Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
44
datasets available to search
ShareScore release 0.9.0
Dataset results
44 results for “energy economics”
Data set for the PLOS ONE paper Expecto transitio: Exploring non-experts' techno-economic expectations of the energy future
<p>Raw data file (SPSS and .cv versions) consisting of all data used for the Plos One publication.</p> <p> </p>
Techno-economic dataset for long-term energy systems modelling in Viet Nam
<p>Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam. This includes data on electricity generation and consumption, electricity imports and exports, fuel prices, emissions, refineries, power transmission and distribution, electricity generation technologies, and renewable energy potential and reserves for the years 2015 to 2050.</p>
Datasets for the study "Hydro-economic analysis of infrastructure investment options within the Water-Energy-Food nexus in the Niger River basin"
<p>This is the dataset used in the study "Hydro-economic analysis of infrastructure investment options within the Water-Energy-Food nexus in the Niger River basin". This study has been conducted within the Department of Environmental Engineering at the Technical University of Denmark as a Master Thesis project. </p> <p>The dataset describes the water-energy-food nexus of the Niger River basin used as input to the open source single objective hydro-economic optimization model WHAT-IF. Note that the Energy Module is partly filled, but has not been used in the study (except the hydropower sheet).</p> <p>More information on data organization <a href="https://zenodo.org/record/2646476#.XxqORudS93g">here</a>.</p> <p>The model is documented in this <a href="https://hess.copernicus.org/articles/23/4129/2019/hess-23-4129-2019-discussion.html">publication</a>.</p> <p>The code is available on <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Impact of Clean Energy on CO2 Emissions and Economic Growth within the Phases of Renewables Diffusion in Selected European Countries
<p>This study explores the impact of clean energy and non-renewable energy consumption on CO<sub>2</sub> emissions and economic growth within two phases (formative and expansion) of renewable energy diffusion for three selected countries (France, Spain, and Sweden). The vector autoregression (VAR) model is estimated on the basis of annual data disaggregated into quarterly data. The Granger causality results reveal distinctive differences in the causality patterns across countries and two phases of renewables diffusion. Clean energy consumption contributes to a decline of emissions more clearly in the expansion phase in France and Spain. However, this effect seems to be counteracted by the increases in emissions due to economic growth and non-renewable energy consumption. Therefore, clean energy consumption has not yet led to a decoupling of economic growth from emissions in France and Spain; in contrast, the findings for Sweden evidence such a decoupling due to the neutrality between economic growth and emissions. Generally, the findings show that despite the enormous growth of renewables and active mitigation policies, CO<sub>2</sub> emissions have not substantially decreased in selected countries or globally. Focused and coordinated policy action, not only at the EU level but also globally, is urgently needed to overhaul existing fossil-fuel economies into low-carbon economies and ultimately meet the relevant climate targets.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for nuclear power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>nuclear power generation</span></span><span> <span>from</span><span> the open literature</span><span>. </span></span><span><span>Nuclear energy is the second-largest source of low-carbon generation, supplying 9% of global electricity</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>604</span></span><span><span> datapoints from </span></span><span><span>19</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span> Technoeconomic data on nuclear power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Global Socio-Economic and Environmental data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided<strong> data files </strong>contain various open data for improving energy system modelling decisions. A thorough description with license restrictions will follow soon.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.</p>
The Economic Integration of Wind Energy: An Analysis of the ECOWAS Subregion
<p>This study evaluates the economic integration of wind energy in the <br>Economic Community of West African States (ECOWAS) between 2010 <br>and 2020. Wind energy is the energy source that can cost-effectively <br>meet the energy needs of the sub-regions due to the theoretical and <br>economic potential of the sub-regions. For this reason, the study uses data <br>from the World Bank Development Indicators using Panel Vector Auto <br>Regression to analyze the determinants underpinning the economic <br>integration of wind energy. The Panel VAR estimate shows a significant <br>direct link between fossil fuel consumption and private sector investment <br>in renewable energy. This implies that the sub-region consumes a <br>significant amount of fossil fuels, hence the need to increase clean energy <br>investments to move the sub-region towards a low-carbon future. <br>Another significant lag variable is the power consumption per capita in <br>the subregion. Per capita electricity consumption in the sub-region is <br>woefully insufficient. Therefore, wind energy can ensure access via the <br>development of small community wind farms where the national power <br>grid cannot be extended to. When assessing the economic justification of <br>wind integration, the LCOE for wind power is 2.98 cents per kilowatt for <br>the lowest cost scenario compared to nuclear power’s 2.26 per kilowatt <br>hour. The FEVD shows that 13.4% of renewable energy investments are <br>self-explanatory within the first and last periods. The FEVD for wind <br>energy illustrates the short-term variance of 16.4 percent and increases to <br>51.1 percent in the following years after system shocks. This implies that <br>the expansion of wind capacity in the sub-region is expected to increase <br>in the long-term. This serves as a blueprint for integrating wind energy <br>into the sub-region.</p>
Data to estimate energy efficiency and environmental friendliness of Ukrainian GDP from indicators of structural, economic, and social development in Ukraine
<p><span>Data to estimate regression dependences of energy efficiency and environmental friendliness of GDP from indicators of structural, economic, and social development in Ukraine </span></p>
PROCEEDİNGS OF ENERGY ECONOMİC RESEARCH CENTER
<p>Economics and Management of Oil and Gas Enterprises</p> <p>Economy and Management of a National Economy</p> <p>Mathematical Methods, Models and Information Technologies in Economics</p> <p> </p>
Data from: Socio-economic impacts of energy access through off-grid systems in rural communities: A case study of South-West Nigeria
<p><span><span>The development of resilient energy systems is important for sustainable cities and communities. However, in countries with insufficient national energy supply, electricity distributors rarely consider remote communities due to their distant settlement, low electricity demand and poor payment capabilities. The United Nations has set a goal to deliver universal energy access by 2030, hence, it has become imperative to deploy clean and affordable off-grid mini-grid solutions to previously abandoned communities. Access to energy in rural communities is expected to result in unlocking their economic potentials. This paper investigates the impact of a solar hybrid mini-grid on the socio-economic growth of local entrepreneurs in Gbamu Gbamu village, Nigeria. A total of 83 Micro and Small Enterprises has been surveyed, descriptive statistics, paired-sample t-test, cross-tabulation, and Chi-square test, were used to assess the performance of businesses before and after electrification. The outcomes include the number of business enterprises created, employment statistics, energy expenses and income generated. Regression analysis was conducted on the relationship between the average income generated by businesses and independent socio-economic variables such as gender, marital status, household size, age, education level, years of business establishment, hours of operation, building tenure, capital source, number of employees, generator ownership and the days of operation. </span></span></p>
Data from: Socio-economic impacts of energy access through off-grid systems in rural communities: A case study of South-West Nigeria
Open the record for dataset details and reuse information.
The effect of renewable and nuclear energy consumption on decoupling economic growth from CO2 emissions in Spain
<p>This study examines the relationship between renewable and nuclear energy consumption, carbon dioxide emissions and economic growth by using the Granger causality and non-linear impulse response function in a business cycle in Spain. We estimate the threshold vector autoregression (TVAR) model on the basis of annual data from the period 1970‒2018, which are disaggregated into quarterly data. Our analysis reveals that economic growth and CO<sub>2</sub> emissions are positively correlated during expansions but not during recessions. Moreover, we find that rising nuclear energy consumption leads to decreased CO<sub>2</sub> emissions during expansions, while the impact of increasing renewable energy consumption on emissions is negative but insignificant. In addition, there is a positive feedback between nuclear energy consumption and economic growth, but unidirectional positive causality running from renewable energy consumption to economic growth in upturns. Our findings do indicate that both nuclear and renewable energy consumption contribute to a reduction in emissions; however, the rise in economic activity, leading to a greater increase in emissions, offsets this positive impact of green energy. Therefore, a decoupling of economic growth from CO<sub>2</sub> emissions is not observed. These results demand some crucial changes in legislation targeted at reducing emissions, as green energy alone is insufficient to reach this goal.</p>
EFFECTIVE USE OF ENERGY RESOURCES AND SAVING THEM IS THE GUARANTEE OF ECONOMIC EFFICIENCY OF INDUSTRIAL ENTERPRISES.
Open the record for dataset details and reuse information.
ENECO-Proceedings of Energy Economic Research Center
<p><strong>ENECO-Proceedings of Energy Economic Research Center</strong></p>
ENECO PROCEEDİNGS OF ENERGY ECONOMİC RESEARCH CENTER
<h1>ENECO PROCEEDİNGS OF ENERGY ECONOMİC RESEARCH CENTER</h1>
ENECO PROCEEDİNGS OF ENERGY ECONOMİC RESEARCH CENTER
<h1>ENECO PROCEEDİNGS OF ENERGY ECONOMİC RESEARCH CENTER</h1>
Data from: Economic uncertainty, geopolitical risk and U.S. energy price risk spillover: An empirical study based on the risk spillover model
<p>This data is mainly used to analyze the risk correlation among economic uncertainly,geopolitical risk and energy price,and can also be applied to the TVP-VAR model to analyze the correlation between different variables using the time-varying parameter model,which has great potential for reuse. The risk relationship between economic uncertainly.At the same time,since it is macroeconomic data,it does not involve any moral and ethical issues.</p>
Utilizing Highway Rest Areas for Electric Vehicle Charging: Economics and Impacts of Renewable Energy Penetration in California
Open the record for dataset details and reuse information.
Data from: Economic uncertainty, geopolitical risk and U.S. energy price risk spillover: An empirical study based on the risk spillover model
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.