Monday, September 30, 2013

Climate Change and Resource Rents

With the next IPCC report coming out, there's been more reporting on climate change issues.  Brad Plumer over a Wonkblog has nice summary that helps to illustrate how much climate change is already "baked in" so to speak.

I'd like to comment one point.  Brad writes "Humans can only burn about one-sixth of their fossil fuel reserves if they want to keep global warming below 2ÂșC."

I'd guess some might quibble with the measurement a bit, since viable reserves depends on price and technology, plus many unknowns about much fossil fuel there really is down there.  But this is probably in the ballpark, and possibly conservative.

Now imagine you own a lot of oil, coal and/or natural gas, you're reading Brad Plumber, and wondering what might happen to climate policy in the coming years.  Maybe not next year or even in the next five or ten years, but you might expect that eventually governments will start doing a lot more to curb fossil fuel use.  You might then want to sell your fossil fuels now or very soon, while you can.   If many resource owners feel this way, fossil fuel prices could fall and CO2 emissions would increase.  

This observation amounts to the so-called "green paradox."  Related arguments suggest that taxing carbon may have little influence on use, and subsidizing renewable fuels and alternative technologies, without taxing or otherwise limiting carbon-based fuels, might make global warming worse, since it could push emissions toward the present.

Research on these ideas, mostly theoretical, is pretty hot in environmental economics right now.  It seems like half the submissions I manage at JEEM touch on the green paradox in one way or another.  

All of it has me thinking about a point my advisor Peter Berck often made when I was in grad school. At the time, we were puzzling over different reasons why prices for non-renewable resources--mostly metals and fossil fuels--were not trending up like Hotelling's rule says they should.  Peter suggested that we may never use the resources up, because if we did, we'd choke on all the pollution.  Resource use would effectively be banned before it could be used. If resource owners recognized this, they'd have no incentive to hold or store natural resources and the resource rent (basically the intrinsic value based on its finite supply) would be zero, which could help explain non-increasing resource prices.

For all practical purposes, Peter understood the green paradox some 15-20 years ago.  Now the literature is finally playing catch up.  

Wednesday, September 25, 2013

David is a confirmed genius

The MacArthur Foundation just confirmed what we've known all long: that G-FEED's very own David Lobell is a genius.  Hopefully the $625k that MacArthur is forking over will free up David to do some additional blogging on his preferred choice of tennis racquet.

Big congrats to David!!

Thursday, September 12, 2013

The noise lovers

I try not to use this blog much for rants, but it’s an easy way to post more frequently. So… we’ve been getting some feedback on our recent paper on drought stress in U.S. maize, some of it positive, some not. One thing that comes up, as with a lot of prior work, is doubts about how important temperature is. Agronomists often talk about how important the timing of rainfall is. And about how heat doesn’t hurt nearly as much if soils are very moist. To me this is another way of saying (1) other factors matter too and (2) there are interactions between temperature and other factors. The answer to both of these is “Of course!”

I am struck by the similarity of these discussions to those that Marshall posted about the empirical work on conflict. They go something like this:
Person A: “We’ve looked at the data and see a clear response that people tend to wake up when you turn the lights on”
Person B: “But people also wake up because they went to bed early last night and aren’t tired any more.”
A: “Yeah, ok”
B: “And the lights probably won’t wake them up if they are passed out drunk.”
A: “Ok, great point”
B: “I’ve woken up thousands of times in my 30-year career, and rarely did I get woken because someone turned the light on”
A: “ok”
B: “So then how can you possibly claim that turning on lights causes people to wake up”
A: “What? Are you serious? Did you even read the paper?”
B: “I don’t need to, I am an expert on waking up.”
And so on. I sometimes don’t know whether people seriously don’t understand the difference between explaining some vs. all of the variance, or if they just look for any opportunity to plug their own area of expertise. When we claim to see a clear signal in the data, it is not a claim that there is no noise around that signal. And some of the noise might include interactions with other variables. In fact, if there wasn’t any noise then the signal would have been known long ago.

The other day I was thinking of replacing my old tennis racquet, so I went to Google and typed in “prince thunder” (the name of my current racquet).  Turns out the top results were about a song that the artist Prince wrote called Thunder. Does that mean that Google is entirely useless? No, it means there is some error if you try to predict what someone wants based only on a couple of words they type. But with their enormous datasets, they are pretty good at picking up signals and getting the answer right more often than not. For most people typing those words, they were probably looking for the song.

Back to the crop example. Of course, heat will matter less or more depending on the situation. And of course getting rainfall right before key stages is more important than getting it after. These are both reasons for the scatter in any plot of heat (or any other variable) against yields. But neither of those refutes the fact that higher temperatures tend to result in more water stress, and lower yields. Or in Sol and Marshall’s case, that higher temperatures tend to increase the risk of conflict.

I sometimes think if one of us were to discover some magic combination of predictors that explained 95% of the variance in some outcome, there would be a chorus of people saying “you left out my favorite 5%!” Don’t get me wrong, there are lots of legitimate questions about cause and effect, stationarity, etc. that are worth looking into. But how much time should we really spend having the same old conversation about the difference between a signal and noise? 

Thursday, September 5, 2013

Yet more cleverness: getting ambient temperature data from cellphones

Following up on an earlier post about some smarty-pantses that figured out how to use cell phone towers to extract estimates of local rainfall, many of these same smarty-pantses have now figured out how to use those same cell phones to provide information on local temperatures.  Here's the new paper, just out in Geophysical Research Letters [HT: Noah Diffenbaugh]:

Crowdsourcing urban air temperatures from smartphone battery temperatures
A. Overeem, J. C. R. Robinson, H. Leijnse, G. J. Steeneveld, B. K. P. Horn, and R. Uijlenhoet

Accurate air temperature observations in urban areas are important for meteorology and energy demand planning. They are indispensable to study the urban heat island effect and the adverse effects of high temperatures on human health. However, the availability of temperature observations in cities is often limited. Here we show that relatively accurate air temperature information for the urban canopy layer can be obtained from an alternative, nowadays omnipresent source: smartphones. In this study, battery temperatures were collected by an Android application for smartphones. A straightforward heat transfer model is employed to estimate daily mean air temperatures from smartphone battery temperatures for eight major cities around the world. The results demonstrate the enormous potential of this crowdsourcing application for real-time temperature monitoring in densely populated areas.

They validate their technique in a few big cities around the world, and it looks pretty neat.  As shown in their Fig 2, which shows temperatures for London over a 4-month period and is reproduced below, raw changes in battery temperature are highly correlated with variation in ambient temperature (compare the orange line and black line, reported correlation r=0.82), and their heat transfer model is able to get the levels close to right (compare the blue dots with the black line).

What we really want to know, of course, is whether this can also work in places where the weather-observing infrastructure is currently really poor (e.g. most of Africa), and thus were techniques like this could be extra useful.  It seems like there are a couple hurdles.  First, you need a lot of people with smartphones.  According to this article, smartphone penetration in Africa is currently around 20%, but Samsung (who might know) puts it at less than 10%.  Nevertheless, smartphone adoption appears to be growing rapidly (you can find them in just about any tiny rural market in western Kenya, for instance), and so this might not be such a limitation in a few years.  And something the authors worry about in colder and richer climes -- that their battery temperature readings are biased because people are in heated or air-conditioned buildings a lot -- is much less of a worry in places where people are outside more and don't keep their houses at a perfect 70F.

Second, to get temperature levels right, it appears that the authors have to calibrate battery temperatures in a given area to data on observed temperatures in that area -- which is obviously not helpful if you don't have observed data to start with.  But if all you care about is temperature deviations -- e.g. if you're running a panel model that is linear in average temperature -- then it seems like the raw battery temperature data give you this pretty well (see figure).  Then if you really need levels -- say you want to estimate how a crop responds to temperatures above a given threshold -- you could do something like David did in his 2011 paper on African maize and add these deviations back to somebody else's estimate of the climatology (David used WorldClim).

Given this, the authors' optimism on future applications seems fitting:

"In the end, such a smartphone application could substantially increase the number of air temperature observations worldwide. This could become beneficial for precision agriculture, e.g., for optimization of irrigation and food production, for human health (urban heat island), and for energy demand planning."

But hopefully the expansion of this technique into rural areas won't have to wait for observed data with which to calibrate their heat transfer model.  If that London plot above is representative, it seems like just getting the raw battery data could be really helpful.