Data Accompanying "International Trade and Air-Quality-Related Mortality"
Dataset Description |
**Data Description:**
**Citation Requirement:**
Wang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w Wang, Shiyuan; Christopher, Tessum; Justin, Johnson; Sumil, Thakrar (2026): Data Accompanying "International Trade and Air-Quality-Related Mortality". University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-0064792_V2 |
Subject |
Physical Sciences |
License |
CC BY |
Corresponding Creator |
Shiyuan Wang |
| Version | DOI | Comment | Publication Date |
|---|---|---|---|
| 2 | 10.13012/B2IDB-0064792_V2 | Added a Readme to explain all the variables and modified some files | 2026-04-17 |
| 1 | 10.13012/B2IDB-0064792_V1 | 2026-01-19 |
Contact the Research Data Service for help interpreting this log.
| Dataset | update: {"all_medusa"=>[nil, true]} | 2026-04-17T16:45:08Z |
| Dataset | update: {"publication_state"=>["version candidate under curator review", "released"], "release_date"=>[nil, Fri, 17 Apr 2026]} | 2026-04-17T16:19:25Z |
| Dataset | update: {"version_comment"=>["", "Added a Readme to explain all the variables and modified some files"]} | 2026-04-17T16:12:11Z |
| Dataset | update: {"description"=>["**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n<b>**Citation Requirement:** </b>\r\nIf you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset:\r\n\r\nWang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w\r\n\r\nWang, Shiyuan; Christopher, Tessum; Justin, Johnson; Sumil, Thakrar (2026): Data Accompanying \"International Trade and Air-Quality-Related Mortality\". University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-0064792_V2\r\n", "<b>**Data Description:** </b>\r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n<b>**Citation Requirement:** </b>\r\nIf you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset:\r\n\r\nWang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w\r\n\r\nWang, Shiyuan; Christopher, Tessum; Justin, Johnson; Sumil, Thakrar (2026): Data Accompanying \"International Trade and Air-Quality-Related Mortality\". University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-0064792_V2\r\n"]} | 2026-04-17T16:06:51Z |
| Dataset | update: {"description"=>["**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n<b>**Citation Requirement:** </b>\r\nIf you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset:\r\n\r\nWang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w\r\n\r\nWang, S., Thakrar, S., Johnson, J., & Tessum, C. Data accompanying “International trade and air-quality-related mortality”. https://doi.org/10.13012/B2IDB-0064792_V2\r\n (2026)\r\n\r\n", "**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n<b>**Citation Requirement:** </b>\r\nIf you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset:\r\n\r\nWang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w\r\n\r\nWang, Shiyuan; Christopher, Tessum; Justin, Johnson; Sumil, Thakrar (2026): Data Accompanying \"International Trade and Air-Quality-Related Mortality\". University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-0064792_V2\r\n"]} | 2026-04-17T16:06:27Z |
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| Dataset | update: {"description"=>["**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n**Citation Requirement:** \r\nIf you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset:\r\n\r\nWang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w\r\n\r\nWang, S., Thakrar, S., Johnson, J., & Tessum, C. Data accompanying “International trade and air-quality-related mortality”. https://doi.org/10.13012/B2IDB-0064792_V2\r\n (2026)\r\n\r\n", "**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n<b>**Citation Requirement:** </b>\r\nIf you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset:\r\n\r\nWang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w\r\n\r\nWang, S., Thakrar, S., Johnson, J., & Tessum, C. Data accompanying “International trade and air-quality-related mortality”. https://doi.org/10.13012/B2IDB-0064792_V2\r\n (2026)\r\n\r\n"]} | 2026-04-17T16:05:07Z |
| Dataset | update: {"description"=>["**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n**Citation Requirement:** \r\nIf you use this dataset in your research, presentations, or derivative works, please cite the associated paper and also the dataset accordingly.\r\n", "**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n**Citation Requirement:** \r\nIf you use this dataset in your research, presentations, or derivative works, please cite both the associated paper and the dataset:\r\n\r\nWang, S., Thakrar, S., Johnson, J. et al. International trade and air-quality-related mortality. Nature Communications, 17, 3518 (2026). https://doi.org/10.1038/s41467-026-71408-w\r\n\r\nWang, S., Thakrar, S., Johnson, J., & Tessum, C. Data accompanying “International trade and air-quality-related mortality”. https://doi.org/10.13012/B2IDB-0064792_V2\r\n (2026)\r\n\r\n"]} | 2026-04-17T14:31:11Z |
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| Dataset | update: {"title"=>["Data for International Trade and Air-Quality-Related Mortality", "Data Accompanying \"International Trade and Air-Quality-Related Mortality\""]} | 2026-03-05T01:56:42Z |
| Creator | create: {"family_name"=>"Sumil", "given_name"=>"Thakrar", "identifier"=>"0000-0003-2205-3333", "email"=>"sumilthakrar@gmail.com", "is_contact"=>false, "row_position"=>4} | 2026-03-04T23:34:15Z |
| Creator | create: {"family_name"=>"Justin", "given_name"=>"Johnson", "identifier"=>"", "email"=>"jajohns@umn.edu", "is_contact"=>false, "row_position"=>3} | 2026-03-04T23:34:15Z |
| Creator | create: {"family_name"=>"Christopher", "given_name"=>"Tessum", "identifier"=>"0000-0002-8864-7436", "email"=>"ctessum@illinois.edu", "is_contact"=>false, "row_position"=>2} | 2026-03-04T23:29:06Z |
| Dataset | update: {"description"=>["**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n**Citation Requirement:** \r\nIf you use this dataset in your research, presentations, or derivative works, please cite the associated paper and acknowledge the dataset accordingly.\r\n", "**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n**Citation Requirement:** \r\nIf you use this dataset in your research, presentations, or derivative works, please cite the associated paper and also the dataset accordingly.\r\n"]} | 2026-03-04T23:29:06Z |
| Dataset | update: {"title"=>["Data for International Trade in Air-Quality-Related Mortality", "Data for International Trade and Air-Quality-Related Mortality"]} | 2026-02-20T23:08:19Z |
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| Dataset | update: {"title"=>["Data for International (Fair) Trade in Air-Quality-Related Mortality", "Data for International Trade in Air-Quality-Related Mortality"], "description"=>["Note: The GTAP dataset includes a total of 140 regions, some of which are aggregated regions. For all map-related supplementary files (S11, S12, S13), we assign values to each individual country to enhance visualization. Countries within the same aggregated region are assigned the same regional value to maintain consistency across the map.\r\n\r\n<b>Data S1 (separate file): S1.csv</b>- CSV file detailing production-related deaths for the GTAP dataset.\r\nRows: Each row represents a country where deaths occur as a result of production activities.\r\nColumns: Each column represents a country-sector pair on the production side.\r\nValues: The values indicate the number of deaths caused by production activities in the country-sector listed in each column and occurring in the country listed in each row.\r\n\r\n<b>Data S2 (separate file): S2.csv</b>- CSV file detailing production-related deaths for the EORA dataset.\r\nStructure: The file has the same structure as S1.csv.\r\n\r\n<b>Data S3 (separate file): S3.csv</b>- CSV file detailing consumption-related deaths for the GTAP dataset.\r\nRows: Each row represents a country where deaths occur as a result of consumption activities.\r\nColumns: Each column represents a consumption country.\r\nValues: The values indicate the number of deaths caused by consumption activities in the country listed in the column and occurring in the country listed in the row.\r\n\r\n<b>Data S4 (separate file): S4.csv</b>- CSV file detailing consumption-related deaths for the EORA dataset.\r\nStructure: The file has the same structure as S3.csv.\r\n\r\n<b>Data S5 (folder of files): S5.zip</b>- a folder containing 141 CSV files, each named after a country's 3-digit code (e.g., USA.csv, CHN.csv), representing production-related spatial PM₂.₅ concentration patterns for all GTAP countries.\r\nRows: Each row corresponds to a grid cell.\r\nColumns: Each column represents an industrial sector. The final column, \"geometry,\" contains the spatial coordinates (latitude and longitude) for each grid cell.\r\nValues: Each value indicates the PM₂.₅ concentration level (in µg/m³) attributable to emissions from the specified sector in the given country, as they occur in each grid cell.\r\n\r\n<b>Data S6 (folder of files): S6.zip</b>- a folder containing 188 CSV files, each named after a country's 3-digit code, representing production-related spatial PM₂.₅ concentration patterns for all EORA countries.\r\nStructure: Each file follows the same format as those in S5.zip, with rows representing grid cells and columns representing industrial sectors, plus a \"geometry\" column containing spatial coordinates.\r\n\r\n<b>Data S7 (separate file): S7.csv</b>- CSV file containing consumption-related spatial PM₂.₅ concentration patterns for all GTAP countries.\r\nRows: Each row represents a grid cell.\r\nColumns: Apart from the last column (\"geometry\"), which contains spatial information for each grid cell in latitude-longitude coordinates, each column represents a consumption country.\r\nValues: Each value indicates the PM₂.₅ concentration level caused by each country’s consumption process and occurring in each grid cell, measured in µg/m³.\r\n\r\n<b>Data S8 (separate file): S8.csv</b>- CSV file containing consumption-related spatial PM₂.₅ concentration patterns for all EORA countries.\r\nStructure: The file has the same structure as S7.csv.\r\n\r\n<b>Data S9 (separate file): S9.csv</b>- CSV file listing the total net bidirectional export of deaths for all countries in GTAP, displaying only positive values.\r\nColumns:\r\n\"from\": The country that exports more consumption-related deaths.\r\n \"to\": The country that imports more consumption-related deaths.\r\n \"values\": The net export of deaths between these two countries, calculated as the difference between the deaths flowing from \"from\" to \"to\" and those from \"to\" to \"from.\"\r\n\r\n<b>Data S10 (separate file): S10.csv</b>- CSV file listing the total net bidirectional export of deaths for all countries in EORA, displaying only positive values.\r\nStructure: The file has the same structure as S9.csv.\r\n\r\n<b>Data S11 (separate file): S11.csv</b>- CSV file listing the Value of Statistical Lives (VSLs), and consumption-related externalities under three scenarios—Business as Usual (BAU), Global Community (GC), and Fair Trade in Deaths (FTD)—along with externalities per GDP and their differences for GTAP countries.\r\nColumns:\r\nVSL, BAU_Externality, GC_Externality, FTD_Externality\r\nBAU_Ext_perGDP, GC_Ext_perGDP, FTD_Ext_perGDP\r\nDiff_GC_BAU, Diff_FTD_BAU, Diff_FTD_GC\r\n\r\n<b>Data S12 (separate file): S12.csv</b>- Same as S11.csv, but for EORA countries.\r\nStructure: Identical to S11.csv.\r\n\r\n<b>Data S13 (separate file): S13.csv</b>- purpose: Includes data used to generate Figures 1, 2, 3, and 5 in the main text.\r\nColumns:\r\ncountry_code: 3-letter country code\r\nGTAP_region, continent, population, GDP, GDP_capita, VSL\r\nexport_of_death, import_of_death, net_export, net_export_capita\r\nallforeign_world, G50foreign_world, G100foreign_world\r\ncause_allforeign_world, cause_L30foreign_world, cause_L50foreign_world\r\nBAU_Externality, GC_Externality, FTD_Externality\r\nBAU_Ext_perGDP, GC_Ext_perGDP, FTD_Ext_perGDP\r\nDiff_GC_BAU, Diff_FTD_BAU, Diff_FTD_GC\r\ngeometry (used for visualization)\r\n\r\n<b>Data S14 (separate file): S14.xlsx</b>- this Excel file contains six sheets summarizing cross-model Pearson correlation coefficients between sectoral economic activity fractions and transboundary mortality impact metrics, based on both GTAP and EORA datasets.\r\nSheets:\r\nOutput_fraction_GTAP\r\nDirect_demand_fraction_GTAP\r\nFinal_demand_fraction_GTAP\r\nOutput_fraction_EORA\r\nDirect_demand_fraction_EORA\r\nFinal_demand_fraction_EORA\r\n\r\nRows: Each row represents an economic sector.\r\nColumns:\r\nG50foreign_world: Fraction of deaths attributable to final demand from regions where demand per capita is more than 50% higher than in the current country.\r\ncause_L50foreign_world: Fraction of deaths caused by consumption within the current country but occurring in countries with more than 50% lower demand per capita.\r\nValues: Each value represents the Pearson correlation between the sectoral fraction and the corresponding transboundary mortality metric.\r\n\r\n<b>Data S15 (separate file): S15.csv</b>- CSV file derived from the GTAP dataset, containing Monte Carlo simulation results (500 draws) for the uncertainty analysis of production-based premature deaths.\r\nColumn Producer: The producing country–sector pair responsible for the emissions leading to health impacts.\r\nColumn Affected Country: The country where the resulting premature deaths occur.\r\nColumn Deaths: The estimated number of deaths corresponding to the one used in the main analysis.\r\nColumns Deaths_median, Deaths_low95, Deaths_high95: The median, 2.5th percentile, and 97.5th percentile values across 500 Monte Carlo draws of the GEMM θ parameter, representing the 95% confidence interval for each producer–affected country pair.\r\n\r\n<b>Data S16 (separate file): S16.csv</b>- CSV file derived from the GTAP dataset, containing Monte Carlo simulation results (500 draws) for the uncertainty analysis of consumption-based premature deaths.\r\nColumn Consumer: The consuming country whose final demand drives the global production and associated health impacts.\r\nColumn Affected Country: The country where the resulting premature deaths occur.\r\nColumn Deaths: The estimated number of deaths corresponding to the one used in the main analysis.\r\nColumns Deaths_median, Deaths_low95, Deaths_high95: The median, 2.5th percentile, and 97.5th percentile values across 500 Monte Carlo draws of the GEMM θ parameter, representing the 95% confidence interval for each consumer–affected country combination.", "**Data Description:** \r\nThis dataset provides country- and sector-level estimates of air-pollution–related health impacts, economic externalities, and associated spatial concentration patterns derived from multi-regional input–output (MRIO) modeling and atmospheric simulations (GTAP and EORA frameworks). Files include production- and consumption-based mortality matrices, gridded PM₂.₅ concentration maps, trade-linked net export metrics, externalities, uncertainty analyses, and cross-model correlation summaries used to generate the figures and tables in the manuscript.\r\n\r\n**Citation Requirement:** \r\nIf you use this dataset in your research, presentations, or derivative works, please cite the associated paper and acknowledge the dataset accordingly.\r\n"]} | 2026-02-01T07:03:06Z |
| Dataset | update: {"hold_state"=>["version candidate under curator review", "none"]} | 2026-01-22T17:59:36Z |
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| Creator | create: {"family_name"=>"Wang", "given_name"=>"Shiyuan", "identifier"=>"0000-0002-7348-147X", "email"=>"shiyuan8@illinois.edu", "is_contact"=>true, "row_position"=>1} | 2026-01-22T04:46:31Z |
| Dataset | update: {"corresponding_creator_name"=>[nil, "Shiyuan Wang"], "corresponding_creator_email"=>[nil, "shiyuan8@illinois.edu"]} | 2026-01-22T04:46:31Z |