CEDA is a multi-regional Environmentally-Extended Input-Output (EEIO) model developed to support a wide range of environmental systems analyses—including corporate carbon accounting and sustainable spend analysis. CEDA provides unparalleled global coverage and granularity, representing 95% of the world's GDP across 148 countries and 400 sectors, enabling robust and geographically comprehensive Scope 3 greenhouse gas (GHG) measurement. Open CEDA is the publicly avaialable version of CEDA, now easy to download and available for free for all use cases. For more information please visit our website at openceda.org CEDA 2024, the latest version of CEDA, uses 2022 as its base year, ensuring that emissions factors and economic data reflect the most recent global economic landscape available. To maintain accuracy and relevance, CEDA is updated annually with the latest data releases. At its core, CEDA connects economic exchanges to GHG emissions by quantifying the life-cycle emissions of products and services. This is achieved through the integration of input-output tables, which represent the full supply-chain network of the global economy, with GHG emissions data. As a result, CEDA provides users with a powerful tool to assess the environmental impacts embedded in corporate value chains.

The dataset provides a set of Import Emission Factors (IEF) for USEEIO models developed using the CEDA model (Watershed Technology, Inc.) along with example USEEIO models built with them for year 2022 and supporting information . The dataset accompanies the addendum "Import Greenhouse Gas Emission Factors Derived from CEDA 2024" to EPA report "Estimating embodied environmental flows in international imports for the USEEIO Model" (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=362470). This dataset is analogous to the "USEEIO Models with Import Emission Factors for Greenhouse Gases for 2017-2022 from EXIOBASE coupled model" dataset (https://doi.org/10.23719/1531676), but its uses CEDA instead of EXIOBASE as the coupled model. See the aforementioned addendum for more information. The factors are provided at the BEA summary and detail levels of sector resolution as reflected in file names. The sector codes for the import factor use the BEA 2017 NAICS based schema used in input-output tables which is the schema used by the associated USEEIO models. US_summary_import_factors_ceda_2022_17sch.csv is the summary level IEFs file and USEEIOv2.4-oriole-22.xlsx is the USEEIO model created with them. US_detail_import_factors_ceda_2022_17sch.csv is the detail level IEFs file and USEEIOv2.4-catbird-22.xlsx is the USEEIO model created with them. Various supporting links and files are provided. Concordance files are provided here that are used to map CEDA commodities and countries to those used in USEEIO (and also available online) to create the import emission factors. The models are named according to an updated USEEIO naming scheme. See the supporting code on the USEEIO github site (link in references) for more details. Model specification files for the detail USEEIO model (USEEIOv2.4-catbird-22.yml) and for the summary model (USEEIOv2.4-oriole-22.yml) that are used to create the USEEIO models in useeior are provided. See the model specification and model data formats on the useeior github site (link in references) for more details. This dataset is associated with the following publication: Ingwersen, W.W., J. Namovich, B. Young, and J. Vendries. Estimating embodied environmental flows in international imports for the USEEIO Model. U.S. Environmental Protection Agency, Washington, DC, USA, 2024.

The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in support of research, survey and monitoring programmes. The ARSF is a unique service providing environmental researchers, engineers and surveyors with synoptic analogue and digital imagery of high spatial and spectral resolution.The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.

This dataset contains monthly maps of dry and wet snow for a Himalayan river basin in northern India. The data were collected as part of the Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat) project aimed at improving our understanding on how water is stored in, and moves through, a Himalayan river system in northern India. The maps were obtained by combining satellite remote sensing images from Sentinel-1 and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resolution of the maps is 500m and the coordinate system is EPSG:4326. The dry snow data correspond to the MODIS land cover product (MCD12Q1). The wet snow data were obtained from Sentinel-1 by applying a -2dB threshold on the backscatter ratio between a Sentinel-1 image with wet snow and a reference Sentinel-1 image with only dry snow. The possible pixel values are: 0: no snow, 1-100: wet snow cover fraction, 101-200: dry snow cover fraction with an offset of 100, 240: missing Sentinel-1 data, 250: pixel wrongly identified as wet snow by Sentinel-1 (false positives), 255: fill value. The images are GeoTIFF formatted.