Technology first?

The idea of a technology-led solution to climate is gaining ground, most recently with a joint AEI-Brookings proposal. Kristen Sheeran has a nice commentary at RCE on the prospects. Go read it.

I’m definitely bearish on the technology-first idea. I agree that technology investment is a winner, with or without environmental externalities. But for high tech to solve the climate problem by itself, absent any emissions pricing, may require technical discontinuities that are less than likely. That makes technology-first the Hail-Mary pass of climate policy: something you do when you’re out of options.

The world isn’t out of options in a physical sense; it’s just that the public has convinced itself otherwise. That’s a pity.

The emerging climate technology delusion

What do you do when feasible policies aren’t popular, and popular policies aren’t feasible?

Let’s start with a joke:

Lenin, Stalin, Khrushchev and Brezhnev are travelling together on a train. Unexpectedly the train stops. Lenin suggests: “Perhaps, we should call a subbotnik, so that workers and peasants fix the problem.” Kruschev suggests rehabilitating the engineers, and leaves for a while, but nothing happens. Stalin, fed up, steps out to intervene. Rifle shots are heard, but when he returns there is still no motion. Brezhnev reaches over, pulls the curtain, and says, “Comrades, let’s pretend we’re moving.” (Apologies to regulars for the repeat.)

The Soviet approach would be funny, if it weren’t the hottest new trend in climate policy. The latest installment is a Breakthrough article, The emerging climate technology consensus. An excerpt: Continue reading “The emerging climate technology delusion”

Enabling an R&D addiction

I actually mean that in a good way. A society addicted to learning and innovation would be pretty cool.

However, it’s not all about money. Quoting the OSTP Science of Science Policy Roadmap,

Investment in science and technology, however, is only one of the policy instruments available to science policy makers; others include fostering the role of competiton and openness in the promotion of discovery, the construction of intellectual property systems, tax policy, and investment in a STEM workforce. However, the probable impact of these various policies and interventions is largely unknown. This lack of knowledge can lead to serious and unintended consequences.

In other words, to spend $16 billion/year wisely, you have to get a number of moving parts coordinated, including:

  1. Prices & tax policy. If prices of natural resources, national security, clean air, health, etc. don’t reflect their true values to society, innovation policy will be pushing against the tide. Innovations will be DOA in the marketplace. The need for markets for products is matched by the need for markets for innovators:
  2. Workforce management. Just throwing money at a problem can create big dislocations in researcher demographics. Put it all into academic research, and you create a big glut of graduates who have no viable career path in science. Put it all into higher education, and your pipeline of talent will be starved by poor science preparation at lower levels. Put it all into labs and industry, and it’ll turn into pay raises for a finite pool of workers. Balance is needed.
  3. Intellectual property law. This needs to reflect the right mix of incentives for private investment and recognition that creations are only possible to the extent that we stand on the shoulders of giants and live in a society with rule of law. Currently I suspect that law has swung too far toward eternal protection that actually hinders innovation.

At the end of the day, #1 is most important. Regardless of the productivity of the science enterprise, someone will probably figure out how to make graphene cables or an aspen tree that bears tomatoes. The key question, then, is how society puts those things to use, to solve its problems and improve welfare. That requires a delicate balancing act, between preserving diversity and individual freedom to explore new ways of doing things, and preventing externalities from harming everyone else.

R&D – crack for techno-optimists

I like R&D. Heck, I basically do R&D. But the common argument, that people won’t do anything hard to mitigate emissions or reduce energy use, so we need lots of R&D to find solutions, strikes me as delusional.

The latest example to cross my desk (via the NYT) is the new American Energy Innovation Council’s recommendations,

Create an independent national energy strategy board.
Invest $16 billion per year in clean energy innovation.
Create Centers of Excellence with strong domain expertise.
Fund ARPA-E at $1 billion per year.
Establish and fund a New Energy Challenge Program to build large-scale pilot projects.

Let’s look at the meat of this – $16 billion per year in energy innovation funding. Historic funding looks like this:

R&D funding

Total public energy R&D, compiled from Gallagher, K.S., Sagar, A, Segal, D, de Sa, P, and John P. Holdren, “DOE Budget Authority for Energy Research, Development, and Demonstration Database,” Energy Technology Innovation Project, John F. Kennedy School of Government, Harvard University, 2007. I have a longer series somewhere, but no time to dig it up. Basically, spending was negligible (or not separately accounted for) before WWII, and ramped up rapidly after 1973.

The data above reflects public R&D; when you consider private spending, the jump to $16 billion represents maybe a factor of 3 or 4 increase. What does that do for you?

Consider a typical model of technical progress, the two-factor learning curve:

cost = (cumulative R&D)^A*(cumulative experience)^B

The A factor represents improvement from deliberate R&D, while the B factor reflects improvement from production experience like construction and installation of wind turbines. A and B are often expressed as learning rates, the multiple on cost that occurs per doubling of the relevant cumulative input. In other words, A,B = ln(learning rate)/ln(2). Typical learning rates reported are .6 to .95, or cost reductions of 40% to 5% per doubling, corresponding with A/B values of -.7 to -.15, respectively. Most learning rate estimates are on the high end (smaller reductions per doubling), particularly when the two-factor function is used (as opposed to just one component).

Let’s simplify so that

cost = (cumulative R&D)^A

and use an aggressive R&D learning rate (.7), for A=-0.5. In steady state, with R&D growing at the growth rate of the economy (call it g), cost falls at the rate A*g (because the integral of exponentially growing spending grows at the same rate, and exp(g*t)^A = exp(A*g*t)).

That’s insight number one: a change in R&D allocation has no effect on the steady-state rate of progress in cost. Obviously one could formulate alternative models of technology where that is not true, but compelling argument for this sort of relationship is that the per capita growth rate of GDP has been steady for over 250 years. A technology model with a stronger steady-state spending->cost relationship would grow super-exponentially.

Insight number two is what the multiple in spending (call it M) does get you: a shift in the steady-state growth trajectory to a new, lower-cost path, by M^A. So, for our aggressive parameter, a multiple of 4 as proposed reduces steady-state costs by a factor of about 2. That’s good, but not good enough to make solar compatible with baseload coal electric power soon.

Given historic cumulative public R&D, 3%/year baseline growth in spending, a 0.8 learning rate (a little less aggressive), a quadrupling of R&D spending today produces cost improvements like this:

R&D future 4x

Those are helpful, but not radical. In addition, even if R&D produces something more miraculous than it has historically, there are still big nontechnical lock-in humps to overcome (infrastructure, habits, …). Overcoming those humps is a matter of deployment more than research. The Energy Innovation Council is definitely enthusiastic about deployment, but without internalizing the externalities associated with energy production and use, how is that going to work? You’d either need someone to pick winners and implement them with a mishmash of credits and subsidies, or you’d have to hope for/wait for cleantech solutions to exceed the performance of conventional alternatives.

The latter approach is the “stone age didn’t end because we ran out of stones” argument. It says that cleantech (iron) will only beat conventional (stone) when it’s unequivocally better, not just for the environment, but also convenience, cost, etc. What does that say about the prospects for CCS, which is inherently (thermodynamically) inferior to combustion without capture? The reality is that cleantech is already better, if you account for the social costs associated with energy. If people aren’t willing to internalize those social costs, so be it, but let’s not pretend we’re sure that there’s a magic technical bullet that will yield a good outcome in spite of the resulting perverse incentives.

Gallagher, K.S., Sagar, A, Segal, D, de Sa, P, and John P. Holdren, “DOE Budget Authority for Energy Research, Development, and Demonstration Database,” Energy Technology Innovation Project, John F. Kennedy School of Government, Harvard University, 2007.

Stop talking, start studying?

Roger Pielke Jr. poses a carbon price paradox:

The carbon price paradox is that any politically conceivable price on carbon can do little more than have a marginal effect on the modern energy economy. A price that would be high enough to induce transformational change is just not in the cards. Thus, carbon pricing alone cannot lead to a transformation of the energy economy.

Put another way:

Advocates for a response to climate change based on increasing the costs of carbon-based energy skate around the fact that people react very negatively to higher prices by promising that action won’t really cost that much. … If action on climate change is indeed “not costly” then it would logically follow the only reasons for anyone to question a strategy based on increasing the costs of energy are complete ignorance and/or a crass willingness to destroy the planet for private gain. … There is another view. Specifically that the current ranges of actions at the forefront of the climate debate focused on putting a price on carbon in order to motivate action are misguided and cannot succeed. This argument goes as follows: In order for action to occur costs must be significant enough to change incentives and thus behavior. Without the sugarcoating, pricing carbon (whether via cap-and-trade or a direct tax) is designed to be costly. In this basic principle lies the seed of failure. Policy makers will do (and have done) everything they can to avoid imposing higher costs of energy on their constituents via dodgy offsets, overly generous allowances, safety valves, hot air, and whatever other gimmick they can come up with.

His prescription (and that of the Breakthrough Institute)  is low carbon taxes, reinvested in R&D:

We believe that soon-to-be-president Obama’s proposal to spend $150 billion over the next 10 years on developing carbon-free energy technologies and infrastructure is the right first step. … a $5 charge on each ton of carbon dioxide produced in the use of fossil fuel energy would raise $30 billion a year. This is more than enough to finance the Obama plan twice over.

… We would like to create the conditions for a virtuous cycle, whereby a small, politically acceptable charge for the use of carbon emitting energy, is used to invest immediately in the development and subsequent deployment of technologies that will accelerate the decarbonization of the U.S. economy.

Stop talking, start solving

As the nation begins to rely less and less on fossil fuels, the political atmosphere will be more favorable to gradually raising the charge on carbon, as it will have less of an impact on businesses and consumers, this in turn will ensure that there is a steady, perhaps even growing source of funds to support a process of continuous technological innovation.

This approach reminds me of an old joke:

Lenin, Stalin, Khrushchev and Brezhnev are travelling together on a train. Unexpectedly the train stops. Lenin suggests: “Perhaps, we should call a subbotnik, so that workers and peasants fix the problem.” Kruschev suggests rehabilitating the engineers, and leaves for a while, but nothing happens. Stalin, fed up, steps out to intervene. Rifle shots are heard, but when he returns there is still no motion. Brezhnev reaches over, pulls the curtain, and says, “Comrades, let’s pretend we’re moving.”

I translate the structure of Pielke’s argument like this:

Pielke Loops

Implementation of a high emissions price now would be undone politically (B1). A low emissions price triggers a virtuous cycle (R), as revenue reinvested in technology lowers the cost of future mitigation, minimizing public outcry and enabling the emissions price to go up. Note that this structure implies two other balancing loops (B2 & B3) that serve to weaken the R&D effect, because revenues fall as emissions fall.

If you elaborate on the diagram a bit, you can see why the technology-led strategy is unlikely to work:

PielkeLoopsSF

First, there’s a huge delay between R&D investment and emergence of deployable technology (green stock-flow chain). R&D funded now by an emissions price could take decades to emerge. Second, there’s another huge delay from the slow turnover of the existing capital stock (purple) – even if we had cars that ran on water tomorrow, it would take 15 years or more to turn over the fleet. Buildings and infrastructure last much longer. Together, those delays greatly weaken the near-term effect of R&D on emissions, and therefore also prevent the virtuous cycle of reduced public outcry due to greater opportunities from getting going. As long as emissions prices remain low, the accumulation of commitments to high-emissions capital grows, increasing public resistance to a later change in direction. Continue reading “Stop talking, start studying?”

NUMMI – an innovation killed by its host's immune system?

This American Life had a great show on the NUMMI car plant, a remarkable joint venture between Toyota and GM. It sheds light on many of the reasons for the decline of GM and the American labor movement. More generally, it’s a story of a successful innovation that failed to spread, due to policy resistance, inability to confront worse-before-better behavior and other dynamics.

I noticed elements of a lot of system dynamics work in manufacturing. Here’s a brief reading list:

Electric Car Wisdumb

The current McKinsey Quarterly feature’s Andy Grove’s editorial, An electric plan for energy resilience. An excerpt:

We believe the United States should consider accelerating this movement by creating an industry of after-market retrofitters. What problems’”technical and economic’”would need to be solved in order to do that? With the help of a team of second-year graduate students in our Bass seminar at the Stanford Business School, we examined this question in the context of a proposed pilot program, whose aim would be to retrofit one million vehicles in three years. We felt that such a project would represent what in game theory is referred to as the ‘minimum winning game’: a significant step toward a long-term strategic objective (see sidebar, ‘Inside Andy’s real-world seminar’).

We estimate the price tag of such a pilot project to be around $10 billion, owing to the present high cost of batteries, which are around $10,000 each. One might expect such costs to drop as volume increases, but because this program is accelerated by design, we have to assume that batteries will remain expensive. Assuming an average gas price of $3 per gallon, the payback period to the owner of a retrofitted vehicle is at least ten years, not a strong economic incentive. But the benefits of this program’”testing and validating a key approach to energy resilience’”accrue to the well-being of the United States at large. As the general population is the predominant beneficiary, economic assistance flowing from everyone to vehicle owners, in the form of tax incentives, is justified.

There are different approaches to retrofitting vehicles. We favor GM’s Volt design, in which the car is directly driven by an electric motor. The vehicle’s existing gasoline engine is replaced by a smaller one, whose sole purpose is to generate electricity and recharge the battery. To simplify the retrofitting task, we would limit the scope of the program to six to ten Chevrolet, Ford, and Dodge models, selected on the basis of two criteria: low fuel efficiency and large numbers of vehicles on the road. Most of these vehicles would be SUVs, pick-ups, and vans.

There’s some wisdom in this proposal, particularly in the recognition that achieving an alt fuel vehicle transformation takes more than a few inventions; it requires changes in infrastructure, marketing, and a variety of other domains, each with bugs to be worked out:

Others wondered why we should bother retrofitting a million cars if that would deal only with a fraction of a percent of the existing cars. That’s one way to look at it. Another, which was the view our students took, is that it is important to strive to do enough conversions that we can encounter all the unknown unknowns, which in my experience characterize every new product or technology as it gets scaled into volume. Should it be 5 million? Should it only be 500,000? We picked a million as a number that is big enough to stress retrofitting capability, battery production capability, manufacturing issues and marketing issues. We described our aim as the ‘minimum winning game’ that would give us a platform from which we could scale further.

However, the retrofit idea strikes me as fundamentally flawed. Targeting low efficiency SUVs, pick-ups, and vans puts batteries exactly where they’d be least effective. If most such vehicles weren’t overweight, un-aerodynamic, saddled with lossy AWD, and bloated with power-hungry accessories, they’d already get decent fuel economy. Adding batteries to them is going to result in some combination of high cost, short range, and poor performance. That sounds like a sure way to poison the public perception of plug in electric vehicles.

RMI has been arguing for years that a coordinated set of chassis innovations could make powertrains with high cost-per-watt, like fuel cells, attractive. It’s no accident that that the only really successful hybrid vehicle (the Prius, responsible for over half of 2007 and 2008 hybrid sales) was designed from scratch. It gets its breakthrough mileage/performance combination from much more than a battery and motor. Lightweight materials, aerodynamics, low rolling resistance tires, and other innovations are also key.

I think Grove and his students are falling for a common fantasy: that technology will step up and allow us to drive exactly as we now do, fossil-free. I personally doubt that will happen. Arnold will probably be one of only a few to ever drive a hydrogen Hummer. The rest of us will have to recognize that if alt fuel vehicles are to accomplish anything really meaningful from an energy standpoint, they’ll be different, as will our land use, commuting, and travel habits.

With that in mind, we should be focusing on creating the new stuff, not fixing the old. That might mean the Chevy Volt, but it might also mean rail or telecommuting. Rather than setting up programs to achieve narrow goals, I’d rather see broad, credible signals (e.g., prices at the pump reflecting environmental and security values) guide the evolution of the new from the bottom up.

Endogenous Energy Technology

I just created an annotated list of links on learning/experience curves, deliberate R&D, and other forms of endogenous energy technology, including a few models and empirical estimates. See del.icio.us/tomfid for details. Comments with more references will be greatly appreciated!

Dangerous Assumptions

Roger Pielke Jr., Tom Wigley, and Christopher Green have a nice commentary in this week’s Nature. It argues that current scenarios are dangerously reliant on business-as-usual technical improvement to reduce greenhouse gas intensity:

Here we show that two-thirds or more of all the energy efficiency improvements and decarbonization of energy supply required to stabilize greenhouse gases is already built into the IPCC reference scenarios. This is because the scenarios assume a certain amount of spontaneous technological change and related decarbonization. Thus, the IPCC implicitly assumes that the bulk of the challenge of reducing future emissions will occur in the absence of climate policies. We believe that these assumptions are optimistic at best and unachievable at worst, potentially seriously underestimating the scale of the technological challenge associated with stabilizing greenhouse-gas concentrations.

They note that assumed rates of decarbonization exceed reality:

The IPCC scenarios include a wide range of possibilities for the future evolution of energy and carbon intensities. Many of the scenarios are arguably unrealistic and some are likely to be unachievable. For instance, the IPCC assumptions for decarbonization in the short term (2000’“2010) are already inconsistent with the recent evolution of the global economy (Fig. 2). All scenarios predict decreases in energy intensity, and in most cases carbon intensity, during 2000 to 2010. But in recent years, both global energy intensity and carbon intensity have risen, reversing the trend of previous decades.

In an accompanying news article, several commenters object to the notion of a trend reversal:

Energy efficiency has in the past improved without climate policy, and the same is very likely to happen in the future. Including unprompted technological change in the baseline is thus logical. It is not very helpful to discredit emission scenarios on the sole basis of their being at odds with the most recent economic trends in China. Chinese statistics are not always reliable. Moreover, the period in question is too short to signify a global trend-break. (Detlef van Vuuren)

Having seen several trend breaks evaporate, including the dot.com productivity miracle and the Chinese emissions reductions coincident with the Asian crisis, I’m inclined to agree that gloom may be premature. On the other hand, Pielke, Wigley and Green are conservative in that they don’t consider the possible pressure for recarbonization created by a transition from conventional oil and gas to coal and tar sands. A look at the long term is helpful:

18 country emissions intensity

Emissions intensity of GDP for 18 major emitters. Notice the convergence in intensity, with high-intensity nations falling, and low-intensity nations (generally less-developed) rising.

Emissions intensity trend for 18 major emitters

Corresponding decadal trends in emissions intensity. Over the long haul, there’s some indication that emissions are falling faster in developed nations – a reason for hope. But there’s also a lot of diversity, and many nations have positive trends in intensity. More importantly, even with major wars and depressions, no major emitter has achieved the kind of intensity trend (about -7%/yr) needed to achieve 80% emissions reductions by 2050 while sustaining 3%/yr GDP growth. That suggests that achieving aggressive goals may require more than technology, including – gasp – lifestyle changes.

6 country emissions intensity

A closer look at intensity for 6 major emitters. Notice intensity rising in China and India until recently, and that Chinese data is indeed suspect.

Pielke, Wigley, and Green wrap up:

There is no question about whether technological innovation is necessary ’” it is. The question is, to what degree should policy focus directly on motivating such innovation? The IPCC plays a risky game in assuming that spontaneous advances in technological innovation will carry most of the burden of achieving future emissions reductions, rather than focusing on creating the conditions for such innovations to occur.

There’s a second risky game afoot, which is assuming that “creating the conditions for such innovations to occur” means investing in R&D, exclusive of other measures. To achieve material reductions in emissions, “occur” must mean “be adopted” not just “be invented.” Absent market signals and institutional changes, it is unlikely that technologies like carbon sequestration will ever be adopted. Others, like vehicle and lighting efficiency, could easily see their gains eroded by increased consumption of energy services, which become cheaper as technology improves productivity.