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139 results for “carbon emissions”
National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide
<p>A complete description of the dataset is given by <a href="http://doi.org/10.1038/s41597-023-02041-1">Jones et al. (2023)</a>. Key information is provided below.</p> <p><strong>Background</strong></p> <p>A dataset describing the global warming response to national emissions CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from fossil and land use sources during 1851-2021.</p> <p>National CO<sub>2 </sub>emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024). </p> <p>National CH<sub>4</sub> and N<sub>2</sub>O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).</p> <p>We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH<sub>4</sub> and N<sub>2</sub>O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).</p> <p>Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).</p> <p>The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or 3-GHG total) and source (fossil emissions, land use emissions or the total).</p> <p><strong>Data records: overview</strong></p> <p>The data records include three comma separated values (.csv) files as described below.</p> <p>All files are in ‘long’ format with one value provided in the <em>Data</em> column for each combination of the categorical variables <em>Year, Country Name, Country ISO3 code, Gas, and Component</em> columns.</p> <p><em>Component</em> specifies fossil emissions, LULUCF emissions or total emissions of the gas.</p> <p><em>Gas</em> specifies CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or the three-gas total (labelled 3-GHG).</p> <p><em>Country ISO3 codes</em> are specifically the unique ISO 3166-1 alpha-3 codes of each country.</p> <p><strong>Data records: specifics</strong></p> <p>Data are provided relative to 2 reference years (denoted <em>ref_year </em>below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.</p> <p><em>EMISSIONS_ANNUAL_{ref_year-20}-2023.csv:</em> <em>Data </em>includes annual emissions of CO<sub>2</sub> (Pg CO<sub>2</sub> year<sup>-1</sup>), CH<sub>4</sub> (Tg CH<sub>4</sub> year<sup>-1</sup>) and N<sub>2</sub>O (Tg N<sub>2</sub>O year<sup>-1</sup>) during the period <em>ref_year-20 </em>to 2023. The <em>Data</em> column provides values for every combination of the categorical variables. Data are provided from <em>ref_year-20</em> because these data are required to calculate GWP* for CH<sub>4</sub>.</p> <p><em>EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data </em>includes the cumulative CO<sub>2</sub> equivalent emissions in units Pg CO<sub>2</sub>-e<sub>100</sub> during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><em>GMST_response_{ref_year+1}-2023.csv:</em> <em>Data</em> includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><strong>Accompanying Code</strong></p> <p>Code is available at: <a href="https://github.com/jonesmattw/National_Warming_Contributions">https://github.com/jonesmattw/National_Warming_Contributions</a> .</p> <p>The code requires Input.zip to run (see README at the GitHub link).</p> <p><strong>Further info: Country Groupings</strong></p> <p>We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:</p> <ul> <li>Annex I countries (number of countries, n = 42)</li> <li>Annex II countries (n = 23)</li> <li>economies in transition (EITs; n = 15)</li> <li>the least developed countries (LDCs; n = 47)</li> <li>the like-minded developing countries (LMDC; n = 24).</li> </ul> <p>And other country groupings:</p> <ul> <li>the organisation for economic co-operation and development (OECD; n = 38)</li> <li>the European Union (EU27 post-Brexit)</li> <li>the Brazil, South Africa, India and China (BASIC) group.</li> </ul> <p>See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.</p>
Summertime methane and carbon dioxide emission rates and associated variables from a national-scale survey of 146 reservoirs in the United States, 2016-2023
Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the conterminous United States, plus data from one reservoir in Washington and another in Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physiochemistry were measured at 15-70 locations during one 22 to 64-hour period during the summers of 2016-2023, with four reservoirs revisited a second time. Concomitant water chemistry measurements were also made at an index site. The dataset is comprised of two geospatial files and seven .csv files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. These data comprise the largest multi-reservoir emissions dataset ever assembled using consistent measurement methods.
Carbon Price Scenarios: Projecting prices for emission certificates
<p>This dataset consists of three different carbon price development scenarios. Each is represented by two growth rates which results in a total of 6 time series. The time frame is from 2020 to 2050. The units of the values are given in € / t CO₂. All values are nominal.</p> <p>Overall, it should be noted that an estimate of the development of CO2 prices in the german nEHS and EU-ETS is subject to great uncertainty due to the major influence of regulatory intervention, a less liquid market towards 2030 and a lack of markets after 2030.</p> <p>The data provided is delivered in frictionless data format (see 2024-03-25_metadata_carbon-price-scenarios.package.json) and can be accessed using the frictionless software (https://frictionlessdata.io/).</p>
COMPAIR carbon footprint calculations and greenhouse gas emissions reduction scenarios
<p>Citizens' carbon footprint calculation results and citizen-created scenarios on how Greenhouse Gas emissions can be reduced by 55% by 2030 are available that were gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.</p>
Commodity-driven deforestation, associated carbon emissions and trade 2001-2022
<p><span>This dataset contains estimates of commodity-driven deforestation and associated carbon emissions for the period 2001-2022, estimated by the Deforestation Driver and Carbon Emission (DeDuCE) model (Singh & Persson 2024), which combines remote sensing data on forest loss and land-use with agricultural statistics to identify and attribute deforestation across the world to expansion of cropland, pastures and forest plantation, and the commodities produced on this land. This also contains data on deforestation embodied in the production, exports, imports, and consumption of agricultural and forestry commodities by country, year, and commodity for the time period 2005-2022 derived using physical and monetary trade models. The data is an update of the results presented in Pendrill et al. (2022) and the differences between the two datasets are detailed in the explainer available here.</span></p>
The Global Carbon Project's fossil CO2 emissions dataset
<p>The <a href="https://www.globalcarbonproject.org/">Global Carbon Project</a> (GCP) has been publishing estimates of global and national fossil CO2 emissions since 2001. In the first instance these were simple re-publications of data from another source, but over subsequent years refinements have been made in response to feedback and identification of inaccuracies. In this article (PDF document) we describe the history of this process leading up to the methodology used in the 2025 release of the GCP's fossil CO2 dataset.</p> <p>The fossil CO2 emissions dataset is included in both its standard, absolute form, and per capita, with associated metadata files in JSON format. A file indicating the source(s) of each data point is also provided.</p> <p>This is the initial release of the 2025 dataset.</p>
Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions
<p>This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO<sub>2</sub> emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data.</p> <p>These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].</p>
Carbonyl Sulfide (OCS/COS) and Carbon Disulfide (CS2): global modelled marine surface concentrations and emissions, 2000-2019
<p>This dataset contains a global ocean emission inventory of the sulfur-containing trace gases carbonyl sulfide (OCS/COS) and carbon disulfide (CS2). It covers the period 2000-2019, and includes a monthly average and an average diel cycle for each month for sea surface concentrations and emissions to the atmosphere. The spatial resolution is 2.8° x 2.8° at the equator (T42 grid), the depth extends from the surface to the mixed layer depth.</p> <p>Carbonyl sulfide (OCS) is the most abundant, long-lived sulphur gas in the atmosphere and a major supplier of sulfur to the stratospheric sulfate aerosol layer. The short-lived gas carbon disulfide (CS<sub>2</sub>) is oxidized to OCS and constitutes a major indirect source to the atmospheric budget of OCS. We encourage the use of the data provided here as input for atmospheric modelling studies to further assess the atmospheric OCS budget and the role of OCS in climate.</p>
Global energy use and carbon emissions from irrigated agriculture
<p>This repository contains supporting data for: "<strong>Global energy use and carbon emissions from irrigated agriculture"</strong></p><p>Email: qinjingxiu17@mails.ucas.ac.cn and duanweili@ms.xjb.ac.cn</p><p>The dataset contains:</p><p>-Global energy consumption and CO2 emissions from irrigation . </p><p>-Global CO2 emissions from groundwater degassing . </p><p>-Energy consumption and CO2 emissions with different irrigation and pumping systems and irrigation water sources. </p><p>-Global energy consumption and CO2 under drip and sprinkler scenarios. </p><p>-Global energy consumption and CO2 under mix electricity scenarios. </p><p>-Energy units: Terajoule (TJ); CO2 emissions units: (Tonnes CO2)</p><p>-Files are uploaded in .tif raster data. </p>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
Carbon Monitor - Global Daily CO2 Emissions in Near-Real-Time
<p><strong><em>Carbon Monitor: A near-real-time global daily CO2 emission dataset</em></strong></p> <p>Carbon dioxide (CO<sub>2</sub>) emissions from the use of fossil fuels and the production of cement are the main driving force of climate change. Carbon Monitor is an international initiative providing for the first time regularly updated, science-based estimates of daily CO<sub>2</sub> emissions.</p> <ul> <li>Website:</li> </ul> <p><a href="https://carbonmonitor.org">https://carbonmonitor.org</a></p> <ul> <li>Citation:</li> </ul> <p>Liu, Z., Ciais, P., Deng, Z. <em>et al.</em> Near-real-time monitoring of global CO<sub>2</sub> emissions reveals the effects of the COVID-19 pandemic. <em>Nat Commun</em> <strong>11, </strong>5172 (2020). https://doi.org/10.1038/s41467-020-18922-7</p> <ul> <li>Data file description:</li> </ul> <table> <thead> <tr> <th scope="col">Field</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>country</td> <td>Brail, China, EU27 & UK, France, Germany, India, Italy, Japan, ROW, Russia, Spain, UK, US, WORLD *</td> </tr> <tr> <td>co2</td> <td>CO2 emissions from fuel combustion and cement production process (unit: kt CO2)</td> </tr> <tr> <td>sector</td> <td>Power, Industry, Residential, Ground Transport, Domestic Aviation, International Aviation, International Shipping, Total **<sup>,</sup>***</td> </tr> <tr> <td>date</td> <td>From 2019/1/1, every day</td> </tr> </tbody> </table> <p>* WORLD = China + US + EU27 & UK + India + Russia + Japan + Brazil + ROW + International Aviation (WORLD) + International Shipping (WORLD)</p> <p>** Total (country level) = Power + Industry + Residential + Ground Transport + Domestic Aviation</p> <p>** Total (WORLD) = Power + Industry + Residential + Ground Transport + Domestic Aviation + International Aviation + International Shipping</p>
Carbon emission and lifecycle costs supporting digital twins for managing railway maintenance and resilience
<p>The development of railway construction increases the system complexity, which results in difficulty in management with traditional methods. Building Information Modelling (BIM) as an interoperable concept is benefits via whole life-cycle assessment (LCA) of the project, and it has been widely adopted in architecture, construction, and engineering (ACE) fields. This dataset of lifecycle cost and carbon footprint supports the digital twins for managing railway maintenance and resilience.</p>
Result data related to "Bersalli et al. (2023) -- Most industrialised countries have peaked carbon dioxide emissions during economic crises through strengthened structural change"
<p>This repository contains the result data of our study investigating the relationship between emission peaks and economic crises. The repository contains mainly two datasets:</p> <ul> <li>multiplicative-contributions.csv / .nc</li> <li>prepost-growth-rates.csv / .nc</li> </ul> <p>Both datasets exist in CSV and NetCDF file format for convenience. The dataset <em>multiplicative-contributions</em> contains year-to-year change factors of GDP, population, energy-intensity, and carbon-intensity for every country in our study. The dataset <em>prepost-growth-rates</em> contains growth over a multi-year period pre- and post- crisis for each country and each crises in our study.</p>
Data for Figures 4, S2, S7-9 and emission data in the Publication "Enhanced Light Absorption and Radiative Forcing by Black Carbon Agglomerates"
<p>This repository contains the data to produce Figure 4, S2, S7-9 and emission data for the paper:</p> <p>"Kelesidis, G. A., Neubauer, D., Fan, L.-S., Lohmann, U., & Pratsinis, S. E. (2022). Enhanced light absorption and radiative forcing by black carbon agglomerates. <em>Environmental Science and Technology</em>, 56(12), 8610– 8618. <a href="https://doi.org/10.1021/acs.est.2c00428">https://doi.org/10.1021/acs.est.2c00428</a> "</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8167401)</p>
Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2019 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2019, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework
<p>Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"</p>
Unpublished data: Quantifying CO2 Emissions and Carbon Sequestration from Digestate-Amended Soil Using Natural 13C Abundance as a Tracer
<p>Unprocessed data of CO2 evolution measured daily on cavity ring-down spectroscopy analyser (G2201-i CRDS isotopic CO2/CH4 analyser, Picarro, Santa Clara, CA, USA).</p>
Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest
<p>This is the data repository for the second version (revised) of the manuscript "Methane and Carbon Dioxide Production and Emission Pathways in the Belowground and Draining Water Bodies of a Tropical Peatland Plantation Forest<strong>"</strong> submitted to Geophysical Research Letters on 10 January 2025.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.