Integrated Assessment Models of the Food, Energy, and Water Nexus: A Review and an Outline of Research Needs

Annual Review of Resource Economics

Volumn 9, Issue , 2017, Pages 143-163

  Kling, Catherine L ^a   Arritt, Raymond W ^a   Calhoun, Gray ^a   Keiser, David A ^a  

^a IOWA STATE UNIVERSITY   (United States)

Author keywords

Energy; Food; Integrated assessment; Water

Indexed keywords

EID: 85031124332     PISSN: 19411340     EISSN: 19411359     Source Type: Journal    
DOI: 10.1146/annurev-resource-100516-033533     Document Type: Review

References (129)

1. Adams RM, Rosenzweig C, Peart RM, Ritchie JT, McCarl BA, et al. 1990. Global climate change and US agriculture. Nature 345:219-24
2. Arnold JG, Fohrer N. 2005. SWAT2000: current capabilities and research opportunities in applied watershed modeling. Hydrol. Process. 19(3):563-72
3. Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, et al. 2012. SWAT: model use, calibration, and validation. Trans. ASABE 55(4):1491-508
4. Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assessment part I: model development. J. Am. Water Resour. Assoc. 34(1):73-89
5. Bates JM, Granger CW. 1969. The combination of forecasts. Oper. Res. Q. 20(4):451-68
6. Borah DK, Yagow G, Saleh A, Barnes PL, Rosenthal W, et al. 2006. Sediment and nutrient modeling for TMDL development and implementation. Trans. ASABE 49(4):967-86
7. Bosch NS, Allan JD, Selegean JP, Scavia D. 2013. Scenario-testing of agricultural best management practices in Lake Erie watersheds. J. Great Lakes Res. 39:429-36
8. Bouraoui F, Grizzetti B. 2014. Modelling mitigation options to reduce diffuse nitrogen water pollution from agriculture. Sci. Total Environ. 468-469:1267-77
9. Bressiani DDA, Gassman PW, Fernandes JG, Garbossa LHP, Srinivasan R, et al. 2015. A review of soil and water assessment tool (SWAT) applications in Brazil: challenges and prospects. Int. J. Agric. Biol. Eng. 8(3):9-35
10. Britz W, Hertel TW. 2011. Impacts of EU biofuels directives on global markets and EU environmental quality: an integrated PE, global CGE analysis. Agric. Ecosyst. Environ. 142(1-2):102-9
11. Carpenter SR, Ludwig D, Brock WA. 1999. Management of eutrophication for lakes subject to potentially irreversible change. Ecol. Appl. 9:751-71
12. Chen X, Huang H, Khanna M, Önal H. 2014. Alternative fuel standards: welfare effects and climate benefits. J. Environ. Econ. Manag. 67:241-57
13. Clark TE, McCracken MW. 2013. Advances in forecast evaluation. In Handbook of Economic Forecasting, Vol. 2, eds. G Elliot, A Timmermann, pp. 1107-1201. North-Holland, Neth.: Elsevier
14. Cohen A, Keiser DA. 2015. The effectiveness of overlapping pollution regulations: evidence from the ban on phosphate in dishwasher detergent. Work. Pap. 14034, Iowa State Univ., Ames
15. Corradi V, Swanson NR. 2006. Predictive density evaluation. Handb. Econ. Forecast. 1:197-284
16. Dalin C, Konar M, Hanasaki N, Rinaldo A, Rodriguez-Iturbe I. 2012. Evolution of the global virtual water trade network. PNAS 109(16):5989-94
17. Daniel EB, Camp JV, LeBoeuf EJ, Penrod JR, Dobbins JP, Abkowitz MD. 2011. Watershed modeling and its applications: a state-of-the-art review. Open Hydrol. J. 5:26-50
18. de Brauwere A, Ouattara NK, Servais P. 2014. Modeling fecal indicator bacteria concentrations in natural surface waters: a review. Crit. Rev. Environ. Sci. Technol. 44:2380-453
19. Diebold FX, Mariano RS. 1995. Comparing predictive accuracy. J. Bus. Econ. Stat. 13(3):253-63
20. Douglas-Mankin KR, Srinivasan R, Arnold JG. 2010. Soil and Water Assessment Tool (SWAT) model: current developments and applications. Trans. ASABE 53(5):1423-31
21. Earnhart D. 2004a. Panel data analysis of regulatory factors shaping environmental performance. Rev. Econ. Stat. 86(1):391-401
22. Earnhart D. 2004b. Regulatory factors shaping environmental performance at publicly-owned treatment plants. J. Environ. Econ. Manag. 48(1):665-81
23. Elliott G, Timmermann A. 2008. Economic forecasting. J. Econ. Lit. 46(1):3-56
24. Elobeid A, Carriquiry M, Dumortier J, Rosas F, Mulik K, et al. 2013. Biofuel expansion, fertilizer use and GHG emissions: unintended consequences of mitigation policies. Econ. Res. Int. 2013:708604
25. Fabiosa JF, Beghin JC, Dong F, Elobeid A, Tokgöz S, Yu T. 2010. Land allocation effects of the global ethanol surge: predictions from the international FAPRI model. Land Econ. 86(4):687-706
26. Farmer JD, Foley D. 2009. The economy needs agent-based modelling. Nature 460:685-86
27. Farrell AE. 2006. Ethanol can contribute to energy and environmental goals. Science 311:506-8
28. Fleming P, Lichtenberg E, Newburn DA. 2015. Agricultural cost sharing and conservation practices for nutrient reduction in the Chesapeake Bay watershed. Presented at Annu. Meet. Agric. Appl. Econ., Assoc. West. Agric. Econ. Assoc., San Francisco
29. Foley JA, Levis S, Costa MH, Cramer W, Pollard D. 2000. Incorporating dynamic vegetation cover within global climate models. Ecol. Appl. 10:1620-32
30. Freeman AM III, Herriges JA, Kling CL. 2014. The Measurement of Environmental and Resource Values: Theory and Methods. Washington, DC: RFF Press. 3rd ed.
31. Gao L, Li D. 2014. A review of hydrological/water-quality models. Front. Agric. Sci. Eng. 1(4):267-76
32. Gassman PW, Reyes MR, Green CH, Arnold JG. 2007. The soil and water assessment tool: historical devel-opment, applications, and future research directions. Trans. ASABE 50(4):1211-50
33. Gassman PW, Sadeghi AM, Srinivasan R. 2014. Applications of the SWAT model special section: overview and insights. J. Environ. Q. 43(1):1-8
34. Gassman PW, Wang Y. 2015. IJABE SWAT Special Issue: innovative modeling solutions for water resource problems. Int. J. Agric. Biol. Eng. 8(3):1-8
35. Gazzaz NM, Yusoff MK, Aris AZ, Juahir H, Ramli MF. 2012. Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar. Pollut. Bull. 64(11):2409-20
36. Gleckler PJ, Taylor KE, Doutriaux C. 2008. Performance metrics for climate models. J. Geophys. Resour. 113:D06104
37. Gneiting T, Katzfuss M. 2014. Probabilistic forecasting. Annu. Rev. Stat. Appl. 1:125-51
38. Goetz RU, Zilberman D. 2000. The dynamics of spatial pollution: the case of phosphorus runoff from agri-cultural land. J. Econ. Dyn. Control 24:143-63
39. Gonzalez-Ramirez MJ, Kling CL, Arbuckle JG Jr. 2015. Cost-share effectiveness in the adoption of cover crops in Iowa. Presented at Annu. Meet. Agric. Appl. Econ., Assoc. West. Agric. Econ. Assoc., San Francisco
40. Gopalakrishnan S, Smith MD, Slott JM, Murray AB. 2011. The value of disappearing beaches: a hedonic pricing model with endogenous beach width. J. Environ. Econ. Manag. 61:297-310
41. Greenstone M, Hanna R. 2014. Environmental regulations, air and water pollution, and infant mortality in India. Am. Econ. Rev. 104(10):3038-72
42. Hansen LP, Sargent TJ. 2007. Recursive robust estimation and control without commitment. J. Econ. Theory 136(1):1-27
43. Hansen LP, Sargent TJ. 2008. Robustness. Princeton, NJ: Princeton Univ. Press
44. Hansen LP, Sargent TJ. 2014. Uncertainty Within Economic Models. Singapore: World Sci.
45. Harvey DI, Leybourne SJ, Newbold P. 1998. Tests for forecast encompassing. J. Bus. Econ. Stat. 16(2):254-59
46. Hayes DJ, Babcock BA, Fabiosa J, Tokgoz S, Elobeid A, et al. 2009. Biofuels: potential production capacity, effects on grain and livestock sectors, and implications for food prices and consumers. J. Agric. Appl. Econ. 41(2):1-27
47. Hendricks NP, Sinnathamby S, Douglas-Mankin K, Smith A, Sumner DA, Earnhart DH. 2014. The envi-ronmental effects of crop price increases: nitrogen losses in the US corn belt. J. Environ. Econ. Manag. 68(3):507-26
48. Hertel TW, Burke MB, Lobell DB. 2010a. The poverty implications of climate-induced crop yield changes by 2030. Glob. Environ. Change 20(4):577-85
49. Hertel TW, Golub A, Jones A, O'Hare M, Plevin R, Kammen D. 2010b. Effects of US maize ethanol on global land use and greenhouse gas emissions: estimating market-mediated responses. Bioscience 60(3):223-31
50. Hertel TW, Villoria NB. 2014. GEOSHARE: geospatial open source hosting of agriculture, resource and environ-mental data for discovery and decision making. Work. Pap., Purdue Univ., West Lafayette, IN. https://mygeohub.org/resources/977/download/GEOSHARE-Prospectus-Final.pdf
51. Hoekstra AY, Mekonnen MM. 2012. The water footprint of humanity. PNAS 109(9):3232-37
52. Housh M, Cai X, Ng T, McIsaac G, Ouyang Y, et al. 2014. System of systems model for analysis of biofuel development. J. Infrastruct. Syst. 21(3). https://doi.org/10.1061/(ASCE)IS.1943-555X.0000238
53. Housh M, Yaeger M, Cai X, McIsaac G, Khanna M, et al. 2015. Managing multiple mandates: a system of systems model to analyze strategies for producing cellulosic ethanol and reducing riverine nitrate loads in the Upper Mississippi River Basin. Environ. Sci. Technol. 49(19):11932-40
54. Howard G, Roe BE. 2013. Stripping because you want to versus stripping because the money is good: a latent class analysis of farmer preferences regarding filter strip programs. Presented at AAEA/CAES Joint Annu. Meet., Washington, DC
55. Howitt R, Medellin-Azuara J, MacEwan D, Lund J, Sumner D. 2014. Economic analysis of the 2014 drought for California agriculture. Rep., Cent. Watershed Sci., Univ. Calif., Davis
56. Hudiburg TW, Wang W, Khanna M, Long SP, Dwivedi P, et al. 2016. Impactsofa32billion gallon bioenergy landscape on land and fossil fuel use in the US. Nat. Energy 1:15005
57. Iho A. 2010. Essays on Socially Optimal Phosphorus Policies in Crop Production. Jokioinen, Finl.: MTT Agrifood Res.
58. Iho A, Laukkanen M. 2012. Precision phosphorus management and agricultural phosphorus loading. Ecol. Econ. 77:91-102
59. Imbens GW, Wooldridge JM. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47(1):5-86
60. Jayakrishnan R, Srinivasan R, Santhi C, Arnold JG. 2005. Advances in the application of the SWAT model for water resources management. Hydrol. Process. 19:749-62
61. Jiang Y, Nan Z, Yang S. 2013. Risk assessment of water quality using Monte Carlo simulation and artificial neural network method. J. Environ. Manag. 122:130-36
62. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, et al. 2003. The DSSAT cropping system model. Eur. J. Agron. 18:235-65
63. Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, et al. 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18:267-88
64. Keiser DA, Shapiro JS. 2016. Consequences of the Clean Water Act and the demand for water quality. Work. Pap., Iowa State Univ., Ames
65. Khanna M, Zilberman D. 2012. Modeling the land-use and greenhouse-gas implications of biofuels. Clim. Change Econ. 3:1250016
66. Khanna M, Zilberman D, Crago C. 2014. Modeling land use change with biofuels. In Oxford Handbook of Land Economics, ed. JM Duke, JJ Wu, pp. 210-35. Oxford, UK: Oxford Univ. Press
67. Kilian L. 2013. Structural vector autoregressions. In Handbook of Research Methods and Applications in Empirical Macroeconomics, ed. N Hashimzade, M Thornton, pp. 515-54. Cheltenham, UK: Edward Elgar
68. Kling C, Phaneuf D, Zhao J. 2012. From Exxon to BP: Has some number become better than no number? J. Econ. Perspect. 26(4):3-26
69. Knapp KC, Schwabe KA. 2008. Spatial dynamics of water and nitrogen management in irrigated agriculture. Am. J. Agric. Econ. 90:524-39
70. Konar M, Hussein Z, Hanasaki N, Mauzerall DL, Rodriguez-Iturbe I. 2013. Virtual water trade flows and savings under climate change. Hydrol. Earth Syst. Sci. Discuss. 10(1):67-101
71. Krysanova V, Arnold JG. 2008. Advances in ecohydrological modelling with SWAT-a review. Hydrol. Sci. J. 53(5):939-47
72. Krysanova V, White M. 2015. Advances in water resources assessment with SWAT-an overview. Hydrol. Sci. J. 60(5):771-83
73. LaBeau MB, Robertson DM, Mayer AS, Pijanowskic BC, Saad DA. 2014. Effects of future urban and biofuel crop expansions on the riverine export of phosphorus to the Laurentian Great Lakes. Ecol. Model. 277:27-37
74. Laborde D. 2011. Assessing the land use change consequences of European biofuel policies. Rep., ATLASS Consort., Int. Food Policy Res. Inst., Washington, DC
75. Landry CE, Hindsley P. 2011. Valuing beach quality with hedonic property models. Land Econ. 87:92-108
76. Lipscomb M, Mobarak AM. 2014. Decentralization and water pollution spillovers: evidence from the redrawing of county boundaries in Brazil. Work. Pap., Yale Univ., New Haven, Conn.
77. Liu J, Mooney H, Hull V, Davis SJ, Gaskell J, et al. 2015. Systems integration for global sustainability. Science 347(6225):1258832
78. Lobell DB, Banziger M, Magorokosho C, Vivek B. 2011. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1:42-45
79. Lotze-Campen H, von Lampe M, Kyle P, Fujimori S, Havlik P, et al. 2014. Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agric. Econ. 45:103-16
80. Manski C. 1990. Nonparametric bounds on treatment effects. Am. Econ. Rev. Pap. Proc. 80:319-23
81. Manski C. 1995. Identification Problems in the Social Sciences. Cambridge, MA: Harvard Univ. Press
82. McCarl BA, Schneider UA. 2001. Greenhouse gas mitigationinU. S. agriculture and forestry. Science 294:2481-82
83. McLellan E, Robertson D, Schilling K, Tomer M, Kostel J, et al. 2015. Reducing nitrogen export from the corn belt to the Gulf of Mexico: agricultural strategies for remediating hypoxia. J. Am. Water Resour. Assoc. 51(1):263-89
84. Mearns LO, Arritt R, Biner S, Bukovsky MS, McGinnis S, et al. 2012. The North American regional climate change assessment program: overview of phase I results. Bull. Am. Meteorol. Soc. 93(9):1337-62
85. Mérel P, Howitt R. 2014. Theory and application of positive mathematical programming in agriculture and the environment. Annu. Rev. Resour. Econ. 6:451-70
86. Nair SS, King KW, Witter JD, Sohngen BL, Fausey NR. 2011. Importance of crop yield in calibrating watershed water quality simulation tools. J. Am. Water Resour. Assoc. 47:1285-97
87. Ng TL, Eheart JW, Cai X, Braden JB. 2011. An agent-based model of farmer decision-making and water quality impacts at the watershed scale under markets for carbon allowances and a second-generation biofuel crop. Water Resour. Res. 47(9). https://doi.org/10.1029/2011WR010399
88. Nordhaus WD. 2008. A Question of Balance: Weighing the Options on Global Warming Policies. New Haven, CT: Yale Univ. Press
89. Nordhaus WD, Boyer J. 2000. Warming the World: Economic Models of Global Warming. Cambridge, MA: MIT Press
90. Olmstead SM, Muehlenbachs LA, Shih J-S, Chu Z, Krupnick AJ. 2013. Shale gas development impacts on surface water quality in Pennsylvania. PNAS 110(13):4962-67
91. Paris Q, Howitt RE. 1998. An analysis of ill-posed production problems using maximum entropy. Am. J. Agric. Econ. 80:124-38
92. Pautsch G, Kurkalova L, Babcock BA, Kling C. 2001. The efficiency of sequestering carbon in agricultural soils. Contemp. Econ. Policy 19:123-34
93. Pelikan J, Britz W, Hertel TW. 2015. Green light for green agricultural policies? An analysis at regional and global scales. J. Agric. Econ. 66(1):1-19
94. Plantinga AJ, Mauldin T, Miller DJ. 1999. An econometric analysis of the costs of sequestering carbon in forests. Am. J. Agric. Econ. 81:812-24
95. Preston SD, Alexander RB, Wolock DM. 2011. Sparrow modeling to understand water-quality conditions in major regions of the United States: a featured collection introduction. J. Am. Water Resour. Assoc. 47(5):887-90
96. Preston SD, Alexander RB, Woodside MD, Hamilton PA. 2009. SPARROW MODELING-enhancing understanding of the nation's water quality. Factsheet 2009-3019, US Geol. Surv., US Dep. Inter., Washington, DC
97. Rabotyagov S, Campbell T, White M, Arnold J, Atwood J, et al. 2014. Cost-effective targeting of conservation investments to reduce the northern Gulf of Mexico hypoxic zone. PNAS 111:18530-35
98. Randhir TO, Hertel TW. 2000. Trade liberalization as a vehicle for adapting to global warming. Agric. Resour. Econ. Rev. 29:159-72
99. Ranson M, Stavins RN. 2015. Linkage of greenhouse gas emissions trading systems: learning from experience. Clim. Policy 16:284-300
100. Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, et al. 2013. The Agricultural Model Intercompar-ison and Improvement Project (AgMIP): protocols and pilot studies. Agric. Forest Meteorol. 170:166-82
101. Rosenzweig C, Parry ML. 1994. Potential impact of climate change on world food supply. Nature 367:133-38
102. Rossi B. 2014. Density forecasts in economics, forecasting and policymaking. Work. Pap., ICREA, Univ. Pompeu Fabra, Barcelona
103. Rudik I. 2016. Optimal climate policy when damages are unknown. SSRN Work. Pap, Iowa State Univ., Ames. https://doi.org/10.2139/ssrn.2516632
104. Schlenker W, Hanemann WM, Fisher AC. 2005. Will US agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. Am. Econ. Rev. 108:395-406
105. Schlenker W, Hanemann WM, Fisher AC. 2006. The impact of global warming on US agriculture: an econometric analysis of optimal growing conditions. Rev. Econ. Stat. 88(1):113-25
106. Schlenker W, Roberts MJ. 2009. Nonlinear temperature effects indicate severe damages to U. S. crop yields under climate change. PNAS 106(37):15594-98
107. Schmitz C, van Meijl H, Kyle P, Nelson GC, Fujimori S, et al. 2014. Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agric. Econ. 45:69-84
108. Schwarz GE, Alexander RB, Smith RA, Preston SD. 2011. The regionalization of national-scale SPARROW models for stream nutrients. J. Am. Water Resour. Assoc. 47(5):1151-72
109. Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A, et al. 2008. Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319:1238-40
110. Segarra E, Ethridge DE, Deussen CR, Onken AB. 1989. Nitrogen carry-over impacts in irrigated cotton production, Southern High Plains of Texas. West. J. Agric. Econ. 14(20):300-9
111. Shimshack JP, Ward MB. 2008. Enforcement and over-compliance. J. Environ. Econ. Manag. 55(1):90-105
112. Sigman H. 2002. International spillovers and water quality in rivers: Do countries free ride? Am. Econ. Rev. 92(4):1152-59
113. Sigman H. 2005. Transboundary spillovers and decentralization of environmental policies. J. Environ. Econ. Manag. 50:82-101
114. Sims CA. 1980. Macroeconomics and reality. Econometrica 48(1):1-48
115. Smith RA, Schwarz GE, Alexander RB. 1997. Regional interpretation of water-quality monitoring data. Water Resour. Res. 33:2781-98
116. Smith VK, Wolloh CV. 2012. Has surface water quality improved since the Clean Water Act? NBER Work. Pap. 18192
117. Sprague LA, Gronberg JAM. 2012. Relating management practices and nutrient export in agricultural watersheds of the United States. J. Environ. Q. 41(6):1939-50
118. Stavins RN. 1999. The costs of carbon sequestration: a revealed-preference approach. Am. Econ. Rev. 89:994-1009
119. Taheripour F, Hertel TW, Liu J. 2013. The role of irrigation in determining the global land use impacts of biofuels. Energy Sustain. Soc. 3(1):1-18
120. Timmermann A. 2006. Forecast combinations. Handb. Econ. Forecast. 1:135-96
121. Tuppad P, Douglas-Mankin KR, Lee T, Srinivasan R, Arnold JG. 2011. Soil and Water Assessment Tool (SWAT) hydrologic/water quality model: extended capability and wider adoption. Trans. ASABE 54(5):1677-84
122. Uhlig H. 2005. What are the effects of monetary policy on output? Results from an agnostic identification procedure. J. Monet. Econ. 52(2):381-419
123. West KD. 1996. Asymptotic inference about predictive ability. Econometrica 64(5):1067-84
124. Williams JR, Arnold JG, Kiniry JR, Gassman PW, Green CH. 2008. Historyofmodel developmentatTemple, Texas. Hydrol. Sci. J. 53(5):948-60
125. Wilson RS, Howard G, Burnett EA. 2014. Improving nutrient management practices in agriculture: the role of risk-based beliefs in understanding farmers' attitudes toward taking additional action. Water Resour. Res. 50:6735-46
126. Xabadia A, Goetz RU, Zilberman D. 2006. Control of accumulating stock pollution by heterogeneous producers. J. Econ. Dyn. Control 30:1105-30
127. Xabadia A, Goetz RU, Zilberman D. 2008. The gains from differentiated policies to control stock pollution when producers are heterogeneous. Am. J. Agric. Econ. 90:1059-73
128. Xie Y, Zilberman D. 2016. Theoretical implications of institutional, environmental, and technological changes for capacity choices of water projects. Water Resour. Econ. 13:19-29
129. Zhao J. 2015. Coupled landscape, atmosphere, and socioeconomic systems (CLASS) in the High Plains region. Presented at Nat. Sci. Found. FEW Workshop, Oct. 12-13, Ames, Iowa