Global models of human decision-making for land-based mitigation and adaptation assessment (original) (raw)

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