Towards agent-based integrated assessment models: examples, challenges, and future developments

  • Francesco LampertiEmail author
  • Antoine Mandel
  • Mauro Napoletano
  • Alessandro Sapio
  • Andrea Roventini
  • Tomas Balint
  • Igor Khorenzhenko
Original Article


Understanding the complex, dynamic, and non-linear relationships between human activities, the environment and the evolution of the climate is pivotal for policy design and requires appropriate tools. Despite the existence of different attempts to link the economy (or parts of it) to the evolution of the climate, results have often been disappointing and criticized. In this paper, we discuss the use of agent-based modeling for climate policy integrated assessment. First, we identify the main limitations of current mainstream models and stress how framing the problem from a complex system perspective might help, in particular when extreme climate conditions are at stake and general equilibrium effects are questionable. Second, we present two agent-based models that serve as prototypes for the analysis of coupled climate, energy, and macroeconomic dynamics. We argue that such models constitute examples of a promising approach for the integrated assessment of climate change and economic dynamics. They allow a bottom-up representation of climate damages and their cross-sectoral percolation, naturally embed distributional issues, and traditionally account for the role of finance in sustaining economic development and shaping the dynamics of energy transitions. All these issues are at the fore-front of the research in integrated assessment. Finally, we provide a careful discussion of testable policy exercises, modeling limitations, and open challenges for this stream of research. Notwithstanding great potential, there is a long way-to-go for agent-based models to catch-up with the richness of many existing integrated assessment models and overcome their major problems. This should encourage research in the area.


Climate change Climate policy Integrated assessment Transitions Agent-based models 



The authors express their gratitude to two anonymous referees whose comments improved the quality of the paper. The authors would like to acknowledge financial support from different European Projects. Francesco Lamperti, Antoine Mandel, Mauro Napoletano, Andrea Roventini, and Alessandro Sapio acknowledge financial support from European Union 7th FP grant agreement no. 603416—IMPRESSIONS. Tomas Balint and Antoine Mandel acknowledge financial support from European Union 7th FP grant agreement no. 610704—SIMPOL. Antoine Mandel, Mauro Napoletano, and Andrea Roventini acknowledge financial support from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 640772—DOLFINS and no. 649186—ISIGrowth. Alessandro Sapio acknowledges financial support by Parthenope University, Bando di sostegno alla ricerca individuale per il triennio 2015–2017, annualitá 2015 & 2016. All the usual disclaimers apply.

Supplementary material

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  1. Acemoglu D, Aghion P, Bursztyn L, Hemous D (2012) The environment and directed technical change. Am Econ Rev 102(1):131–166. CrossRefGoogle Scholar
  2. Ackerman F, DeCanio SJ, Howarth RB, Sheeran K (2009) Limitations of integrated assessment models of climate change. Clim Chang 95(3):297–315. CrossRefGoogle Scholar
  3. Akhtar MK, Wibe J, Simonovic SP, MacGee J (2013) Integrated assessment model of society-biosphere-climate-economy-energy system. Environ Model Softw 49:1–21. CrossRefGoogle Scholar
  4. Anthoff D, Tol RSJ (2009) The impact of climate change on the balanced growth equivalent: an application of fund. Environ Resour Econ 43(3):351–367. CrossRefGoogle Scholar
  5. Anthoff D, Tol RSJ (2010) On international equity weights and national decision making on climate change. J Environ Econ Manag 60(1):14–20. CrossRefGoogle Scholar
  6. Balbi S, Giupponi C (2010) Agent-based modelling of socio-ecosystems: a methodology for the analysis of adaptation to climate change. Int J Agent Technol Syst 2(4):17–38. CrossRefGoogle Scholar
  7. Balint T, Lamperti F, Mandel A, Napoletano M, Roventini A, Sapio A (2017) Complexity and the economics of climate change: a survey and a look forward. Ecol Econ 138(Supplement C):252–265. CrossRefGoogle Scholar
  8. Bardoscia M, Battiston S, Caccioli F, Caldarelli G (2015) Debtrank: a microscopic foundation for shock propagation. PLoS One 10(6):e0130406. CrossRefGoogle Scholar
  9. Bartelsman EJ, Doms M (2000) Understanding productivity: lessons from longitudinal microdata. J Econ Lit 38(3):569–594. CrossRefGoogle Scholar
  10. Battiston S, Puliga M, Kaushik R, Tasca P, Caldarelli G (2012) Debtrank: too central to fail? Financial networks, the FED and systemic risk. Sci Rep 2:541 EPCrossRefGoogle Scholar
  11. Battiston S, Mandel A, Monasterolo I, Schütze F, Visentin G (2017) A climate stress-test of the financial system. Nat Clim Chang 7(4):283 EP–283288. CrossRefGoogle Scholar
  12. Beckenbach F, Briegel R (2010) Multi-agent modeling of economic innovation dynamics and its implications for analyzing emission impacts. IEEP 7(2):317–341. CrossRefGoogle Scholar
  13. Bierkandt R, Wenz L, Willner SN, Levermann A (2014) Acclimate—a model for economic damage propagation. Part 1: basic formulation of damage transfer within a global supply network and damage conserving dynamics. Environ Syst Decis 34(4):507–524. CrossRefGoogle Scholar
  14. Bosetti V, Maffezzoli M (2013) Taxing carbon under market incompleteness. Working Papers 2013.72, Fondazione Eni Enrico Mattei.
  15. Bosetti V, Carraro C, Galeotti M, Massetti E, Tavoni M (2006) WITCH: a world induced technical change hybrid model. Energy J 27:13–37 URL Google Scholar
  16. Bottazzi G, Secchi A (2006) Explaining the distribution of firm growth rates. RAND J Econ 37(2):235–256. CrossRefGoogle Scholar
  17. Bouwer LM (2013) Projections of future extreme weather losses under changes in climate and exposure. Risk Anal 33(5):915–930. CrossRefGoogle Scholar
  18. Brouwers L, Hansson K, Verhagen H, Boman M (2001) Agent models of catastrophic events. In: modelling autonomous agents in a multi-agent world, 10th European workshop on multi agent systemsGoogle Scholar
  19. Burke M, Craxton M, Kolstad CD, Onda C, Allcott H, Baker E, Barrage L, Carson R, Gillingham K, Graff-Zivin J, Greenstone M, Hallegatte S, Hanemann WM, Heal G, Hsiang S, Jones B, Kelly DL, Kopp R, Kotchen M, Mendelsohn R, Meng K, Metcalf G, Moreno-Cruz J, Pindyck R, Rose S, Rudik I, Stock J, Tol RSJ (2016) Opportunities for advances in climate change economics. Science 352(6283):292–293. URL, CrossRefGoogle Scholar
  20. 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 112(15):4606–4611. URL 15/4606.abstract, CrossRefGoogle Scholar
  21. Carney M (2016) Resolving the climate paradox. Speech given by Mark Carney at the Arthur Burns Memorial Lecture, BerlinGoogle Scholar
  22. Cirillo P, Gallegati M (2012) The empirical validation of an agent-based model. East Econ J 38(4):525–547. CrossRefGoogle Scholar
  23. Claessens S, Kose A (2013) Financial crises explanations, types, and implications. IMF Working Papers 13/28, International Monetary Fund, URL
  24. 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 Econ 31(Supplement 2):S64–S81. URL article/pii/S0140988309001960, international, U.S. and E.U. Climate Change Control Scenarios: Results from EMF 22CrossRefGoogle Scholar
  25. de Vries B (2010) Interacting with complex systems: models and games for a sustainable economy. Tech. report, Netherlands Environmental Assessment AgencyGoogle Scholar
  26. DeCanio SJ, Watkins WE (1998) Investment in energy efficiency: do the characteristics of firms matter? Rev Econ Stat 80(1):95–107. CrossRefGoogle Scholar
  27. Dell M, Jones BF, Olken BA (2012) Temperature shocks and economic growth: evidence from the last half century. Am Econ J Macroecon 4(3):66–95. URL CrossRefGoogle Scholar
  28. Dennig F, Budolfson MB, Fleurbaey M, Siebert A, Socolow RH (2015) Inequality, climate impacts on the future poor, and carbon prices. Proc Natl Acad Sci 112(52):15,827–15,832. URL 15827.abstract, CrossRefGoogle Scholar
  29. Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M, Agwe J, Buys P, Kjevstad O, Lyon B, Yetman G (2005) Natural disaster hotspots: a global risk analysis (English). World Bank, Washington, DCGoogle Scholar
  30. Dosi G (1988) Sources, procedures, and microeconomic effects of innovation. J Econ Lit 26(3):1120–1171 URL Google Scholar
  31. Dosi G (2012) Economic organization, industrial dynamics and development. Edward Elgar, Chel- tenhamGoogle Scholar
  32. Dosi G, Fagiolo G, Roventini A (2010) Schumpeter meeting Keynes: a policy-friendly model of endogenous growth and business cycles. J Econ Dyn Control 34(9):1748–1767. URL CrossRefGoogle Scholar
  33. Dosi G, Fagiolo G, Napoletano M, Roventini A (2013) Income distribution, credit and fiscal policies in an agent-based Keynesian model. J Econ Dyn Control 37(8):1598–1625. URL article/pii/S0165188913000213, rethinking Economic Policies in a Landscape of Heterogeneous AgentsCrossRefGoogle Scholar
  34. Dosi G, Fagiolo G, Napoletano M, Roventini A, Treibich T (2015) Fiscal and monetary policies in complex evolving economies. J Econ Dyn Control 52:166–189. URL pii/S016518891400311X CrossRefGoogle Scholar
  35. Dosi G, Napoletano M, Roventini A, Treibich T (2016) Micro and macro policies in the Keynes + Schumpeter evolutionary models J Evol Econ forthcoming 1-28.
  36. Dosi G, Pereira M, Roventini A, Virgillito M (2017) When more flexibility yields more fragility: the microfoundations of Keynesian aggregate unemployment. J Econ Dyn Control 81(Supplement C):162–186. URL, international Conference Large-scale Crises: 1929 vs. 2008CrossRefGoogle Scholar
  37. Emmerling J, Drouet LD, Reis LA, Bevione M, Berger L, Bosetti V, Carrara S, De Cian E, De Maere D’Aertrycke G, Longden T, Malpede M, Marangoni G, Sferra F, Tavoni M, Witajewski-Baltvilks J, Havlik P. (2016) The WITCH 2016 model—documentation and implementation of the shared socioeconomic pathways. MITP: Mitigation, Innovation, and Transformation Pathways 240748, Fondazione Eni Enrico Mattei (FEEM),
  38. Fagiolo G, Roventini A (2012) Macroeconomic policy in DSGE and agent-based models. Revue de l’OFCE (5):67–116, URL
  39. Fagiolo G, Roventini A (2017) Macroeconomic policy in DSGE and agent-based models redux: new developments and challenges ahead. J Artif Soc Soc Simul 20(1):1–1. CrossRefGoogle Scholar
  40. Fagiolo G, Guerini M, Lamperti F, Moneta A, Roventini A (2017) Validation of agent-based models in economics and finance. LEM papers series 2017/23. Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, PisaGoogle Scholar
  41. Fankhauser S, Tol RS, Pearce DW (1997) The aggregation of climate change damages: a welfare theoretic approach. Environ Resour Econ 10(3):249–266. CrossRefGoogle Scholar
  42. Farmer JD, Foley D (2009) The economy needs agent-based modelling. Nature 460(7256):685–686. CrossRefGoogle Scholar
  43. Farmer JD, Hepburn C, Mealy P, Teytelboym A (2015) A third wave in the economics of climate change. Environ Resour Econ 62(2):329–357. CrossRefGoogle Scholar
  44. Fiddaman TS (1997) Feedback complexity in integrated climate-economy models. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  45. Fiddaman TS (2002) Exploring policy options with a behavioral climate economy model. Syst Dyn Rev 18(2):243–267. CrossRefGoogle Scholar
  46. Flake GW (1998) The computational beauty of nature: computer explorations of fractals, chaos, complex systems, and adaptation. MIT press, Cambridge (US)Google Scholar
  47. Forni M, Lippi M (1997) Aggregation and the microfoundations of dynamic macroeconomics. Oxford University Press, OxfordGoogle Scholar
  48. Forrester JW (1958) Industrial dynamics: a major breakthrough for decision makers. Harv Bus Rev 36(4):37–66Google Scholar
  49. Gerst M, Wang P, Roventini A, Fagiolo G, Dosi G, Howarth R, Borsuk M (2013) Agent-based modeling of climate policy: an introduction to the ENGAGE multi-level model framework. Environ Model Softw 44:62–75. URL http://, thematic Issue on InnovativeApproaches to Global Change ModellingCrossRefGoogle Scholar
  50. Gillingham K, Nordhaus WD, Anthoff D, Blanford G, Bosetti V, Christensen P, McJeon H, Reilly J, Sztorc P (2015) Modeling uncertainty in climate change: a multi-model comparison. Working Paper 21637, National Bureau of Economic Research. URL papers/w21637
  51. Gintis H (2006) The emergence of a price system from decentralized bilateral exchange. B E J Theor Econ 6(1):1302–1322. Google Scholar
  52. Gintis H (2007) The dynamics of general equilibrium*. Econ J 117(523):1280–1309. CrossRefGoogle Scholar
  53. Goudriaan J, Ketner P (1984) A simulation study for the global carbon cycle, including man’s impact on the biosphere. Clim Chang 6(2):167–192. CrossRefGoogle Scholar
  54. Greenwald BC, Stiglitz JE (1993) Financial market imperfections and business cycles. Q J Econ 108(1):77–114, URL CrossRefGoogle Scholar
  55. Guerini M, Moneta A (2017) A method for agent-based models validation. J Econ Dyn Control 82(Supplement C):125–141. URL http: // CrossRefGoogle Scholar
  56. Guha-Sapir D, Santos I (eds) (2013) The economic impacts of natural disasters. Oxford University Press, New York (UK). Google Scholar
  57. Haas A, Jaeger C (2005) Agents, Bayes, and climatic risks-a modular modelling approach. Adv Geosci 4(4):3–7. CrossRefGoogle Scholar
  58. Hallegatte S (2008) An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina. Risk Anal 28(3):779–799. CrossRefGoogle Scholar
  59. Hallegatte S (2014) Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters. Risk Anal 34(1):152–167. CrossRefGoogle Scholar
  60. Hallegatte S, Ranger N, Mestre O, Dumas P, Corfee-Morlot J, Herweijer C, Wood RM (2010) Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen. Clim Chang 104(1):113–137. CrossRefGoogle Scholar
  61. Hasselmann K (2010) The climate change game. Nat Geosci 3(8):511 EP–511512. CrossRefGoogle Scholar
  62. Hasselmann K, Kovalevsky DV (2013) Simulating animal spirits in actor-based environmental models. Environ Model Softw 44(Supplement C):10–24. URL S136481521200134X, thematic Issue on Innovative Approaches to Global Change ModellingCrossRefGoogle Scholar
  63. Heckman J (2001) Micro data, heterogeneity, and the evaluation of public policy: Nobel lecture. J Polit Econ 109(4):673–748. CrossRefGoogle Scholar
  64. Helbing D (2013) Globally networked risks and how to respond. Nature 497(7447):51–59. CrossRefGoogle Scholar
  65. Henriet F, Hallegatte S, Tabourier L (2012) Firm-network characteristics and economic robustness to natural disasters. J Econ Dyn Control 36(1):150–167. CrossRefGoogle Scholar
  66. IPCC (2001) Contribution of working group II the IPCC third assessment report. In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate change 2001: impacts, adaptation, and vulnerability. Cambridge University Press, Cambridge (US)Google Scholar
  67. IPCC (2014) Climate change Working Group III contribution to the IPCC Fifth Assessment report. 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, Schlomer S, von Stechow C, Zwickel T, Minx JC (eds) Climate change 2014: mitigation of climate change. Cambridge University Press, CambridgeGoogle Scholar
  68. Isley S, Lempert R, Popper S, Vardavas R (2013) An evolutionary model of industry transformation and the political sustainability of emission control policies. RAND Corporation, Technical reportGoogle Scholar
  69. Jaeger C (2012) Scarcity and coordination in the global commons. In: Jaeger C, Hasselmann K, Leipold G, Mangalagiu D, Tàbara JD (eds) Reframing the problem of climate change: from zero sum game to win-win solutions. Earthscan from Routledge, New York (US), pp 85–101Google Scholar
  70. Jaeger C, Hasselmann K, Leipold G, Mangalagiu D, Tàbara JD (2013) Reframing the problem of climate change: from zero sum game to win-win solutions. Earthscan from Routledge, New York (US)Google Scholar
  71. Jenkins K, Surminski S, Hall J, Crick F (2017) Assessing surface water flood risk and management strategies under future climate change: insights from an agent-based model. Sci Total Environ 595(Supplement C):159–168. CrossRefGoogle Scholar
  72. Kelly RA, Jakeman AJ, Barreteau O, Borsuk ME, ElSawah S, Hamilton SH, Henriksen HJ, Kuikka S, Maier HR, Rizzoli AE, van Delden H, Voinov AA (2013) Selecting among five common modelling approaches for integrated environmental assessment and management. Environ Model Softw 47:159–181. CrossRefGoogle Scholar
  73. Kirman AP (1992) Whom or what does the representative individual represent? J Econ Perspect 6(2):117–136. CrossRefGoogle Scholar
  74. Kirman A (2016) Ants and nonoptimal self-organization: lessons for macroeconomics. Macroecon Dyn 20(02):601–621. CrossRefGoogle Scholar
  75. Kousky C (2014) Informing climate adaptation: a review of the economic costs of natural disasters. Energy Econ 46:576–592. CrossRefGoogle Scholar
  76. Kovalevsky DV, Hasselmann K (2014) Assessing the transition to a low-carbon economy using actor-based system-dynamic models. In: Ames DP, Quinn NWT, Rizzoli AE (Eds.). Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15–19, San Diego, California, USAGoogle Scholar
  77. Kregel J (2009) Financial experimentation, technological paradigm revolutions and financial crises. In: Drechsler W, Kattel R, Reinert ES (eds) Techo-economic paradigms: essays in honor of Carlota Perez. Anthem Press, London, pp 203–220Google Scholar
  78. Kriegler E, Riahi K, Bauer N, Schwanitz VJ, Petermann N, Bosetti V, Marcucci A, Otto S, Paroussos L, Rao S, Currs TA, Ashina S, Bollen J, Eom J, Hamdi-Cherif M, Longden T, Kitous A, Mjean A, Sano F, Schaeffer M, Wada K, Capros P, van Vuuren DP, Edenhofer O (2015) Making or breaking climate targets: the ampere study on staged accession scenarios for climate policy. Technol Forecast Soc Chang 90:24–44. CrossRefGoogle Scholar
  79. Lamperti F (2017a) Empirical validation of simulated models through the gsl-div: an illustrative application. J Econ Interac Coord.
  80. Lamperti F (2017b) An information theoretic criterion for empirical validation of simulation models. Econometrics Stat 5:83–106. CrossRefGoogle Scholar
  81. Lamperti F, Napoletano M, Roventini A (2015) Preventing environmental disasters: market-based vs. command-and-control policies. LEM papers series 2015/34. Laboratory of Economics and Manage-ment (LEM), Sant’Anna School of Advanced Studies, Pisa URL ssa/lemwps/2015-34.html Google Scholar
  82. Lamperti F, Dosi G, Napoletano M, Roventini A, Sapio A (2017) Faraway, so close: coupled climate and economic dynamics in an agent-based integrated assessment model. LEM papers series 2017/12. Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa URL Google Scholar
  83. Li N, Liu X, Xie W, Wu J, Zhang P (2013) The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution. Risk Anal 33(1):134–145. CrossRefGoogle Scholar
  84. 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 Chang 5(5):441–444. URL CrossRefGoogle Scholar
  85. Magliocca NR, Shelley M, Smorul M (2014) Agent-based virtual laboratories for a novel experimental approach to socio-environmental synthesis. In: Ames DP, Quinn NWT, Rizzoli AE (Eds.). Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15–19, San Diego, California, USAGoogle Scholar
  86. Mandel A, Fürst S, Lass W, Meissner F, Jaeger C (2009) Lagom generiC: an agent-based model of growing economies. Tech. rep., European Climate Forum, Working Paper 1/2009Google Scholar
  87. Mastrandrea MD, Schneider SH (2001) Integrated assessment of abrupt climatic changes. Clim Pol 1(4):433–449. CrossRefGoogle Scholar
  88. Meadows DH, Meadows DL, Randers J, Behrens WW (1972) The limits to growth. Universe Books, New YorkGoogle Scholar
  89. Mercure JF, Pollitt H, Bassi AM, Viñuales JE, Edwards NR (2016) Modelling complex systems of heterogeneous agents to better design sustainability transitions policy. Glob Environ Chang 37:102–115. CrossRefGoogle Scholar
  90. Michel-Kerjan E, Hochrainer-Stigler S, Kunreuther H, Linnerooth-Bayer J, Mechler R, Muir-Wood R, Ranger N, Vaziri P, Young M (2013) Catastrophe risk models for evaluating disaster risk reduction investments in developing countries. Risk Anal 33(6):984–999. CrossRefGoogle Scholar
  91. Monasterolo I, Raberto M (2018) The eirin flow-of-funds behavioural model of green fiscal policies and green sovereign bonds. Ecol Econ 144(Supplement C):228–243. CrossRefGoogle Scholar
  92. Monasterolo I, Jones A, Tonelli F, Natalini D (2014) A hybrid system dynamics-agent based model to simulate complex adaptive systems: a new methodological framework for sustainability analysis. In: Proceedings of the System Dynamics Society Annual Conference, vol 5Google Scholar
  93. Monasterolo I, Battiston S, Janetos A, Zheng Z (2016) Understanding investors’ exposure to climate stranded assets to inform the post-carbon policy transition in the eurozone. Available at SSRN:
  94. Moss S (2002a) Agent based modelling for integrated assessment. Integr Assess 3(1):63–77. CrossRefGoogle Scholar
  95. Moss S (2002b) Policy analysis from first principles. Proc Natl Acad Sci U S A 99(10):7267–7274. CrossRefGoogle Scholar
  96. Moss S, Pahl-Wostl C, Downing T (2001) Agent-based integrated assessment modelling: the example of climate change. Integr Assess 2(1):17–30. CrossRefGoogle Scholar
  97. NBER (2010) US business cycle expansions and contractions. URL
  98. Nordhaus WD (1992) An optimal transition path for controlling greenhouse gases. Science 258(5086):1315–1319. URL, CrossRefGoogle Scholar
  99. Nordhaus WD (2008) A question of balance: economic modeling of global warming. Yale University Press, New HavenGoogle Scholar
  100. Nordhaus W (2014) Estimates of the social cost of carbon: concepts and results from the DICE-2013R model and alternative approaches. J Assoc Environ Resour Econ 1(1/2):273–312. Google Scholar
  101. Nordhaus WD, Yang Z (1996) A regional dynamic general-equilibrium model of alternative climate-change strategies. Am Econ Rev 86:741–765. Google Scholar
  102. Oeschger H, Siegenthaler U, Schotterer U, Gugelmann A (1975) A box diffusion model to study the carbon dioxide exchange in nature. Tellus 27(2):168–192. CrossRefGoogle Scholar
  103. Okuyama Y, Santos JR (2014) Disaster impact and input-output analysis. Econ Syst Res 26(1):1–12. CrossRefGoogle Scholar
  104. Olivier JG, Janssens-Maenhout G, Peters JA (2015) Trends in global CO2 emissions: 2015 report. Tech. Rep. 1803, PBL Netherlands Environmental Assessment Agency and Institute for Environment and Sustainability of the European Commissions Joint Research CentreGoogle Scholar
  105. Pasqualino R, Jones AW, Monasterolo I, Phillips A (2015) Understanding global systems todaya calibration of the world3-03 model between 1995 and 2012. Sustainability 7(8):9864–9889. CrossRefGoogle Scholar
  106. Peck SC, Teisberg TJ (1992) CETA: a model for carbon emissions trajectory assessment. Energy J 13(1):55–77 CrossRefGoogle Scholar
  107. Perez C (2003) Technological revolutions and financial capital. Edward Elgar PublishingGoogle Scholar
  108. Petrick S (2013) Carbon efficiency, technology, and the role of innovation patterns: evidence from German plant-level microdata. No. 1833. Kiel Working PaperGoogle Scholar
  109. Pindyck RS (2013) Climate change policy: what do the models tell us? J Econ Lit 51(3):860–872. CrossRefGoogle Scholar
  110. Pindyck RS (2017) The use and misuse of models for climate policy. Rev Environ Econ Policy 11(1):100–114. CrossRefGoogle Scholar
  111. Rengs B, Scholz-Wäckerle M, Gazheli A, Antal M, van den Bergh J (2015) Testing innovation, employment and distributional impacts of climate policy packages in a macro-evolutionary systems setting. WWWforEurope, 83. European Commission, bmwfw, ViennaGoogle Scholar
  112. 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(7495):173–175. CrossRefGoogle Scholar
  113. Robalino DA, Lempert RJ (2000) Carrots and sticks for new technology: abating greenhouse gas emissions in a heterogeneous and uncertain world. Integr Assess 1(1):1–19. CrossRefGoogle Scholar
  114. Rogoff K (2016) Extreme weather and global growth. URL commentary/extreme-weather-impact-global-economy-by-kenneth-rogoff-2016-01, project Syndicate - Sustainability and Environment + Economics
  115. Rosser JB (2011) Complex evolutionary dynamics in urban-regional and ecologic-economic systems: from catastrophe to chaos and beyond. Springer Science & Business Media, New York (US) CrossRefGoogle Scholar
  116. Siegel LS, Homer J, Fiddaman T, McCauley S, Franck T, Sawin E, J ones AP, Sterman J (2015) En-roads simulator reference guide. Tech. report, Climate InteractiveGoogle Scholar
  117. Smajgl A, Brown DG, Valbuena D, Huigen MG (2011) Empirical characterisation of agent behaviours in socio-ecological systems. Environ Model Softw 26(7):837–844. CrossRefGoogle Scholar
  118. Solow RM (2005) Reflections on growth theory. In: Aghion P, Durlauf SN (eds) Handbook of economic growth, vol 1, Part A. Elsevier, pp 3–10.
  119. Sonnenschein H (1972) Market excess demand functions. Econometrica 40(3):549–563. URL CrossRefGoogle Scholar
  120. Sterman J, Fiddaman T, Franck T, Jones A, McCauley S, Rice P, Sawin E, Siegel L (2012) Climate interactive: the C-ROADS climate policy model. Syst Dyn Rev 28(3):295–305. CrossRefGoogle Scholar
  121. Sterman JD, Fiddaman T, Franck T, Jones A, McCauley S, Rice P, Sawin E, Siegel L (2013) Management flight simulators to support climate negotiations. Environ Model Softw 44:122–135. CrossRefGoogle Scholar
  122. 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. CrossRefGoogle Scholar
  123. Stern N (2016) Current climate models are grossly misleading. Nature 530(7591):407–409. CrossRefGoogle Scholar
  124. Stiglitz JE, Weiss A (1981) Credit rationing in markets with imperfect information. Am Econ Rev 71(3):393–410 URL Google Scholar
  125. Tesfatsion L, Judd KL (2006) Handbook of computational economics: agent-based computational economics, vol 2. North-Holland for Elsevier, Amsterdam (NL)Google Scholar
  126. Tol RS (1997) On the optimal control of carbon dioxide emissions: an application of fund. Environ Model Assess 2(3):151–163. CrossRefGoogle Scholar
  127. Weitzman ML (2013) Tail-hedge discounting and the social cost of carbon. J Econ Lit 51(3):873–882. CrossRefGoogle Scholar
  128. Wenz L, Willner SN, Bierkandt R, Levermann A (2014) Acclimate—a model for economic damage propagation. Part II: a dynamic formulation of the backward effects of disaster-induced production failures in the global supply network. Environ Syst Decis 34(4):525–539. CrossRefGoogle Scholar
  129. Windrum P, Fagiolo G, Moneta A (2007) Empirical validation of agent-based models: alternatives and prospects. J Artif Soc Soc Simul 10(2):8 URL Google Scholar
  130. Wolf S, Bouchaud JP, Cecconi F, Cincotti S, Dawid H, Gintis H, van der Hoog S, Jaeger CC, Kovalevsky DV, Mandel A, Paroussos L (2013a) Describing economic agent-based models—DAHLEM ABM documentation guidelines. Complex Econ 2(1):63–74. CrossRefGoogle Scholar
  131. Wolf S, Fürst S, Mandel A, Lass W, Lincke D, Pablo-Martí F, Jaeger C (2013b) A multi-agent model of several economic regions. Environ Model Softw 44:25–43. CrossRefGoogle Scholar
  132. Wright EL, Erickson JD (2003) Incorporating catastrophes into integrated assessment: science, impacts, and adaptation. Clim Chang 57(3):265–286

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Authors and Affiliations

  • Francesco Lamperti
    • 1
    • 2
    Email author
  • Antoine Mandel
    • 3
    • 4
  • Mauro Napoletano
    • 5
    • 6
  • Alessandro Sapio
    • 6
    • 7
  • Andrea Roventini
    • 6
    • 8
  • Tomas Balint
    • 3
  • Igor Khorenzhenko
    • 3
    • 9
  1. 1.Scuola Superiore Sant’AnnaInstitute of EconomicsPisaItaly
  2. 2.Fondazione Eni Enrico MatteiMilanItaly
  3. 3.Université Paris 1 Panthéon-SorbonneParisFrance
  4. 4.CNRSParisFrance
  5. 5.OFCE Sciences Po, Université Côte d’Azur, GREDEG, SKEMA, CNRSSophia AntipolisFrance
  6. 6.Scuola Superiore Sant’AnnaPisaItaly
  7. 7.Parthenope University of NaplesNaplesItaly
  8. 8.OFCE Sciences PoSophia AntipolisItaly
  9. 9.Bielefeld UniversityBielefeldGermany

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