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A Third Wave in the Economics of Climate Change

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

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

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

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

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

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

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

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Authors and Affiliations

  1. Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Eagle House, Walton Well Road, Oxford, OX2 6ED, UK

    J. Doyne Farmer, Cameron Hepburn, Penny Mealy & Alexander Teytelboym

  2. Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, 87501, USA

    J. Doyne Farmer

  3. Mathematical Institute, University of Oxford, Woodstock Road, Oxford, OX2 6GG, UK

    J. Doyne Farmer

  4. Smith School of Enterprise and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK

    Cameron Hepburn, Penny Mealy & Alexander Teytelboym

  5. Grantham Research Institute, London School of Economics and Political Science, Tower 3, Clement’s Inn, London, WC2A 2AZ, UK

    Cameron Hepburn

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Correspondence to Cameron Hepburn.

Additional information

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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