Trade Emissions & Cosmic Rays

Two interesting abstracts I ran across today:

Testing the proposed causal link between cosmic rays and cloud cover

A decrease in the globally averaged low level cloud cover, deduced from the ISCCP infrared data, as the cosmic ray intensity decreased during the solar cycle 22 was observed by two groups. The groups went on to hypothesize that the decrease in ionization due to cosmic rays causes the decrease in cloud cover, thereby explaining a large part of the currently observed global warming. We have examined this hypothesis to look for evidence to corroborate it. None has been found and so our conclusions are to doubt it. From the absence of corroborative evidence, we estimate that less than 23%, at the 95% confidence level, of the 11 year cycle change in the globally averaged cloud cover observed in solar cycle 22 is due to the change in the rate of ionization from the solar modulation of cosmic rays.

Trading Kyoto

Almost one-quarter of carbon dioxide released to the atmosphere is emitted in the production of internationally traded goods and services. Trade therefore represents an unrivalled, and unused, tool for reducing greenhouse gas emissions.

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.

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.