428
Scott, M. J., Daly, D. S., Hejazi, M. I., Kyle, G. P., Liu, L., McJeon, H. C., Mundra, A., Patel, P. L.,
Rice, J. S., & Voisin, N. (2016). Sensitivity of future U.S. Water shortages to socioeconomic
and climate drivers: a case study in Georgia using an integrated human-earth system modeling
framework, Climatic Change 136:233–246.
Sokolov, A.P., Stone, P. H., Forest, C. E., Prinn, R., Sarofim, M. C., Webster, M., Paltsev, S.,
Schlosser, C. A., Kicklighter, D., Dutkiewicz, S. Reilly, J., Wang, C., Felzer, B., & Jacoby,
H. D. (2009). Probabilistic Forecast for 21st Century Climate Based on Uncertainties in
Emissions (without Policy) and Climate Parameters, Report No. 169, MIT Joint Program on
the Science and Policy of Global Change, Feb 2009.
Voisin, N., Liu, L., Hejazi, M., Tesfa, T., Li, H., Huang, M., Liu, Y., & Leung, L. R. (2013). Oneway coupling of an integrated assessment model and a water resources model: Evaluation and
implications of future changes over the U.S. Midwest. Hydrology and Earth System Sciences,
17(11), 4555–4575. https://doi.org/10.5194/hess-17-4555-2013.
Von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S.,
Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Lotze-Campen, H., Mason d’Croz, D., Nelson,
G. C., Sands, R. D., Schmitz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., & van
Meijl, H. (2014). Why do global long-term scenarios for agriculture differ? An overview
of the AgMIP global economic model intercomparison. Agricultural Economics, 45(1), 3–20.
https://doi.org/10.1111/agec.12086.
Webster, M. D., Babiker, M., Mayer, M., Reilly, J. M., Harnisch, J., Hyman, R., Sarofim, M. C.,
& Wang, C. (2002). Uncertainty in emissions projections for climate models. Atmospheric
Environment, 36(22), 3659–3670. https://doi.org/10.1016/S1352-2310(02)00245-5.
Weyant, J. P., de la Chesnaye, F. C., & Blanford, G. J. (2006). Overview of EMF-21: Multigas
mitigation and climate policy. The Energy Journal, 27, 1–32.
Weyant, J. P. (2004). Special issue-EMF 19 Alternative technology strategies for climate change
policy. Energy Economics, 26, 501–755.
Wigley, T. M. L., Richels, R., & Edmonds, J. A. (1996). Economic and environmental choices in
the stabilization of atmospheric CO2 concentrations. Nature, 379(6562), 240–243. https://doi.
org/10.1038/379240a0.
Further Reading
Graham, N. T., et al. (2018). Water sector assumptions for the shared socioeconomic pathways in
an integrated modeling framework. Water Resources Research, 54, 6423–6440 Retrieved from
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018WR023452.
Motesharrei, S., et al. (2014). Human and nature dynamics (HANDY): Modeling inequality and use
of resources in the collapse or sustainability of societies. Ecological Economics, 101, 90–102
Retrieved from https://www.sciencedirect.com/science/article/pii/S0921800914000615.
Motesharrei, S., et al. (2016). Modeling sustainability: Population, inequality, consumption, and
bidirectional coupling of the earth and human systems. National Science Review, 3(4), 470–494
Retrieved from https://academic.oup.com/nsr/article/3/4/470/2669331.
Muñoz-Castillo, R., et al. (2017). Uncovering the green, blue, and grey water footprint and virtual
water of biofuel production in brazil: A nexus perspective. Sustainability, 9(11), 2049 Retrieved
from https://www.mdpi.com/2071-1050/9/11/2049.
National Science Foundation (NSF). (2014). Food, energy and water transformative research
opportunities in the mathematical and physical sciences.
Perrone, D., & Hornberger, G. (2014). Water, food, and energy security: Scrambling for resources
or solutions? WIREs Water, 1, 49–68. https://doi.org/10.1002/wat2.1004.
F. R. Miralles-Wilhelm
Scott, M. J., Daly, D. S., Hejazi, M. I., Kyle, G. P., Liu, L., McJeon, H. C., Mundra, A., Patel, P. L.,
Rice, J. S., & Voisin, N. (2016). Sensitivity of future U.S. Water shortages to socioeconomic
and climate drivers: a case study in Georgia using an integrated human-earth system modeling
framework, Climatic Change 136:233–246.
Sokolov, A.P., Stone, P. H., Forest, C. E., Prinn, R., Sarofim, M. C., Webster, M., Paltsev, S.,
Schlosser, C. A., Kicklighter, D., Dutkiewicz, S. Reilly, J., Wang, C., Felzer, B., & Jacoby,
H. D. (2009). Probabilistic Forecast for 21st Century Climate Based on Uncertainties in
Emissions (without Policy) and Climate Parameters, Report No. 169, MIT Joint Program on
the Science and Policy of Global Change, Feb 2009.
Voisin, N., Liu, L., Hejazi, M., Tesfa, T., Li, H., Huang, M., Liu, Y., & Leung, L. R. (2013). Oneway coupling of an integrated assessment model and a water resources model: Evaluation and
implications of future changes over the U.S. Midwest. Hydrology and Earth System Sciences,
17(11), 4555–4575. https://doi.org/10.5194/hess-17-4555-2013.
Von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S.,
Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Lotze-Campen, H., Mason d’Croz, D., Nelson,
G. C., Sands, R. D., Schmitz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., & van
Meijl, H. (2014). Why do global long-term scenarios for agriculture differ? An overview
of the AgMIP global economic model intercomparison. Agricultural Economics, 45(1), 3–20.
https://doi.org/10.1111/agec.12086.
Webster, M. D., Babiker, M., Mayer, M., Reilly, J. M., Harnisch, J., Hyman, R., Sarofim, M. C.,
& Wang, C. (2002). Uncertainty in emissions projections for climate models. Atmospheric
Environment, 36(22), 3659–3670. https://doi.org/10.1016/S1352-2310(02)00245-5.
Weyant, J. P., de la Chesnaye, F. C., & Blanford, G. J. (2006). Overview of EMF-21: Multigas
mitigation and climate policy. The Energy Journal, 27, 1–32.
Weyant, J. P. (2004). Special issue-EMF 19 Alternative technology strategies for climate change
policy. Energy Economics, 26, 501–755.
Wigley, T. M. L., Richels, R., & Edmonds, J. A. (1996). Economic and environmental choices in
the stabilization of atmospheric CO2 concentrations. Nature, 379(6562), 240–243. https://doi.
org/10.1038/379240a0.
Further Reading
Graham, N. T., et al. (2018). Water sector assumptions for the shared socioeconomic pathways in
an integrated modeling framework. Water Resources Research, 54, 6423–6440 Retrieved from
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018WR023452.
Motesharrei, S., et al. (2014). Human and nature dynamics (HANDY): Modeling inequality and use
of resources in the collapse or sustainability of societies. Ecological Economics, 101, 90–102
Retrieved from https://www.sciencedirect.com/science/article/pii/S0921800914000615.
Motesharrei, S., et al. (2016). Modeling sustainability: Population, inequality, consumption, and
bidirectional coupling of the earth and human systems. National Science Review, 3(4), 470–494
Retrieved from https://academic.oup.com/nsr/article/3/4/470/2669331.
Muñoz-Castillo, R., et al. (2017). Uncovering the green, blue, and grey water footprint and virtual
water of biofuel production in brazil: A nexus perspective. Sustainability, 9(11), 2049 Retrieved
from https://www.mdpi.com/2071-1050/9/11/2049.
National Science Foundation (NSF). (2014). Food, energy and water transformative research
opportunities in the mathematical and physical sciences.
Perrone, D., & Hornberger, G. (2014). Water, food, and energy security: Scrambling for resources
or solutions? WIREs Water, 1, 49–68. https://doi.org/10.1002/wat2.1004.
F. R. Miralles-Wilhelm