Tom – Page 78

Ethanol Odd Couple & the California LCFS

I started sharing items from my feed reader, here. Top of the list is currently a pair of articles from Science Daily:

Corn-for-ethanol’s Carbon Footprint Critiqued

To avoid creating greenhouse gases, it makes more sense using today’s technology to leave land unfarmed in conservation reserves than to plow it up for corn to make biofuel, according to a comprehensive Duke University-led study.

“Converting set-asides to corn-ethanol production is an inefficient and expensive greenhouse gas mitigation policy that should not be encouraged until ethanol-production technologies improve,” the study’s authors reported in the March edition of the research journal Ecological Applications.

Corn Rises After Government Boosts Estimate for Ethanol Demand

Corn rose for a fourth straight session, the longest rally this year, after the U.S. government unexpectedly increased its estimate of the amount of grain that will be used to make ethanol.

…

House Speaker Nancy Pelosi, a California Democrat, and Senator Amy Klobuchar, a Minnesota Democrat, both said March 9 they support higher amounts of ethanol blended into gasoline. On March 6, Growth Energy, an ethanol-industry trade group, asked the Environmental Protection Agency to raise the U.S. ratio of ethanol in gasoline to 15 percent from 10 percent.

This left me wondering where California’s assessments of low carbon fuels now stand. Last March, I attended a collaborative workshop on life cycle analysis of low carbon fuels, part of a series (mostly facilitated by Ventana, but not this one) on GHG policy. The elephant in the room was indirect land use emissions from biofuels. At the time, some of the academics present argued that, while there’s a lot of uncertainty, zero is the one value that we know to be wrong. That left me wondering what plan B is for biofuels, if current variants turn out to have high land use emissions (rendering them worse than fossil alternatives) and advanced variants remain elusive.

It turns out to be an opportune moment to wonder about this again, because California ARB has just released its LCFS staff report and a bunch of related documents on fuel GHG intensities and land use emissions. The staff report burdens corn ethanol with an indirect land use emission factor of 30 gCO2eq/MJ, on top of direct emissions of 47 to 75 gCO2eq/MJ. That renders 4 of the 11 options tested worse than gasoline (CA RFG at 96 gCO2eq/MJ). Brazilian sugarcane ethanol goes from 27 gCO2eq/MJ direct to 73 gCO2eq/MJ total, due to a higher burden of 46 gCO2eq/MJ for land use (presumably due to tropical forest proximity).

These numbers are a lot bigger than the zero, but also a lot smaller than Michael O’Hare’s 2008 back-of-the-envelope exercise. For example, for corn ethanol grown on converted CRP land, he put total emissions at 228 gCO2eq/MJ (more than twice as high as gasoline), of which 140 gCO2eq/MJ is land use. Maybe the new results (from the GTAP model) are a lot better, but I’m a little wary of the fact that the Staff Report sensitivity ranges on land use (32-57 gCO2eq/MJ for sugarcane, for example) have such a low variance, when uncertainty was previously regarded as rather profound.

But hey, 7 of 11 corn ethanol variants are still better than gasoline, right? Not so fast. A low carbon fuel standard sets the constraint:

(1-x)*G = (1-s)*G + s*A

where x is the standard (emissions intensity cut vs. gasoline), s is the market share of the low-carbon alternative, G is the intensity of gasoline, and A is the intensity of the alternative. Rearranging,

s = x / (1-A/G)

In words, the market share of the alternative fuel needed is proportional to the size of the cut, x, and inversely proportional to the alternative’s improvement over gasoline, (1-A/G), which I’ll call i. As a result, the required share of an alternative fuel increases steeply as it’s performance approaches the limit required by the standard, as shown schematically below:

Intensity-share schematic

Clearly, if a fuel’s i is less than x, s=x/i would have to exceed 1, which is impossible, so you couldn’t meet the constraint with that fuel alone (though you could still use it, supplemented by something better).

Thus land use emissions are quite debilitating for conventional ethanol fuels’ role in the LCFS. For example, ignoring land use emissions, California dry process ethanol has intensity ~=59, or i=0.39. To make a 10% cut, x=0.1, you’d need s=0.26 – 26% market share is hard, but doable. But add 30 gCO2eq/MJ for land use, and i=0.07, which means you can’t meet the standard with that fuel alone. Even the best ethanol option, Brazilian sugarcane at i=0.24, would have 42% market share to meet the standard. This means that the alternative to gasoline in the LCFS would have to be either an advanced ethanol (cellulosic, not yet evaluated), electricity (i=0.6) or hydrogen. As it turns out, that’s exactly what the new Staff Report shows. In the new gasoline compliance scenarios in table ES-10, conventional ethanol contributes at most 5% of the 2020 intensity reduction.

Chapter VI of the Staff Report describes compliance scenarios in more detail. Of the four scenarios in the gasoline stovepipe, each blends 15 to 20% ethanol into gasoline. That ethanol is in turn about 10% conventional (Midwest corn or an improved CA variant with lower intensity) and up to 10% sugarcane. The other 80 to 90% of ethanol is either cellulosic or “advanced renewable” (from forest waste).

That makes the current scenarios a rather different beast from those explored in the original UC Davis LCFS technical study that provides the analytical foundation for the LCFS. I dusted off my copy of VISION-CA (the model used, and a topic for another post some day) and ran the 10% cut scenarios. Some look rather like the vision in the current staff report, with high penetration of low-intensity fuels. But the most technically diverse (and, I think, the most plausible) scenario is H10, with multiple fuels and vehicles. The H10 scenario’s ethanol is still 70% conventional Midwest corn in 2020. It also includes substantial “dieselization” of the fleet (which helps due to diesel’s higher tank-to-wheel efficiency). I suspect that H10-like scenarios are now unavailable, due to land use emissions (which greatly diminish the value of corn ethanol) and the choice of separate compliance pathways for gasoline and diesel.

The new beast isn’t necessarily worse than the old, but it strikes me as higher risk, because it relies on the substantial penetration of fuels that aren’t on the market today. If that’s going to happen by 2020, it’s going to be a busy decade.

The Growth Bubble

I caught up with my email just after my last post, which questioned the role of the real economy in the current financial crisis. I found this in my inbox, by Thomas Friedman, currently the most-emailed article in the NYT:

Let’s today step out of the normal boundaries of analysis of our economic crisis and ask a radical question: What if the crisis of 2008 represents something much more fundamental than a deep recession? What if it’s telling us that the whole growth model we created over the last 50 years is simply unsustainable economically and ecologically and that 2008 was when we hit the wall ’” when Mother Nature and the market both said: ‘No more.’

Certainly there are some parallels between the housing bubble and environment/growth issues. You have your eternal growth enthusiasts with plausible-sounding theories, cheered on by people in industry who stand to profit.

There’s plenty of speculation about the problem ahead of time:

Google news timeline – housing bubble

People in authority doubt that there’s a problem, and envision a soft landing. In any case, nobody does anything about it.

Sound familiar so far?

However, I think it’s a bit of a leap to attribute our current mess to unsustainability in the real economy. For one thing, in hindsight, it’s clear that we weren’t overshooting natural carrying capacity in 1929, so it’s clearly possible to have a depression without an underlying resource problem. For another, we had ridiculously high commodity prices, but not many other direct impacts of environmental catastrophe (other than all the ones that have been slowly worsening for decades). My guess is that environmental overshoot has a lot longer time constant than housing or tech stock markets, both on the way up and the way down, so overshoot will evolve in more gradual and diverse ways at first. I think at best you can say that detecting the role of unsustainable resource management is like the tropical storm attribution problem. There are good theoretical reasons to think that higher sea surface temperatures contribute to tropical storm intensity, but there’s little hope of pinning Katrina on global warming specifically.

Personally, I think it’s possible that EIA is right, and peak oil is a little further down the road. With a little luck, asset prices might stabilize, and we could get another run of growth, at least from the perspective of those who benefit most from globalization. If so, will we learn from this bubble, and take corrective action before the next? I hope so.

I think the most important lesson could be the ending of the housing bubble, as we know it so far. It’s not a soft landing; positive feedbacks have taken over, as with a spark in a dry forest. That seems like a really good reason to step back and think, not just how to save big banks, but how to turn our current situation into a storm of creative destruction that mitigates the bigger one coming.

What about the real economy?

I sort of follow a bunch of economics blogs. Naturally they’re all very much preoccupied with the financial crisis. There’s a lot of debate about Keynesian multipliers, whether the stimulus will work, liquidity traps, bursting bubbles, and the like. If you step back, it appears to be a conversation about how to use fiscal and monetary policy to halt a vicious cycle of declining expectations fueled by financial instruments no one really understands – essentially an attempt to keep the perceived economy from dragging down the real economy (as it is clearly now doing). The implicit assumption often seems to be that, if we could only untangle the current mess, the economy would return to its steady state growth path.

What I find interesting is that there’s little mention of what might have been wrong in the real economy to begin with, and its role in the current crisis. Clearly the last decade was a time of disequilibrium, not just in the price of risk, but in the real capital investments and consumption patterns that flowed from it. My working hypothesis is that we were living in a lala land of overconsumption, funded by deficits, sovereign wealth funds, resource drawdown, and failure to invest in our own future. In that case, the question for the real economy is, how much does consumption have to fall to bring things back into balance? My WAG is 15% – which implies a heck of a lot of reallocation of activity in the real economy. What does that look like? Could we see it through the fog of knock-on effects that we’re now experiencing? Is there something we could be doing, on top of fiscal and monetary policy, to ease the transition?

Killer Models?

I was just looking up Archimedean copulas, and stumbled across a bunch of articles blaming the Gaussian copula for the crash, like this interesting one at Wired.

Getting into trouble by ignoring covariance actually has a long and glorious history. Want to make your complex device look super reliable? Decompose it into a zillion parts, then assess their collective probability of failure without regard for collateral damage and other feedbacks that correlate the failure of one part with another. Just don’t check for leaks with a candle afterwards.

Still, blaming copulas, or any other model, for the financial crisis strikes me as a lot like blaming a telephone pole for your car crash. Never mind that you were speeding, drunk, and talking on the phone. It’s not the models, but a general predisposition to ignore systemic risk that brought down the system.

SD on Long Waves, Boom & Bust

Two relevant conversations from the SD email list archive:

Where are we in the long wave?

Bill Harris asks, in 2003,

… should a reasonable person think we are now in the down side of a long wave? That the tough economic times we’ve seen for the past few years will need years to work through, as levels adjust? That simple, short-term economic fixes wont work as they may have in the past? That the concerns we’ve heard about deflation should be seen in a longer context of an entire cycle, not as an isolated event to be overcome? Is there a commonly accepted date for the start of this decline?

Was Bill 5 years ahead of schedule?

Preventing the next boom and bust

Kim Warren asks, in 2001,

This is a puzzle – we take a large fraction of the very brightest and best educated people in the world, put them through 2 years of further intensive education in how business, finance and economics are supposed to work, set them to work in big consulting firms, VCs, and investment banks, pay them highly and supervise them with very experienced and equally bright managers. Yet still we manage to invent quite implausible business ideas, project unsustainable earnings and market performance, and divert huge sums of money and talented people from useful activity into a collective fantasy. Some important questions remain unanswered, like who they are, what they did, how they got away with it, and why the rest of us meekly went along with them? So the challenge to SDers in business is … where is the next bubble coming from, what will it look like, and how can we stop it?

Clearly this is one nut we haven’t cracked.

Can Montana Escape Recession Ravages?

The answer is evidently now “no”, but until recently the UofM’s Bureau of Business and Economic Research director Patrick Barkey thought so:

“As early as last summer we still thought Montana would escape this recession,” he said. “We knew the national economic climate was uncertain, but Montana had been doing pretty well in the previous two recessions. We now know this is a global recession, and it is a more severe recession, and it’s a recession that’s not going to leave Montana unscathed.”

Indeed, things aren’t as bad here as they are in a lot of other places – yet. Compare our housing prices to Florida’s:

On the other hand, our overall economic situation shows a bigger hit than some places with hard-hit housing markets. Here’s the Fed’s coincident index vs. California:

As one would expect, the construction and resource sectors are particularly hard hit by the double-whammy of housing bubble and commodity price collapse. In spite of home prices that seem to have held steady so far, new home construction has fallen dramatically:

Interestingly, that hasn’t hit construction employment as hard as one would expect. Mining and resources employment has taken a similar hit, though you can hardly see it here because the industry is comparatively small (so why is its influence on MT politics comparatively large?).

So, where’s the bottom? For metro home prices nationwide, futures markets think it’s 10 to 20% below today, some time around the end of 2010. If the recession turns into a depression, that’s probably too rosy, and it’s hard to see how Montana could escape the contagion. But the impact will certainly vary regionally. The answer for Montana likely depends a lot on two factors: how bubbly was our housing market, and how recession-resistant is our mix of economic activity?

On the first point, here’s the Montana housing market (black diamonds), compared to the other 49 states and DC:

Prices above are normalized to 2000 levels, using the OFHEO index of conforming loan sales (which is not entirely representative – read on). At the end of 2003, Montana ranked 20th in appreciation from 2000. At the end of 2008, MT was 8th. Does the rise mean that we’re holding strong on fundamentals while others collapse? Or just that we’re a bunch of hicks, last to hear that the party’s over? Hard to say.

It’s perhaps a little easier to separate fundamentals from enthusiasm by looking at prices in absolute terms. Here, I’ve used the Census Bureau’s 2000 median home prices to translate the OFHEO index into $ terms:

Among its western region peers, a few other large states, and random states I like, Montana starts to look like a relative bargain still. The real question then is whether demographic trends (latte cowboys like me moving in) can buoy the market against an outgoing tide. I suspect that we’ll fare reasonably well in the long run, but suffer a significant undershoot in the near term.

The OFHEO indices above are a little puzzling, in that so many states seem to be just now, or not yet, peaking. For comparison, here are the 20 metro areas in the CSI index (lines), together with Gallatin County’s median prices (bars):

These more representative indices still show Montana holding up comparatively well, but with Gallatin County peaking in 2006. I suspect that the OFHEO index is a biased picture of the wider market, due to its exclusion of nonconforming loans, and that this is a truer picture.

Real Estate Roundup

Ira Artman takes a look at residential real estate price indices – S&P/Case-Shiller (CSI), OFHEO, and RPX. The RPX comes out on top, for (marginally) better correlation with foreclosures and, more importantly, a much shorter reporting lag than CSI. This is a cause for minor rejoicing, as we at Ventana helped create the RPX and are affiliated with Radar Logic. Perhaps more importantly, rumor has it that there’s more trading volume on RPX.

In spite of the lag it introduces, the CSI repeat sales regression is apparently sexy to economists. Calculated Risk has been using it to follow developments in prices and price/rent ratios. Econbrowser today looks at the market bottom, as predicted by CSI forward contracts on CME. You can find similar forward curves in Radar’s monthly analysis. As of today, both RPX and CSI futures put the bottom of the market in Nov/Dec 2010, another 15% below current prices. Interestingly, the RPX forward curve looks a little more pessimistic than CSI – an arbitrage opportunity, if you can find the liquidity.

Artman notes that somehow the Fed, in its flow of funds reporting, missed most of the housing decline until after the election.

OCO Loses Gamble

The launch of the Orbiting Carbon Observatory failed today. Nature News has the story. This must be incredibly frustrating for everyone who worked on the project, or anticipated using the data.

MIT Updates Greenhouse Gamble

For some time, the MIT Joint Program has been using roulette wheels to communicate climate uncertainty. They’ve recently updated the wheels, based on new model projections:

	No Policy	Policy
New
Old

The changes are rather dramatic, as you can see. The no-policy wheel looks like the old joke about playing Russian Roulette with an automatic. A tiny part of the difference is a baseline change, but most is not, as the report on the underlying modeling explains:

The new projections are considerably warmer than the 2003 projections, e.g., the median surface warming in 2091 to 2100 is 5.1Â°C compared to 2.4Â°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the 20th century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting GDP growth which eliminated many low emission scenarios. However, if recently published data, suggesting stronger 20th century ocean warming, are used to determine the input climate parameters, the median projected warning at the end of the 21st century is only 4.1Â°C. Nevertheless all our simulations have a very small probability of warming less than 2.4Â°C, the lower bound of the IPCC AR4 projected likely range for the A1FI scenario, which has forcing very similar to our median projection.

I think the wheels are a cool idea, but I’d be curious to know how users respond to it. Do they cheat, and spin to get the outcome they hope for? Perhaps MIT should spice things up a bit, by programming an online version that gives users’ computers the BSOD if they roll a >7C world.

Hat tip to Travis Franck for pointing this out.

The Blood-Hungry Spleen

OK, I’ve stolen another title, this time from a favorite kids’ book. This post is really about the thyroid, which is a little less catchy than the spleen.

Your hormones are exciting!
They stir your body up.
They’re made by glands (called endocrine)
and give your body pluck.

Allan Wolf & Greg Clarke, The Blood-Hungry Spleen

A friend has been diagnosed with hypothyroidism, so I did some digging on the workings of the thyroid. A few hours searching citations on PubMed, Medline and google gave me enough material to create this diagram:

(This is a LARGE image, so click through and zoom in to do it justice.)

The bottom half is the thyroid control system, as it is typically described. The top half strays into the insulin regulation system (borrowed from a classic SD model), body fat regulation, and other areas that seem related. A lot of the causal links above are speculative, and I have little hope of turning the diagram into a running model. Unfortunately, I can’t find anything in the literature that really digs into the dynamics of the system. In fact, I can’t even find the basics – how much stuff is in each stock, and how long does it stay there? There is a core of the system that I hope to get running at some point though:

(another largish image)

This is the part of the system that’s typically involved in the treatment of hypothyroidism with synthetic hormone replacements. Normally, the body runs a negative feedback loop in which thyroid hormone levels (T3/T4) govern production of TSH, which in turn controls the production of T3 and T4. The problem begins when something (perhaps an autoimmune disease, i.e. Hashimoto’s) diminishes the thyroid’s ability to produce T3 and T4 (reducing the two inflows in the big yellow box at center). Then therapy seeks to replace the natural control loop, by adjusting a dose of synthetic T4 (levothyroxine) until the measured level of TSH (left stock structure) reaches a desired target.

This is a negative feedback loop with fairly long delays, so dosage adjustments are made only at infrequent intervals, in order to allow the system to settle between changes. Otherwise, you’d have the aggressive shower taker problem: water’s to cold, crank up the hot water … ouch, too hot, turn it way down … eek, too cold …. Measurements of T3 and T4 are made, but seldom paid much heed – the TSH level is regarded as the “gold standard.”

This black box approach to control is probably effective for many patients, but it leaves me feeling uneasy about several things. The “normal” range for TSH varies by an order of magnitude; what basis is there for choosing one or the other end of the range as a target? Wouldn’t we expect variation among patients in the appropriate target level? How do we know that TSH levels are a reliable indicator, if they don’t correlate well with T3/T4 levels or symptoms? Are extremely sparse measurements of TSH really robust to variability on various time scales, or is dose titration vulnerable to noise?

One could imagine alternative approaches to control, using direct measurements of T3 and T4, or indirect measurements (symptoms). Those might have the advantage of less delay (fewer confounding states between the goal state and the measured state). But T3/T4 measurements seem to be regarded as unreliable, which might have something to do with the fact that it’s hard to find any information on the scale or dynamics of their reservoirs. Symptoms also take a back seat; one paper even demonstrates fairly convincingly that dosage changes +/- 25% have no effect on symptoms (so why are we doing this again?).

I’d like to have a more systemic understanding of both the internal dynamics of the thyroid regulation system, and its interaction with symptoms, behaviors, and other regulatory systems. Here’s hoping that one of you lurkers (I know you’re out there) can comment with some thoughts or references.

So the spleen doesn’t feel shortchanged, I’ll leave you with another favorite:

Lovely
I think that I ain’t never seen
A poem ugly as a spleen.
A poem that could make you shiver
Like 3.5 … pounds of liver.
A poem to make you lose your lunch,
Tie your intestines in a bunch.
A poem all gray, wet, and swollen,
Like a stomach or a colon.
Something like your kidney, lung,
Pancreas, bladder, even tongue.
Why you turning green, good buddy?
It’s just human body study.

John Scieszka & Lane Smith, Science Verse