by Judith Curry
Climate change is an externality that is global, pervasive, long-term, and uncertain–but even though the scale and complexity of this externality is unprecedented, economic theory is well equipped for such problems–and advice based on rigorous economic analysis is any way preferred to wishy-washy thinking. – Richard Tol
Integrated Assessment Model-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading. – Robert Pindyck
In the preceding post Social Cost of Carbon, we were left with two outstanding issues: what is the uncertainty/ignorance associated with the SCC estimates, and how should the SCC be used in policy making. In this post, I present perspectives from two economists that are arguably in the ‘middle of the road’, at least in relative terms, on this topic.
Targets for global climate policy: an overview
Abstract. A survey of the economic impact of climate change and the marginal damage costs shows that carbon dioxide emissions are a negative externality. The estimated Pigou tax and its growth rate are too low to justify the climate policy targets set by political leaders. A lower discount rate or greater concern for the global distribution of income would justify more stringent climate policy, but would imply an overhaul of other public policies. Catastrophic risk justifies more stringent climate policy, but only to a limited extent.
Published by Journal of Economic Dynamics and Control, full manuscript available [here]. Some excerpts:
There is broad agreement between these studies in four areas. First, the welfare effect of a doubling of the atmospheric concentration of greenhouse gas emissions on the current economy is relatively small—a few percentage points of GDP. Second, the initial benefits of a modest increase in temperature (up to ~2C) are probably positive, followed by losses as temperatures increase further. Third, the uncertainty is vast and right-skewed. Undesirable surprises are more likely than desirable surprises. Fourth, poorer countries tend to be more vulnerable to climate change.
Although Table 2 reveals a large estimated uncertainty about the social cost of carbon, there is reason to believe that the actual uncertainty is larger still. First of all, the social cost of carbon derives from the total economic impact estimates, of which there are few, incomplete estimates. Second, the researchers who published impact estimates are from a small and close-knit community who may be subject togroup-thinking, peer pressure and self-censoring.
The second paper is by MIT economist Robert Pindyck:
Climate Change Policy, What do the Models Tell Us?
Abstract. Very little. A plethora of integrated assessment models (IAMs) have been constructed and used to estimate the social cost of carbon (SCC) and evaluate alternative abatement policies. These models have crucial flaws that make them close to useless as tools for policy analysis: certain inputs (e.g., the discount rate) are arbitrary, but have huge effects on the SCC estimates the models produce; the models’ descriptions of the impact of climate change are completely ad hoc, with no theoretical or empirical foundation; and the models can tell us nothing about the most important driver of the SCC, the possibility of a catastrophic climate outcome. IAM-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading.
Published in Journal of Economic Literature, [link] to full paper. Summary excerpts:
I have argued that IAMs are of little or no value for evaluating alternative climate change policies and estimating the SCC. On the contrary, an IAM-based analysis suggests a level of knowledge and precision that is nonexistent, and allows the modeler to obtain almost any desired result because key inputs can be chosen arbitrarily.
So how can we bring economic analysis to bear on the policy implications of possible catastrophic outcomes? Given how little we know, a detailed and complex modeling exercise is unlikely to be helpful. (Even if we believed the model accurately represented the relevant physical and economic relationships, we would have to come to agreement on the discount rate and other key parameters.) Probably something simpler is needed. Perhaps the best we can do is come up with rough, subjective estimates of the probability of a climate change sufficiently large to have a catastrophic impact, and then some distribution for the size of that impact (in terms, say, of a reduction in GDP or the effective capital stock).
First, consider a plausible range of catastrophic outcomes (under, for example, BAU), as measured by percentage declines in the stock of productive capital (thereby reducing future GDP). Next, what are plausible probabilities? Here, “plausible” would mean acceptable to a range of economists and climate scientists. Given these plausible outcomes and probabilities, one can calculate the present value of the benefits from averting those outcomes, or reducing the probabilities of their occurrence. The benefits will depend on preference parameters, but if they are sufficiently large and robust to reasonable ranges for those parameters, it would support a stringent abatement policy. Of course this approach does not carry the perceived precision that comes from an IAM-based analysis, but that perceived precision is illusory. To the extent that we are dealing with unknowable quantities, it may be that the best we can do is rely on the “plausible.”
Robert Samuelson discusses Pindyck’s paper in the Washington Post titled Global Warming Pragmatism. Excerpt:
Pindyck sounds like a “global warming denier.” He isn’t. True, he thinks climate change and its adverse economic consequences could be wildly overstated. He also thinks they could be wildly understated. The effects might ultimately be catastrophic. We simply don’t know. Ignorance reigns. The best course, he says, would be to adopt a modest carbon tax — because there are certainly some ill effects of global warming — and adjust it as we learn more. Meanwhile, we shouldn’t assume that computer models convey scientific truth. “The models create an illusion of knowledge,” he says. “For me, the issue is being honest.”
JC comments: I like both of these papers; they pay substantial attention to uncertainty, and both recognize the issues associated not just with statistical uncertainty but also methodological reliability (e.g. see recent reliability thread on the paper by Petersen and Smith) as well as flat out ignorance. Tol is an IPCC insider (who is unusually open minded for an IPCC insider, IMO) while Pindyck does not have any apparent connection to the IPCC (and the issue of IAM and SCC does not seem to be a primary focus). Hence Pindyck’s perspective partially addresses Tol’s concerns about narrow perspectives from a small close-knit community.
But all this still leaves us with the issue of what to do re climate policy. Even with high levels of uncertainty and ignorance, looking at the model results and sensitivities is useful, provided that this information is used in context of a broad scenario approach such as suggested by Pindyck. The use of these model results to drive policy in an optimal decision making mode, such as what seems implied by the White House doc, does not seem defensible given these analyses of uncertainty and areas of ignorance.