Facing up to uncertainty in climate-change economics

Geoffrey Heal, Antony Millner

Uncertainty is intrinsic in climate-change economics. This column argues that it’s here to stay. There will be no accurate predictive tool for predicting economic growth, the emergence of clean-energy technology, or economic vulnerability in light of climate change in the near future. But this is not an excuse not to think about climate economics. Research and policy would do well to be more explicit about what we don’t know. We should avoid subjective guesses, and focus more on credible forecasts from empirically sound, if uncertain, models.

Uncertainty is intrinsic in climate change economics. We know that increases in greenhouse gas concentrations are causing shifts in the climate, but not precisely how large these shifts will be, nor when and where they will occur. Neither do we understand fully the social and economic consequences of these changes, or the options that will be available for coping with them in the future. Characterising our knowledge of these uncertainties, and finding decision tools that are appropriate to our state of knowledge, is a vital part of sensible evaluations of climate-policy options.

It is helpful to decompose uncertainty about the climate problem into scientific and socioeconomic components. Scientific uncertainty in turn has three components. In the short to medium run, i.e. over the next 20-60 years (depending on the spatial resolution of the prediction), uncertainty in climate predictions is dominated by ‘initial condition’ uncertainty and ‘model uncertainty’ (Hawkins and Sutton 2009). Initial condition uncertainty arises from the properties of chaotic systems, of which the climate system is an archetypal example. These systems exhibit sensitive dependence on initial conditions – predictions of the evolution of the system that start out very close to one another can diverge rapidly over time. Thus small errors in the initial specifications of climate models can lead to widely divergent predictions over time. Climate scientists capture this uncertainty by running their models with many initial configurations, leading to a distribution of predictions at each lead-time.

Climate model uncertainty

But this is only part of the story. We are also uncertain about the structure of climate models – both the values of their many parameters, and the detailed forms of some of the equations used to model physical, chemical, and biological processes – cloud formation being a prominent example. These uncertainties are collectively called ‘model uncertainty’, and are captured by running many climate models with different structures and parameter values. Because these climate models share some components, and are calibrated on common datasets, we cannot view their predictions as independent estimates, and it is not possible to combine them into a unique probabilistic prediction without making strong subjective judgements about how to weight different models (Knutti et. al. 2010). Thus model uncertainty has quite a different character to initial condition uncertainty. Finally, in the long run, the dominant uncertainty in climate predictions derives from our own actions – we don’t know how much more CO 2 we will put into the atmosphere over the coming century, and this largely determines the trajectory of the global climate in the long run.

All of these uncertainties are at play in scientific predictions of changes in globally averaged climate variables. But the effects of climate change will be local, not global. There is much spatial heterogeneity in central estimates of predicted changes, the uncertainty around them, and the factors that contribute to this uncertainty. In general, the larger the spatial scale of the prediction, the more credible it is, and the lower the uncertainty. However, as we magnify down to scales relevant to individual countries or districts, uncertainties increase, often dramatically (Masson and Knutti 2011).

While the uncertainties in climate predictions are large, they are derived from well-confirmed physical principles with long track records of making credible predictions. When it comes to characterising the socioeconomic uncertainties associated with the climate problem, we seldom have this luxury. These uncertainties have both empirical and normative components. Empirical uncertainties arise from our inability to predict the evolution of the global economy (how rich we will be), the future costs of CO 2 abatement (which technologies will be available), how damaging climate change will be to future economies, and how we will adapt to its effects. All of these factors are crucial inputs to designing ‘optimal’ climate policies, yet the tools we use to forecast them are educated guesses at best.

Normative uncertainties are not really uncertainties at all, but rather disagreements. There are wide divergences of opinion as to the appropriate ethical framework for welfare analysis of climate policy. Any such policies will have important distributive effects, both within and across generations. Ethical parameters such as the pure rate of time preference (how much we value our own welfare relative to the next generation’s) and the elasticity of marginal utility (how much we care about inequality in consumption) have been shown to be crucial determinants of the timing and intensity of optimal policy responses (Nordhaus 2008). A democratic approach to this ethical heterogeneity requires a social choice based approach to welfare analysis – we need to aggregate the ethical preferences of diverse individuals. This aggregation process can lead to frameworks for welfare analysis that are quite different from those that are prevalent in the literature today (Heal and Millner 2013a).

Deep uncertainty

Taking all the empirical factors together, it is clear that our uncertainty about climate policy is ‘deep’. There are many models that make predictions of the relevant empirical factors, but we don’t know how to combine them, or indeed, in the case of the economic models, whether they are credible predictive tools. We are thus forced to make strong subjective judgments if we wish to define a unique probability distribution over future outcomes. Whose subjective beliefs about the level of World GDP in 2100, or the economic damages to future societies from four degrees of temperature change, should enter climate-policy analyses? While there are many models that provide estimates of these effects, none can claim to be a gold standard predictive tool.

This implies that when it comes to making policy choices concerning climate, we need to find ways of incorporating information of varying and uncertain quality into our decisions. We argue that the traditional tool of expected utility maximisation is not well suited to the nature of our knowledge about the climate problem (Heal and Millner 2013b). Because this criterion describes our knowledge with a unique probability distribution, which is treated no differently from the uncertainty in the roll of a die or the toss of a coin, it neglects the fact that in many cases we are ‘uncertain about our uncertainty’. Decision theorists have long been aware of this difficulty, and have developed powerful alternatives to expected utility theory that are designed for use when our information is incomplete, inconsistent, or non-existent (see e.g. Gilboa 2009). It is time for these methods to be applied to the climate problem.

Alternatives theories

Alternatives to expected utility theory come in several flavours. The simplest models do not require any likelihood information at all – policy options are evaluated based purely on outcomes, and not the likelihood of outcomes. Most of these methods tend to advocate an avoidance of ‘worst-case’ outcomes, whether by maximising the minimum payoff, or minimising the maximum ‘regret’ of a policy option. These methods may be appropriate in situations where we have little or no information (e.g. in some very local adaptation decisions), but in many cases the scientific literature provides us with much more detail. It is often the case that we have many competing probabilistic forecasts from different models – the trouble is that we cannot meaningfully compare the models’ performance in order to arrive at an objective weighting scheme for these forecasts. The decision theorists have provided tools that are suited to this case too. These methods rely on there being multiple probability distributions over possible future states, which cannot be reduced to a single composite distribution. In this case, the decision criteria tend to emphasise the more pessimistic distributions (not outcomes), and advocate policies that avoid the worst expected welfare losses. There are thus several competing decision criteria in the literature, each with its strengths and weaknesses. We advocate an exploration of these approaches in economic analysis of climate policy (Heal and Millner 2013b).

Conclusions

There are three broad conclusions that can be reached from this discussion.

First, uncertainty is here to stay;

While we are making incremental progress in characterising the uncertainty in climate predictions, as well as how climate change impacts current economies and ecosystems, big parts of the picture are likely to remain obscured. We have no ‘magic bullet’ predictive tool that credibly forecasts the pace of economic growth over the coming centuries, the emergence of new clean energy technologies, or how vulnerable future economies will be to climate change.

Second, although there are some tough problems in quantitative modeling of the effects of climate policies, this is not an excuse for not thinking about these policies at all;

The problem is very real, even if aspects of it are not well described by conventional probabilistic tools. We do have an enormous amount of useful information about the perils of climate change, certainly more than enough to recognise that it is an issue requiring immediate policy attention.

Third, if economic models are to provide genuinely useful inputs to the policy selection process, they must account for the nature of our knowledge about the climate problem, be explicit about what we know and what we don’t, and base their recommendations on decision tools that recognise the difference between subjective guesses and credible forecasts from empirically sound models;

These decision tools tend to place more weight on ‘bad’ states of the world. Since the downside risk from ignoring the climate problem is likely to be far worse than the downside risk from aggressive mitigation policy, it is likely that decision tools that account for deep uncertainty will advocate more aggressive mitigation policies than those that do not (Millner et al. 2013).

References

Gilboa, I (2009), Theory of Decision under Uncertainty, Cambridge University Press.

Hawkins, E and R Sutton (2009), “The potential to narrow uncertainty in regional climate predictions', Bulletin of the American Meteorological Society 90:1095-1107.

Heal, G and A Millner (2013a), “Discounting under disagreement”, NBER Working paper No. 18999.

Heal, G and A Millner (2013b), “Uncertainty and decision in climate change economics”, NBER Working paper No. 18929.

Knutti, R et al. (2010), “Challenges in Combining Projections from Multiple Climate Models”, Journal of Climate 23(10), 2739-2758.

Masson, D and R Knutti (2011), “Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble”, Journal of Climate 24(11), 2680-2692.

Millner, A et al. (2013), “Scientific ambiguity and climate policy”, Environmental and Resource Economics 55(1), 21-46.

Nordhaus, W (2008), A question of balance, Yale University Press.