Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Global models of human decision-making for land-based mitigation and adaptation assessment

An Erratum to this article was published on 30 July 2014

This article has been updated

Abstract

Understanding the links between land-use change (LUC) and climate change is vital in developing effective land-based climate mitigation policies and adaptation measures. Although mitigation and adaptation are human-mediated processes, current global-scale modelling tools do not account for societal learning and other human responses to environmental change. We propose the agent functional type (AFT) method to advance the representation of these processes, by combining socio-economics (agent-based modelling) with natural sciences (dynamic global vegetation models). Initial AFT-based simulations show the emergence of realistic LUC patterns that reflect known LUC processes, demonstrating the potential of the method to enhance our understanding of the role of people in the Earth system.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Concept of plant functional types in dynamic global vegetation models (DGVMs).
Figure 2: Concept of agent functional types in global agent-based models.
Figure 3: Outcomes from an example simulation of an ABM application for a hypothetical region based on three farmer AFTs (high, medium and low intensity farmers) and a conservationist AFT that compete for capital resources.

Similar content being viewed by others

Change history

  • 25 June 2014

    In the print version of this Perspective, references 20 and 21 were omitted from the reference list and should have appeared as: 20. Adger, N. W., Barnett, J., Brown, K., Marshall, N. & O'Brien, K. Cultural dimension of climate change impacts and adaptation. Nature Clim. Change 3, 112–117 (2013). 21. Moser, S. C. & Ekstrom, J. A. A framework to diagnose barriers to climate change adaptation. Proc. Natl Acad. Sci. USA 104, 22026–22031 (2012). These omissions have been corrected in the HTML and PDF versions of the Perspective.

References

  1. Houghton, R. A. et al. Carbon emissions from land use and land-cover change. Biogeosciences 9, 5125–5142 (2012).

    Article  CAS  Google Scholar 

  2. Pitman, A. J. et al. Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study. Geophys. Res. Lett. 36, L14814 (2009).

    Article  Google Scholar 

  3. Gornall, J. et al. Implications of climate change for agricultural productivity in the early twenty-first century. Phil. Trans Roy. Soc. B 365, 2973–2989 (2010).

    Article  Google Scholar 

  4. Easterling, W. E. et al. in Climate Change 2007: Impacts, Adaptation and Vulnerability (eds Parry, M. L. et al.) 273–313 (Cambridge Univ. Press, 2007).

    Google Scholar 

  5. Ashmore, M. R. Assessing the future global impacts of ozone on vegetation. Plant Cell Environ. 28, 949–964 (2005).

    Article  CAS  Google Scholar 

  6. Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 22, http://dx.doi.org/10.1029/2007GB002952 (2008).

  7. Le Quere, C. et al. Trends in the sources and sinks of carbon dioxide. Nature Geosci. 2, 831–836 (2009).

    Article  CAS  Google Scholar 

  8. Zaehle, S., Ciais, P., Friend, A. D. & Prieur, V. Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions. Nature Geosci. 4, 601–605 (2011).

    Article  CAS  Google Scholar 

  9. Arora, V. K. & Montenegro, A. Small temperature benefits provided by realistic afforestation efforts. Nature Geosci. 4, 514–518 (2011).

    Article  CAS  Google Scholar 

  10. Pongratz, J., Reick, C. H., Raddatz, T. & Claussen, M. Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett. 37, L08702 (2010).

    Article  Google Scholar 

  11. UN-REDD Beyond Carbon: Ecosystem-based benefits of REDD+ (UNEP-WCMC, 2009).

  12. IPCC The National Greenhouse Gas Inventories Programme (eds Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K.) (IGES, 2006).

  13. Fargione, J. Energy: Boosting biofuel yields. Nature Clim. Change 1, 445–446 (2011).

    Article  Google Scholar 

  14. Rounsevell, M. D. A. et al. Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dynam. 5, 117–137 (2014). This paper is the outcome of a community effort that brought together the natural, economic and social sciences to provide a review of the current state-of-the art of global land-use change modelling; the main challenges and ways forward to address them.

    Article  Google Scholar 

  15. Melillo, J. M. et al. Indirect Emissions from Biofuels: How Important? Science 326, 1397–1399 (2009).

    Article  CAS  Google Scholar 

  16. Fargione, J., Hill, J., Tilman, D., Polasky, S. & Hawthorne, P. Land clearing and the biofuel carbon debt. Science 319, 1235–1238 (2008).

    Article  CAS  Google Scholar 

  17. Crutzen, P. J., Mosier, A. R., Smith, K. A. & Winiwarter, W. N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels. Atm. Chem. Phys. 7, 11191–11205 (2007).

    Google Scholar 

  18. deMenocal, P. B. Cultural Responses to Climate Change During the Late Holocene. Science 292, 667–673 (2001).

    Article  CAS  Google Scholar 

  19. Oglesby, R. J., Sever, T. L., Saturno, W., Erickson, D. J. III & Srikishen, J. Collapse of the Maya: Could deforestation have contributed? J. Geophys. Res. 115, D12106 (2010).

    Article  Google Scholar 

  20. Adger, N. W., Barnett, J., Brown, K., Marshall, N. & O'Brien, K. Cultural dimension of climate change impacts and adaptation. Nature Clim. Change, 3, 112–117 (2013

    Article  Google Scholar 

  21. Moser, S.C. & Ekstrom, J. A. A framework to diagnose barriers to climate change adaptation. Proc. Natl Acad. Sci. USA 104, 22026–22031 (2012).

    Google Scholar 

  22. Acosta-Michlik, L. et al. A spatially explicit scenario-driven model of adaptive capacity to global change in Europe. Glob. Environ. Change 23, 1211–1224 (2013).

    Article  Google Scholar 

  23. van Vuuren, D. P. et al. The use of scenarios as the basis for combined assessment of climate change mitigation and adaptation. Glob. Environ. Change 21, 575–591 (2011).

    Article  Google Scholar 

  24. Warren, R. The role of interactions in a world implementing adaptation and mitigation solutions to climate change. Phil. Trans. R. Soc. A 369, 217–241 (2011).

    Article  CAS  Google Scholar 

  25. Hertel, T. W. The global supply and demand for agricultural land in 2050: A perfect storm in the making? Am. J. Agric. Econ. 93, 259–275 (2011).

    Article  Google Scholar 

  26. Nakicenovic, N. & Swart, R. Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2000).

    Google Scholar 

  27. Smith, P. et al. Competition for land. Phil. Trans. R. Soc. B 365, 2941–2957 (2010).

    Article  Google Scholar 

  28. van Vuuren, D. P. et al. A proposal for a new scenario framework to support research and assessment in different climate research communities. Glob. Environ. Change 22, 21–35 (2012).

    Article  Google Scholar 

  29. Schmitz, C. et al. Trading more food: Implications for land use, greenhouse gas emissions, and the food system. Glob. Environ. Change 22, 189–209 (2012).

    Article  Google Scholar 

  30. Sarofim, M. C. & Reilly, J. M. Applications of integrated assessment modeling to climate change. Wiley Interdis. Rev. Clim. Chang. 2, 27–44 (2011).

    Article  Google Scholar 

  31. Popp, A., Lotze-Campen, H. & Bodirsky, B. Food consumption, diet shifts and associated non-CO2 greenhouse gases from agricultural production. Glob. Environ. Change 20, 451–462 (2010).

    Article  Google Scholar 

  32. Fuessel, H.-M. Modelling impacts and adaptation in global IAMs. Wiley Interdis. Rev. Clim. Chang 1, 288–303 (2010).

    Article  Google Scholar 

  33. Busch, G. Future European agricultural landscapes — What can we learn from existing quantitative land use scenario studies? Agric. Ecosys. Environ. 114, 121–140 (2006).

    Article  Google Scholar 

  34. Giupponi, C., Borsuk, M. E., Vries, B. J. M. d. & Hasselmann, K. Innovative approaches to integrated global change modelling. Environ. Modelling Software 44, 1–9 (2013).

    Article  Google Scholar 

  35. Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G. & Gotts, N. M. Agent-based land-use models: a review of applications. Landscape Ecol. 22, 1447–1459 (2007).

    Article  Google Scholar 

  36. Filatova, T., Verburg, P., Parker, D. C. & Stannard, C. A. Spatial agent-based models for socio-ecological systems: challenges and prospects. Environ. Modelling Software 45, 1–7 (2013).

    Article  Google Scholar 

  37. Nolan, J., Parker, D. & van Kooten, G. C. An Overview of Computational Modeling in Agricultural and Resource Economics. Can. J. Agric. Econ. 57, 417–429 (2009).

    Article  Google Scholar 

  38. An, L. Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecol. Modelling 229, 25–36 (2012).

    Article  Google Scholar 

  39. Wolf, S. et al. A multi-agent model of several economic regions. Environ. Modelling Software 44, 25–43 (2013).

    Article  Google Scholar 

  40. Brede, M. & de Vries, B. J. M. The energy transition in a climate-constrained world: Regional vs. global optimization. Environ. Modelling Software 44, 44–61 (2013).

    Article  Google Scholar 

  41. Purnomo, H., Suyamto, D. & Irawati, R. H. Harnessing the climate commons: an agent-based modelling approach to making reducing emission from deforestation and degradation (REDD)+work. Mitigation Adapt. Strategies for Glob. Change 18, 471–489 (2013).

    Article  Google Scholar 

  42. Bonabeau, E. Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl Acad. Sci. USA 99, 7280–7287 (2002).

    Article  CAS  Google Scholar 

  43. Farmer, J. D. & Foley, D. The economy needs agent-based modelling. Nature 460, 685–686 (2009).

    Article  CAS  Google Scholar 

  44. Valbuena, D., Verburg, P. H., Bregt, A. K. & Ligtenberg, A. An agent-based approach to model land-use change at a regional scale. Landscape Ecol. 25, 185–199 (2010).

    Article  Google Scholar 

  45. Rounsevell, M. D. A., Robinson, D. & Murray-Rust, D. From actors to agents in socio-ecological systems models. Phil. Trans. R. Soc. B 367, 259–269 (2012).

    Article  CAS  Google Scholar 

  46. Boisier, J.-P. et al. Attributing the impacts of land-cover changes in temperate regions on surface temperature and heat fuxes to specific causes. Results from the first LUCID set of simulations. J. Geophys. Res. 117, D12116 (2012).

    Article  Google Scholar 

  47. Hulme, M. Meet the humanities. Nature Clim. Change 1, 177–179 (2011).

    Article  Google Scholar 

  48. Roco, M. C., Bainbridge, W. S., Tonn, B. & Whitesides, G. Converging Knowledge, Technology and Society: Beyond Convergenc of Nano-Bio-Info-Cognitive Technologies (WTEC, 2013).

    Book  Google Scholar 

  49. Smajgl, A., Brown, D. G., Valbuena, D. & Huigen, M. G. A. Empirical characterisation of agent behaviours in socioecological systems. Environ. Modelling Software 26, 837–844 (2011).

    Article  Google Scholar 

  50. Ernst, A. in Empirical Agent-Based Modelling-Challenges and Solutions (eds Smajgl, A. & Barretau, O.) 85–104 (Springer, 2014).

    Book  Google Scholar 

  51. Smajgl, A. & Barreteau, O. in Empirical Agent-Based Modelling-Challenges and Solutions Vol. 1: The Characterisation and parameterisation of empirical agent-based models (eds Smajgl, A. & Barretau, O.) 1–26 (Springer, 2014).

    Book  Google Scholar 

  52. Magliocca, N. R., Brown, D. G. & Ellis, E. C. Exploring agricultural livelihood transitions with an agent-based virtual laboratory: Global forces to local decision-making. PLoS One 8, e73241 (2013).

    Article  CAS  Google Scholar 

  53. Prentice, I. C. et al. in Terrestrial Ecosystems in a Changing World IGBP Series (eds Canadell, J. G., Pataki, D. E. & Pitelka, L. F.) 175–192 (Springer, 2007). A review on the plant functional types concept, its application in dynamic global vegetation models and their application to key issues of global environmental change.

    Book  Google Scholar 

  54. Harrison, S. P. et al. Ecophysiological and bioclimatic foundations for a global plant functional classification. J. Veg. Sci. 21, 300–317 (2010).

    Article  Google Scholar 

  55. Prentice, I. C. et al. A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeography 19, 117–134 (1992).

    Article  Google Scholar 

  56. Arneth, A. et al. From biota to chemistry and climate: towards a comprehensive description of trace gas exchange between the biosphere and atmosphere. Biogeosciences 7, 121–149 (2010).

    Article  CAS  Google Scholar 

  57. Bondeau, A. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706 (2007).

    Article  Google Scholar 

  58. Lindeskog, M. et al. Implications of accounting for land use in simulations of ecosystem services and carbon cycling in Africa. Earth Sys. Dynam. 4, 385–407 (2013).

    Article  Google Scholar 

  59. Foley, J. A. et al. Global consequences of land use. Science 09, 570–574 (2005).

    Article  CAS  Google Scholar 

  60. Bandura, A. Toward a Psychology of Human Agency. Perspectives Psychol. Sci. 1, 164–180 (2006). The paper summarises the important properties of human agency, including core aspects related to planning, decision making and adaptation as fundamental, endogeneous traits of people within social systems.

    Article  Google Scholar 

  61. Spiggle, S. & Sanders, C. R. in Advances in Consumer Research Volume 11 (ed. Kinnear, T. C.) 337–342 (Association for Consumer Research, 1984).

    Google Scholar 

  62. Dickmann, M. & Müller-Camen, M. A typology of international human resource management strategies and processes. Int. J. Human Res. Manage. 17, 580–601 (2006).

    Google Scholar 

  63. Rounsevell, M. D. A. & Arneth, A. Representing human behaviour and decisional processes in land system models as an integral component of the earth system. Glob. Environ. Change 21, 840–843 (2011).

    Article  Google Scholar 

  64. Sheffer, M., Westley, F., Brock, W. A. & Holmgren, M. in Panarchy: Understanding Transformations in Human and Natural Systems (eds Gunderson, L. H. & Holling, C. S.) 195–239 (Island Press, 2002).

    Google Scholar 

  65. Rindfuss, R. R., Walsh, S. J., Turner, B. L., Fox, J. & Mishra, V. Developing a science of land change: Challenges and methodological issues. Proc. Natl Acad. Sci. USA 101, 13976–13981 (2004).

    Article  CAS  Google Scholar 

  66. Poritt, J. Capitalism as if the World Matters (Earthscan, 2005).

    Google Scholar 

  67. Fraser, E. D. G. in Assessing Vulnerability to Global Environmental Change (eds Patt, A. G., Schröter, D., Klein, R. J. T. & de la Vega-Leinert, A. C.) (Earthscan, 2009).

    Google Scholar 

  68. Guillem, E. E., Barnes, A. P., Rounsevell, M. D. A. & Renwick, A. Refining perception-based farmer typologies with the analysis of past census data. J. Environ. Manage. 110, 226–235 (2012).

    Article  CAS  Google Scholar 

  69. Carpenter, S. R. et al. Science for managing ecosystem services: Beyond the Millennium Ecosystem Assessment. Proc. Natl. Acad. Sci. USA 106, 1305–1312 (2009).

    Article  CAS  Google Scholar 

  70. Rounsevell, M. D. A. et al. A coherent set of future land use change scenarios for Europe. Agric. Ecosys. Environ. 114, 57–68 (2006).

    Article  Google Scholar 

  71. Alexander, P., Moran, D., Rounsevell, M. D. A. & Smith, P. Modelling the perennial energy crop market: the role of spatial diffusion. J. R. Soc. Interface 10, 20130656 (2013).

    Article  Google Scholar 

  72. Giavazzi, F., Jappelli, T. & Pagano, M. Searching for non-linear effects of fiscal policy: Evidence from industrial and developing countries. European Econ. Rev. 44, 1259–1289 (2000).

    Article  Google Scholar 

  73. Walters, B. B., Sabogal, C., Snook, L. K. & de Almeida, E. Constraints and opportunities for better silvicultural practice in tropical forestry: an interdisciplinary approach. For. Ecol. Manage. 209, 3–18 (2005).

    Article  Google Scholar 

  74. Filatova, T., Van Der Veen, A. & Parker, D. C. Land market interactions between heterogeneous agents in a heterogeneous landscape — tracing the macro-scale effects of individual trade-offs between environmental amenities and disamenities. Can. J. Agric. Econ. 57, 431–457 (2009).

    Article  Google Scholar 

  75. Kattge, J. et al. TRY — a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).

    Article  Google Scholar 

  76. Hertel, T. & Villoria, N. B. GEOSHARE: Geospatial Open Source Hosting of Agriculture, Resource & Environmental Data for Discovery and Decision Making (Purdue University, 2012).

    Google Scholar 

  77. Ellis, E. C. & Ramankutty, N. Putting people in the map: anthropogenic biomes of the world. Frontiers Ecol. Environ. 6, 439–447 (2008).

    Article  Google Scholar 

  78. Rudel, T. K. Meta-analyses of case studies: A method for studying regional and global environmental change. Glob. Environ. Change 18, 18–25 (2008).

    Article  Google Scholar 

  79. Lotze-Campen, H. et al. Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agric. Econ. 39, 325–338 (2008).

    Google Scholar 

  80. Ligmann-Zielinska, A. & Sun, L. B. Applying time-dependent variance-based global sensitivity analysis to represent the dynamics of an agent-based model of land use change. Int. J. Geo. Info. Sci. 24, 1829–1850 (2010).

    Article  Google Scholar 

  81. Murray-Rust, D. et al. Combining agent functional types, capitals and services to model land use dynamics. Environ. Modelling Software (in the press).

Download references

Acknowledgements

The idea for this paper was conceived at a workshop jointly sponsored by the IGBP Global Land Project, and CSIRO. The work contributes to the EU FP7 project LUC4C (grant agreement no. 603542) and the Swedish Research Council Formas Strong Research Environment “Land use today and tomorrow”. A.A. acknowledges support from the Helmholtz Association, especially through its Initiative and Networking funds. M.R. and C.B. acknowledge funding by the European Union through the VOLANTE project (grant agreement no. 265104).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Arneth.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arneth, A., Brown, C. & Rounsevell, M. Global models of human decision-making for land-based mitigation and adaptation assessment. Nature Clim Change 4, 550–557 (2014). https://doi.org/10.1038/nclimate2250

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate2250

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing