Abstract
The shared socio-economic pathways (SSPs) provide storylines for plausible future global developments under a consistent set of assumptions. Primarily, the SSPs have been used to derive quantitative scenarios of greenhouse gas emissions for use in general circulation models and other large-scale, physically based models of environmental change. Their use in large-scale socio-environmental simulation modelling has been less forthcoming. Behavioural models of land systems, typically operating within agent-based frameworks, attempt to explicitly represent the varied socio-environmental factors that characterise the SSP storylines. Such simulation models are often geospatial and grid-based, capable of representing dynamics at resolutions of up to 10s km2 over large extents (continental to global). However, such approaches demand geospatial (usually gridded) input data for multiple social and human variables - often together known as capitals - such as education levels and infrastructure. The lack of such input data for the future SSP storylines has been a substantial limitation in the application of large-scale behavioural models of land systems specifically and of socio-environmental modelling more generally. Here, we present global gridded data for socio-economic capitals useful for simulation models and consistent with the SSP storylines, disaggregated spatially to a 0.25o grid. We analyse the spatial and temporal characteristics of the projected data, comparing them to projections of GDP, and discuss their utility and limitations for large-scale socio-environmental modelling.
References
Ackerman, F., DeCanio, S. J., Howarth, R. B., & Sheeran, K. (2009). Limitations of integrated assessment models of climate change. Climatic Change, 95(3), 297–315. https://doi.org/10.1007/s10584-009-9570-x
Arneth, A., Brown, C., & Rounsevell, M. D. A. (2014). Global models of human decision-making for land-based mitigation and adaptation assessment. Nature Climate Change, 4(7), Article 7. https://doi.org/10.1038/nclimate2250
Bauer, N., Calvin, K., Emmerling, J., Fricko, O., Fujimori, S., Hilaire, J., Eom, J., Krey, V., Kriegler, E., Mouratiadou, I., Sytze de Boer, H., van den Berg, M., Carrara, S., Daioglou, V., Drouet, L., Edmonds, J. E., Gernaat, D., Havlik, P., Johnson, N., … van Vuuren, D. P. (2017). Shared Socio-Economic Pathways of the Energy Sector – Quantifying the Narratives. Global Environmental Change, 42, 316–330. https://doi.org/10.1016/j.gloenvcha.2016.07.006
Billing, M., Forrest, M., Bowring, S., Oberhagemann, L., Neidermeier, A., Hetzer, J., von Bloh, W., Muller, C., Rolinski, S., Hickler T., & Thonicke, K. (2026). Future projections of burned area in Europe highlight the importance of human action. Global Change Biology, 32 (8) e71043. https://doi.org/10.1111/gcb.71043
Blanco, V., Brown, C., Holzhauer, S., Vulturius, G., & Rounsevell, M. D. A. (2017). The importance of socio-ecological system dynamics in understanding adaptation to global change in the forestry sector. Journal of Environmental Management, 196, 36–47. https://doi.org/10.1016/j.jenvman.2017.02.066
Botzen, W. J. W., Deschenes, O., & Sanders, M. (2019). The Economic Impacts of Natural Disasters: A Review of Models and Empirical Studies. Review of Environmental Economics and Policy, 13(2), 167–188. https://doi.org/10.1093/reep/rez004
Brown, C., Holman, I., & Rounsevell, M. (2021). How modelling paradigms affect simulated future land use change. Earth System Dynamics, 12(1), 211–231. https://doi.org/10.5194/esd-12-211-2021
Brown, C., Seo, B., Alexander, P., Burton, V., Chacón-Montalván, E. A., Dunford, R., Merkle, M., Harrison, P. A., Prestele, R., Robinson, E. L., & Rounsevell, M. (2022). Agent-Based Modeling of Alternative Futures in the British Land Use System. Earth’s Future, 10(11), e2022EF002905. https://doi.org/10.1029/2022EF002905
CIESIN (Center for International Earth Science Information Network) - Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW.
de Castro Victoria, D., da Silva, R.F.B., Millington, J.D., Katerinchuk, V. & Batistella, M. (2021) Transport cost to port though Brazilian federal roads network: Dataset for years 2000, 2005, 2010 and 2017. Data in brief, 36, 107070. https://doi.org/10.1016/j.dib.2021.107070
EIA (U.S. Energy Information Administration). Global energy consumption [online resource]. Available at: https://www.eia.gov/opendata/bulkfiles.php, last accessed 15 Nov 2024.
EI (Energy Institute). (2024). Statistical review of world energy [online resource]. Available at: https://www.energyinst.org/statistical-review, last accessed 15 Nov 2024.
Frieler, K., Volkholz, J., Lange, S., Schewe, J., Mengel, M., Lopez, M., Otto, C., Reyer, C., Karger, D., Malle, J., True, S., Menz, C., Blanchard, J., Harrison, C., Petrik, C., Eddy, T., Ortega-Cisneros, Novaglio, C., … & Bechtold, M. (2024). Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Geoscientific Model Development, 17, 1-51. https://doi.org/10.5194/gmd-17-1-2024
Gaffin, S. R., Rosenzweig, C., Xing, X., & Yetman, G. (2004). Downscaling and geo-spatial gridding of socio-economic projections from the IPCC Special Report on Emissions Scenarios (SRES). Global Environmental Change, 14(2), 105–123. https://doi.org/10.1016/j.gloenvcha.2004.02.004
Hausfather, Z., & Peters, G. (2020). Emissions - the 'business as usual' story is misleading. Nature, 577, 618-620. https://doi.org/10.1038/d41586-020-00177-3
Hijmans, R. (2025). terra: spatial data analysis [software], version 1.10. Available at: https://CRAN.R-project.org/package=terra, last accessed 6 November 2025.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., … & Zhang, X. (2020). Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geoscientific Model Development, 13(11), 5425–5464. https://doi.org/10.5194/gmd-13-5425-2020
Jones, B., & O’Neill, B. C. (2016). Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environmental Research Letters, 11(8), 084003. https://doi.org/10.1088/1748-9326/11/8/084003
KC, S., & Lutz, W. (2017). The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Global Environmental Change, 42, 181–192. https://doi.org/10.1016/j.gloenvcha.2014.06.004
Keys, P. W., Badia, L., & Warrier, R. (2024). The Future in Anthropocene Science. Earth’s Future, 12(1), e2023EF003820. https://doi.org/10.1029/2023EF003820
Koch, J., & Leimbach, M. (2023). SSP economic growth projections: Major changes of key drivers in integrated assessment modelling. Ecological Economics, 206, 107751. https://doi.org/10.1016/j.ecolecon.2023.107751
Kummu, M., Taka, M., & Guillaume, J. H. A. (2018). Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Scientific Data, 5(1), Article 1. https://doi.org/10.1038/sdata.2018.4
Mahmoud, M. R., Roushdi, M., & Aboelkhear, M. (2024). Potential benefits of climate change on navigation in the northern sea route by 2050. Scientific Reports, 14(1), 2771. https://doi.org/10.1038/s41598-024-53308-5
Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J., & Schipper, A. M. (2018). Global patterns of current and future road infrastructure. Environmental Research Letters, 13(6), 064006. https://doi.org/10.1088/1748-9326/aabd42
Millington, J. D. A., Katerinchuk, V., Silva, R. F. B. da, Victoria, D. de C., & Batistella, M. (2021). Modelling drivers of Brazilian agricultural change in a telecoupled world. Environmental Modelling & Software, 139, 105024. https://doi.org/10.1016/j.envsoft.2021.105024
Mirza, M. U., Xu, C., Bavel, B. V., Van Nes, E. H., & Scheffer, M. (2021). Global inequality remotely sensed. Proceedings of the National Academy of Sciences, 118(18), e1919913118. https://doi.org/10.1073/pnas.1919913118
Murakami, D., Yoshida, T., & Yamagata, Y. (2021). Gridded GDP Projections Compatible With the Five SSPs (Shared Socioeconomic Pathways). Frontiers in Built Environment, 7. https://www.frontiersin.org/articles/10.3389/fbuil.2021.760306
Murray-Rust, D., Brown, C., van Vliet, J., Alam, S. J., Robinson, D. T., Verburg, P. H., & Rounsevell, M. (2014). Combining agent functional types, capitals and services to model land use dynamics. Environmental Modelling & Software, 59, 187–201. https://doi.org/10.1016/j.envsoft.2014.05.019
NASA (2009). Distance to the nearest coastline [online resource]. Available at: https://oceancolor.gsfc.nasa.gov/resources/docs/distfromcoast/, last accessed November 4 2025.
Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., Börger, L., Bennett, D. J., Choimes, A., Collen, B., Day, J., De Palma, A., Díaz, S., Echeverria-Londoño, S., Edgar, M. J., Feldman, A., Garon, M., Harrison, M. L. K., Alhusseini, T., … Purvis, A. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520(7545), Article 7545. https://doi.org/10.1038/nature14324
O’Neill, B. C., Carter, T. R., Ebi, K., Harrison, P. A., Kemp-Benedict, E., Kok, K., Kriegler, E., Preston, B. L., Riahi, K., Sillmann, J., van Ruijven, B. J., van Vuuren, D., Carlisle, D., Conde, C., Fuglestvedt, J., Green, C., Hasegawa, T., Leininger, J., Monteith, S., & Pichs-Madruga, R. (2020). Achievements and needs for the climate change scenario framework. Nature Climate Change, 10(12), 1074–1084. https://doi.org/10.1038/s41558-020-00952-0
O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., & Solecki, W. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004
O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., Mathur, R., & van Vuuren, D. P. (2014). A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Climatic Change, 122(3), 387–400. https://doi.org/10.1007/s10584-013-0905-2
Panwar, V., & Sen, S. (2019). Economic Impact of Natural Disasters: An Empirical Re-examination. Margin: The Journal of Applied Economic Research, 13(1), 109–139. https://doi.org/10.1177/0973801018800087
Perkins, O., Kasoar, M., Voulgarakis, A., Edwards, T., Haas, O., & Millington, J. D. A. (2025a). The Spatial Distribution and Temporal Drivers of Changing Global Fire Regimes: A Coupled Socio-Ecological Modeling Approach. Earth’s Future, 13 (5), e2024EF004770. https://doi.org/10.1029/2024EF004770
Perkins, O., Haas, O., Kasoar, M., Voulgarakis, A., & Millington, J. D. A. (2025b). Structural uncertainty in the direct human forcing of future global burned area. EGUsphere, https://doi.org/10.5194/egusphere-2025-3728
Perkins, O., Kasoar, M., Voulgarakis, A., Smith, C., Mistry, J., & Millington, J. D. A. (2024). A global behavioural model of human fire use and management: WHAM! v1.0. Geoscientific Model Development, 17(9), 3993–4016. https://doi.org/10.5194/gmd-17-3993-2024
Perkins, O., Matej, S., Erb, K., & Millington, J. (2022). Towards a global behavioural model of anthropogenic fire: The spatiotemporal distribution of land-fire systems. Socio-Environmental Systems Modelling, 4, 18130–18130. https://doi.org/10.18174/sesmo.18130
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest, E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M., Hasegawa, T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin, H., Waldhoff, S., Weindl, I., Wise, M., … Vuuren, D. P. van. (2017). Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42, 331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002
Potančoková, M., K. C., S., & Goujon, A. (2014, April). Global Estimates of Mean Years of Schooling: A New Methodology [Monograph]. IR-14-005. https://iiasa.dev.local/
Price, O., Ondei, S., & Bowman, D. (2025). Progress and prospects for predicting wildfire spread through the wildland-urban interface. International Journal of Disaster Risk Reduction, 121, 105392. https://doi.org/10.1016/j.ijdrr.2025.105392
Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., Yue, C., Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T., … Arneth, A. (2017). The Fire Modeling Intercomparison Project (FireMIP), phase 1: Experimental and analytical protocols with detailed model descriptions. Geoscientific Model Development, 10(3), 1175–1197. https://doi.org/10.5194/gmd-10-1175-2017
Rao, N. D., Sauer, P., Gidden, M., & Riahi, K. (2019). Income inequality projections for the Shared Socioeconomic Pathways (SSPs). Futures, 105, 27–39. https://doi.org/10.1016/j.futures.2018.07.001
Raworth, K. (2018). Doughnut Economics: Seven Ways to Think Like a 21st Century Economist. Chelsea Green Publishing.
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
Ritchie, H., Rosado, P., and Roser, M. Primary energy consumption per capita [ online resource], available at: https://ourworldindata.org/grapher/per-capita-energy-use, last accessed 15 Nov 2024.
Rouhette, T., Van de Ven, D., Narayan, K., Tebaldi, C., Perkins, O., Haas, O., & Escobar, N. (2026). IAM-FIRE: A climate emulator-based framework to project wildfire impacts and risks for integrated assessment models. Global Change Biology, 32 (6), e70951. https://doi.org/10.1111/gcb.70951
Saxena, A., Brown, C., Winkler, K., Arneth, A., & Rounsevell, M. (2026). Spatial patterns of global land management intensity are influenced by socioeconomic, biophysical and behavioural factors. Earth System Science Data, 18 (9), 6251-6292. https://doi.org/10.5194/essd-18-6251-2026
Schandl, H., Hatfield-Dodds, S., Wiedmann, T., Geschke, A., Cai, Y., West, J., Newth, D., Baynes, T., Lenzen, M., & Owen, A. (2016). Decoupling global environmental pressure and economic growth: Scenarios for energy use, materials use and carbon emissions. Journal of Cleaner Production, 132, 45–56. https://doi.org/10.1016/j.jclepro.2015.06.100
Teckentrup, L., Harrison, S. P., Hantson, S., Heil, A., Melton, J. R., Forrest, M., Li, F., Yue, C., Arneth, A., Hickler, T., Sitch, S., & Lasslop, G. (2019). Response of simulated burned area to historical changes in environmental and anthropogenic factors: A comparison of seven fire models. Biogeosciences, 16(19), 3883–3910. https://doi.org/10.5194/bg-16-3883-2019
Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267–276. https://doi.org/10.1007/BF02289263
UNDESA (United Nations Department of Economic and Social Affairs). World Population Prospects [online resource]. Available at: https://population.un.org/wpp/Download/Standard/Population/, last accessed 15 Nov 2024.
UNDP (United Nations Development Programme). (2025). Human Development Report 2025 technical notes. Available at: https://hdr.undp.org/data-center/documentation-and-downloads, last accessed 12 Oct 2026.
Verburg, P. H., Ellis, E. C., & Letourneau, A. (2011). A global assessment of market accessibility and market influence for global environmental change studies. Environmental Research Letters, 6(3), 034019. https://doi.org/10.1088/1748-9326/6/3/034019
World Bank. The world by region [online resource]. Available at: https://datatopics.worldbank.org/sdgatlas/archive/2017/the-world-by-region.html, last accessed 21 Nov 2024.
Wright, M. N., & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77, 1–17. https://doi.org/10.18637/jss.v077.i01
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Oliver Perkins, Ankita Saxena, James Millington, Bumsuk Seo, Calum Brown, Mark Rounsevell