Just because we've always done it that way doesn't mean we still should...

At the end of November 2012 I met with Professor Rong Zeng of Tsinghua University, and he told me of his long-term vision of an electric power system that only used Direct Current (DC).    There is much to recommend this vision if one were starting a power system from scratch, but the US (and thus the world) settled on AC power about a century ago, after fierce competition between Edison and Tesla. Edison advocated DC power, Tesla advocated AC power, and eventually the AC power proponents triumphed. This historical conflict is one of several terrific examples of the importance of path dependence in technological and economic systems.

I was reminded of Professor Zeng’s vision when I read an article in EE times about the power systems needed for Light Emitting Diode (LED) fixtures.    This article describes the circuitry needed to minimize electromagnetic interference from the switching power supplies in LED, circuitry that adds costs and complexity to end-user devices.  If the houses were wired for DC, this complex circuitry simply wouldn’t be needed.

Of course, there might be other disadvantages to widespread use of DC power for power systems, but it’s not at all a given that the current state of a technological system is how we’d design it if we were creating it from scratch.  And in fact, the point of whole systems design is to capture the benefits of designing from scratch, precisely because most technological systems are characterized by path dependence.  So it’s important for those approaching problems for the first time not to assume that the way things are is the way things have to be.  The future is ours to choose, and technological developments often push us into new design spaces that simply weren’t reachable before.  Your goal is to find those spaces and use them to your advantage.

Black carbon: a short-lived warming agent with big effects

Climate Progress reported yesterday on a new study about the warming effects of black carbon published in the peer reviewed journal Journal of Geophysical Research-Atmospheres.  The study reveals that black carbon (dark particles of soot emitted by various kinds of combustion) is roughly twice as powerful a warming agent as previously thought, and it is the second most important climate forcing agent after carbon dioxide in terms of warming experienced thus far (methane is a close third).

The figure below, taken from the article, summarizes the complexities surrounding black carbon’s emissions and effects.

This research is important because:

1) It is yet another example (like the melting rate of Greenland’s glaciers) where the climate models have in the past significantly underestimated the warming effects of human activities.  As we learn more, the climate problem continues to grow more worrisome.

2) Unlike carbon dioxide, nitrous oxide, and methane, black carbon (BC) has a very short lifetime in the atmosphere (on the order of a week).  Reductions of BC emissions will lead to rapid reductions in the warming effect, so we can have a big impact on warming relatively quickly, if we focus on the sources of those emissions that exert the highest warming effect.

3) BC emissions (in the form of small particulate matter) are tied to significant human health effects, and the benefits of reducing these emissions are large enough to justify reducing them, irrespective of climate benefits.  The World Health Organization estimates that about 2 million people die each year from pollution from indoor combustion of solid fuels, the same kinds of combustion tied to BC emissions.

4) The biggest sources of BC are burning forests and savannas, coal and biomass in residential applications, and diesel fuel.  As the world warms, it’s likely that black carbon from burning forests will increase, which is a positive feedback associated with a climate change.

5) Even if we are successful in reducing BC emissions, we’ll still need to reduce emissions of carbon dioxide, methane, nitrous oxide, and other long-lived forcing agents, because their long-term effects remain a real concern.

Related posts

Cold Cash, Cool Climate:  A summary of the argument

Why climate change causes BIG increases in severe weather

An effective graph summarizing the climate conundrum

Why fossil fuel abundance is an illusion

The danger of disembodied "facts"

My friend Alex Zwissler, who heads up the Chabot Space and Science Center, published an interesting blog post today giving guidance about how to separate fact from fiction on scientific topics, with the promise of more to come.  He focused today on determining the source of any claim, assessing whether the source is truly an authority and what their underlying motivations might be.  Here are the intro two paragraphs:

In one of my recent posts I had some fun with the topic “…the six things I want our kids to know about science” … of course there are more, but it was a good start.  Among the comments I received on the post, one from a friend posed a troubling question, which could be restated as, “OK wise guy, how DO we help our kids figure out what to believe with all this seemingly conflicting and confusing sciency stuff?”  In re-reading my rants on this subject I realize that while I have done a passable job of laying out the challenge of figuring out how to decide what to believe, I’ve done a crap job in providing any answers to the question how.  This led to some rapid self- reflection, asking myself the question, “OK wise guy, how do YOU figure out what to believe with all this seemingly conflicting and confusing sciency stuff?”  …fie on self-reflection. But the effort did allow me to see that I do have a bit of method to my madness, outlined here.

My first step in assessing the validity of a claim is pretty much always the same… I take a really, really close look at the source. I feel this is the best place to start, and while taking a bit of time and effort it can often yield immediate results. If the source does not pass the smell test, then move on.  This exercise breaks down into two broad categories, qualification and motivation.

I’ve written about some aspects of this topic in several places in Turning Numbers into Knowledge, including Chapter 11, which focuses on applying critical thinking to assessing arguments.   Alex’s post got me thinking about the practical complexities that often arise, even for researchers in a specific field, and I saw a good example of such complexities in an news report in Science Daily that was just posted today.

This report summarized a peer reviewed article that appeared in a well regarded journal (Environmental Science and Technology).  Even better, the report gave the actual citation with a link to the article’s DOI (digital object identifier, which is a record locator for scholarly papers).  It also states at the end that it is a summary of materials supplied by the American Chemical Society, so it’s not original reporting by Science Daily. So far, so good.

Now it gets interesting–here’s the first paragraph of the article:

Researchers from the Centre for Energy-Efficient Telecommunications (CEET) and Bell Labs explain that the information communications and technology (ICT) industry, which delivers Internet, video, voice and other cloud services, produces more than 830 million tons of carbon dioxide (CO₂), the main greenhouse gas, annually. That’s about 2 percent of global CO₂emissions – the same proportion as the aviation industry produces. Projections suggest that ICT sector’s share is expected to double by 2020. The team notes that controlling those emissions requires more accurate but still feasible models, which take into account the data traffic, energy use and CO₂production in networks and other elements of the ICT industry. Existing assessment models are inaccurate, so they set out to develop new approaches that better account for variations in equipment and other factors in the ICT industry.

A reader might reasonably conclude that the research article added up carbon dioxide emissions and showed that the ICT industry emits about the same amount of greenhouse gases as global aviation, roughly 2 percent of global emissions.   When you read the article itself, however, you realize that the authors were simply summarizing the results of six other studies, three peer reviewed, three not, with the two key sources dating to 2007 and 2008, respectively.

In order to really understand if this claim is true you’d need to go back to those sources and read them all.  If you did that, you’d realize that the 2 percent estimate is from non-peer reviewed reports published 4-5 years ago, and that the cited research article was simply reproducing those figures as context for presenting their conclusions.  In essence, the “factoid” that 2% of the world’s carbon dioxide emissions come from ICT has become disembodied from the original source, making it difficult and time consuming for people unfamiliar with this literature to determine if it’s true or not.

None of this should discourage the lay reader from following Alex’s advice and assessing the credibility and motives of any information source, but it also highlights the importance of actually reading the original published source for any particular claim.  Summaries of other people’s results almost invariably create disembodied statistics and other confusions, so it’s incumbent on anyone who wants to use information for an important decision to go back to the original source.  That’s the only way to make sure you’ve really gotten it right.

In the Epilogue to the second edition of Turning Numbers into Knowledge I summarize a related example, in which some rather wild claims about Internet electricity use required detailed debunking.  Email me if you’d like a copy of the Epilogue and a few related articles–it’s a terrific illustration of disembodied statistics run amok.

Finally, I highly recommend William Hughes’ book titled Critical Thinking:  An Introduction to the Basic Skills.   The book is a marvelous introduction to critical thinking, and it discusses how to evaluate whether an authority is credible in some detail.  I have the 1997 edition, which was written by Hughes alone, but there seem to be two later editions coauthored by Jonathan Lavery and William Hughes, and used copies seem to be reasonably priced.  Here’s the link to the 2008 edition on Amazon US.  Amazon Canada has the 2008 edition new for about $48 Canadian.

The National Center for Science Education highlights the book Cold Cash, Cool Climate

The National Center for Science Education (NCSE) states as its mission “defending the teaching of evolution and climate science.  NCSE highlighted Cold Cash, Cool Climate:  Science-based Advice for Ecological Entrepreneurs in a post on December 21, 2012, and it remains on their top page as of tonight.  At their request, I made "Chapter 5:  The Scope of the Problem” available as a free download through their site.  Please spread the word!

An effective graph summarizing the climate conundrum

Bill McKibben of 350.org created a graph summarizing the picture for carbon in a particularly effective way.  It’s part of a Washington Post compilation of the most important graphs of 2012.

This graph summarizes the warming limit approach to the climate problem nicely (for more details, see my post about “Why fossil fuel abundance is a illusion”, Chapters 1 to 3 and Appendix A in Cold Cash, Cool Climate, and Bill McKibben’s Rolling Stone article titled “Global Warming’s Terrifying New Math”).   When you compare the amount of carbon we can emit and stay within the 2 Celsius degree warming limit to proved reserves of fossil fuels, it’s clear that we just can’t burn it all.  This reality hasn’t yet sunk in, because the valuation of fossil fuel companies still implies that we can.

As an aside, readers who know my book Turning Numbers into Knowledge are aware that I’m not a fan of 3D bar charts, and the point could have been better made with a 2D bar overlaid on top of Google Earth.  The message of the graph is the most important thing, however, and the image is striking, so I’ll give Bill a pass on this issue.

Addendum (December 28, 2012): The graph uses a term called “declared reserves”, which isn’t one that is used much in the literature, but it’s likely that it represents proved reserves plus some part of what geologists call “resources”.  The latter category represents fossil fuel deposits that we expect to be there based on our current knowledge, but we don’t know their quantities as accurately as the proved reserves (which we are pretty sure we can extract at current prices using current technologies).

More on causality and the national debt

As I pointed out in this post and this one, growth in the national debt can only be fairly evaluated by assigning causality to the different contributors to that debt.  Simple-minded comparisons of debt when a president came into office with current day debt will be misleading if previous occupants of the office implemented policies that continue into the next administration.

I recently discovered that the New York Times and the Washington Post have conducted analysis of the contributors to debt based on the policies of the Bush and Obama administrations, and the bottom line is about the same in each case.  President Bush’s policies increased the debt by about $5 trillion (T) from 2001 to 2009, while President Obama’s policies increased the debt by $1 to 1.4T.  By attributing debt to specific policies, these two analyses exclude debt attributable to previous administrations, so the comparison is a consistent one.

These analyses don’t seem to include explicit treatment of the effect of the Great Recession on costs and revenues, which is something worth exploring (for one such analysis, see this graph via Paul Krugman).  They also aren’t explicit about how they treat inflation and the time value of money, both of which make money spent in earlier years more valuable than money spent in later years.  Someone evaluating these numbers would need to understand how those two effects were treated to use the data in other comparisons.  In any case, correcting for those two effects would tend to make President Bush’s relative contribution to debt even larger if they are not currently included in these comparisons.

Energy harvesting in the news

The world is starting to pay greater attention to energy harvesting, through which ultra-low power sensors and controls can be powered by ambient energy flows (like light, heat, motion, or stray radio and TV signals).  This week, Electronics Weekly reported on a study that estimated the current market for energy harvesting at $19M/year, and projected that it will grow roughly tenfold by 2017.  Putting aside the difficulty of projecting the future for economic and social systems, it’s clear that people are waking up to the potential for energy harvesting. Thus far it’s mostly been a niche application, the most widely used example of which is tire pressure sensors in cars (they use the motion of the wheel to power themselves).

There has also been more interest recently in biomedical applications.  Proteus Digital Health has an ingestible sensor that has no battery.  Instead, it has a cathode and anode, and uses your stomach juices as the electrolyte.  It goes inside a pill, and when the pill dissolves in your stomach it sends a tiny signal to a patch on your skin, which relays the signal to your cell phone or other mobile device, recording accurately when you took your medicine.  This is what Proteus calls “partial energy harvesting”, since the energy extracted is really embedded in the anode/cathode pair, and the electrolyte simple enables us to tap that energy for as long as the electrodes last.

Nature Biotechnology published an article recently on a device that can extract power from a biologic battery found in the inner ears of certain animals, including humans.

Mercier, Patrick P., Andrew C. Lysaght, Saurav Bandyopadhyay, Anantha P. Chandrakasan, and Konstantina M. Stankovic. 2012. “Energy extraction from the biologic battery in the inner ear."  Nat Biotech. Advance online publication, 11/08/online. [http://dx.doi.org/10.1038/nbt.2394]

The Wall Street Journal reported on the Nature Biotechnology paper as well as some other examples of energy harvesting in medicine, where the concept seems to be taking off.

It’s important to remember that energy harvesting is still in its infancy, and that it’s competing against single use batteries that are also improving over time.  Once an electronic device has achieved very low power (averaging micro watts or nano watts) then it’s relatively easy to attach it to a single use battery and achieve battery lifetimes in years (or even a decade or two).  For many applications, that’s more than sufficient, so the cost of energy harvesting needs to be compared to that for a single use lithium or lithium thionyl chloride battery, and in many cases the battery will come out ahead. That won’t always be true, but it’s often true now.

A real-world example of how wrong "likely voter" screens can be

As if in reply to my missive about hazards in political polling, Politico has a great story today about just how far astray likely voter screens can lead even seasoned political professionals:

For Republicans, one of the worst parts of the GOP’s 2012 trouncing was that they didn’t see it coming.

Top party strategists and officials always knew there was a chance that President Barack Obama would get reelected, or that Republicans wouldn’t gain control of the Senate. But down to the final days of the national campaign, few anticipated the severe setbacks that Republicans experienced on Nov. 6.

The reason: Across the party’s campaigns, committees and super PACs, internal polling gave an overly optimistic read on the electorate. The Romney campaign entered the last week of the election convinced that Colorado, Florida and Virginia were all but won, that the race in Ohio was neck and neck and that the Republican nominee had a legitimate shot in Pennsylvania.

In other words, the likely voter screen the Republican pollsters applied to figure out who would actually vote were grossly inaccurate.  And this conclusion is confirmed by Democratic pollsters and the Obama Campaign:

Democrats had argued for months before the election that Republican polling was screening out voters who would ultimately turn up to support Obama. In fact, Obama advisers said, if you applied a tighter likely voter screen to Democratic polling — counting only the very likeliest voters as part of the electorate — you could come up with results similar to what the GOP was looking at.

Keep in mind the twofold purposes of political polling next time you see polling results.  The first is to take an accurate snapshot of the electorate’s opinions on a certain date, but the second is to predict the results on election day.  The first goal isn’t easy to achieve, but the second one is even harder (because predicting behavior of human behavior is difficult in all circumstances, impossible in many).   Be much more skeptical of likely voter polls and focus more on polling averages than on a single poll, because you’re much more likely to have an accurate picture that way.  You should also read “polling postmortems” (like the one just published by Nate Silver at 538) to understand how each pollster stacked up against actual results.

This last conclusion applies to all kinds of forecasts, which is why I’m a strong advocate of retrospective comparisons of forecasting results to actual events (see for example Koomey, Jonathan G., Paul Craig, Ashok Gadgil, and David Lorenzetti. 2003. “Improving long-range energy modeling:  A plea for historical retrospectives.”  The Energy Journal (also LBNL-52448).  vol. 24, no. 4. October. pp. 75-92.   Email me for a copy.  Also check out this short post on a retrospective for a 1981 climate forecast).

My ARM Tech Con keynote ("Why ultra-low power computing will change everything") is now posted

I had great fun this past Wednesday (October 31st, 2012) talking at ARM’s Tech Con event, which is probably the world’s largest gathering of technologists devoted to low power innovation.  Compared to my Authors@Google talk, I’ve added some additional examples of ultra-low-power computing and communications and really boiled the talk down to its essential messages. Check it out!:

If political polls are driving you crazy, read this

As the election approaches, I’ve been musing over the nature of political polling.  There are many folks who make a living reporting on poll results, and a few who actually do solid analysis using such polls (with Nate Silver at 538 being the most prominent and sophisticated example).  Unfortunately, there are problems inherent in the enterprise of measuring public opinion that make it impossible to say with certainty what the outcome will be (at least for a close presidential election like this one promises to be).

There are two goals of a poll:  1) to create a “snapshot” of public opinion during the period over which the poll was conducted, and 2) to predict who will win the election.  It’s important to distinguish these two goals.

Taking a snapshot of public opinion seems straightforward, but it’s getting harder to do so, as different parts of the public change their preferences about answering calls from strangers (in part aided by technologies like call-waiting, which are becoming more widespread).   People are increasingly shifting to not having landline phones, and that may also introduce biases into the results.

The spread of polling results is largely the result of differences in how factors such as these are treated by the pollsters (and there are always buried assumptions and judgment calls in such analyses).  So it’s not at all clear that the snapshot of public preferences is accurate, even for the pollsters who are most sophisticated and use human interviewers and careful statistical methods.  Analysts try to adjust for these variations by taking averages of polling results, but such methods only work when there is no systematic bias affecting the results (as an aside, the “margin of error” that is commonly reported for polls is a simple statistical measure based on the number of respondents and does not reflect the kinds of structural biases I describe above).

I want to turn now to the second goal, which has received comparatively little attention (it’s the one that prompted me to write this post in the first place).  Doing a prediction of what will happen is fraught with problems, unless you are dealing with a physical system like planets orbiting the sun, and it is in attempting to do predictions that I think most pollsters get into real trouble.

The methods used to convert samples of “registered voters” to samples of “likely voters” are where the snapshot formally becomes a prediction.  In that conversion the pollster needs to decide who will submit a valid ballot by Tuesday November 6th.  While it is possible to make educated guesses based on historical data, each election is different.  Will an energized Republican base offset increased enthusiasm in the hispanic community?  Will Hurricane Sandy make voting difficult in some states?  Will undecided voters break for the incumbent or the challenger?  Will efforts to require photo ID reduce turnout of certain voter groups, and if so, how much?  Will someone be able to manipulate the voting results?  None of these things will be known with certainty until after November 6th, and history may give no guidance at all.

So I’m convinced that polling simply can’t tell us with certainty who will win the election, at least when it’s close.  According to Nate Silver, the state level polls suggest an electoral advantage for President Obama (with about 75% probability today), but what the polls can’t say is what will actually happen on November 6th, and the margins in many states are small enough that the election could go either way.

And that’s where we, the people come in.  Our choices are what determine the future. In an election where polls are close, your vote really does count.  What we decide to do will be what makes the difference on Tuesday. So don’t get hung up on contradictory polling results, just go out and vote!

“The best way to predict the future is to invent it.”  –Alan Kay

Addendum, October 30, 2012:  The statistician Andrew Gelman wrote a nice piece in The New York Times that analyzes what “too close to call” means in the context of this election.  Highly recommended reading.  It addresses the ostensible contradiction that President Obama has a 75% chance of winning but that the election could go either way based on unpredictable factors.

Why we need to stop coal exports and keep coal in the ground

Many observers have been heartened by the increase in natural gas production, which has contributed to significant significant declines in US greenhouse gas emissions.   It is not actually the most important factor reducing emissions in the first half of 2012, as my friends at CO2 Scorecard and I showed earlier this year.  And the methane emissions from fracking haven’t been measured very accurately, so there may be increased warming from methane that significantly offsets the reductions in other fossil fuels from the use of natural gas.

Another important issue addressed in the research note from CO2 Scorecard is the reduced price of natural gas resulting from fracking, which increases use of natural gas not just in the electricity sector (where gas displaces coal) but also in buildings and industrial sectors, and those increases offset emissions savings in the electricity sector.

Today there’s a story in the Guardian that shows another interesting (and troubling) price-related effect of natural gas fracking:  as US coal use has declined, an increase in coal exports from the US has reduced global prices of coal.  That price decrease makes it harder for countries with modest natural gas reserves to reduce use of coal-fired electricity, as the Guardian story demonstrates.  The pressure is particularly intense in developing countries, which are often more price sensitive than developed countries.

This story makes a compelling case for reducing and ultimately stopping exports of US coal, in order to keep global coal prices higher than they otherwise would be.  In part, US subsidies to the coal industry are subsidizing exports and reducing world electricity prices, and that’s just perverse, but even without subsidies, we need to slow and soon stop coal exports.

We need to either develop ways to sequester carbon from coal burning or keep the coal in the ground.  Since the first option is being tested but is nowhere near implementation on a large scale, our only current option is not burning the coal.  So that means not approving additional coal export terminals, diligently enforcing existing environmental regulations, and eliminating subsidies for coal mining.  It makes absolutely no sense to export coal that we don’t burn in the US, because no matter where it is burned, it will contribute to warming just the same.

US coal in decline: New Brattle Group report on coal-fired power plant retirements

On October 1, 2012, the Brattle Group published an update to its 2010 numbers on coal-fired power plant retirements, and it “finds that 59,000 to 77,000 MW of coal plant capacity are likely to retire over the next five years, which is approximately 25,000 MW more than previously estimated”.

The news release for the study states

Since December 2010 when the prior estimates of potential coal plant retirements were released, both natural gas prices and the projected demand for power have decreased, and environmental rules have been finalized with less restrictive compliance requirements and deadlines than previously foreseen. These shifts in market and regulatory conditions have resulted in an acceleration in announced coal plant retirements. As of July 2012, about 30,000 MW of coal plants (roughly 10% of total U.S. coal capacity) had announced plans to retire by 2016.

The updated study takes into account the most recent market conditions and the shifting regulatory outlook facing coal plants. To reflect the remaining regulatory uncertainty, the authors developed both “strict” and “lenient” regulatory scenarios for required environmental control technology. About 59,000 MW will likely retire under lenient rules versus 77,000 MW under strict regulations. Final regulatory requirements are still unresolved, but the authors suspect they will be akin to the lenient scenario. The study highlights that retirement projections are even more sensitive to future market conditions than to regulations, particularly natural gas prices. Likely coal plant retirements drop to between 21,000 and 35,000 MW if natural gas prices increase by just $1.00/MMBtu relative to April 2012 forward prices. Similarly, projected coal plant retirements would increase to between 115,000 and 141,000 MW if natural gas prices were to decrease by $1.00/MMBtu.“

Coal will continue to decline in importance in the US in the medium term, and it isn’t principally because of environmental regulations.  Of course, regulations are getting tighter, but cheap natural gas is the main culprit.  In addition, few new coal plants are likely to be built to replace the retiring plants.  Instead, natural gas and wind plants will likely pick up most of the slack.

My Authors@Google talk on computing trends, now posted

I had great fun at Google on September 12th, 2012 talking about the implications of computing efficiency trends for mobile sensors, controls, and computing more generally.  This version contains my latest thinking and is my most polished version to date.  You can watch it below (or by clicking here).

My friend Luiz Barroso introduced me, and he was gracious enough to highlight my latest book, Cold Cash, Cool Climate:  Science-based Advice for Ecological Entrepreneurs.

For background on computing trends, see my Technology Review article, and the supporting academic article:    Koomey, Jonathan G., Stephen Berard, Marla Sanchez, and Henry Wong. 2011. “Implications of Historical Trends in The Electrical Efficiency of Computing."  IEEE Annals of the History of Computing.  vol. 33, no. 3. July-September. pp. 46-54.

I'm on KCRW today, talking about the NYT story and its implications

KCRW in Santa Monica, CA had a discussion show this morning (“To the Point”) about electricity used by data centers, prompted by the NY Times article by Jim Glanz.  Jim led off the show in discussions with the host (Warren Olney), and then I, Andrew Blum of Wired, and Andy Lawrence of 451 Group/Uptime Institute added context and commentary.  It was a useful discussion, and the interviewer asked good questions.  You can listen in here.

Giga Om on the NYT data center articles

Katie Fehrenbacher over at GigaOm did a service for those of us interested in data centers by compiling some of the issues with the recent New York Times article.  She summarizes her conclusions (with which I agree) here:

I feel the same way about the NYT’s series that I do about Greenpeace’s dirty cloud reports. Yeah, they got a few things wrong, but the overall thesis is right, and can be used to make the Internet industry even more conscientious about their carbon emissions and energy footprint.

There are still a few Internet leaders who haven’t publicly embraced energy efficiency and greener technologies for data centers. For example, Amazon and its web services haven’t really stepped up to touting energy efficiency and clean power technologies so far, despite its prominent role in the industry. Though, they have made some strides.

Additionally while the largest and leading Internet companies have widely adopted energy efficiency practices, businesses running their own IT services haven’t adopted these technologies. That’s one of the biggest problems with the article, that the reporter is lumping together businesses’ in house IT server practices, with the webscale cloud giants. But clearly there’s still a lot more work to be done when it comes to the Internet an its massive power consumption.

I applaud Jim Glanz of the NYT for shining a light on the need for greater energy efficiency in data centers, but feel strongly that tackling the problem will require critical insights that someone just reading that article would not pick up.  Let’s hope this is the beginning of a deeper conversation about these issues.