A Third Wave in the Economics of Climate Change

Abstract

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

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Notes

  1. 1.

    The influential Stern Review (Stern 2007) underestimated such risks, as Hepburn and Stern (2008) acknowledged.

  2. 2.

    One perspective is to distinguish between “objective uncertainty”, which may be modelled as probability distributions, and “subjective assumptions”, being both distributions for outcomes that are poorly known (such as damages from higher temperature changes) and welfare parameters (such as the pure rate of time preference) (Baldwin 2015). The advantage of this approach is that the sensitivity of the social cost of carbon and of desirable climate policy (i.e. carbon tax or cap-and-trade) to inherent uncertainty can be made explicit.

  3. 3.

    Some models use different equity weights to account for disproportionate effect of consumption losses in poorer regions.

  4. 4.

    Another particularly difficult source of uncertainty to deal with is the extent to which adaptation to climate change is going to reduce mitigation costs. For example, Diaz (2014) argues that with appropriate adaptation the costs of sea-level rise could be reduced by a factor of five. Several POMs have been extended to allow for adaptation (e.g. AD-WITCH and AD-DICE/RICE) and some allow for a mixture of adaptation and mitigation policies (e.g. PAGE09).

  5. 5.

    The focus on equilibrium has been questioned in other areas of economic modeling, including macroeconomic forecasting (Howitt 2012).

  6. 6.

    There is no fundamental reason why a complex economic system should simply tend toward a single given, known equilibrium or indeed why a stable equilibrium should even exist. “Chaotic” dynamics can emerge in very simple models of economic growth with rational consumers, decreasing returns to capital and overlapping generations (Benhabib and Day 1981; Day 1982, 1983; Benhabib and Day 1982). The economy is sufficiently complicated and nonlinear that it would be surprising if the attractors of the dynamics were not high-dimensional (Galla and Farmer 2013).

  7. 7.

    There are minor exceptions. For example, Krussel and Smith (2009) assume that financial markets are incomplete.

  8. 8.

    It also goes under the names of “hind-casting” or “back-casting”, and is a form of cross-validation.

  9. 9.

    Speech at the ECB Annual Central Banking Conference, November 2010.

  10. 10.

    In a sense, although the necessity of estimating so many parameters appears to be a drawback compared to other modelling approaches, this is only because other modelling approaches implicitly assume specific values for such parameters, without empirical estimation.

  11. 11.

    This is the physical science uncertainty about where around a billion tonnes of anthropogenic carbon disappear from the atmosphere every year i.e. why there is a discrepancy between carbon-uptake models and the field studies (Burgermeister 2007).

  12. 12.

    “A lower bound of the welfare loss from uncertainty over the climate’s sensitivity to \(\hbox {CO}_{2}\) is 2-3 orders of magnitude higher than the best guess of the welfare loss from uncertainty over the carbon flows. A clear quantitative message from economics to science to shift more attention to the feedback processes on the temperature side” (p. 30).

  13. 13.

    For example, Arthur (1991) developed a parameterised learning algorithm to simulate how agents learn to choose among different, discrete actions with initially unknown payoffs. He then calibrated the parameters against learning data measured in human subjects. Epstein (2000) used an ABM to characterise and simulate the evolution of social norms with reference to the inverse relationship between the strength of a norm and the amount of time an agent spends thinking about a particular behaviour. Holland (1975) was the first to develop genetic algorithms, which model learning from a more evolutionary perspective. Using a genetic algorithm approach, Janssen and de Vries (1998) incorporated learning agents into a very simple IAM, and showed that learning can significantly influence outcomes at the aggregate level, especially in environments with imperfect information.

  14. 14.

    The tails of distributions can be characterized by the tail exponent, which roughly speaking is the absolute value of the power law exponent of the cumulative distribution. For thin tailed distributions the tail exponent is infinite, but for fat-tailed distributions it is finite. Moments higher than the tail exponent do not exist. Thus if the tail exponent is 1.8, the mean exists but the variance does not exist. When moments do not exist, this means that empirical estimates do not converge (but rather increase without bound) in the limit as the number of data points become large. Many meteorological series have tail exponents close to one, meaning that the mean doesn’t exist. (A classic example is flood levels, which is why it is meaningless to discuss an “average flood”. see e.g. Embrechts et al. (1997)).

    A remarkable fact from extreme value theory is that there are only three types of extremal distributions, corresponding to fat tails (which are generically power laws), thin tails (such as the normal distribution) and bounded support.

  15. 15.

    A description of models used in SSPs can be found here: https://secure.iiasa.ac.at/web-apps/ene/SspDb/download/iam_scenario_doc/SSP_Model_Documentation.pdf.

  16. 16.

    This recursive preference formulation allows the modeler to disentangle the degree of risk aversion from intertemporal substitution enabling us to see more clearly how changes in fundamental parameters of the utility function and in types of uncertainty affect outcomes. For example, Ackerman et al. (2013) find that risk aversion has a remarkably small effect on optimal policy while intertemporal substitution has a large one. Crost and Traeger (2013) show that uncertainty associated with the steepness, rather than level of damages associated with temperature increases is what makes an impact on policy outcomes. Uncertainty about steepness of the damage function can result in a much higher level of optimal mitigation.

  17. 17.

    For instance, Galla and Farmer (2013) show that in a game-theoretic context where two players must learn their strategies, under some conditions they settle into fixed-point equilibrium as usually assumed in economic theory, but in many other cases there are multiple equilibria, or no stable fixed point equilibria at all. In the latter case their strategies never settle into a steady state, but instead wander around a chaotic attractor, which in some cases is sufficiently high dimensional that the resulting behavior is for most purposes effectively random.

  18. 18.

    Meaning that the model is a demonstration of the feasibility (but usually not the full realization or implementation) of a particular approach, idea or analytical technique.

  19. 19.

    Other ABIAMs include Desmarchelier et al. (2013); Giupponi et al. 2013); Hasselmann and Kovalevsky 2013)

References

  1. Acemoglu D, Aghion P, Bursztyn L, Hemous D (2012) The environment and directed technical change. Am Econ Rev 102(1):131–166

    Article  Google Scholar 

  2. Ackerman F, DeCanio SJ, Howarth RB, Sheeran K (2009) Limitations of integrated assessment models of climate change. Clim Change 95(3–4):297–315

    Article  Google Scholar 

  3. Ackerman F, Stanton EA, Bueno R (2013) Epstein–Zin utility in DICE: Is risk aversion irrelevant to climate policy? Environ Resour Econ 56(1):73–84. doi:10.1007/s10640-013-9645-z

    Article  Google Scholar 

  4. Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica 60(2):323–351

    Article  Google Scholar 

  5. Aghion P, Dechezleprêtre A, Hemous D, Martin R, Van Reenen J (2014a) Carbon taxes, path dependency and directed technical change: evidence from the auto industry. J Political Econ. http://personal.lse.ac.uk/dechezle/adhmv_jpe_sept21_2014.pdf

  6. Aghion P, Hepburn C, Teytelboym A, Zenghelis D (2014b) Path dependence, innovation, and the economics of climate change. Centre for Climate Change Economics and Policy/Grantham Research Institute on Climate Change and the Environment Policy Paper & Contributing paper to New Climate Economy

  7. Allais M (1953) Le comportement de l’Homme Rationnel Devant Le Risque: Critique Des Postulats et Axiomes de l’Ecole Americaine. Econometrica 21(4):503–546

    Article  Google Scholar 

  8. Allen F, Gale D (2000) Financial contagion. J Polit Econ 108(1):1–33

    Article  Google Scholar 

  9. Allen F, Frame DJ (2007) Call off the quest. Science 318(5850):582–583

    Article  Google Scholar 

  10. An L (2012) Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol Model 229:25–36

    Article  Google Scholar 

  11. Anderson PW, Arrow KJ, Pines D (eds) (1988) The economy as a complex evolving system. Addison-Wesley, Redwood City

  12. Anderson B, Borgonovo E, Galeotti M, Roson R (2014) Uncertainty in climate change modeling: can global sensitivity analysis be of help? Risk Anal 34(2):271–293. doi:10.1111/risa.12117

    Article  Google Scholar 

  13. Anthoff D, Tol RSJ (2013) The uncertainty about the social cost of carbon: a decomposition analysis using fund. Clim Change 117(3):515–530

    Article  Google Scholar 

  14. Arent DJ, Tol RSJ, Faust E, Hella JP, Kumar S, Strzepek KM, Tóth FL, Yan D (2014) Key economic sectors and services. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 659–708

  15. Arthur WB (1991) Designing economic agents that act like human agents: a behavioral approach to bounded rationality. Am Econ Rev 81(2):353–359

    Google Scholar 

  16. Arthur WB (2006) Out-of-equilibrium economics and agent-based modeling. Handbook of computational economics. Retrieved from http://www.sciencedirect.com/science/article/pii/S1574002105020320

  17. Arthur WB (2013) Complexity economics: a different framework for economic thought. SFI Working Paper 2013-04-012

  18. Axtell R (1999) The emergence of firms in a population of agents: local increasing returns, unstable Nash equilibria, and power law size distributions. Brookings Institution Working Paper

  19. Axtell R (2013) Endogenous firms and their dynamics. Working paper, http://www.acefinmod.com/docs/esrc/axtell-firms.pdf

  20. Axelrod R (1997) The complexity of cooperation: agent-based models of competition and collaboration. Princeton University Press, Princeton

    Google Scholar 

  21. Atkinson G, Dietz S, Helgeson J, Hepburn C, Saelen H (2009) Siblings, not triplets: social preferences for risk, inequality and time in discounting climate change. Econ E-J 2009-14

  22. Aymanns C, Farmer JD (2015) The dynamics of the leverage cycle. J Econ Dyn Control 50:155–179

    Article  Google Scholar 

  23. Balbi S, Giupponi C (2009) Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability. Available at SSRN 1457625. http://www.researchgate.net/publication/220141236_Agent-Based_Modelling_of_Socio-Ecosystems_A_Methodology_for_the_Analysis_of_Adaptation_to_Climate_Change/file/72e7e51bd779a1e608.pdf

  24. Baldwin E (2015) Choosing in the dark: incomplete preferences, and climate policy. Mimeo

  25. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  Google Scholar 

  26. Batty M (2009) Urban modeling. In: International encyclopedia of human geography. Elsevier, Oxford

  27. Beinhocker ED (2006) The origin of wealth: evolution, complexity and the radical remaking of economics. Harvard Business School Press, Boston

    Google Scholar 

  28. Benhabib J, Day RH (1981) Rational choice and erratic behaviour. Rev Econ Stud 48(3):459–471 http://www.jstor.org/stable/2297158

  29. Benhabib J, Day RH (1982) A characterization of erratic dynamics in the overlapping generations model. J Econ Dyn Control 4(1):37–55

    Article  Google Scholar 

  30. Benhabib J, Farmer REA (1994) Indeterminacy and increasing returns. J Econ Theory 63(1):19–41. doi:10.1006/jeth.1994.1031

    Article  Google Scholar 

  31. Blanchard OJ, Summers LH (1987) Hysteresis in unemployment. Eur Econ Rev 31(1–2):288–295

    Article  Google Scholar 

  32. Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. In: Proceedings of the National Academy of Sciences of the United States of America 99(suppl. 3):7280–7287

  33. Bonaccorsi A, Rossi C (2003) Why open source software can succeed. Res Policy 32(7):1243–1258

    Article  Google Scholar 

  34. Bosetti V, Carraro C, Massetti E, Tavoni M (2008) International energy R&D spillovers and the economics of greenhouse gas atmospheric stabilization. Energy Econ 30(6):2912–2929

    Article  Google Scholar 

  35. Bosetti V, Carraro C, Massetti E, Sgobbi A, Tavoni M (2009) Optimal energy investment and R&D strategies to stabilize atmospheric greenhouse gas concentrations. Resour Energy Econ 31(2):123–137

    Article  Google Scholar 

  36. Bousquet F, Page C (2004) Multi-agent simulations and ecosystem management: a review. Ecol Model 176(3–4):313–332. doi:10.1016/j.ecolmodel.2004.01.011

    Article  Google Scholar 

  37. Brekke KA, Johansson-Stenman O (2008) The behavioural economics of climate change. Oxf Rev Econ Policy 24(2):280–297

    Article  Google Scholar 

  38. Bretschger L, Vinogradova A (2014) Growth and mitigation policies with uncertain climate damage. http://dx.doi.org/10.2139/ssrn.2485200

  39. Brown DG, Robinson DT (2006) Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecol Soc 11(1):46

    Google Scholar 

  40. Bryant BP, Lempert RJ (2010) Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol Forecast Soc Change 77(1):34–49

    Article  Google Scholar 

  41. Burke M, Dykema J, Lobell DB, Miguel E, Satyanath S (2015) Incorporating climate uncertainty into estimates of climate change impacts. Rev Econ Stat 97(2):461–471. doi:10.1162/REST

    Article  Google Scholar 

  42. Burgermeister J (2007) Missing carbon mystery: case solved? Nat Rep Clim Change 3:36–37. doi:10.1038/climate.2007.35

    Google Scholar 

  43. Butler MP, Reed PM, Fisher-Vanden K, Keller K, Wagener T (2014) Inaction and climate stabilization uncertainties lead to severe economic risks. Clim Change 127(3–4):463–474. doi:10.1007/s10584-014-1283-0

  44. Caccioli F, Bouchaud JP, Farmer JD (2012) Impact-adjusted valuation and the criticality of leverage. Risk 74–77

  45. Cai Y, Judd KL, Lontzek TS (2012) DSICE: a dynamic stochastic integrated model of climate and economy. Working Paper No. 12-02, The Center for Robust Decision Making on Climate and Energy Policy

  46. Cai Y, Judd KL, Lontzek TS (2013) The social cost of stochastic and irreversible climate change. NBER Working Paper No. 18704

  47. Cai Y, Judd KL, Lenton TM, Lontzek TS, Narita D (2015) Environmental tipping points significantly affect the cost-benefit assessment of climate policies. Proc Natl Acad Sci 201503890. doi:10.1073/pnas.1503890112

  48. Caldara D, Fernandez-Villaverde J, Rubio-Ramirez JF, Yao W (2012) Computing DSGE models with recursive preferences and stochastic volatility. Rev Econ Dyn 15(2):188–206

    Article  Google Scholar 

  49. Cane MA, Miguel E, Burke M, Hsiang SM, Lobell DB, Meng CK, Satyanath S (2014) Temperature and violence. Nat Clim Change 4:234–235

    Article  Google Scholar 

  50. Canova F (2008) How Much Structure in Empirical Models? In: Mills T, Patterson K (eds) Palgrave handbook of econometrics, vol 2, Applied Econometrics. Palgrave Macmillan

  51. Canova F, Sala L (2009) Back to square one: identification issues in DSGE models. J Monet Econ 56(4):431–449

  52. Carbon Tracker Initiative (2013) Unburnable Carbon 2013: Wasted capital and stranded assets. Report in collaboration with the Grantham Research Institute on Climate Change and the Environment, LSE. http://carbontracker.live.kiln.it/Unburnable-Carbon-2-Web-Version.pdf

  53. Carrillo-Hermosilla J (2006) A policy approach to the environmental impacts of technological lock-in. Ecol Econ 58:717–742

    Article  Google Scholar 

  54. Cass D, Shell K (1983) Do sunspots matter? J Polit Econ 91(2):193–227. doi:10.1086/261139

    Article  Google Scholar 

  55. Cincotti S, Raberto M, Teglio A (2010) Credit money and macroeconomic instability in the agent-based model and simulator Eurace. Econ Open-Access Open-Assess E-J 4:2010–2026

    Article  Google Scholar 

  56. Clarke L, Edmonds J, Krey V, Richels R, Rose S, Tavoni M (2009) International climate policy architectures: overview of the EMF 22 International Scenarios. Energy Economics 31(suppl. 2)

  57. Clarke L, Jiang K, Akimoto K, Babiker M, Blanford G, Fisher-Vanden K, Hourcade J-C, Krey V, Kriegler E, Löschel A, McCollum D, Paltsev S, Rose S, Shukla PR, Tavoni M, van der Zwaan BCC, van Vuuren DP (2014) Assessing transformation pathways. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds). Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  58. Cole HL, Kehoe TJ (2000) Self-fulfilling debt crises. Rev Econ Stud 67(1):91–116. doi:10.2307/2567030

    Article  Google Scholar 

  59. Cooper R (1999) Coordination games: complementarities and macroeconomics. Cambridge University Press, Cambridge

    Google Scholar 

  60. Crost B, Traeger CP (2013) Optimal climate policy: uncertainty versus Monte Carlo. Econ Lett 120(3):552–558. doi:10.1016/j.econlet.2013.05.019

    Article  Google Scholar 

  61. Dawid H, Gemkow S, Harting P, Neugart M (2009) One the effects of skill upgrading in the presence of spatial labor market frictions: an agent-based analysis of spatial policy design. J Artif Soc Soc Simul 12(4):5. http://jasss.soc.surrey.ac.uk/12/4/5.html

  62. Day RH (1982) Irregular growth cycles. Am Econ Rev 72(3):406–414

    Google Scholar 

  63. Day RH (1983) The emergence of chaos from classical economic growth. Q J Econ 98(2). President & Fellows of Harvard University: 201–13. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4624269&site=ehost-live&scope=site

  64. Deissenberg C, van der Hoog S, Dawid H (2008) EURACE: a massively parallel agent-based model of the European economy. Appl Math Comput 204(2):541–552. doi:10.1016/j.amc.2008.05.116

    Article  Google Scholar 

  65. Del Negro M, Eggertsson G, Ferrero A, Kiyotaki N (2011) The great escape? A quantitative evaluation of the fed’s liquidity facilities. Staff Report. Federal Reserve Bank of New York, 520

  66. Delre S, Jager W, Bijmolt T, Janssen M (2007) Targeting and timing promotional activities: an agent-based model for the takeoff of new products. J Bus Res 60(8):826–835

    Article  Google Scholar 

  67. Desmarchelier B, Djellal F, Gallouj F (2013) Environmental policies and eco-innovations by service firms: an agent-based model. Technol Forecast Soc Change 80(7):1395–1408

    Article  Google Scholar 

  68. Diamond PA (1982) Aggregate demand management in search equilibrium. J Polit Econ 90(5):881–894. doi:10.1086/261099

    Article  Google Scholar 

  69. Diaz DB (2014) Estimating global damages from sea level rise with the coastal impact and adaption model CIAM, FEEM Note di Lavoro working paper series, originally presented at European Summer School in Resource and Environmental Economics San Servolo, Venice, Italy July 12

  70. Dietz S, Hepburn C (2013) Benefit-cost analysis of non-marginal climate and energy projects. Energy Econ 40:61–71

    Article  Google Scholar 

  71. Dietz S, Stern N (2015) Endogenous growth, convexity of damages and climate risk?: how Nordhaus’ framework supports deep cuts in carbon emissions. Econ J 125(583):574–620

    Article  Google Scholar 

  72. Ebi KL, Hallegatte S, Kram T, Arnell NW, Carter TR, Edmonds J, Zwickel T (2014) A new scenario framework for climate change research: background, process, and future directions. Clim Change 122(3):363–372. doi:10.1007/s10584-013-0912-3

    Article  Google Scholar 

  73. Eboli F, Parrado R, Roson R (2010) Climate-change feedback on economic growth: explorations with a dynamic general equilibrium model. Environ Dev Econ 15(5):515–533

    Article  Google Scholar 

  74. Embrechts P, Klüppelberg C, Mikosch T (1997) Modelling extremal events for insurance and finance. Springer, Berlin

    Google Scholar 

  75. Epstein JM (1999) Agent-based computational models and generative social science. Generative Social Science: Studies in Agent-Based Computational Modelling. Retrieved from http://books.google.com/books?hl=en&lr=&id=543OS3qdxBYC&oi=fnd&pg=PA4&dq=agent-based+computaitonal+models+and+generative+social+science&ots=j0ByRbZ9J5&sig=s-wW7_UYmi-XZhbOHEps0GgdmqU

  76. Epstein JM (2000) Learning to be thoughtless: social norms and individual computation. Working Paper No. 6, 2000. The Brookings Institution and Santa Fe Institute, Center on Social and Economic Dynamics

  77. Epstein JM (2002) Modeling civil violence: an agent-based computational approach. Proc Natl Acad Sci USA 99(suppl. 3):7243–7250

    Article  Google Scholar 

  78. Epstein JM (2009) Modelling to contain pandemics. Nature 460(7256):687–687. doi:10.1038/460687a

    Article  Google Scholar 

  79. Epstein L, Zin SE (1989) Substitution, risk aversion, and the temporal behavior of consumption and asset returns: a theoretical framework. Econometrica 57(4):937–969. doi: 10.2307/1913778

  80. Epstein L, Zin SE (1991) Substitution, risk aversion, and the temporal behavior of consumption and asset returns: an empirical analysis. J Political Econ. doi: 10.1086/261750

  81. Faber A, Valente M, Janssen P (2010) Exploring domestic micro-cogeneration in the Netherlands: an agent-based demand model for technology diffusion. Energy Policy 38(6):2763–2775

    Article  Google Scholar 

  82. Faglio G, Roventini A (2012) Macroeconomic policy in DSGE and agent-basd models. Working Paper 2012-17, Economix

  83. Farmer REA, Guo JT (1994) Real business cycles and the animal spirits hypothesis. J Econ Theory 63(1):42–72

    Article  Google Scholar 

  84. Farmer JD, Foley D (2009) The economy needs agent-based modelling. Nature 460(7256):685–686. doi:10.1038/460685a

    Article  Google Scholar 

  85. Farmer JD, Geanakoplos J (2009) Hyperbolic discounting is rational: valuing the far future with uncertain discount rates, http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=278280m

  86. Farmer JD, Hepburn C (2014) Less precision, more truth: uncertainty in climate economics and macroprudential policy. Paper Prepared for Bank of England Interdisciplinary Workshop on 2 April 2014 on “The Role of Uncertainty in Central Bank Policy.” http://www.bankofengland.co.uk/research/Documents/pdf/hepburn_0414.pdf

  87. Farmer JD, Lafond F (2015) How predictable is technological progress? SSRN Working Paper 2566810

  88. Farmer JD, Geanakoplos J, Masoliver J, Montero M, Perello P (2014) Discounting the distant future. J Public Econ. http://lanl.arxiv.org/abs/1311.4068

  89. Fukač M, Pagan A (2010) Limited information estimation and evaluation of DSGE models. J Appl Econom 25(1):55–70

  90. Frenken K, Izquierdo L, Zeppini P (2012) Branching innovation, recombinant innovation, and endogenous technological transitions. Environ Innov Soc Transit 4:25–35

    Article  Google Scholar 

  91. Galí J (2009) Monetary policy, inflation, and the business cycle: an introduction to the new keynesian framework. Princeton University Press, Princeton

    Google Scholar 

  92. Galla T, Farmer JD (2013) Complex dynamics in learning complicated games. Proc Natl Acad Sci 110(4):1232–1236

    Article  Google Scholar 

  93. Geanakoplos J, Axtell R, Farmer JD, Howitt P, Conlee B, Goldstein J, Hendrey M, Palmer N, Yang C-Y (2012) Getting at systemic risk via an agent-based model of the housing market. Am Econ Rev 102(3):53–58

    Article  Google Scholar 

  94. Gerali A, Neri S, Sessa L, Signoretti FM (2010) Credit and banking in a DSGE model of the Euro area. J Money Credit Bank 42(6):107–141

    Article  Google Scholar 

  95. Germann T, Kadau K, Longini I, Macken C (2006) Mitigation strategies for pandemic influenza in the United States. Proc Natl Acad Sci 103(15):5935–5940

    Article  Google Scholar 

  96. Gerst M, Wang P, Borsuk M (2013a) Discovering plausible energy and economic futures under global change using multidimensional scenario discovery. Environ Model Softw 44:7686

    Google Scholar 

  97. Gerst M, Wang P, Roventini A, Fagiolo G, Dosi G, Howarth R, Borsuk M (2013b) Agent-based modeling of climate policy: an introduction to the ENGAGE multi-level model framework. Environ Model Softw 44:62–75. doi:10.1016/j.envsoft.2012.09.002

    Article  Google Scholar 

  98. Gilbert N, Terna P (2000) How to build and use agent-based models in social science. Mind Soc 1(1):57–72

    Article  Google Scholar 

  99. Gillingham K, Newell RG, Pizer WA (2008) Modeling endogenous technological change for climate policy analysis. Energy Econ 30(6):2734–2753

    Article  Google Scholar 

  100. Gintis H (2007) The dynamics of general equilibrium*. Econ J 117(523):1280–1309. doi:10.1111/j.1468-0297.2007.02083.x

    Article  Google Scholar 

  101. Giupponi C, Borsuk M, Vries B, Hasselmann K (2013) Innovative approaches to integrated global change modelling. Environ Model Softw 44:1–9. doi:10.1016/j.envsoft.2013.01.013

    Article  Google Scholar 

  102. Golosov M, Hassler J, Krusell P, Tsyvinski A (2014) Optimal taxes on fossil fuel in general equilibrium. Econometrica 82(1):41–88

    Article  Google Scholar 

  103. Gomez W (2014) A DSGE model with loss aversion in consumption and leisure: an explanation for business cycles asymmetries. Working Paper No. 011100

  104. Gowdy JM (2008) Behavioral economics and climate change policy. J Econ Behav Organ 68(3):632–644. doi:10.1016/j.jebo.2008.06.011

    Article  Google Scholar 

  105. Grauwe P (2010) The scientific foundation of dynamic stochastic general equilibrium (DSGE) models. Public Choice 144(3):413–443

    Article  Google Scholar 

  106. Grimm V (1999) Ten years of individual-based modeling in ecology: what have we learned and what could we learn in the future? Ecol Model 115:129–148

    Article  Google Scholar 

  107. Grimm V, Railsback SF (2012) Designing, formulating, and communicating agent-based models. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Netherlands, pp 361–377

    Google Scholar 

  108. Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, DeAngelis DL (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198(1):115–126

    Article  Google Scholar 

  109. Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF (2010) The ODD protocol: a review and first update. Ecol Model 221(23):2760–2768

    Article  Google Scholar 

  110. Groom B, Hepburn C, Koundouri P, Pearce D (2005) Declining discount rates: the long and the short of it. Environ Resour Econ 32(4):445–493. doi:10.1007/s10640-005-4681-y

    Article  Google Scholar 

  111. Guerrero O, Axtell R (2013) Employment growth through labor flow networks. PLoS ONE 8(5). doi: 10.1371/journal.pone.0060808

  112. Halloran M, Ferguson N, Eubank S, Longini I, Cummings D, Lewis B, Cooley P (2008) Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci 105(12):4639–4644. doi:10.1073/pnas.0706849105

    Article  Google Scholar 

  113. Hansen AH (1939) Economic progress and declining population growth. Am Econ Rev 29(1):1–15

    Google Scholar 

  114. Harberger AC (1959) Using the resources at hand more effectively. Am Econ Rev 49(2):134–146

    Google Scholar 

  115. Hasselmann K, Kovalevsky D (2013) Simulating animal spirits in actor-based environmental models. Environ Model Softw 44:10–24. doi:10.1016/j.envsoft.2012.04.007

    Article  Google Scholar 

  116. Hasselmann K, Jaeger C, Leipold G, Mangalagiu D, Tabara J (2013) Reframing the problem of climate change: from zero sum game to win–win solutions. Routlege

  117. Hassler J, Krusell P (2012) Economics and climate change: integrated assessment in a multi-region world. J Eur Econ Assoc 10(5):974–1000

    Article  Google Scholar 

  118. Heal G, Millner A (2014) Uncertainty and decision making in climate change economics. Rev Environ Econ Policy 8(1):120–137

    Article  Google Scholar 

  119. Heckman JJ (2001) Micro data, heterogeneity, and the evaluation of public policy: Nobel lecture. J Polit Econ 109(4):673–748. doi:10.1086/322086

    Article  Google Scholar 

  120. Helgeson JF (2007) Climate ethics survey: disentangling public risk preference from inequality & time. Msc Environmental Change & Management Dissertation, http://www.eci.ox.ac.uk/teaching/msc/downloads/helgeson-summary.pdf

  121. Hepburn C, Stern N (2008) A new global deal on climate change. Oxf Rev Econ Policy 24:259–279. doi:10.1093/oxrep/grn020

    Article  Google Scholar 

  122. Hepburn C, Koundouri P, Panopoulou E, Pantelidis T (2009) Social discounting under uncertainty: a cross-country comparison. J Environ Econ Manag 57(2):140–150. doi:10.1016/j.jeem.2008.04.004

    Article  Google Scholar 

  123. Hepburn C, Duncan S, Papachristodoulou A (2010) Behavioural economics, hyperbolic discounting and environmental policy. Environ Resour Econ 46(2):189–206. doi:10.1007/s10640-010-9354-9

    Article  Google Scholar 

  124. Hoel M, Karp L (2001) Taxes and quotas for a stock pollutant with multiplicative uncertainty. J Public Econ 82:91–114. doi:10.1016/S0047-2727(00)00136-5

    Article  Google Scholar 

  125. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  126. Holland JH, Miller JH (1991) Artificial adaptive agents in economic theory. Am Econ Rev Pap Proc 81(2):365–370

    Google Scholar 

  127. Holcombe M, Coakley S, Kiran M, Chin S, Greenough C, Worth D, Neugart M (2013) Large-scale modeling of economic systems. Complex Syst 22(2):175–191

    Google Scholar 

  128. Holmstrom B, Tirole J (1997) Financial intermediation, loanable funds, and the real sector. Q J Econ 112(3):663–691

    Article  Google Scholar 

  129. Hope C (2013) Critical issues for the calculation of the social cost of CO2: why the estimates from PAGE09 are higher than those from PAGE2002. Clim Change 117(3):531–543. doi:10.1007/s10584-012-0633-z

    Article  Google Scholar 

  130. Hope C, Anderson J, Wenman P (1993) Policy analysis of the greenhouse effect: an application of the PAGE model. Energy Policy 21(3):327–338. doi:10.1016/0301-4215(93)90253-C

    Article  Google Scholar 

  131. Howitt P (2012) What have central bankers learned from modern macroeconomic theory? J macroecon 34(1):11–22

    Article  Google Scholar 

  132. Hsiang SM, Meng KC, Cane MA (2011) Civil conflicts are associated with the global climate. Nature 476:438–441. doi:10.1038/nature10311

    Article  Google Scholar 

  133. Interagency Working Group on Social Cost of Carbon (2010) Social cost of carbon for regulatory impact analysis under executive order 12866. United States Government. http://www.epa.gov/oms/climate/regulations/scc-tsd.pdf

  134. IPCC (2001) Climate change 2001: working group III: mitigation. Cambridge University Press, Cambridge

  135. IPCC (2014) Summary for policymakers. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds) Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  136. Iyer GC, Clarke LE, Edmonds JA, Flannery BP, Hultman NE, McJeon HC, Victor DG (2015) Improved representation of investment decisions in assessments of CO2 mitigation. Nat Clim Change 5:436–440. doi:10.1038/nclimate2553

    Article  Google Scholar 

  137. Janssen MA, de Vries B (1998) The battle of perspectives: a multi-agent model with adaptive responses to climate change. Ecol Econ 26(1):43–65

  138. Janssen MA, Ostrom E (2006) Empirically based, agent-based models. Ecol Soc 11(2):37 Retrieved from http://groups.forestry.oregonstate.edu/fpf/system/files/Janssen%20and%20Ostrom%202006.pdf

  139. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica, 47. The Econometric Society: 263–92

  140. Kelly DL, Kolstad CD (1999) Bayesian learning, growth, and pollution. J Econ Dyn Control 23:491–518

    Article  Google Scholar 

  141. Kelly DL, Kolstad CD (2001) Solving infinite horizon growth models with an environment sector. Comput Econ 182:217–231

    Article  Google Scholar 

  142. Kirman AP (1992) Whom or what does the representative agent represent? J Econ Perspect 6(2):117–136

    Article  Google Scholar 

  143. Kirman AP (1997) The economy as an evolving network. J Evolut Econ 7(4):339–353

  144. Kirman AP (2008) Economy as a complex system. In: Durlauf SN, Blume LE (ed) The New palgrave dictionary of economics, 2nd edn. Palgrave Macmillan

  145. Kolstad C, Urama K, Broome J, Bruvoll A, Cariño Olvera M, Fullerton D, Gollier C, Hanemann WM, Hassan R, Jotzo F, Khan MR, Meyer L, Mundaca L (2014) Social, economic and ethical concepts and methods. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds) Climate change 2014: mitigation of limate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovern- mental Panel on Climate Change. Cambridge University Press, Cambridge

  146. Köszegi B, Rabin M (2006) A model of reference-dependent preferences. Q J Econ 121(4):1133–1165

    Google Scholar 

  147. Krey V, Masera O, Blanford G, Bruckner T, Cooke R, Fisher-Vanden K, Haberl H, Hertwich E, Kriegler E, Mueller D, Paltsev S, Price L, Schlömer S, Ürge-Vorsatz D, van Vuuren D, Zwickel T (2014) Annex II: metrics & methodology. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds) Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  148. Krugman P (1991) History versus expectations. Q J Econ 106(2):651–67. http://qje.oxfordjournals.org/content/106/2/651.full.pdf

  149. Krussel P, Smith Jr AA (2009) Macroeconomics and global climate change: transition for a many-region economy. Mimeo

  150. Kunreuther H, Gupta S, Bosetti V, Cooke R, Dutt V, Ha-Duong M, Held H, Llanes-Regueiro J, Patt A, Shittu E, Weber E (2014) Integrated risk and uncertainty assessment of climate change response policies. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds) Climate change 2014: mitigation of climate change. contribution of working group iii to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  151. Law AM (2009) How to build valid and credible simulation models. In: Simulation conference (WSC), Proceedings of the 2009 Winter pp 24–33. IEEE

  152. Lemoine D, Traeger C (2014) Watch your step: optimal policy in a tipping climate. Am Econ J Econ Policy 61 B:137–166. doi:10.1257/pol.6.1.137

    Article  Google Scholar 

  153. Lenton TM, Held H, Kriegler E, Hall J, Lucht W, Rahmstorf S, Schellnhuber HJ (2008) Tipping elements in the earth’s climate system. Proc Natl Acad Sci USA 105:1786–1793. doi:10.1073/pnas.0705414105

    Article  Google Scholar 

  154. Ligtenberg A, Bregt AK, Van Lammeren R (2001) Multi-actor-based land use modelling: spatial planning using agents. Landsc Urban Plan 56(1):21–33

    Article  Google Scholar 

  155. Lim M, Metzler R, Bar-Yam Y (2007) Global pattern formation and ethnic/cultural violence. Science 317(5844):1540–1544. doi:10.1126/science.1142734

    Article  Google Scholar 

  156. Lobell DB, Roberts MJ, Schlenker W, Braun N, Little BB, Rejesus RM, Hammer GL (2014) Greater sensitivity to drought accompanies maize yield increase in the US midwest. Science 344(6183):516–519. doi:10.1126/science.1251423

    Article  Google Scholar 

  157. Lontzek TS, Cai Y, Judd KL, Lenton TM (2015) Stochastic integrated assessment of climate tipping points indicates the need for strict climate policy. Nat Clim Change 5:441–444. doi:10.1038/nclimate2570

    Article  Google Scholar 

  158. Löschel A (2002) Technological change in economic models of environmental policy: a survey. Ecol Econ 43(2–3):105–126

    Article  Google Scholar 

  159. Luderer G, Pietzcker RC, Bertram C et al (2013) Economic mitigation challenges: how further delay closes the door for achieving climate targets. Environ Res Lett 8(3):034033. doi:10.1088/1748-9326/8/3/034033

    Article  Google Scholar 

  160. Ma T, Nakamori Y (2005) Agent-based modeling on technological innovation as an evolutionary process. Eur J Oper Res 166(3):741–755

    Article  Google Scholar 

  161. Ma T, Grubler A, Nakamori Y (2009) Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents. Eur J Oper Res 195(1):296–306

    Article  Google Scholar 

  162. Macal CM, North MJ (2010) Tutorial on agent-based modelling and simulation. J Simul 4:151–162

    Article  Google Scholar 

  163. Manne A, Mendelsohn R, Richels R (1995) MERGE: a model for evaluating regional and global effects of GHG reduction policies. Energy Policy 23(1):17–34

    Article  Google Scholar 

  164. Maréchal K (2007) The economics of climate change and the change of climate in economics. Energy Policy 35(10):5181–5194

    Article  Google Scholar 

  165. Martin IWR, Pindyck RS (2014) Averting catastrophes: the strange economics of scylla and charybdis. NBER Working Paper 20215

  166. Matsuyama K (1991) Increasing returns, industrialization, and indeterminacy of equilibrium. Q J Econ 106(2):617–650. doi:10.2307/2937949

    Article  Google Scholar 

  167. Matthews R, Gilbert N, Roach A, Polhill J, Gotts N (2007) Agent-based land-use models: a review of applications. Landsc Ecol 22(10):1447–1459. doi:10.1007/s10980-007-9135-1

    Article  Google Scholar 

  168. Metcalf GE, Stock J (2015) The role of integrated assessment models in climate policy: a user’s guide and assessment. Working paper, http://scholar.harvard.edu/stock/publications/role-integrated-assessment-models-climate-policy-users-guide-and-assessment

  169. Miller JH, Page SE (2007) Complex adaptive systems: an introduction to computational models of social life. Princeton University Press, Princeton

    Google Scholar 

  170. Millner A, Simon D, Geoffrey H (2013) Scientific ambiguity and climate policy. Environ Resour Econ 55(1):21–46. doi:10.1007/s10640-012-9612-0

  171. Moore FC, Diaz DB (2015) Temperature impacts on economic growth warrant stringent mitigation policy. Nat Clim Change. doi:10.1038/nclimate2481

  172. Moss S, Pahl-Wostl C, Downing T (2001) Agent-based integrated assessment modelling: the example of climate change. Integr Assess 2(1):17–30. doi:10.1023/A:1011527523183

    Article  Google Scholar 

  173. Nagy B, Farmer JD, Bui Q, Trancik J (2013) Statistical basis for predicting technological progress. PloS One 8(2):e52669

    Article  Google Scholar 

  174. Nordhaus WD (1994) Managing the global commons: the economics of climate change. MIT Press, Cambridge

    Google Scholar 

  175. Nordhaus WD (2010) Economic aspects of global warming in a post-Copenhagen environment. Proc Natl Acad Sci USA 107(26):11721–11726

    Article  Google Scholar 

  176. Nordhaus WD, Yang Z (1996) A regional dynamic general-equilibrium model of alternative climate-change strategies. Am Econ Rev 86(4):741–765

    Google Scholar 

  177. Nordhaus W, Sztorc P (2013) DICE 2013R: introduction and user’s manual. http://www.econ.yale.edu/~nordhaus/homepage/documents/DICE_Manual_103113r2.pdf

  178. O’Donoghue T, Rabin M (1999) Doing it now or later. Am Econ Rev 89(1):103–24. http://ideas.repec.org/a/aea/aecrev/v89y1999i1p103-124.html

  179. Obstfeld M (1986) Rational and self-fulfilling balance-of-payments crises. Am Econ Rev 76(1):72–81. doi:10.1126/science.151.3712.867a

    Google Scholar 

  180. O’Neill BC, Kriegler E, Riahi K, Ebi KL, Hallegatte S, Carter TR, van VuurenDP(2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Change 122:387–400. doi:10.1007/s10584-013-0905-2

  181. Paltsev S, Reilly JM, Jacoby HD, Eckaus RS, Mcfarland J, Sarofim M, Asadoorian M, Babiker M (2005) MIT joint program on the science and policy of global change (EPPA) model: version 4. Policy analysis report no. 125, p 78

  182. Parker D, Manson S, Janssen M, Hoffmann M, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93(2):314–337

    Article  Google Scholar 

  183. Peck SC, Teisberg TJ (1992) CETA: a model for carbon emissions trajectory assessment. Energy J 13(1):55–77

    Article  Google Scholar 

  184. Pindyck RS (2013) Climate change policy: what do the models tell us? J Econ Lit 51(3):860–872

    Article  Google Scholar 

  185. Pindyck RS (2015) The use and misuse of models for climate policy. Working paper, http://web.mit.edu/rpindyck/www/Papers/PindyckClimateModels2015.pdf

  186. Pittel K (2002) Sustainability and endogenous growth. Edward Elgar Publishing

  187. Poledna S, Thurner S, Farmer JD, Geanakoplos J (2014) Leverage-induced systemic risk under Basle II and other credit risk policies. J Bank Finance 42(1):199–212

    Article  Google Scholar 

  188. Popp D (2004) ENTICE: Endogenous Technological Change in the DICE Model ofglobal warming. J Environ Econ Manag 48(1):742–768

  189. Powell W (2007) Approximate dynamic programming: solving the curses of dimensionality. Wiley, New York

    Google Scholar 

  190. Raberto M, Teglio A, Cincotti S (2012) Debt deleveraging and business cycles/ an agent-based perspective. Econ Open-Access Open-Assess E-J 6(2012-27)

  191. Rabin M (1993) Incorporating fairness into game theory and economics. Am Econ Rev 83(5):1281–1302

    Google Scholar 

  192. Rabin M (2000) Risk aversion and expected utility theory: a calibration theorem. Econometrica 68(5):1281–1292

    Article  Google Scholar 

  193. Railsback S, Lytinen S, Jackson S (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82(9):609–623

    Article  Google Scholar 

  194. Revesz RL, Howard PH, Arrow K, Goulder LH, Kopp RE, Livermore MA, Oppenheimer M, Sterner T (2014) Global warming: improve economic models of climate change. Nature 508:173–175

    Article  Google Scholar 

  195. Rezai A, van der Ploeg F (2014) Intergenerational inequality aversion, growth and the role of damages: Occam’s rule for theglobal carbon tax. Centre for Economic Policy Research, London.http://www.cepr.org/active/publications/discussion_papers/dp.php?dpno=10292

  196. Richiardi M, Leombruni R, Saam N, Sonnessa M (2006) A common protocol for agent-based social simulation. J Artif Soc Soc Simul 9(1):15. Retrieved from http://jasss.soc.surrey.ac.uk/9/1/15.html

  197. Roe GH, Baker MB (2007) Why Is Climate Sensitivity so Unpredictable?Science 318(5850):629–632. doi:10.1126/science.1144735

  198. Romer PM (1990) Endogenous technological change. J Polit Econ 98(5):71–102

    Article  Google Scholar 

  199. Roozmand O, Ghasem-Aghaee N, Hofstede G, Nematbakhsh M, Baraani A, Verwaart T (2011) Agent-based modeling of consumer decision making process based on power distance and personality. Knowl-Based Syst 24(7):1075–1095

    Article  Google Scholar 

  200. Sassi O, Crassous R, Hourcade JC, Gitz V, Waisman H, Guivarch C (2010) IMACLIM-R: a modelling framework to simulate sustainable development pathways. Int J Global Environ Issues 10(1–2):5–24

    Article  Google Scholar 

  201. 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 95(1):395–406

    Article  Google Scholar 

  202. Schularick M, Taylor MA (2012) Credit booms gone bust: monetary policy, leverage cycles, and financial crises. Am Econ Rev 102(2):1029–1061

    Article  Google Scholar 

  203. Schwarz N, Ernst A (2009) Agent-based modeling of the diffusion of environmental innovations: an empirical approach. Technol Forecast Soc Change 76(4):497–511

    Article  Google Scholar 

  204. Schweizer VJ, O’Neill BC (2013) Systematic construction of global socioeconomic pathways using internally consistent element combinations. Clim Change 2014:1–15. doi:10.1007/s10584-013-0908-z

    Google Scholar 

  205. Shafiei E, Thorkelsson H, Ásgeirsson E, Davidsdottir B, Raberto M, Stefansson H (2012) An agent-based modeling approach to predict the evolution of market share of electric vehicles: a case study from Iceland. Technol Forecast Soc Change 79(9):1638–1653

    Article  Google Scholar 

  206. Shleifer A (1986) Implementation cycles. J Polit Econ 94(6):1163–1190

    Article  Google Scholar 

  207. Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65–94

    Article  Google Scholar 

  208. Smulders S, Di Maria C (2012) The cost of environmental policy under induced technical change. CESifo working paper no. 3886. https://www.cesifogroup.de/DocDL/cesifo1_wp3886.pdf

  209. Stern N (2007) Stern review: the economics of climate change. Cambridge University Press, Cambridge

    Google Scholar 

  210. Stern N (2013) The structure of economic modeling of the potential impacts of climate change: grafting gross underestimation of risk onto already narrow science models. J Econ Lit 51(3):838–859

    Article  Google Scholar 

  211. Sun J, Tesfatsion L (2007) Dynamic testing of wholesale power market designs: an open-source agent-based framework. Comput Econ 30(3):291–327

    Article  Google Scholar 

  212. Tesfatsion L (2002) Hysteresis in an evolutionary labour market with adaptive search. In: Chen SH (ed) Evolutionary computation in economics and finance. Physica-Verlag Heidelberg, New York, pp 189–210

    Google Scholar 

  213. Thurner S, Farmer JD, Geanakoplos J (2012) Leverage causes fat tails and clustered volatility. Quant Finance 12(5):695–707

    Article  Google Scholar 

  214. Tol RSJ (1997) On the optimal control of carbon dioxide emissions: an application of fund. Environ Model Assess 2(3):151–163

    Article  Google Scholar 

  215. Traeger CP (2014) A 4-stated DICE: quantitatively addressing uncertainty effects in climate change. Environ Resour Econ 59(1):1–37. doi:10.1007/s10640-014-9776-x

    Article  Google Scholar 

  216. Traeger CP (2015) Analytic integrated assessment and uncertainty. Working paper, http://www.lse.ac.uk/GranthamInstitute/wp-content/uploads/2015/04/Traeger_AnalyticIAM.pdf

  217. van der Meijden G, Smulders S (2014) Carbon lock-in: the role ofexpectations. Tinbergen Institute discussion paper 14-100/VIII.http://ssrn.com/abstract=2475876

  218. Van der Mensbrugghe D (2010) The environmental impact and sustainability applied general equilibrium (ENVISAGE) model. Version 7:1

  219. Vivid Economics (2013) The macroeconomics of climate change. Report prepared for DEFRA

  220. Waisman H, Guivarch C, Grazi F, Hourcade JC (2012) The imaclim-R model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight. Clim Change 114(1):101–120. doi:10.1007/s10584-011-0387-z

    Article  Google Scholar 

  221. Webster M, Santen N, Parpas P (2012) An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty. CMS 93:339–362. doi:10.1007/s10287-012-0147-1

    Article  Google Scholar 

  222. Weidlich A, Veit D (2008) A critical survey of agent-based wholesale electricity market models. Energy Econ 30(4):17281759. doi:10.1016/j.eneco.2008.01.003

    Article  Google Scholar 

  223. Weitzman ML (1974) Prices versus quantities. Rev Econ Stud 41(4):477–491

    Article  Google Scholar 

  224. Weitzman ML (1998) Why the far-distant future should be discounted at its lowest possible rate? J Environ Econ Manag 36(3):201–208

  225. Weitzman ML (2001) Gamma discounting. Am Econ Rev 91(1):261–271

    Article  Google Scholar 

  226. Weitzman ML (2009) On modeling and interpreting the economics of catastrophic climate change. Rev Econ Stat 91(1):1–19

    Article  Google Scholar 

  227. Weitzman ML (2011) Fat-tailed uncertainty in the economics of catastrophic climate change. Rev Environ Econ Policy 5(2):275–292

    Article  Google Scholar 

  228. Weitzman ML (2013) Tail-hedge discounting and the social cost of carbon. J Econ Lit 51(3):873–882

    Article  Google Scholar 

  229. Weyant JP (2009) A perspective on integrated assessment: an editorial comment. Clim Change 95(3–4):317–323

    Article  Google Scholar 

  230. Wolf S, Fürst S, Mandel A, Lass W, Lincke D, Pablo-Martí F, Jaeger C (2013) A multi-agent model of several economic regions. Environ Model Softw 44:25–43. doi:10.1016/j.envsoft.2012.12.012

    Article  Google Scholar 

  231. Woodford M (1986) Stationary sunspot equilibria in a finance constrained economy. J Econ Theory 40(1):128–137. doi:10.1016/0022-0531(86)90011-6

    Article  Google Scholar 

  232. Zhang T, Zhang D (2007) Agent-based simulation of consumer purchase decision-making and the decoy effect. J Bus Res 60(8):912–922

    Article  Google Scholar 

  233. Zhang B, Zhang Y, Bi J (2011) An adaptive agent-based modeling approach for analyzing the influence of transaction costs on emissions trading markets. Environ Model Softw 26(4):482491. doi:10.1016/j.envsoft.2010.10.011

    Article  Google Scholar 

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Correspondence to Cameron Hepburn.

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We are grateful for comments on Hepburn’s presentation from participants at the ‘Beyond IPCC—Future Paths for Climate Research in Gothenburg on 17 October 2014, particularly Scott Barrett, Ottmar Edenhofer, Gunnar Eskeland and Michael Hanemann. We would also like to thank David Anthoff, Elizabeth Baldwin, Chris Hope, Richard Millar, Thomas Sterner and two anonymous referees for extremely helpful comments and Nichola Kitson for excellent research assistance. We also thank Thomas Sterner for his initiative in editing this special issue, and Otto Poon for financial support. We remain responsible for all errors and omissions.

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Table 2 Summary of features of POMs

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Farmer, J.D., Hepburn, C., Mealy, P. et al. A Third Wave in the Economics of Climate Change. Environ Resource Econ 62, 329–357 (2015). https://doi.org/10.1007/s10640-015-9965-2

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Keywords

  • Climate change
  • Integrated assessment models
  • Agent based models
  • DSGE models
  • Uncertainty
  • Technological innovation
  • Heterogeneity
  • Damage function