Author: Tom

Space Tourism & Climate

The Saturn V used for the Apollo missions burned 203,000 gallons of RP-1 (basically kerosene) in its first stage. At 820 kg/m^3, that’s 630 metric tons of fuel. Liquid hydrocarbons tend to be close to CxH2x, or about 85% carbon by mass, so that’s 536 metric tons of carbon, which yields 1965 tons CO2 when burned, or 655 TonCO2/astronaut. Obviously that’s not personal consumption, but it is a lot of carbon in the atmosphere.

The emerging space tourism industry, on the other hand, is primarily personal consumption. I’d love to take the trip, but I’d be a little put off if the consequences of seeing the big blue marble from above were to make a major contribution to climate change. So, what are the consequences?

Big Blue Marble from TerraMODIS, NASA

TerraMODIS, NASA Continue reading “Space Tourism & Climate”

On Limits to Growth

It’s a good idea to read things you criticize; checking your sources doesn’t hurt either. One of the most frequent targets of uninformed criticism, passed down from teacher to student with nary a reference to the actual text, must be The Limits to Growth. In writing my recent review of Green & Armstrong (2007), I ran across this tidbit:

Complex models (those involving nonlinearities and interactions) harm accuracy because their errors multiply. Ascher (1978), refers to the Club of Rome’s 1972 forecasts where, unaware of the research on forecasting, the developers proudly proclaimed, “in our model about 100,000 relationships are stored in the computer.” (page 999)

Setting aside the erroneous attributions about complexity, I found the statement that the MIT world models contained 100,000 relationships surprising, as both can be diagrammed on a single large page. I looked up electronic copies of World Dynamics and World3, which have 123 and 373 equations respectively. A third or more of those are inconsequential coefficients or switches for policy experiments. So how did Ascher, or Ascher’s source, get to 100,000? Perhaps by multiplying by the number of time steps over the 200 year simulation period – hardly a relevant measure of complexity.

Meadows et al. tried to steer the reader away from focusing on point forecasts. The introduction to the simulation results reads,

Each of these variables is plotted on a different vertical scale. We have deliberately omitted the vertical scales and we have made the horizontal time scale somewhat vague because we want to emphasize the general behavior modes of these computer outputs, not the numerical values, which are only approximately known. (page 123)

Many critics have blithely ignored such admonitions, and other comments to the effect of, “this is a choice, not a forecast” or “more study is needed.” Often, critics don’t even refer to the World3 runs, which are inconvenient in that none reaches overshoot in the 20th century, making it hard to establish that “LTG predicted the end of the world in year XXXX, and it didn’t happen.” Instead, critics choose the year XXXX from a table of resource lifetime indices in the chapter on nonrenewable resources (page 56), which were not forecasts at all. Continue reading “On Limits to Growth”

A modest bailout proposal

The Fed has just doled out over $300 billion in loans to bail out Bear Stearns and other bad actors in the subprime mortgage mess. It’s hard to say what fraction of that capital is really at risk, but let’s say 10%. That’s a pretty big transfer to shareholders, especially considering that there’s nothing in it for the general public other than avoidance of financial contagion effects. If this were an environmental or public health issue, skeptics would be lined up to question whether contagion in fact exists, whether fixing it does more harm than good (e.g., by creating future moral hazard), and whether there’s a better way to spend the money. Contagion would have to be proven with models, subject to infinite scrutiny and delay. Yet here, billions are doled out with no visible analysis or public process, based on policies invented ad hoc. Perhaps a little feedback control is needed here: let’s create a bailout fund, supported by taxes on firms that are deemed too big to fail by some objective criteria. Then two negative feedbacks will operate: firms that get too large will be encouraged to split themselves into manageable chunks, and the potential beneficiaries of bailouts will have to ask themselves how badly they really want insurance. Let’s try it, and see how long the precautionary principle lasts in the financial sector.

Update: Paul Krugman has a nice editorial on the problem.

And if financial players like Bear are going to receive the kind of rescue previously limited to deposit-taking banks, the implication seems obvious: they should be regulated like banks, too.

Unintended Consequences

Olive Heffernan has an interesting tidbit on Climate Feedback about unintended consequences of climate policy.

It’s worth noting that most of these side-effects are not consequences of climate policy per se. They are consequences of pursuing climate policy piecemeal, from the bottom up, and seeking technological fixes in the absence of market signals. If climate policy were pursued as part of a general agenda of internalizing environmental and social externalities through market signals, some of these perverse behaviors would not occur.

The side effects of the corn ethanol boom should not be laid at the door of climate policy. Apart from hopes for cellulosic, ethanol has little to offer with respect to greenhouse gas emissions, and perhaps much to answer for. Its real motivations are oil independence and largesse to the ag sector.

Surveys and Quizzes as Propaganda

Long ago I took an IATA survey to relieve the boredom of a long layover. Ever since, I’ve been on their mailing list, and received “invitations” to take additional surveys. Sometimes I do, out of curiosity – it’s fun to try to infer what they’re really after. The latest is a “Global Survey on Aviation and Environment” so I couldn’t resist. After a few introductory questions, we get to the meat:

History/Fact
1. Air transport contributes 8% to the global economy and supports employment for 32 million people. But, aviation is responsible for only 2% of global CO2 emissions.

Wow … an energy intensive sector that somehow manages to be less carbon intensive than the economy in general? Sounds too good to be true. Unfortunately, it is. The illusion of massive scale of the air transport sector is achieved by including indirect activity, i.e. taking credit for what other sectors produce when it might involve air transport. Federal cost-benefit accounting practices generally banish the use of such multiplier effects, with good reason. According to an ATAG report hosted by IATA, the indirect effects make up the bulk of activity claimed above. ATAG peels the onion for us:

Air transport direct and indirect GDP contributions

So, direct air transport is closer to 1% of GDP. Comparing direct GDP of 1% to direct emissions of 2% no longer looks favorable, though – especially when you consider that air transport has other warming effects (contrails, non-CO2 GHG emissions) that might double or triple its climate impact. The IPCC Aviation and the Global Atmosphere report, for example, places aviation at about 2% of fossil fuel use, and about 4% of total radiative forcing. If IATA wants to count indirect GDP and employment, fine with me, but then they need to count indirect emissions on the same basis. Continue reading “Surveys and Quizzes as Propaganda”

Evidence on Climate Predictions

Last Year, Kesten Green and Scott Armstrong published a critique of climate science, arguing that there are no valid scientific forecasts of climate. RealClimate mocked the paper, but didn’t really refute it. The paper came to my attention recently when Green & Armstrong attacked John Sterman and Linda Booth Sweeney’s paper on mental models of climate change.

I reviewed Green & Armstrong’s paper and concluded that their claims were overstated. I responded as follows: Continue reading “Evidence on Climate Predictions”

Confused at the National Post

A colleague recently pointed me to a debate on an MIT email list over Lorne Gunter’s National Post article, Forget Global Warming: Welcome to the New Ice Age.

The article starts off with anecdotal evidence that this has been an unusually cold winter. If it had stopped where it said, “OK, so one winter does not a climate make. It would be premature to claim an Ice Age is looming just because we have had one of our most brutal winters in decades,” I wouldn’t have faulted it. It’s useful as a general principle to realize that weather has high variance, so it’s silly to make decisions on the basis of short term events. (Similarly, science is a process of refinement, so it’s silly to make decisions on the basis of a single paper.)

But it didn’t stop. It went on to assemble a set of scientific results of varying quality and relevance, purporting to show that, “It’s way too early to claim the same is about to happen again, but then it’s way too early for the hysteria of the global warmers, too.” That sounds to me like a claim that the evidence for anthropogenic global warming is of the same quality as the evidence that we’re about to enter an ice age, which is ridiculous. It fails to inform the layman either by giving a useful summary of accurately characterized evidence or by demonstrating proper application of logic.

Some further digging reveals that the article is full of holes: Continue reading “Confused at the National Post”

The blank sheet of paper

I’ve been stymied for some time over how to start this blog. Finally (thanks to my wife) I’ve realized that it’s really the same problem as conceptualizing a model, with the same solution.

Beginning modelers frequently face a blank sheet of paper with trepidation … where to begin? There’s lots of good advice that I should probably link here. Instead I’ll just observe that there’s really no good answer … you just have to start. The key is to remember that modeling is highly iterative. It’s OK if the first 10 attempts are bad; their purpose is not to achieve perfection. Colleagues and I are currently working on a model that is in version 99, and still full of challenges. The purpose of those first few rounds is to explore the problem space and capture as much of the “mess” as possible. As long as the modeling process exposes the work-in-progress to lots of user feedback and reality checks, and captures insight along the way, there’s nothing to worry about.