Global Gridded Sectoral and Labor‑Type Heat Stress Based on ERA5 and Bias‑Corrected CMIP6 Projections

Overview This dataset provides global, gridded estimates of sector‑specific labor capacity under heat stress, derived from Wet Bulb Globe Temperature (WBGT) and mapped to economic sectors and labor categories. It extends the global occupational heat‑stress labor capacity dataset of Kong & Huber (2026) by adding a new economic‑sector and labor‑type disaggregation, enabling detailed analysis of how warming affects different parts of the economy and different groups of workers. The underlying WBGT fields are based on ERA5 historical reanalysis (1995–2015) and bias‑corrected CMIP6 multi‑model ensemble projections at seven global warming levels (1–4°C above pre‑industrial) . WBGT is computed using the bias‑corrected data following Kong & Huber (2025). Files File Description lancet_model_ensemble_v2607.zip Percent change in labor capacity- Model ensemble vs ERA5 reanalysis, 1995–2015 average - by economic activity and labor type lancet_png.zip Plots showing % change by economic acticity and labor category iso_model_ensemble_v2607.zip Percent change in labor capacity- Model ensemble vs ERA5 reanalysis, 1995–2015 average - by economic activity and labor type iso_png.zip Plots showing % change by economic acticity and labor category Data Structure Each NetCDF files follows the naming pattern: change_<Sector>_Labor_<Category>.nc Each file contains: labor_capacity_change - percent change relative to ERA5 baseline economic sector (embedded in filename) labor category (embedded in filename) warming level (1.0°C, 1.5°C, 2.0°C, 2.5°C, 3.0°C, 3.5°C, 4.0°C above pre‑industrial) 0.25° × 0.25° global grid, land‑only coverage What This Dataset Adds While the original dataset by Kong & Huber (2026) provides labor capacity for indoor/outdoor conditions, metabolic rates, and warming levels, the dataset provided here differentiates by economic sector and labor type. This dataset introduces: Economic sector classification (e.g., Agriculture, Manufacturing, Services, Construction, Extraction, Trade, Transportation) Labor categories (e.g., office, services, techpro, unskilled) Sector‑specific labor capacity change, expressed as percent change relative to the ERA5 1995–2015 baseline Future warming scenarios consistent with bias‑corrected CMIP6 ensemble mean projections (15 models) This enables researchers and policymakers to quantify which sectors lose labor capacity, which labor groups are most affected, and how impacts scale with warming. Methodology Input Data Labor capacity is computed using the same two independent response functions used in Kong & Huber (2026): ISO 7243 threshold‑based response (steep decline once WBGT exceeds metabolic‑rate‑specific limits) Lancet cumulative‑normal response, adapted from field observations (gradual decline across WBGT range) These functions bracket plausible upper and lower bounds on heat‑stress impacts on labor. Economic Activities and Labor Categories Sectoral and labor-type computation follows the methodology of Saeed et al. (2022), in which global WBGT-derived labor capacity fields are combined with spatially explicit sectoral employment distributions and labor-type shares to estimate sector-specific labor capacity based on labor parameters based on BLS provided by Saeed et al. (2022). Monthly labor capacity is calculated as the average daily labor capacity over all days within each calendar month. Monthly percentage changes are then computed by comparing each future warming scenario with the historical ERA5 climatological baseline. No. BLS Occupations Label in the Dataset 1 Management Occupations office 2 Business and Financial Operations Occupations office 3 Office and Administrative Support Occupations office 4 Computer and Mathematical Occupations techpro 5 Architecture and Engineering Occupations techpro 6 Life, Physical, and Social Science Occupations techpro 7 Community and Social Service Occupations techpro 8 Legal Occupations techpro 9 Education, Training, and Library Occupations techpro 10 Arts, Design, Entertainment, Sports, and Media Occupations techpro 11 Healthcare Practitioners and Technical Occupations services 12 Healthcare Support Occupations services 13 Protective Service Occupations services 14 Food Preparation and Serving Related Occupations services 15 Building and Grounds Cleaning and Maintenance Occupations services 16 Personal Care and Service Occupations services 17 Sales and Related Occupations services 18 Farming, Fishing, and Forestry Occupations unskilled 19 Construction and Extraction Occupations unskilled 20 Installation, Maintenance, and Repair Occupations unskilled 21 Production Occupations unskilled 22 Transportation and Material Moving Occupations unskilled For the agricultural sector, monthly averages and percentage changes are calculated only for the crop-specific growing season months, thereby representing periods of active agricultural labor demand. For all other economic sectors, calculations include all twelve months of the year representing average changes. No. BLS Economic Activities Economic Activities in the Dataset 1 Agriculture and related Agriculture 2 Mining, quarrying, and oil and gas extraction Extraction 3 Construction Construction 4 Manufacturing Manufacturing 5 Wholesale and retail trade Trade 6 Transportation and utilities Transport 7 Information Services 8 Financial activities Services 9 Professional and business services Services 10 Education and health services Services 11 Leisure and hospitality Services 12 Other services Services 13 Public administration Services Sun Exposure Agricultural outdoor labor exposure is allowed to vary across countries to reflect differences in production systems, mechanization, and technological development, following the exposure assumptions described in Saeed et al. (2022). For non-agricultural sectors, labor exposure is determined using sector- and labor-type-specific exposure factors. Air Conditioning Country-specific air conditioning (AC) penetration rates are incorporated to account for the mitigating effects of indoor climate control on occupational heat exposure. These adjustment factors reduce effective heat exposure for workers in occupations and regions where air conditioning is available, resulting in country-specific estimates of labor capacity under each warming scenario. Applications This dataset is designed for: sectoral economic impact assessments labor productivity modeling risk analysis for supply chains adaptation planning and occupational safety research integration with IAMs, CGE models, and input‑output frameworks Citation If you use this dataset, please cite: Haqiqi, I., Kong, Q., & Moore, F. (2026). Global Gridded Sectoral Labor Capacity Change Under Heat Stress Based on ERA5 and Bias‑Corrected CMIP6 Projections. Zenodo. DOI: 10.5281/zenodo.21712766 References Kong, Q., & Huber, M. (2022). Explicit calculations of Wet Bulb Globe Temperature compared with approximations and why it matters for labor productivity. Earth's Future. Kong, Q., & Huber, M. (2025). A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics. Scientific Data, 12(1), 246. Kong, Q., & Huber, M. (2026). Global Gridded Occupational Heat Stress Labor Capacity Based on ERA5 and Bias‑Corrected CMIP6 Projections. Zenodo. DOI: 10.5281/zenodo.21636523 Saeed, W., Haqiqi, I., Kong, Q., Huber, M., Buzan, J. R., Chonabayashi, S., ... & Hertel, T. W. (2022). The poverty impacts of labor heat stress in West Africa under a warming climate. Earth's Future, 10(11), e2022EF002777.