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National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide V2025
No full textA complete description of the dataset is given by Jones et al. (2023). Key information is provided below.
Background
A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1851-2024.
National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2025; Friedlingstein et al., 2025).
National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).
We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O 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).
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).
The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions, land use emissions or the total).
Data records: overview
The data records include three comma separated values (.csv) files as described below.
All files are in ‘long’ format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns.
Component specifies fossil emissions, LULUCF emissions or total emissions of the gas.
Gas specifies CO2, CH4, N2O or the three-gas total (labelled 3-GHG).
Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country.
Data records: specifics
Data are provided relative to 2 reference years (denoted ref_year 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.
EMISSIONS_ANNUAL_{ref_year-20}-2024.csv: Data includes annual emissions of CO2 (Pg CO2 year-1), CH4 (Tg CH4 year-1) and N2O (Tg N2O year-1) during the period ref_year-20 to 2024. The Data column provides values for every combination of the categorical variables. Data are provided from ref_year-20 because these data are required to calculate GWP* for CH4.
EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2024.csv: Data includes the cumulative CO2 equivalent emissions in units Pg CO2-e100 during the period ref_year+1 to 2024 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.
GMST_response_{ref_year+1}-2024.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period ref_year+1 to 2024 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.
Accompanying Code
Code is available at: https://github.com/jonesmattw/National_Warming_Contributions .
The code requires Input.zip to run (see README at the GitHub link).
Further info: Country Groupings
We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:
Annex I countries (number of countries, n = 42)
Annex II countries (n = 23)
economies in transition (EITs; n = 15)
the least developed countries (LDCs; n = 47)
the like-minded developing countries (LMDC; n = 24).
And other country groupings:
the organisation for economic co-operation and development (OECD; n = 38)
the European Union (EU27 post-Brexit)
the Brazil, South Africa, India and China (BASIC) group.
See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group
The key role of production efficiency changes in livestock methane emission mitigation V4
No full textThis dataset contains the R code, the input data, the parameters used, and the updated livestock methane emission for the period 1961-2023 using methods from Chang, J., Peng, S., Yin, Y., Ciais, P., Havlik, P., Herrero, M. (2021). The key role of production efficiency changes in livestock methane emission mitigation. AGU Advances, 2, e2021AV000391. DOI: https://doi. org/10.1029/2021AV000391
1. R code: Chang_et_al_Global_Livestock_CH4_Assessment_1961_2023.R
2. Input data and parameters: Data.zip (statistics on historical livestock numbers and production need to be downloaded from FAOSTAT (http://www.fao.org/faostat/en/)
3. Results on global livestock methane emissions during 1961-2023 were presented in the Global_Results.xlsx
4. Results on livestock methane emissions from enteric fermentation and manure management during the period 1961-2023 in each country/area were shown in the folder named Country_Results: Files are organized as "Country_[XX]CH4_[YY]_[ZZ].csv" where XX indicate emission from enteric fermentation (EF) or manure management (MM); YY indicates method used for the estimates; and ZZ indicates livestock categories.
5. Results on gridded livestock methane emissions at a resolution of 5 arc-min using the IPCC Mixed Tier 1 and Tier 2 (2019MT) and Tier 1 (2019T1) methods following the 2019 refinement to the 2006 IPCC guidelines for National Greenhouse Gas Inventories (Vol. 4) (IPCC, 2019): Livestock_CH4_map_5arcmin_1961_2023_2019MT_2019T1.nc4
Please contact: Dr. Jinfeng Chang ([email protected]) for any question on the dataset
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2022) V2-beta
No full textSub-dataset: Dominant grassland class, 2021-2023
Global annual grassland class and extent for 2000—2022 produced by Parente et al. (2024) within the scope of the Global Pasture Wath initiative. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a:
maximum of 50% tree canopy cover (greater than 5 meters),
maximum of 70% of other woody vegetation (scrubs and open shrubland), and
maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.
The grassland extent is classified into two classes:
Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.
Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.
The dataset is organized in 69 global mosaics (23 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:
Probabilities of cultivated grassland (values range from 0–100),
Probabilities of natural/semi-natural grassland (values range from 0–100), and
Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland.
All raster files are in unsigned 8-bit integer format and use 255 as no-data value (pixels ignored by prediction), following an specific naming convention:
Project name: Global Pasture Watch (gpw)
Class name: cultivated grassland (cultiv.grassland), natural/semi-natural grassland (nat.semi.grassland) and dominant grassland (grassland)
Procedure combination: Random Forest (rf), Savitzky-golay (savgol), balanced threshold (bthr) and mean absolute difference (madi).
Variable type: probability (p)
Spatial resolution: 30m
Begin of time reference: date of first Landsat composite used by the modeling (20220101)
End of time reference: date of last Landsat composite used by the modeling (20221231)
Spatial extent: global (go)
Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)
Version: v1
Related resources
Maps of dominant grassland:
2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2022
Probability maps of cultivated grassland:
2000-2022 (All URLs)
Probability maps of natural/semi-natural grassland:
2000-2022 (All URLs)
Grassland reference samples based on VHR imagery (2000–2022):
GeoPackage files
Global machine learning models (Random Forest):
Parquet and joblib python files
Reference sampling design derived by FSCV:
GeoPackage and raster files
Harmonized reference samples based on existing LULC dataset:
GeoPackage and raster files
Source code for reproducibility:
GitHub release
Mapping feedback tool:
GeoWiki
Data catalogues:
OpenLandMap STAC Google Earth Engine
Support
For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watc
MAGICC AR7 fast track probabilistic distribution v0.3.0
No full textProbabilistic distribution for use with MAGICC7 as part of the CMIP7 AR7 fast track.
This is a work in progress, do not use this file for final runs of anything
Replication archive for: "Using net-zero carbon debt to track climate overshoot responsibility"
No full textReplication archive for: Pelz S, Ganti G, Lamboll R, Grant L, Smith C, Pachauri S, Rogelj J, Riahi K, Thiery W, Gidden M (2025) Using net-zero carbon debt to track climate overshoot responsibility. PNAS (in press) 10.1073/pnas.2409316122
Dataset: World Emissions Clock V2
No full textThis dataset contains data presented on the World Emissions Clock hosted by the World Data Lab.
The World Emissions Clock provides trajectories of future greenhouse gas emissions until 2050 for 182 countries (and international aviation + shipping), five main sectors and up to 24 subsectors, and three different scenarios. These hypothetical scenarios are:
Business as usual (BAU), where technological advancement and policy-making roughly follows past trends without major shifts.
Nationally determined contributions (NDC), where countries fully implement their unconditional climate pledges as submitted to the United Nations Framework Convention on Climate Change (UNFCCC).
Achieving 1.5°C, where secotral emissions within countries follow a cost-efficient pathway towards limiting global warming to 1.5° Celsius by 2100.
For further information, see the Methodology section of the World Emissions Clock. Contact [email protected] for access information.
The World Emissions Clock was created in a cooperation of the World Data Lab with the International Institute for Applied Systems Analysis (IIASA), the Vienna University of Economics and Business (WU Vienna), and the University of Oxford and was supported from the German Federal Ministry for Economic Cooperation and Development (BMZ), the German Agency for International Cooperation (GIZ), and the Patrick J. McGovern Foundation
LAMASUS NUTS-level forest land rents
No full textThis dataset provides spatially explicit estimates of forest land rents, expressed as the Net Present Value (NPV) of forestry operations in EUR per hectare. The estimates are based on biophysical and economic parameters describing wood production, planting costs, and forest productivity across Europe, and are aggregated to different NUTS levels (NUTS0–NUTS3)
Deep learning four decades of human migration: datasets
No full textThis Zenodo repository contains all migration flow estimates associated with the paper "Deep learning four decades of human migration." Evaluation code, training data, trained neural networks, and smaller flow datasets are available in the main GitHub repository, which also provides detailed instructions on data sourcing. Due to file size limits, the larger datasets are archived here.
Data is available in both NetCDF (.nc) and CSV (.csv) formats. The NetCDF format is more compact and pre-indexed, making it suitable for large files. In Python, datasets can be opened as xarray.Dataset objects, enabling coordinate-based data selection.
Each dataset uses the following coordinate conventions:
Year: 1990–2023
Birth ISO: Country of birth (UN ISO3)
Origin ISO: Country of origin (UN ISO3)
Destination ISO: Destination country (UN ISO3)
Country ISO: Used for net migration data (UN ISO3)
The following data files are provided:
T.nc: Full table of flows disaggregated by country of birth. Dimensions: Year, Birth ISO, Origin ISO, Destination ISO
flows.nc: Total origin-destination flows (equivalent to T summed over Birth ISO). Dimensions: Year, Origin ISO, Destination ISO
net_migration.nc: Net migration data by country. Dimensions: Year, Country ISO
stocks.nc: Stock estimates for each country pair. Dimensions: Year, Origin ISO (corresponding to Birth ISO), Destination ISO
test_flows.nc: Flow estimates on a randomly selected set of test edges, used for model validation
Additionally, two CSV files are provided for convenience:
mig_unilateral.csv: Unilateral migration estimates per country, comprising:
imm: Total immigration flows
emi: Total emigration flows
net: Net migration
imm_pop: Total immigrant population (non-native-born)
emi_pop: Total emigrant population (living abroad)
mig_bilateral.csv: Bilateral flow data, comprising:
mig_prev: Total origin-destination flows
mig_brth: Total birth-destination flows, where Origin ISO reflects place of birth
Each dataset includes a mean variable (mean estimate) and a std variable (standard deviation of the estimate).
An ISO3 conversion table is also provided
Replication data: Systemic Cooling Poverty across world countries
No full textThis repository contains the complete replication data for constructing the Systemic Cooling Poverty Index (SCPI) presented in:
Falchetta, G., Mazzone, A., Bhasin, S., Davide, M., Bezerra, P., Fabbri, K., Bertarelli, G., Pistorio, A., Dal Barco, I., De Cian, E. (2025). "Systemic Cooling Poverty across World Countries." Under review at Nature Sustainability
Global Pasture Watch - Annual cattle density layers at 1-km for 2000–2022 (including 95% prediction interval)
No full textGlobal annual layers of cattle density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 313,604 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution).
Pixels values represent heads km-square including:
Mean predicted values (_m_)
Upper prediction interval based on 97.5th percentiles (_p.975_)
Lower prediction interval based on 2.5th percentiles (_p.025_)
Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.
Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.
In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine