Travis Kniffin, JD Anticipated May 2023
In February of last year, The Royal Society – a London-based organization which describes itself as “a [f]ellowship of the world’s most eminent scientists” and “the oldest scientific academy in continuous existence – held a virtual event titled “The Economics of Biodiversity.” After a brief introduction, Prince Charles appeared via Zoom. In his remarks, the Prince called for “the restoration of our degraded natural systems.” The imperative to restore nature, he warned, is “not only vital for reasons of ethics and common sense, but also economics.” And as the Prince continued, he didn’t just make the case that restoring nature was vital to economic well-being. He also likened our natural assets to financial ones: “Just as diversity within a portfolio of financial assets reduces risk and uncertainty, diversity in ecosystems, in species, and in genes – in other words, biodiversity – directly and indirectly increases nature’s resilience to shocks.” He declared that “[n]ow…is surely the moment when we must see the world as it truly is.” Then, taking his point past a mere analogy, the Prince stated that it was high time we “find the means to ensure that economy and ecology are viewed as one.”
The Prince may not have known it, but he has the experts on his side. The way in which economists, bureaucrats and legal thinkers conceive of policies to regulate our air, water, and soil has long been grounded in prices. But these regulatory approaches are only as useful as their ability to properly ‘price’ the things they seek to regulate. And it’s particularly difficult to put a price on the inherent uncertainty that comes with forecasting the severity of future harms to the environment.
Cost-Benefit Analysis
The predominant way of using price to determine policy is called cost-benefit analysis (CBA). In 1981, the Reagan administration mandated that federal rulemaking – and therefore much of environmental rulemaking – use CBA to analyze policy. CBA has many virtues. It allows environmentalists and economists to speak a common language, and it does this by translating environmental problems into straightforward quantitative comparisons.
To assess potential environmental regulation, policymakers and regulators use CBA to express the benefits of the regulation in economic terms. In this analysis, the costs of mitigating a given risk are weighed against the damage that would be done in the absence of regulation. Thus, the cost of avoiding such damage equates, in turn, to the ‘benefit’ of some aspect of a healthy and viable environment.
At first blush, CBA seems like an eminently reasonable way to plan and calibrate our response to future environmental harm. In order to weigh costs and benefits against one another, CBA expresses all values in a dollar amount – and thus CBA allows the direct quantitative comparison of competing cost and benefit values. Although the premise is intuitive, CBA comes out of a particular ideological context. And the story behind this line of thinking starts with the discipline of welfare economics.
Welfare economics is motivated by a noble goal: it aims to maximize the aggregate well-being of all members of a given population. But as Professor Amy Sinden notes, the task of “[m]easuring aggregate ‘welfare’ has always been problematic,” and it’s little wonder that even the discipline’s early practitioners were skeptical that well-being could be accurately measured from one person to the next. However, welfare economics soon found a clearer articulation in something called the Pareto Principle, named for the Italian economist who devised it. Sinden describes how in Pareto’s formulation, a “Pareto Improvement” is an action that improves the lot of a single person, while leaving others untroubled. As these “improvements” play out in voluntary transactions between people, the thinking goes, society will eventually reach a state in which no further improvement is possible.
However, welfare economists also recognize that population-wide improvement doesn’t play out according to Pareto’s thinking – that social actors in the real world will fail to achieve “Pareto efficiency.” In light of this issue, welfare economists concede that the state will sometimes need to intervene. But these same economists maintain that “when [the] government does step in, it should calibrate its regulation to mimic the economically efficient outcome that a perfectly functioning market would have produced.” And in order to most accurately mimic that outcome, economists use CBA.
The core advantage of CBA is that it reduces the analysis of competing values to a direct numerical comparison. And in these comparisons, regulation of an environmental harm is justified if the dollar cost of that regulation is lower than the dollar costs that would result from the harm. Of course, as the level of regulation – its ‘stringency’ – increases, its costs increase as well. At a certain point, additional stringency yields greater costs than benefits. As a result, the quality of CBA depends a great deal on how much certainty is fed into it. When risks are well understood, CBA is a powerful and effective tool. But when there is uncertainty about just how bad a potential future harm could be, CBA begins to fall apart. And unfortunately, the risks we’re least certain of also happen to involve the most catastrophic potential harms of climate change.
A useful way to understand this phenomenon is called the “fat tail” of risk. Many people are familiar with the bell curve, which represents a distribution of outcomes visually, in histogram form. In bell curve representations, the most likely outcomes are clustered in the middle, giving the curve its familiar shape. But as it happens, the usual bell curve distribution produced by CBA isn’t suited to describing the risks from climate change. Instead, as climatologist Michael Mann contends, the right way to think about climate risks might be familiar to anyone who’s purchased automobile or home insurance. As he puts it, “we don’t purchase fire insurance on our homes because our homes are likely to burn down.” Of course, we know that a residential fire is unlikely. But “if it did happen, it would be catastrophic.” As obvious as Mann’s observation seems, CBA does not lead us to it. A CBA approach would probably fail to account for threat posed by an unlikely — but disastrous — house fire. This is partly because CBA, with its basis in welfare economics, implicitly underweights remote-yet-catastrophic possibilities — and thus lessens the anticipated impact of things like the “fat tail.”
Another irksome characteristic of CBA is something it inherited from welfare economics: the assumption of an ever-expanding economy. Douglas Kysar has observed how the assumption of a continually expanding economy means that climate change CBA models project “that global GDP can continue to pour forth even after all presently inhabited land on earth has been rendered unsuitable for human existence.” Therefore, a policymaker using CBA to predict the worst-case effects of an environmental harm is bound to conclude that any foreseeable catastrophes won’t be especially catastrophic.
The Social Cost of Carbon
Since CBA is only as good as its inputs, there is much at stake in calculating the harm, in dollar terms, that each additional ton of carbon wreaks on the atmosphere. The name of this measure, an input into climate change CBA, is the Social Cost of Carbon (SCC).
Michael A. Livermore and Richard L. Revesz have told the origin story of this effort. In a 2008 federal case called Center for Biological Diversity v. NHTSA, nearly a dozen states brought suit against National Highway Traffic Safety Administration (NHTSA) because it failed to account for the ill effects of greenhouse gases (GHGs) in its fuel economy standards. For its part, NHTSA argued that accounting for the effects of GHGs involved too much uncertainty. The Ninth Circuit, however, found the agency’s approach untenable. Even if the precise effects of GHGs are difficult to quantify, the court reasoned, reducing the amount of GHGs emitted clearly has some value, rather than none.
From there, the Obama administration charged several government agencies – which together formed the Interagency Working Group (IWG) – with the task of determining the SCC. In turn, the IWG based its findings on “the three most widely cited, peer-reviewed models that link the physical impacts of carbon dioxide emissions to economic damages.” The models worked by first translating emissions GHGs into levels of atmospheric carbon, and then translating those levels into projected changes in average global temperature. Finally, the projected changes were converted into dollar amounts of economic damage.
The IWG’s source models had a problem, though. In their article “The Social Cost of Carbon,” Frank Ackerman and Elizabeth A. Stanton call out the problem, by way of a question: “What importance should be given to, for instance, the loss of endangered species, unique habitats and environments, and human lives and communities?” As it happens, the IWG’s models shrank from the task. The model-makers chose to exclude these considerations. To be sure, placing a dollar value on them is a thorny proposition. Indeed, this possibility is probably not lost on economists. In a rather famous episode, economist and former Secretary of the Treasury Larry Summers released a memo in which he argued for the virtues of exporting rich countries’ pollution to poorer countries. The thinking, described in Harvard Magazine, was straightforward enough: “underpopulated” African nations are vastly “underpolluted.” So in Summers’s formulation, “dumping toxic wastes there” made perfect economic sense. Summers (who claims that the memo was partially fabricated and improperly credited to him) was rightly excoriated for that vile thesis. But when it comes to climate change, the failure to assign an approximate dollar value on the loss of priceless assets such as endangered species makes CBA untenable. As Ackerman and Stanton see it, excluding these risks from the CBA equation is tantamount to suggesting that they “have no value at all.”
What is more, Ackerman and Stanton also describe how the IWG’s models tended to give little weight to the cost of saddling future generations with the runaway warming of our planet. This is because when considering the impact of economic damage in the future, the models applied what’s called a ‘discount rate.’ Since modelers need to aggregate the cost of damages in the near term along with those in the more distant future, they choose to underweight future damage relative to near-term damage – and the discount rate refers to this practice of underweighting future damages. But as the authors point out, the trade-off is that “[t]he higher the ‘discount rate’ that is chosen, the less future costs are valued in present-day terms.”
Repeating NHTSA’s Mistake
As noted above, Center for Biological Diversity v. NHTSA helped to set in motion the effort to find the SCC. NHTSA, the court said, “cannot put a thumb on the scale by undervaluing the benefits” of avoiding additional GHG emissions. Thus the court found that NHTSA’s contention – that uncertainty precluded them from factoring in the cost of carbon – was untenable. And despite the scale and ambition of the effort to find the SCC, the IWG repeated NHTSA’s mistake. By choosing to not specifically quantify the toll of harms which are ethically difficult to price, and choosing to heavily discount the cost of catastrophe in the far future, the IWG showed that it was unwilling to deal with uncertainty head-on.
For all their efforts, the IWG (and the models it drew upon) ultimately repeated NHTSA’s mistake. The IWG failed to ‘price in’ the unknown. And at the same time, their long and comprehensive process lent legitimacy to an SCC that merely formalized NHTSA’s error.
Conclusion
In 2017, the Trump administration abandoned the IWG. But on the day of his inauguration, President Biden reestablished the group through Executive Order 13990. With this executive action, the administration seeks to revise federal policymakers’ approach to the SCC, “to the extent that current methodologies do not adequately take account of climate risk, environmental justice, and intergenerational equity.” To that end, the IWG has indicated that the new SCC will include adjustments to the discount rate based upon “intergenerational ethical considerations.” It has also acknowledged the need to “account for global damages,” by recognizing “the diverse ways in which U.S. interests, businesses, and residents may be impacted by climate change beyond U.S. borders.”
While the IWG’s new focus is likely to drive down the discount rate and yield a model that is more data-rich, we ought to remain skeptical. Still grounded in CBA and saddled with the limitations it’s bound to inherit from IGW’s previous SCC effort, the new model will struggle to overcome the in-built assumption of an ever-expanding economy, and a general aversion to ethically troubling price estimates and dealing head-on with uncertainty.
So, let’s return to Prince Charles’s declaration that we ought to treat economy and ecology “as one.” It’s a broad directive, and CBA and SCC are hardly the only ways to represent the environment in economic terms. But the example of CBA and SCC should caution us. Policymakers implement these approaches to make hard-to-describe factors more quantifiable and tangible. Rather than making the impacts of potential harms more concrete, CBA and SCC tend to leave us where we started: hindered by uncertainty. In formulating approaches that put prices on the virtues and drawbacks of measures to combat climate change, policymakers would be wise to avoiding reproducing the dilemmas that their new approaches are designed to address.