoir region (Paranapanema River basin), including the Laranjinha River (L1–L4), Cinzas River main channel (CIU - upstream and CID - downstream from Salto Cavalcante falls), Capivara Reservoir (CA) and Tibagi River (TI), as well as the site (ST) of a restocking event in the Laranjinha River. Source: modified from Google Earth, 2018 (https://www.google.com.br/maps).
FIGURE 3 in Different stocks of Prochilodus lineatus along the Cinzas River basin: contributions from a free-flowing tributary in the Capivara Reservoir region, Paranapanema River basin (upper Paraná River)
FIGURE 3 | Results from mtDNA (D-Loop) of Prochilodus lineatus in Cinzas River basin, Capivara Reservoir and Tibagi River. A. Mismatch distributions of mitochondrial haplotypes for all samples. B. Haplotype network, including GenBank data revealing previous records of some haplotypes in Paraná River main channel (PR) and in the Pardo (PA), Mogi-Guaçu (MG), Verde (VE), Peixe (PE), Aguapeí (AG), and Iguaçu (IG) rivers. Circle sizes are proportional to haplotype frequency.
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>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. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
Data from: Estimates of gene flow and dispersal in wild riverine Brook Trout (Salvelinus fontinalis) populations reveal ongoing migration and introgression from stocked fish
As anthropogenic impacts accelerate changes to landscapes across the globe, understanding how genetic population structure is influenced by habitat features and dispersal is key to preserving evolutionary potential at the species level. Furthermore, knowledge of these interactions is essential to identifying potential constraints on local adaptation and for the development of effective management strategies. We examined these issues in Brook Trout (Salvelinus fontinalis) populations residing in the Upper Hudson River watershed of New York State by investigating the spatial genetic structure of over 350 fish collected from 14 different sampling locations encompassing three river systems. Population genetic analyses of microsatellite data suggest that fish in the area exhibit varying degrees of introgression from nearby State-directed supplementation activities. Levels of introgression in these populations correlate with water-way distance to stocking sites, although genetic population structure at the level of individual tributaries as well as their larger, parent river systems is also detectable and is dictated by migration and influenced by habitat connectivity. These findings represent a significant contribution to the current literature surrounding Brook Trout migration and dispersal, especially as it relates to larger interconnected systems. This work also suggests that stocking activities may have far-reaching consequences that are not directly limited to the immediate area where stocking occurs. The framework and data presented here may aid in the development of other local aquatic species-focused conservation plans that incorporate molecular tools to answer complex questions regarding diversity mapping, and genetically important conservation units.
Data from: Integrating continuous stocks and flows into state-and-transition simulation models of landscape change
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Data from: Estimates of gene flow and dispersal in wild riverine Brook Trout (Salvelinus fontinalis) populations reveal ongoing migration and introgression from stocked fish
Open the record for dataset details and reuse information.
Data for "Uncovering the global plastics flows, stock and end-of-life fates for a pragmatic resolution to end plastic pollution"
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OpenNeuro
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