Reflections on Virgin Earth

Colleagues just pointed out the Virgin Earth Challenge, “a US$25 million prize for an environmentally sustainable and economically viable way to remove greenhouse gases from the atmosphere.”

John Sterman writes:

I think it inevitable that we will see more and more interest in CO2 removal. And IF it can be done without undermining mitigation I’d be all for it. I do like biochar as a possibility; though I am very skeptical of direct air capture and CCS. But the IF in the prior sentence is clearly not true: if there were effective removal technology it would create moral hazard leading to less mitigation and more emissions.

Even more interesting, direct air capture is not thermodynamically favored; needs lots of energy. All the finalists claim that they will use renewable energy or “waste” heat from other processes to power their removal technology, but how about using those renewable sources and waste heat to directly offset fossil fuels and reduce emissions instead of using them to power less efficient removal processes? Clearly, any wind/solar/geothermal that is used to power a removal technology could have been used directly to reduce fossil emissions, and will be cheaper and offset more net emissions. Same for waste heat unless the waste heat is too low temp to be used to offset fossil fuels. Result: these capture schemes may increase net CO2 flux into the atmosphere.

Every business knows it’s always better to prevent the creation of a defect than to correct it after the fact. No responsible firm would say “our products are killing the customers; we know how to prevent that, but we think our money is best spent on settling lawsuits with their heirs.” (Oh: GM did exactly that, and look how it is damaging them). So why is it ok for people to say “fossil fuel use is killing us; we know how to prevent that, but we’ve decided to spend even more money to try to clean up the mess after the pollution is already in the air”?

To me, many of these schemes reflect a serious lack of systems thinking, and the desire for a technical solution that allows us to keep living the way we are living without any change in our behavior. Can’t work.

I agree with John, and I think there are some additional gaps in systemic thinking about these technologies. Here are some quick reflections, in pictures.

EmittingCapturingA basic point for any system is that you can lower the level of a stock (all else equal) by reducing the inflow or increasing the outflow. So the idea of capturing CO2 is not totally bonkers. In fact, it lets you do at least one thing that you can’t do by reducing emissions. When emissions fall to 0, there’s no leverage to reduce CO2 in the atmosphere further. But capture could actively draw down the CO2 stock. However, we are very far from 0 emissions, and this is harder than it seems:

AirCapturePushbackNatural sinks have been graciously absorbing roughly half of our CO2 emissions for a long time. If we reduce emissions dramatically, and begin capturing, nature will be happy to give us back that CO2, ton for ton. So, the capture problem is actually twice as big you’d think from looking at the excess CO2 in the atmosphere.

Currently, there’s also a problem of scale. Emissions are something like two orders of magnitude larger than potential markets for CO2, so there’s a looong way to go. And capture doesn’t scale like like a service running on Amazon Elastic Cloud servers; it’s bricks and mortar.

EmitCaptureScaleAnd where does that little cloud go, anyway? Several proposals gloss over this, as in:

The process involves a chemical solution (that naturally absorbs CO2) being brought into contact with the air. This solution, now containing the captured CO2, is sent to through a regeneration cycle which simultaneously extracts the CO2 as a high-pressure pipeline-quality product (ready to be put to numerous commercial uses) …

The biggest commercial uses I know of are beverage carbonation and enhanced oil recovery (EOR). Consider the beverage system:

BeverageCO2CO2 sequestered in beverages doesn’t stay there very long! You’d have to start stockpiling vast quantities of Coke in salt mines to accumulate a significant quantity. This reminds me of Nike’s carbon-sucking golf ball. EOR is just as bad, because you put CO2 down a hole (hopefully it stays there), and oil and gas come back up, which are then burned … emitting more CO2. Fortunately the biochar solutions do not suffer so much from this problem.

Next up, delays and moral hazard:

CO2moralHazardThis is a cartoonish view of the control system driving mitigation and capture effort. The good news is that air capture gives us another negative loop (blue, top) by which we can reduce CO2 in the atmosphere. That’s good, especially if we mismanage the green loop. The moral hazard side effect is that the mere act of going through the motions of capture R&D reduces the perceived scale of the climate problem (red link), and therefore reduces mitigation, which actually makes the problem harder to solve.

Capture also competes with mitigation for resources, as in John’s process heat example:

ProcessHeat

It’s even worse than that, because a lot of mitigation efforts have fairly rapid effects on emissions. There are certainly long-lived aspects of energy and infrastructure that must be considered, but behavior can change a lot of emissions quickly and with off-the-shelf technology. The delay between air capture R&D and actual capturing, on the other hand, is bound to be fairly long, because it’s in its infancy, and has to make it through multiple discover/develop/deploy hurdles.

One of those hurdles is cost. Why would anyone bother to pay for air capture, especially in cases where it’s a sure loser in terms of thermodynamics and capital costs? Altruism is not a likely candidate, so it’ll take a policy driver. There are essentially two choices: standards and emissions pricing.

A standard might mandate (as the EPA and California have) that new power plants above a certain emissions intensity must employ some kind of offsetting capture. If coal wants to stay in business, it has to ante up. The silly thing about this, apart from inevitable complexity, is that any technology that meets the standard without capture, like combined cycle gas electricity currently, pays 0 for its emissions, even though they too are harmful.

Similarly, you could place a subsidy or bounty on tons of CO2 captured. That would be perverse, because taxpayers would then have to fund capture – not likely a popular measure. The obvious alternative would be to price emissions in general – positive for emissions, negative for capture. Then all sources and sinks would be on a level playing field. That’s the way to go, but of course we ought to do it now, so that mitigation starts working, and air capture joins in later if and when it’s a viable competitor.

I think it’s fine if people work on carbon capture and sequestration, as long as they don’t pretend that it’s anywhere near a plausible scale, or even remotely possible without comprehensive changes in incentives. I won’t spend my own time on a speculative, low-leverage policy when there are more effective, immediate and cheaper mitigation alternatives. And I’ll certainly never advise anyone to pursue a geoengineered world, any more than I’d advise them to keep smoking but invest in cancer research.

 

 

Climate Interactive – #12 climate think tank

Climate Interactive is #12 (out of 210) in the International Center for Climate Governance’s Standardized Ranking of climate think tanks (by per capita productivity):

  1. Woods Hole Research Center (WHRC)
  2. Basque Centre for Climate Change (BC3)
  3. Centre for European Policy Studies (CEPS)*
  4. Centre for European Economic Research (ZEW)*
  5. International Institute for Applied Systems Analysis (IIASA)
  6. Worldwatch Institute
  7. Fondazione Eni Enrico Mattei (FEEM)
  8. Resources for the Future (RFF)
  9. Mercator Research Institute on Global Commons and Climate Change (MCC)
  10. Centre International de Recherche sur l’Environnement et le De?veloppement (CIRED)
  11. Institut Pierre Simon Laplace (IPSL)
  12. Climate Interactive
  13. The Climate Institute
  14. Buildings Performance Institute Europe (BPIE)
  15. International Institute for Environment and Development (IIED)
  16. Center for Climate and Energy Solutions (C2ES)
  17. Global Climate Forum (GCF)
  18. Potsdam Institute for Climate Impact Research (PIK)
  19. Sandbag Climate Campaign
  20. Civic Exchange

That’s some pretty illustrious company! Congratulations to all at CI.

Where's my stuff?

I’ve just acquired a pair of 18″ Dell XPS portable desktop tablets. It’s one slick piece of hardware, that makes my iPad seem about as sexy as a beer coaster.

They came with Win8 installed. Now I know why everyone hates it. It makes a good first impression with pretty colors and a simple layout. But after a few minutes, you wonder, where’s all my stuff? There’s no obvious way to run a desktop application, so you end up scouring the web for ways to resurrect the Start menu.

It’s bizarre that Microsoft seems to have forgotten the dynamics that made it a powerhouse in the first place. It’s basically this:

Software is a big nest of positive feedbacks, producing winner-take-all behavior. A few key loops are above. The bottom pair is the classic Bass diffusion model – reinforcing feedback from word of mouth, and balancing feedback from saturation (running out of potential customers). The top loop is an aspect of complementary infrastructure – the more users you have on your platform, the more attractive it is to build apps for it; the more apps there are, the more users you get.

There are lots of similar loops involving accumulation of knowledge, standards, etc. More importantly, this is not a one-player system; there are multiple platforms competing for users, each with its own reinforcing loops. That makes this a success-to-the-successful situation. Microsoft gained huge advantage from these reinforcing loops early in the PC game. Being the first to acquire a huge base of users and applications carried it through many situations in which its tech was not the most exciting thing out there.

So, if you’re Microsoft, and Apple throws you a curve ball by launching a new, wildly successful platform, what should you do? It seems to me that the first imperative should be to preserve the advantages conferred by your gigantic user and application base.

Win8 does exactly the opposite of that:

  • Hiding the Start menu means that users have to struggle to find their familiar stuff, effectively chucking out a vast resource, in favor of new apps that are slicker, but pathetically few in number.
  • That, plus other decisions, enrage committed users and cause them to consider switching platforms, when a smoother transition would have them comfortably loyal.

This strategy seems totally bonkers.

The dynamics of UFO sightings

The Economist reports on UFO sightings:

UFOdataThis deserves a model:

UFOs

UFOs.vpm (Vensim published model, requires Pro/DSS or the free Reader)

The model is a mixed discrete/continuous simulation of an individual sleeping, working and drinking. This started out as a multi-agent model, but I realized along the way that sleeping, working and drinking is a fairly ergodic process on long time scales (at least with respect to UFOs), so one individual with a distribution of behaviors over time or simulations is as good as a population of agents.

The model replicates the data somewhat faithfully:

UFOdistributionThe model shows a morning peak (people awake but out and about) and a workday dip (inside, lurking near the water cooler) but the data do not. This suggests to me that:

  • Alcohol is the dominant factor in sightings.
  • I don’t party nearly enough to see a UFO.

Actually, now that I’ve built this version, I think the interesting model would have a longer time horizon, to address the non-ergodic part: contagion of sightings across individuals.

h/t Andreas Größler.

Footing the bill for Iraq

Back in 2002, when invasion of Iraq was on the table and many Democrats were rushing patriotically to the President’s side rather than thinking for themselves, William Nordhaus (staunchest critic of Limits) went out on a limb a bit to attempt a realistic estimate of the potential cost.

All the dangers that lead to ignoring or underestimating the costs of war can be reduced by a thoughtful public discussion. Yet neither the Bush administration nor the Congress – neither the proponents nor the critics of war – has presented a serious estimate of the costs of a war in Iraq. Neither citizens nor policymakers are able to make informed judgments about the realistic costs and benefits of a potential conflict when no estimate is given.

His worst case: about $755 billion direct (military, peacekeeping and reconstruction) plus indirect effects totaling almost $2 trillion for a decade of conflict and its aftermath.

NordhausIraqNordhaus’ worst case is pretty close to actual direct spending in Iraq to date. But with another trillion for Afghanistan and 2 to 4 in the pipeline from future obligations related to the war, the grand total is looking like a lowball estimate. Other pre-invasion estimates, in the low billions, look downright ludicrous.

Recent news makes Nordhaus’ parting thought even more prescient:

Particularly worrisome are the casual promises of postwar democratization, reconstruction, and nation-building in Iraq. The cost of war may turn out to be low, but the cost of a successful peace looks very steep. If American taxpayers decline to pay the bills for ensuring the long-term health of Iraq, America would leave behind mountains of rubble and mobs of angry people. As the world learned from the Carthaginian peace that settled World War I, the cost of a botched peace may be even higher than the price of a bloody war

Early economic dynamics: Samuelson's multiplier-accelerator

Paul Samuelson’s 1939 analysis of the multiplier-accelerator is a neat piece of work. Too bad it’s wrong.

Interestingly, this work dates from a time in which the very idea of a mathematical model was still questioned:

Contrary to the impression commonly held, mathematical methods properly employed, far from making economic theory more abstract, actually serve as a powerful liberating device enabling the entertainment and analysis of ever more realistic and complicated hypotheses.

Samuelson should be hailed as one of the early explorers of a very big jungle.

The basic statement of the model is very simple:

NationalIncome

In quasi-System Dynamics notation, that looks like:

SamuelsonDiagramB

A caveat:

The limitations inherent in so simplified a picture as that presented here should not be overlooked. In particular, it assumes that the marginal propensity to consume and the relation are constants; actually these will change with the level of income, so that this representation is strictly a marginal analysis to be applied to the study of small oscillations. Nevertheless it is more general than the usual analysis.

Samuelson hand-simulated the model (it’s fun – once – but he runs four scenarios):Simulated Samuelson then solves the discrete time system, to identify four regions with different behavior: goal seeking (exponential decay to a steady state), damped oscillations, unstable (explosive) oscillations, and unstable exponential growth or decline. He nicely maps the parameter space:

parameterSpace

ParamRegionBehaviorSo where’s the problem?

The first is not so much of Samuelson’s making as it is a limitation of the pre-computer era. The essential simplification of the model for analytic solution is;

Simplified

This is fine, but it’s incredibly abstract. Presented with this equation out of context – as readers often are – it’s almost impossible to posit a sensible description of how the economy works that would enable one to critique the model. This kind of notation remains common in econometrics, to the detriment of understanding and progress.

At the first SD conference, Gil Low presented a critique and reconstruction of the MA model that addressed this problem. He reconstructed the model, providing an operational description of the economy that remains consistent with the multiplier-accelerator framework.

LowThe mere act of crafting a stock-flow description reveals problem #1: the basic multiplier-accelerator doesn’t conserve stuff.

inventory1 InventoryCapital2Non-conservation of stuff leads to problem #2. When you do implement inventories and capital stocks, the period of multiplier-accelerator oscillations moves to about 2 decades – far from the 3-7 year period of the business cycle that Samuelson originally sought to explain. This occurs in part because the capital stock, with a 15-year lifetime, introduces considerable momentum. You simply can’t discover this problem in the original multiplier-accelerator framework, because too many physical and behavioral time constants are buried in the assumptions associated with its 2 parameters.

Low goes on to introduce labor, finding that variations in capacity utilization do produce oscillations of the required time scale.

ShortTermI think there’s a third problem with the approach as well: discrete time. Discrete time notation is convenient for matching a model to data sampled at regular intervals. But the economy is not even remotely close to operating in discrete annual steps. Moreover a one-year step is dangerously close to the 3-year period of the business cycle phenomenon of interest. This means that it is a distinct possibility that some of the oscillatory tendency is an artifact of discrete time sampling. While improper oscillations can be detected analytically, with discrete time notation it’s not easy to apply the simple heuristic of halving the time step to test stability, because it merely compresses the time axis or causes problems with implicit time constants, depending on how the model is implemented. Halving the time step and switching to RK4 integration illustrates these issues:

RK4

It seems like a no-brainer, that economic dynamic models should start with operational descriptions, continuous time, and engineering state variable or stock flow notation. Abstraction and discrete time should emerge as simplifications, as needed for analysis or calibration. The fact that this has not become standard operating procedure suggests that the invisible hand is sometimes rather slow as it gropes for understanding.

The model is in my library.

See Richardson’s Feedback Thought in Social Science and Systems Theory for more history.

Samuelson’s Multiplier Accelerator

This is a fairly direct implementation of the multiplier-accelerator model from Paul Samuelson’s classic 1939 paper,

“Interactions between the Multiplier Analysis and the Principle of Acceleration” PA Samuelson – The Review of Economics and Statistics, 1939 (paywalled on JSTOR, but if you register you can read a limited number of publications for free)

SamuelsonDiagramB

This is a nice example of very early economic dynamics analyses, and also demonstrates implementation of discrete time notation in Vensim. Continue reading “Samuelson’s Multiplier Accelerator”

How many things can you get wrong on one chart?

Let’s count:

  1. stupidGraphTruncate records that start ca. 1850 at an arbitrary starting point.
  2. Calculate trends around a breakpoint cherry-picked to most favor your argument.
  3. Abuse polynomial fits generally. (See this series.)
  4. Report misleading linear trends by simply dropping the quadratic term.
  5. Fail to notice the obvious: that temperature in the second period is, on average, higher than in the first.
  6. Choose a loaded color scheme that emphasizes #5.
  7. Fail to understand that temperature integrates CO2.
  8. Fallacy of the single cause (only CO2 affects temperature – in good company with Burt Rutan).

Another field ponders rationality

The reasoning criminal vs. Homer Simpson: conceptual challenges for crime science

A recent disciplinary offshoot of criminology, crime science (CS) defines itself as “the application of science to the control of crime.” One of its stated ambitions is to act as a cross-disciplinary linchpin in the domain of crime reduction. Despite many practical successes, notably in the area of situational crime prevention (SCP), CS has yet to achieve a commensurate level of academic visibility. The case is made that the growth of CS is stifled by its reliance on a model of decision-making, the Rational Choice Perspective (RCP), which is inimical to the integration of knowledge and insights from the behavioral, cognitive and neurosciences (CBNs).