Land-use changes across distant places: design of a telecoupled agent-based model

References

• An, L., & Liu, J. (2010). Long-term effects of family planning and other determinants of fertility on population and environment: Agent-based modeling evidence from wolong nature reserve, China. Population & Environment, 31(6), 427–459.
   Google Scholar
• Barnaud, C, Bousquet, F, & Trebuil, G. (2008). Multi-agent simulations to explore rules for rural credit in a highland farming community of northern thailand. Ecological Economics, 66(4), 615–627. doi: http://dx.doi.org/10.1016/j.ecolecon.2007.10.022
   Google Scholar
• Barnaud, C, Promburom, T, Trébuil, G, & Bousquet, F. (2007). An evolving simulation/gaming process to facilitate adaptive watershed management in northern mountainous thailand. Simulation & Gaming, 38, 398–420. doi:10.1177/1046878107300670
   Google Scholar
• Barona, E., Ramankutty, N., Hyman, G., & Coomes, O.T. (2010). The role of pasture and soybean in deforestation of the Brazilian Amazon. Environmental Research Letters, 5(2), 024002.
   Google Scholar
• Barreteau, O., & Le Page, C. (2011). Using social simulation to explore the dynamics at stake in participatory participatory research. Journal of Artificial Societies and Social Simulation, 14(4), 12–15.
   Google Scholar
• Berger, T. (2001). Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics, 25(2–3), 245–260. doi:10.1111/agec.2001.25.issue-2-3
   Google Scholar
• Berger, T., & Ringler, C. (2002). Trade-offs, efficiency gains and technical change – modeling water management and land use within a multiple-agent framework. Quarterly Journal of International Agriculture, 41(1), 119–144. Retrieved from http://www.ifpri.org/publication/tradeoffs-efficiency-gains-and-technical-change-modeling-water-management-and-land-use
   Google Scholar
• Berger, T., & Schreinemachers, P. (2009). Agent-based land use models for teaching, extension and collaborative learning. In S. J. Goetz & F. Brouwer (Eds.), New Perspectives on Agri-environmental Policies: A multidisciplinary and transatlantic approach (Routledge Explorations in Environmental Economics) (pp. 1–18). Routledge
   Google Scholar
• Berger, T, Schreinemachers, P, & Woelcke, J. (2006). Multi-agent simulation for the targeting of development policies in less-favored areas. Agricultural Systems, 88(1), 28–43. doi:10.1016/j.agsy.2005.06.002
   Google Scholar
• Balmann, A. (1996). Farm-based modelling of regional structural change: A cellular automata approach. European Review of Agricultural Economics, 24(August 1995), 85–108.
   Google Scholar
• 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.
   Google Scholar
• Brown, D.G., Page, S., Riolo, R., Zellner, M., & Rand, W. (2007). Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science, 19(2), 153–174.
   Google Scholar
• Brown, J.D. (2004). Knowledge, uncertainty and physical geography : Towards the development of methodologies for questioning belief. Transactions of the Institute of British Geographers, 29(3), 367–381.
   Google Scholar
• Cabrera, A. R., Deadman, P., & Brondizio, E. S., & Pinedo-Vasquez, M. (2010). Exploring the choice of decision making method in an agent based model of land use change. In D. A. Swayne, W. Yang, A. A. Voinov, A. Rizzoli, & T. Filatova (Eds.), 2010 International Congress on Environmental Modelling and Software (p. 373). Retrieved from https://scholarsarchive.byu.edu/iemssconference/2010/all/373/?utm_source=scholarsarchive.byu.edu%2Fiemssconference%2F2010%2Fall%2F373&utm_medium=PDF&utm_campaign=PDFCoverPages
   Google Scholar
• Cabrera, R., Deadman, P., & Moran, E., Brondízio, E. S., & Vanwey, L. K. (2012). Exploring demographic and lot effects in an abm/lucc of agriculture in the brazilian amazon. In A. Heppenstall, A. Crooks, L. See, & M. Batty (Eds.), Agent-Based Models of Geographical Systems (pp. 663–676). doi.10.1007/978-90-481-8927-4_33
   Google Scholar
• Carlson, A.K., Zaehringer, J.G., Garrett, R.D., Silva, R.F.B., Furumo, P.R., Rey, A.N.R., … Liu, J. (2018). Toward rigorous telecoupling causal attribution: A systematic review and typology. Sustainability, 10(12). doi:10.3390/su10124426
   Google Scholar
• Castella, J.-C., & Verburg, P.H. (2007). Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecological Modelling, 202(3–4), 410–420. Retrieved from: http://www.sciencedirect.com/science/article/pii/S0304380006005436
   Google Scholar
• Chen, X., Vina, A., Shortridge, A., An, L., & Liu, J. (2014). Assessing the effectiveness of payments for ecosystem services: An agent-based modeling approach. Ecology and Society, 19(1), 7.
   Google Scholar
• Deadman, P., Robinson, D., Moran, E., & Brondizio, E. (2004). Colonist household decisionmaking and land-use change in the Amazon rainforest: An agent-based simulation. Environment and Planning B: Planning and Design, 31(5), 693–709. Retrieved from: http://www.envplan.com/abstract.cgi?id=b3098
   Google Scholar
• Diniz, F.H., Kok, K., Hoogstra-Klein, M.A., & Arts, B. (2015). Mapping future changes in livelihood security and environmental sustainability based on perceptions of small farmers in the Brazilian Amazon. Ecology and Society, 20(2), art26.
   Google Scholar
• Dou, Y., Silva, R.F.B.D., Yang, H., & Liu, J. (2018). Spillover effect offsets the conservation effort in the Amazon. Journal of Geographical Sciences, 28(11), 1715–1732.
   Google Scholar
• Dou, Y., Yao, G., Herzberger, A., Silva, R.F.B., Da, Song, Q., Hovis, C., … Liu, J. (n.d.). Land-use changes across distant places: Implementation of a telecoupled agent-based model. Journal of Artificial Societies and Social Simulation, (In review).
   Google Scholar
• Evans, A. (2012). Chapter 15 uncertainty and error. In M. Batty, A.T.A.T. Crooks, L.M.L.M. See, A.J.A.J. Heppenstall, A.T.A.T. Crooks, L.M.L.M. See, & W. Lane (Eds.), Agent-based models of geographical systems (pp. 309–346).Dordrecht, Netherlands: Springer. doi:10.1007/978-90-481-8927-4.
   Google Scholar
• Evans, T. P, & Kelley, H. (2004). Multi-scale analysis of a household level agent-based model of landcover change. Journal Of Environmental Management, 72(1–2), 57–72. doi:10.1016/j.jenvman.2004.02.008
   Google Scholar
• Evans, T. P, & Kelley, H. (2008). Assessing the transition from deforestation to forest regrowth with an agent-based model of land cover change for south-central indiana (usa). Geoforum, 39(2), 819–832. doi:10.1016/j.geoforum.2007.03.010
   Google Scholar
• Evans, T. P, Phanvilay, K, Fox, J, & Vogler, J. (2011). An agent-based model of agricultural innovation, land-cover change and household inequality: the transition from swidden cultivation to rubber plantations in laos pdr. Journal Of Land Use Science, 6(2–3), 151–173. doi:10.1080/1747423X.2011.558602
   Google Scholar
• Foley, J., Defries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., … Snyder, P.K. (2005). Global consequences of land use. Science, 309(5734), 570–574.
   Google Scholar
• Food and Agriculture Organization of the United Nations. (2016). FAOSTAT statistics database. Retrieved from http://www.fao.org/faostat/en/#data
   Google Scholar
• Friis, C., Nielsen, J.O., Otero, I., Haberl, H., Niewohner, J., & Hostert, P. (2016). From teleconnection to telecoupling: Taking stock of an emerging framework in land system science. Journal of Land Use Science, 11(2), 131–153.
   Google Scholar
• Fuchs, R., Alexander, P., Brown, C., Cossar, F., Henry, R.C., & Rounsevell, M. (2019). Why the US–China trade war spells disaster for the Amazon. Nature, 567(7749), 451–454.
   Google Scholar
• Gibbs, H.K., Munger, J., L’Roe, J., Barreto, P., Pereira, R., Christie, M., … Walker, N.F. (2016). Did ranchers and slaughterhouses respond to zero-deforestation agreements in the Brazilian Amazon? Conservation Letters, 9(1), 32–42.
   Google Scholar
• Gibbs, H.K., Rausch, L., Munger, J., Schelly, I., Morton, D.C., Noojipady, P., … Cerrado, E. (2015). Brazil’s Soy Moratorium. Science, 347(6220), 377–378.
   Google Scholar
• Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., … Toulmin, C. (2010). Food security: The challenge of feeding 9 billion people. Science, 327(February), 812.
   Google Scholar
• Gotts, N.M., & Polhill, J. G., & Law, A. N. R. (2003). Aspiration levels in a land use simulation. Cybernetics & Systems, 34(21), 663–683. Retrieved from doi10.1080/716100277
   Google Scholar
• Gotts, Nicholas, & Polhill, J. G. (2009). When and How to Imitate Your Neighbours : Lessons from and for FEARLUS. Journal of Artificial Societies and Social Simulation, 12(3), 1–2. Retrieved from http://jasss.soc.surrey.ac.uk/12/3/2.html
   Google Scholar
• Gray, S.A., Gray, S., de Kok, J.L., Helfgott, A.E.R., O’Dwyer, B., Jordan, R., & Nyaki, A. (2015). Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20(2). doi:10.5751/ES-07396-200211
   Google Scholar
• Grimm, V., & Railsback, S.F. (2012). Pattern-oriented modelling: A “multi-scope” for predictive systems ecology. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1586), 298–310.
   Google Scholar
• Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W.M., Railsback, S.F., … DeAngelis, D.L. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(11), 984–987.
   Google Scholar
• Happe, K. (2004). Agricultural policies and farm structures. Agent-based modelling and application to EU-policy reform. (Institute of Agricultural Development in Central and Eastern Europe, IAMO; Vol. 30). Retrieved from http://ideas.repec.org/p/wpa/wuwpot/0504011.html
   Google Scholar
• Happe, K., Balmann, A., Kellermann, K., & Sahrbacher, C. (2008). Does structure matter? The impact of switching the agricultural policy regime on farm structures. Journal of Economic Behavior & Organization, 67(2), 431–444.
   Google Scholar
• Happe, K., Hutchings, N.J., Dalgaard, T., & Kellerman, K. (2011). Modelling the interactions between regional farming structure, nitrogen losses and environmental regulation. Agricultural Systems, 104(3), 281–291.
   Google Scholar
• Happe, K., Kellermann, K., & Balmann, A. (2006). Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior. Ecology and Society, 11(1). Retrieved from http://www.ecologyandsociety.org/vol11/iss1/art49/main.html
   Google Scholar
• Heilongjiang Provincial Bureau of Statistics, & Survey Office of the National Bureau of Statistics in Heilongjiang. (n.d.). Heilongjiang statistical yearbook. Beijing: China Statistics Press.
   Google Scholar
• Holzhauer, S, Brown, C, & Rounsevell, M. (2019). Modelling dynamic effects of multi-scale institutions on land use change. Regional Environmental Change, 19, 733–746. doi:10.1007/s10113-018-1424-5
   Google Scholar
• Huang, Q., Parker, D.C., Sun, S., & Filatova, T. (2013). Effects of agent heterogeneity in the presence of a land-market: A systematic test in an agent-based laboratory. Computers, Environment and Urban Systems, 41, 188–203.
   Google Scholar
• Huber, R., Bakker, M., Balmann, A., Berger, T., Bithell, M., Brown, C., … Finger, R. (2018). Representation of decision-making in European agricultural agent-based models. Agricultural Systems, 167, 143–160.
   Google Scholar
• ICSU. (2010). Grand challenges in global sustainability research : Research priorities for the decade. Paris: International council for science.
   Google Scholar
• Instituto Brasileiro de Geografia e Estatistica. (n.d.). Agencia de Noticias. Retrieved March 1, 2019, from https://sidra.ibge.gov.br/tabela/825
   Google Scholar
• Kapsar, K.E., Hovis, C.L., Silva, R.F.B., Da, Buchholtz, E.K., Carlson, A.K., Dou, Y., … Liu, J. (2019). Telecoupling research : The first five years. Sustainability, 11, 1–13.
   Google Scholar
• Lane, S.N. (2001). Constructive comments on D massey ‘space-time, “science” and the relationship between physical geography and human geography: Rsquo. Transactions of the Institute of British Geographers, 26(2), 243–256.
   Google Scholar
• Lapola, D.M., Schaldach, R., Alcamo, J., Bondeau, A., Koch, J., Koelking, C., & Priess, J.A. (2010). Indirect land-use changes can overcome carbon savings from biofuels in Brazil. Proceedings of the National Academy of Sciences of the United States of America, 107(8), 3388–3393.
   Google Scholar
• Le, Q. B, Park, S. J, & Vlek, Paul L.G. (2010). Land use dynamic simulator (ludas): a multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system. Ecological Informatics, 5(3), 203–221. doi:10.1016/j.ecoinf.2010.02.001
   Google Scholar
• Le, Q. B, Park, S. J, Vlek, Paul L.G, & Cremers, A. B. (2008). Land-use dynamic simulator (ludas): a multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system. i. structure and theoretical specification. Ecological Informatics, 3(2), 135–153. doi:10.1016/j.ecoinf.2008.04.003
   Google Scholar
• Le, Q. B, Seidl, R, & Scholz, R. W. (2012). Feedback loops and types of adaptation in the modelling of land-use decisions in an agent-based simulation. Environmental Modelling & Software, 27–28 27–28 27-28(0), 83–96. doi:10.1016/j.envsoft.2011.09.002
   Google Scholar
• Liu, J. (2014). Forest sustainability in China and implications for a telecoupled world. Asia & the Pacific Policy Studies, 1(1), 230–250.
   Google Scholar
• Liu, J., Dietz, T., Carpenter, S.R., Alberti, M., Folke, C., Moran, E., … Taylor, W.W. (2007). Complexity of coupled human and natural systems. Science, 317(5844), 1513–1516.
   Google Scholar
• Liu, J., Dou, Y., Batistella, M., Challies, E., Connor, T., Friis, C., … Sun, J. (2018). Spillover systems in a telecoupled anthropocene: Typology, methods, and governance for global sustainability. Current Opinion in Environmental Sustainability, 33, 58–69.
   Google Scholar
• Liu, J., Hertel, T., Nichols, S., Moran, E., & Viña, A. (2015). Complex dynamics of telecoupled human and natural systems. Retrieved from National Science Foundation Grant website: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1518518
   Google Scholar
• Liu, J., Hull, V., Batistella, M., Defries, R., Dietz, T., Fu, F., … Zhu, C. (2013). Framing sustainability in a telecoupled world. Ecology And Society, 18(2(26)), 19. Retrieved from: http://www.ecologyandsociety.org/vol18/iss2/art26/ES-2013-5873.pdf
   Google Scholar
• Liu, J., Mooney, H., Hull, V., Davis, S.J., Gaskell, J., Hertel, T., … Li, S. (2015). Systems integration for global sustainability. Science, 347(6225), 963–973.
   Google Scholar
• Luus, K.A., Robinson, D.T., & Deadman, P.J. (2011). Representing ecological processes in agent-based models of land use and cover change. Journal of Land Use Science, 8(2), 175–198.
   Google Scholar
• Manson, S M. (2005). Agent-based modeling and genetic programming for modeling land change in the southern yucatán peninsular region of mexico. Agriculture, Ecosystems & Environment, 111(1–4), 47–62. doi:10.1016/j.agee.2005.04.024
   Google Scholar
• Manson, S M. (2006). Bounded rationality in agent‐based models: experiments with evolutionary programs. International Journal of Geographical Information Science, 20(9), 991–1012. Retrieved from http://dx.doi.org/10.1080/13658810600830566 20 267-1012 doi:10.1080/13658810600830566
   Google Scholar
• Manson, Steven, & Evans, T. (2007). Agent-based modeling of deforestation in southern Yucatán, Mexico, and reforestation in the Midwest United States. Proceedings of the National Academy of Sciences of the United States of America, 104(52), 20678–20683. Retrieved from http://www.pnas.org/content/104/52/20678.abstract
   Google Scholar
• Matthews, R. (2006). The people and landscape model (palm): towards full integration of human decision-making and biophysical simulation models. Ecological Modelling, 194(4), 329–343. doi:10.1016/j.ecolmodel.2005.10.032
   Google Scholar
• Meyfroidt, P., & Lambin, E.F. (2009). Forest transition in Vietnam and displacement of deforestation abroad. Proceedings of the National Academy of Sciences of the United States of America, 106(38), 16139–16144.
   Google Scholar
• Millington, J., Xiong, H., Peterson, S., & Woods, J. (2017). Integrating modelling approaches for understanding telecoupling: Global food trade and local land use. Land, 6(3), 56.
   Google Scholar
• Millington, J.D.A., Demeritt, D., & Romero-Calcerrada, R. (2011). Participatory evaluation of agent-based land-use models. Journal of Land Use Science, 6(2–3), 195–210.
   Google Scholar
• 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.
   Google Scholar
• Murungweni, C., van Wijk, M.T., Andersson, J.A., Smaling, E.M.A., Giller, K.E., Wijk, M.T.V., … Giller, K.E. (2011). Application of fuzzy cognitive mapping in livelihood vulnerability. Ecology and Society, 16(4), 8.
   Google Scholar
• Nepstad, D., Mcgrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., … Hess, L. (2014). Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science, 344(6188), 1118–1123.
   Google Scholar
• Ngo, T.A., & See, L. (2012). Calibration and validation of agent-based models of land cover change. In A.J. Heppenstall, A.T. Crooks, L.M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 181–197). doi:10.1007/978-90-481-8927-4.
   Google Scholar
• O’Sullivan, D., Evans, T., Manson, S., Metcalf, S., Ligmann-Zielinska, A., & Bone, C. (2015). Strategic directions for agent-based modeling: Avoiding the YAAWN syndrome. Journal of Land Use Science, (August), 1–11. doi:10.1080/1747423X.2015.1030463
   Google Scholar
• Özesmi, U., & Özesmi, S.L. (2004). Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1–2), 43–64.
   Google Scholar
• Parker, D.C., Entwisle, B., Rindfuss, R.R., Vanwey, L.K., Manson, S.M., Moran, E., … Malanson, G. (2008). Case studies, cross-site comparisons, and the challenge of generalization: Comparing agent-based models of land-use change in frontier regions. Journal of Land Use Science, 3(1), 41–72.
   Google Scholar
• Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2), 314–337.
   Google Scholar
• Polhill, J Gary, & Parker, D. C., & Gotts, N. M. (2007). Effects of land markets on competition between innovators and imitators in land use : results from fearlus-elmm. In B. Edmonds, K. G. Troitzsch, & C. H. Iglesias (Eds.), Social Simulation: Technologies, Advances and New Discoveries (pp. 81–97). doi.10.4018/978-1-59904-522-1.ch007
   Google Scholar
• Polhill, J.G., Sutherland, L.-A., & Gotts, N. M. (2010). Using qualitative evidence to enhance an agent-based modelling system for studying land use change. Journal of Artificial Societies and Social Simulation, 13(2), 10. Retrieved from http://jasss.soc.surrey.ac.uk/13/2/10.html
   Google Scholar
• Reimer, J.J., Zheng, X., & Gehlhar, M.J. (2012). Export demand elasticity estimation for major U.S. crops. Journal of Agricultural and Applied Economics, 44(4), 501–515. Retrieved from: http://purl.umn.edu/137120
   Google Scholar
• Richards, P. (2015). What drives indirect land use change? How Brazil’s agriculture sector influences frontier deforestation. Annals of the Association of American Geographers, 105(5), 1026–1040.
   Google Scholar
• Richards, P.D., Walker, R.T., & Arima, E.Y. (2014). Spatially complex land change: The indirect effect of Brazil’s agricultural sector on land use in Amazonia. Global Environmental Change, 29(November), 1–9.
   Google Scholar
• Robinson, D.T., Brown, D.G., Parker, D.C., Schreinemachers, P., Janssen, M.A., Huigen, M., … Barnaud, C. (2007). Comparison of empirical methods for building agent-based models in land use science. Journal of Land Use Science, 2(1), 31–55. .
   Google Scholar
• Schreinemachers, P. (2005). The (Ir) relevance of the Crop Yield Gap Concept to Food Security in Developing Countries-With an Application of Multi Agent Modeling to Farming Systems in Uganda (University of Bonn Dissertation, Cuvillier Verlag Gottingen). Retrieved from https://igm.uni-hohenheim.de/cms/fileadmin/documents/ProjectDocs/OtherDocuments/Schreinemachers_2006 cuv.pdf
   Google Scholar
• Schreinemachers, P., & Berger, T. (2006b). Simulating farm household poverty: from passive victims to adaptive agents. International Association of Agricultural Economists Conference, 1–22. Retrieved from http://ideas.repec.org/p/ags/iaae06/25479.html
   Google Scholar
• Schreinemachers, P, & Berger, T. (2011). An agent-based simulation model of human–environment interactions in agricultural systems. Environmental Modelling & Software, 26(7), 845–859. doi:10.1016/j.envsoft.2011.02.004
   Google Scholar
• Seto, K.C., & Reenberg, A. (Eds.). (2014). Rethinking global land use in an urban era. Massachusetts, Cambridge: MIT Press.
   Google Scholar
• Seto, K.C., Reenberg, A., Boone, C.G., Fragkias, M., Haase, D., Langanke, T., … Simon, D. (2012). Urban land teleconnections and sustainability. Proceedings of the National Academy of Sciences of the United States of America, 109(20), 7687–7692.
   Google Scholar
• Silva, R., Batistella, M., Dou, Y., Moran, E., Torres, S., & Liu, J. (2017). The Sino-Brazilian telecoupled soybean system and cascading effects for the exporting country. Land, 6(53). doi:10.3390/land6030053
   Google Scholar
• Smajgl, A., Brown, D.G., Valbuena, D., & Huigen, M.G.A. (2011). Empirical characterisation of agent behaviours in socio-ecological systems. Environmental Modelling and Software, 26(7), 837–844.
   Google Scholar
• Sun, J., Mooney, H., Wu, W., Tang, H., Tong, Y., Xu, Z., … Liu, J. (2018). Importing food damages domestic environment: Evidence from global soybean trade. Proceedings of the National Academy of Sciences of the United States of America, 115(21), 5415–5419.
   Google Scholar
• Sun, J., Tong, Y., & Liu, J. (2017). Telecoupled land-use changes in distant countries. Journal of Integrative Agriculture, 16(2), 368–376.
   Google Scholar
• Tong, Y., Liu, J., Li, X., Sun, J., Herzberger, A., Wei, D., … Zhang, F. (2017). Cropping system conversion led to organic carbon change in China’s mollisols regions. Scientific Reports, 7(1), 1–9.
   Google Scholar
• Turner, B.L., II, Lambin, E.F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences of the United States of America, 104(52), 20666–20672. Retrieved from: http://www.pnas.org/content/104/52/20666.short
   Google Scholar
• Valbuena, D., Verburg, P.H., & Bregt, A.K. (2008). A method to define a typology for agent-based analysis in regional land-use research. Agriculture, Ecosystems & Environment, 128(1–2), 27–36.
   Google Scholar
• van Vliet, M., Kok, K., & Veldkamp, T. (2010). Linking stakeholders and modellers in scenario studies: The use of fuzzy cognitive maps as a communication and learning tool. Futures, 42(1), 1–14.
   Google Scholar
• Verburg, P.H., Alexander, P., Evans, T., Magliocca, N.R., Malek, Z., Rounsevell, M.D., & van Vliet, J. (2019). Beyond land cover change: Towards a new generation of land use models. Current Opinion in Environmental Sustainability, 38(May), 77–85.
   Google Scholar
• Verburg, P.H., Crossman, N., Ellis, E., Heinimann, A., Hostert, P., Mertz, O., … Lin, Y.-P. (2016). Science Plan and Implementation Strategy. Bern: GLP.
   Google Scholar
• Verburg, P.H., Crossman, N., Ellis, E.C., Heinimann, A., Hostert, P., Mertz, O., … Zhen, L. (2015). Land system science and sustainable development of the earth system: A global land project perspective. Anthropocene, 12, 29–41.
   Google Scholar
• Villamor, G. B., & Van, Troitzsch, Noordwijk, M., K. G., & Vlek, P. L. G. (2012). Human decision making for empirical agent-based models : construction and validation. In R. Seppelt, A. A. Voinov, S. Lange, & D. Bankamp (Eds.), International Environmental Modelling and Software Society (iEMSs), 2012 International Congress on Environmental Modelling and Software. Leipzig, Germany: Sixth Biennial Meeting.
   Google Scholar
• Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmental Modelling & Software, 25(11), 1268–1281.
   Google Scholar
• Wang, F., & Liu, J. (2016). Conservation planning beyond giant pandas: The need for an innovative telecoupling framework. Science China Life Sciences, 60(5), 1–4.
   Google Scholar
• Yang, W., Hyndman, D.W., Winkler, J.A., Viña, A., Deines, J.M., Lupi, F., … Liu, J. (2016). Urban water sustainability: Framework and application. Ecology and Society, 21(4(4)), 14.
   Google Scholar
• Yao, G., Hertel, T.W., & Taheripour, F. (2018). Economic drivers of telecoupling and terrestrial carbon fluxes in the global soybean complex. Global Environmental Change, 50(March), 190–200.
   Google Scholar