Recent days have seen a flurry of blogospheric back-and-forth about the new CBO and EPA reports, and more generally about the costs and benefits of climate change legislation. As someone who believes the costs are overestimated and the benefits underrated, I thought I’d weigh in.

Here are three key questions:

  1. How available and affordable are low-carbon alternatives today?
  2. How available and affordable will they be in the future?
  3. Do the economic models used to project the cost of carbon regulations accurately reflect the answers to Nos. 1 and 2?

If the answer to No. 1 is “scarce and expensive” and the answer to No. 2 is “still scarce and expensive” then an extremely high price signal and much economic pain will be required to force providers to scale up alternatives and consumers to substitute them, especially in the short-term. That pessimism is endemic to official projections.

Luckily, those aren’t the right answers, at least I don’t think so. Cost-effective low-carbon alternatives are plentiful. Many remain unexploited not because they can’t compete in a free market, but because there isn’t one. A variety of market barriers, market failures, and behavioral failures plague the energy sector: monopolies, oligarchs, myopic accounting, misaligned incentives, perverse regulation, information bottlenecks, immature business models, cultural inertia, plain old bad habits. Underutilization of cost-effective clean alternatives is especially true in efficiency. (See: McKinsey & ACEEE.) Hell, recycled waste heat alone could generate 742 terawatt hours of power a year in the U.S., according to Lawrence Berkeley National Lab (PDF).

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Market failures can be overcome through smart legislation, regulation, and investment designed to encourage not just alternative technologies but alternative systems. When we get our accounting right, we see they’re all over. The era of cheap energy in the U.S. has produced, among other things, a relatively sclerotic and unimaginative energy sector, particularly in electricity, which is dominated by monopolies. (The average power plant is no more efficient today than it was 50 years ago.)

But that languid pace of innovation is changing, and quickly. The past or even present pace of energy innovation is no adequate predictor of the explosion on its way.

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cat playing piano“Am I cute enough to make you read a post about macroeconomics?”It’s only a model

All those competing climate bill cost estimates you see coming from think tanks, government agencies, and industry groups? They’re from macroeconomic models. The reason different models produce different results is that they value various parameters and inputs differently. For instance, making a different choice about the proper discount rate — how the interests of future people weigh against present-day interests — can change a Nordhaus model, which recommends a low carbon tax and a little research, into a Stern model, which recommends immediate, large-scale crisis mobilization. (How do you determine the “right” discount rate? I once asked ex-macroeconomics professor and then-McCain advisor Douglas Holtz-Eakin that question. His answer: “Insoluble.”)

An EDF survey demonstrated that the consensus among the most reputable models is that the costs of climate mitigation will be modest; between 2010 and 2030, it’s projected to reduce GDP by around one half of one percent.

Predictions are hard, especially about the future

But is even that overstated? Brad Plumer and Ryan Avent both argue that economists consistently overestimate the cost of environmental regulations. Jim Manzi disputes, with an atypically facile response:

Presumably the same awareness of the track record of asserted prior under-estimation of environmental costs was available to both the EPA and CBO as they prepared their cost estimates. Unless we wish to assert that they are biased or simply irrational, why would we assume they failed to incorporate this information into their (very similar) forecasts of costs by 2020?

In other words, if cost-overestimation has been a consistent result in modeling, current day modelers must know that, and incorporate it into their models. But that presumes the flaws in models are well-understood and that it’s possible to incorporate optimism — debatable propositions both.

I once asked economist Peter Dorman about the computable general equilibrium (CGE) models frequently used in policy assessment; he said there’s been shockingly little effort to assess the success of past projections and determine how and why they failed. “They claim they’re data driven,” he said, “but in fact there’s no retrospective analysis.” UCLA economics professor Matthew Khan once said, “To be honest, these CGE models are crap.” You’d never know that from press coverage. In a well-known 1991 paper, sociologist Amitai Etzioni complained about journalists presenting economic forecasts “as though the odds are in their favor, instead of as possessing all the dependability of omens in the astrology column.” That hasn’t changed.

Putting the generally voodoo-esque nature of macroeconomics aside, though, it’s worth considering whether modelers could consistently incorporate positive answers to questions 1 and 2 above.

Faith in models

Market and behavioral failures tend to fade from view at the macro level, where models assume full employment — i.e., that resources are optimally deployed. It’s down in microeconomics, behavioral economics, even psychology and history where the extent of failures in energy markets become clear. It’s in bottom-up studies based on empirical examination of what’s possible and what’s been done. ACEEE, which has intensively studied the barriers to and potential of efficiency, says Waxman-Markey efficiency provisions will produce annual household savings of $4,400 by 2030.

As for modeling innovation, that’s always been the Achilles heel of economic forecasting. In this piece, Eban Goodstein and Hart Hodges trace a history of cost overestimations around environmental regulation. Again and again, models have underestimated the pace of business and technological innovation.

Today’s modelers surely do all they can to incorporate innovation. (As Brad notes, the CBO tries.) But there are constraints to how precisely this can be done. In 1980, McKinsey reported to AT&T that mobile subscriptions would rise to 0.9 million by 2000. The real number turned out to be … 109 million. (This factoid is among many interesting tidbits in this presentation from Vinod Khosla.) What if a modeler had come along in 1980 and said, “There will be massive innovation and new infrastructure and new business models and costs will fall by orders of magnitude, so much so that the prediction of our friends at McKinsey is 121 times too low!”

They would have been roundly mocked. And rightly so. How could they presume to predict so many fortuitous twists, turns, and serendipities? They obviously couldn’t see the future.

The thing is, neither could McKinsey. The reason its report sounded reasonable is that it was a modest number, roughly what you’d expect from linear extension of existing trends. In a sense, that’s always the responsible prediction, the small-c conservative one. That things can change hugely, quickly, is almost by definition unpredictable. For McKinsey to have forecast it would have been an act of faith.

Professional economists can’t go around saying, “At this point, magic will happen and costs will plunge.” That’s true even though magic usually happens. The American entrepreneurial spirit gets ‘er done!

Yes, this post is still going on

Point being: economic models like the ones on which Manzi bases his opposition to Waxman-Markey tend to undercount the accessible low-carbon alternatives (especially efficiency) and underestimate innovation.

They also tend to be wrong about oil and gas prices. They miss difficult-to-quantify benefits, the kind of systems-of-systems benefits Adam Siegel discusses here. They haven’t foreseen the bubbles and busts we keep going through. They can’t anticipate further regulations and investments from the Obama administration. And they don’t take account of the avoided costs of climate change.

These blind spots are by no means unique to macroeconomic forecasts. Models simply put a sheen of scientific precision on conventional wisdom.

Still, despite their unblemished record of failure, to object to making policy on the basis of cost projections from macroeconomists is to come off as vaguely obscurantist and anti-science. Advocating policy based on historically grounded optimism is seen as ideological.

The real question is: do you believe the American people can figure out innovative, profitable ways to transition to clean energy if they put their shoulders to it? In the end, it’s an expression of faith. But as conservatives like Manzi are eager to point out in other contexts, faith in American entrepreneurialism tends to pay off.