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:
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:
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”
15,000 MIT theses are online at Dspace, including some SD classics. The trick is finding things. Searching for terms like “system dynamics” is often unhelpful. You might try some of the following authors:
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”
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”
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.