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557 results for “demand”
Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona
<p>This dataset contains the simulation results of the combined effects of future urban growth and climate change on irrigation water use in the Phoenix Metropolitan Area, central Arizona. The simulation is conducted with the Variable Infiltration Capacity (VIC) model at 1-km, hourly resolution from 1981-2100 and aggregated to 30-yr average in this dataset. </p> <p>The 30-yr average results are compressed and organized into three files: <strong>Baseline</strong>, <strong>ICLUS2050</strong>, and <strong>ICLUS2100</strong>. The Baseline file contains results using the historical land cover map (year 2010). The <strong>ICLUS2050</strong> and <strong>ICLUS2100</strong> contain results using future land cover maps. The filename of modeling results contains the associated land cover and climate change scenario as follows: "fluxes.irri.ICLUS_<em>$YEAR</em>_<em>$LCSCE</em>.<em>$CLSCE.$GCM</em>.nc", where <em>$YEAR</em> is the year of land cover change projection (2050 or 2100), <em>$LCSCE</em> is the land cover change scenario (SSP2 or SSP5), <em>$CLSCE</em> is the climate change scenario (RCP45 or RCP85), and <em>$GCM</em> is the GCM used (eight in total) </p> <p>More details can be found on the associated paper (this record will be updated when the paper is published):</p> <p>Wang, Z., and Vivoni, E.R. 2021. Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona. <em>Journal of the American Water Resources Association (in revision)</em>.</p>
Experimental source data for "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"
<p>Experimental data for the manuscript "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"</p>
Global Transportation Demand Dataset using the Shared Socioeconomic Pathways (SSPs) Scenario Framework
<p>We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand <em><strong>source</strong>: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. <a href="https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en">https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en</a></em></p> <p>We collect the historical socioeconomic variables from the World Bank’s global open data bank <em><strong>source</strong>: World Bank. (2020). Data Bank: World Development Indicators. <a href="https://databank.worldbank.org/source/world-development-indicators">https://databank.worldbank.org/source/world-development-indicators</a></em></p> <p>For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). <em><strong>source: </strong>Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. <a href="https://doi.org/10.1016/j.gloenvcha.2016.05.009">https://doi.org/10.1016/j.gloenvcha.2016.05.009</a> Available Online: <a href="https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about">https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about</a></em></p> <p>The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Hands-on: How to add a service demand in MUSE
<p>Files available in this repository:</p> <p>1) Default example of MUSE</p> <p>2) Final input data</p> <p>3) Results spreadsheet</p>
Hands-on 2: How to add a service demand in MUSE
<p>Files available:</p> <ol> <li>default.zip - the default, unedited example</li> <li>default_final.zip - the final, edited example</li> <li>capacity_results.xlsx - the visualised results</li> </ol>
MAED Data Note: Selected socio-economic and technical demand modelling data for Sierra Leone.
<p>This MAED Data Note contains historic annual data on electricity demand (2018 to 2023) segregated by sector (industry, services, households). Additionally, historic social and economic data on population (total, growth, urban-rural split, household size), GDP (total, sectoral split, growth) and electrification are included within this data note. This historic data can be used to create base year energy intensitities used as a foundation for energy demand modelling using the Model for the Analysis of Energy Demand (MAED) simulation software. Two illustrative scenarios are developed alongside a baseline demand projection from 2018 to 2050. </p> <p>This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.</p>
Fracture metamaterials with on-demand crack paths enabled by bending
Open the record for dataset details and reuse information.
Influence of Nuclear Investment Costs and Baseload Demand on the Optimal Energy Mix
<p>Results from the study on "Influence of Nuclear Investment Costs and Baseload Demand on the Optimal Energy Mix".</p>
Replication package for: Excess Capacity and Demand Driven Business Cycles
<div> <p>This package contains replication files for results in "Excess Capacity and Demand Driven Business Cycles" by Tiancheng Sun forthcoming in the <em>Review of Economic Studies</em>. The README file provides details on data sources, how to obtain access to the confidential microdata from the KOF Swiss Economic Institute, and how to run the code. </p> </div>
Reducing the Allocation of Software Testing Demand: A Study on the Pros and Cons using STELA Tool
<p><strong>Qualitative Research</strong></p> <p>1. How long have you been on the testing team?<br><br>2. How long have you been assigning requests?<br><br>3. Do you encounter difficulties in the manual assignment process?<br><br>4. If yes, which ones?<br><br>5. Do you use automated acceptance with STELA support?<br>6. If not, why?<br><br>7. What main differences do you see between the acceptance and distribution of requests using automated acceptance with STELA support and the manual process?<br><br>8. What advantages do you see in automated acceptance with the support of STELA?<br><br>9. What disadvantages do you see in automated acceptance with STELA support?<br><br>10. How do you feel about using automated acceptance within the STELA tool?<br><br>11. What are the differences between STELA and the system used previously?</p>
Root Canal Treatment and Demand for Continuing Education among Thai Dental Practitioners
Open the record for dataset details and reuse information.
The simulation results of runoff components and estimated surface water demands for the Yarkant River basin
<p>This dataset contains two directories as below. </p> <p>1. Hydrological_Simulation</p> <p>Historical_runoff_simulation.csv : the four runoff components at monthly scale (km^3/month) aggregated from daily runoff simulation results for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP1-2.6 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP2-4.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the simulation results from a different GCM for the SSP5-8.5 scenario. Each file contains four runoff components at monthly scale (km³/month) aggregated from daily runoff simulation results.</p> <p>The subdirectory "48 experiments" contains the four runoff components at monthly scale (km^3/month) simulated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p> <p>2. Water_Demand_Estimation</p> <p>Historical_water_demand_estimation.csv : the monthly water demands (km^3/month) from irrigation (including wheat, cotton, and corn) and other sectors for the historical period</p> <p>The subdirectory "SSP126" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP1-2.6 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP245" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP2-4.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "SSP585" contains 9 CSV files, each representing the estimation results from a different GCM for the SSP5-8.5 scenario. Each file contains the monthly water demands (km³/month) from irrigation (including wheat, cotton, and corn) and other sectors.</p> <p>The subdirectory "48 experiments" contains the monthly water demands (km^3/month) estimated for 48 climate scenarios, with the file name indicating the scenario settings:<br>t[+x]_p[+/-yy]_runoff_simulation.csv means the scenario with temperature increase at x C degree and precipitation change at +/- yy%.</p>
Supplementary material 2 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Average yield (in t/ha) of agricultural crops in Schleswig-Holstein in 2010 (data from Statistikamt Nord 2010), nitrogen content (in kg N/dt fresh weight) of crop plants (data from DüV 2007) and corresponding estimated nitrogen content per hectare.
Supplementary material 3 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Relevant nitrogen application and input rates considering nitrogen losses (data from Landwirtschaftskammer Niedersachsen 2017b).
Supplementary material 1 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Nitrogen excretion (in kg N/year) of livestock (data from the Ministerium für Landwirtschaft, Umwelt und Verbraucherschutz Mecklenburg-Vorpommern 2008)
Low-Input Estimation of Site-Specific Lime Demand - Table S1
<p>Overview of combined observations for soil apparent electrical conductivity (via EM38 MK1 (Geonics, Canada) in vertical mode), soil humidity, ex situ determined topsoil pH (pHREF), in situ determined topsoil pH (pHFE), modified in situ determined topsoil pH (pHFEM2) and the respective estimated lime demands based on ECa and pH (pHREF, pHFE, pHFEM2) values.</p>
Replication package for: What Drives Demand for State-Run Lotteries? Evidence and Welfare Implications
<p>Replication package:</p> <p>Lockwood, Benjamin B., Hunt Allcott, Dmitry Taubinsky, and Afras Y. Sial. 2024. "What Drives Demand for State-Run Lotteries? Evidence and Welfare Implications." Review of Economic Studies. </p> <p>This package contains all replication scripts and non-confidential data sets necessary to reproduce results in the paper. </p>
Data Set - Seasonal variability in the global relevance of mountains to satisfy lowland water demand
<p>GENERAL INFORMATION</p> <p>The data and scripts used for the analysis of the manuscript "Seasonal variability in the global relevance of mountains to satisfy lowland water demand"</p> <p><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</strong></p> <p>DATA & FILE OVERVIEW</p> <p>please have a look at readme.txt </p> <p>Don't hesitate to contact us in case of any questions (sarah.hanus@geo.uzh.ch)</p>
Simulation result from Meeting climate target with realistic demand-side policies in the residential sector
<p>Simulation result from EU Implementation of Message-ix Buildings. Result are detailed in the working paper: Meeting climate target with realistic demand-side policies in the residential sector.</p> <p>It contains 3 folders: all scenarios, optimal scenarios detailed in last section of Result and the counterfactual scenario.</p> <p> </p>
Weather- and climate-driven power supply and demand time series for European countries
<p>This repository contains times series of wind power generation, solar power generation, hydropower inflow, heating demand, and cooling demand that use both historical and projected climate variables. Historical climate data are from the ERA5 reanalysis (1940-2023), while projected climate variables are from three climate models of the CMIP5 EURO-CORDEX (2006-2100) and three representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5). The time series are at country level for the EU27 countries, Balkan countries, the United Kingdom, Norway, and Switzerland.</p> <p>Each country has the following files for the historical period, where ** is the country’s ISO Alpha-2 code:</p> <p>1) **__ERA5__wind__capacity_factor_time_series__onshore.nc<br>2) **__ERA5__wind__capacity_factor_time_series__offshore.nc<br>3) **__ERA5__solar__capacity_factor_time_series.nc<br>4) **__ERA5__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>5) **__ERA5__hydropower__inflow_time_series__run_of_river.nc<br>6) **__ERA5__heating__demand_time_series__residential_space.nc<br>7) **__ERA5__heating__demand_time_series__services_space.nc<br>8) **__ERA5__cooling__demand_time_series.nc</p> <p>Each country has also the following files for the climate projections, where ** is the country’s ISO Alpha-2 code, #_# is the value of the RCP scenario (e.g., 2_6 is RCP 2.6), ££££__&&&& is the name of the combination of global-regional climate models (e.g., CNRM_CERFACS_CM5__CNRM_ALADIN63):</p> <p>9) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__onshore.nc<br>10) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__offshore.nc<br>11) **__CORDEX__RCP_#_#__££££__&&&&__solar__capacity_factor_time_series.nc<br>12) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>13) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__run_of_river.nc<br>14) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__residential_space.nc<br>15) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__services_space.nc<br>16) **__CORDEX__RCP_#_#__££££__&&&&__cooling__demand_time_series.nc</p> <p>The repository also contains the caliration coefficients that resulted from the calibration procedure for the countries in which data were available.</p> <p> </p>
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