Global Climate Change Negotiations

 

There were many interesting talks at the Interdisciplinary Climate Change Workshop last week—too many for me to describe them all in detail. But I really must describe the talks by Radoslav Dimitrov. They were full of important things I didn’t know. Some are quite promising.

Radoslav S. Dimitrov is a professor at the Department of Political Science at Western University. What’s interesting is that he’s also been a delegate for the European Union at the UN climate change negotiations since 1990! His work documents the history of climate negotiations from behind closed doors.

Here are some things he said:

• In international diplomacy, there is no questioning the reality and importance of human-caused climate change. The question is just what to do about it.

• Governments go through every line of the IPCC reports twice. They cannot add anything the scientists have written, but they can delete things. All governments have veto power. This makes the the IPCC reports more conservative than they otherwise would be: “considerably diluted”.

• The climate change negotiations have surprised political scientists in many ways:

1) There is substantial cooperation even without the USA taking the lead.

2) Developing countries are accepting obligations, with many overcomplying.

3) There has been action by many countries and subnational entities without any treaty obligations.

4) There have been repeated failures of negotiation despite policy readiness.

• In 2011, China and Saudi Arabia rejected the final agreement at Durban as inadequate. Only Canada, the United States and Australia had been resisting stronger action on climate change. Canada abandoned the Kyoto Protocol the day after the collapse of negotiations at Durban. They publicly blamed China, India and Brazil, even though Brazil had accepted dramatic emissions cuts and China had, for the first time, accepted limits on emissions. Only India had taken a “hardline” attitude. Publicly blaming some other country for the collapse of negotiations is a no-no in diplomacy, so the Chinese took this move by Canada as a slap in the face. In return, they blamed Canada and “the West” for the collapse of Durban.

• Dimitrov is studying the role of persuasion in diplomacy, recording and analyzing hundreds of hours of discussions. Countries try to change each other’s minds, not just behavior.

• The global elite do not see climate change negotiations as an environmental issue. Instead, they feel they are “negotiating the future economy”. They focus on the negative economic consequences of inaction, and the economic benefits of climate action.

• In particular, the EU has managed to persuade many countries that climate change is worth tackling now. They do this with economic, not environmental arguments. For example, they argue that countries who take the initiative will have an advantage in future employment, getting most of the “green jobs”. Results include China’s latest 5-year plan, which some have called “the most progressive legislation in history”, and also Japan’s plan for a 60-80% reduction of carbon emissions. The EU itself also expects big returns on investment in climate change.

I apologize for any oversimplifications or downright errors in my notes here.

References

You can see some slides for Dimitrov’s talks here:

• Radoslav S. Dimitrov, A climate of change.

For more, try reading this article, which is free online:

• Radoslav S. Dimitrov, Inside Copenhagen: the state of climate governance, Global Environmental Politics 10 (2010), 18–24.

and these more recent book chapters, which are apparently not as easy to get:

• Radoslav S. Dimitrov, Environmental diplomacy, in Handbook of Global Environmental Politics, edited by Paul Harris, Routledge, forthcoming as of 2013.

• Radoslav S. Dimitrov, International negotiations, in Handbook of Global Climate and Environmental Policy, edited by Robert Falkner, Wiley-Blackwell forthcoming as of 2013.

• Radoslav S. Dimitrov, Persuasion in world politics: The UN climate change negotiations, in Handbook of Global Environmental Politics, edited by Peter Dauvergne, Edward Elgar Publishing, Cheltenham, UK, 2012.

• Radoslav S. Dimitrov, American prosperity and the high politics of climate change, in Prospects for a Post-American World, edited by Sabrina Hoque and Sean Clark, University of Toronto Press, Toronto, 2012.

What To Do About Climate Change?

Here are the slides for my second talk in the Interdisciplinary Climate Change Workshop at the Balsillie School of International Affairs:

• What To Do About Climate Change?

Like the first it’s just 15 minutes long, so it’s very terse.

I start by noting that slowing the rate of carbon burning won’t stop global warming: most carbon dioxide stays in the air over a century, though individual molecules come and go. Global warming is like a ratchet.

So, we will:

1) leave fossil fuels unburnt,

2) sequester carbon,

3) actively cool the Earth, and/or

4) live with a hotter climate.

Of course we may do a mix of these…. though we’ll certainly do some of option 4), and we might do only this one. My goal in this short talk is not mainly to argue for a particular mix! I mainly want to present some information about the various options.

I do not say anything about the best ways to do option 4); I merely provide some arguments that we’ll wind up doing a lot of this one… because I’m afraid some of the participants in the workshop may be in denial about that.

I also argue that we should start doing research on option 3), because like it or not, I think people are going to become very interested in geoengineering, and without enough solid information about it, people are likely to make bad mistakes: for example, diving into ambitious projects out of desperation.

As usual, if you click on a phrase in blue in this talk, you can get more information.

I want to really thank everyone associated with Azimuth for helping find and compile the information used in this talk! It’s really been a team effort!

What is Climate Change?

Here are the slides for a 15-minute talk I’m giving on Friday for the Interdisciplinary Climate Change Workshop at the Balsillie School of International Affairs:

• What is Climate Change?

This will be the first talk of the workshop. Many participants are focused on diplomacy and economics. None are officially biologists or ecologists. So, I want to set the stage with a broad perspective that fits humans into the biosphere as a whole.

I claim that climate change is just one aspect of something bigger: a new geological epoch, the Anthropocene.

I start with evidence that human civilization is having such a big impact on the biosphere that we’re entering a new geological epoch.

Then I point out what this implies. Climate change is not an isolated ‘problem’ of the sort routinely ‘solved’ by existing human institutions. It is part of a shift from the exponential growth phase of human impact on the biosphere to a new, uncharted phase.

In this new phase, institutions and attitudes will change dramatically, like it or not:

• Before we could treat ‘nature’ as distinct from ‘civilization’. Now, there is no nature separate from civilization.

• Before, we might imagine ‘economic growth’ an almost unalloyed good, with many externalities disregarded. Now, many forms of growth have reached the point where they push the biosphere toward tipping points.

In a separate talk I’ll say a bit about ‘what we can do about it’. So, nothing about that here. You can click on words in blue to see sources for the information.

What Is Climate Change and What To Do About It?

Soon I’m going to a workshop on Interdisciplinary Perspectives on Climate Change at the Balsillie School of International Affairs, or BSIA, in Waterloo, Canada. It’s organized by Simon Dalby, who has a chair in the political economy of climate change at this school.

The plan is to gather people from many different disciplines to provide views on two questions: what is climate change, and what to do about it?

We’re giving really short talks, leaving time for discussion. But before I get there I need to write a 2000-word paper on my view of climate change—‘as a mathematician’, supposedly. That’s where I want your help. I think I know roughly what I want to say, and I’ll post some drafts here as soon as I write them. But I’d like get your ideas, too.

For starters, the program looks like this:

Friday 25 October: What is Climate Change?

9:00 – 9:30 Introductory remarks
John Ravenhill, Director, BSIA
Dan Scott, University of Waterloo, Interdiscipinary Centre for Climate Change.
Simon Dalby, BSIA

9:30 – 10:45 Presentation Session 1
Chair: Sara Koopman, BSIA
John Baez, University of California (Mathematics)
Jean Andrey, University of Waterloo (Geography)
Byron Williston, Wilfrid Laurier University (Philosophy)

11:15 – 12:30 Presentation Session 2
Chair: Marisa Beck, BSIA
Chris Russill, Carleton University (Communications)
Mike Hulme, Kings’ College London (Climate Science)
Radoslav Dimitrov, Western University (Political Science)

1:30 – 2:30 Presentation Session 3
Chair: Matt Gaudreau, BSIA
Jatin Nathwani, University of Waterloo (Engineering)
Sarah Burch, University of Waterloo (New Social Media and Education)

3:00 – 5:00 Roundtable 1 (all presenters)
Chair: Lucie Edwards, BSIA
Discussant: Vanessa Schweizer, University of Waterloo

5:00 – 5:15 Wrap-up
Dan Scott and Simon Dalby

Saturday 26 October: What Should We Do About It?

9:00 – 10:15 Presentation Session 4
Chair: Matt Gaudreau, BSIA
Radoslav Dimitrov, Western University (Political Science)
Mike Hulme, Kings’ College London (Climate Science)
Jean Andrey, University of Waterloo (Geography)

10:45 – 12:00 Presentation Session 5
Chair: Lucie Edwards, BSIA
Jatin Nathwani, University of Waterloo (Engineering)
Sarah Burch, University of Waterloo (Environmental Education)
Chris Russill, Carleton University (Communications)

1:00 – 2:00 Presentation Session 6
Chair: Marisa Beck, BSIA
Byron Williston, Wilfrid Laurier University (Philosophy)
John Baez, University of California (Mathematics)

2:30 – 4:30 Roundtable 2 (all presenters)
Chair: Sara Koopman, BSIA
Discussant: James Orbinski, CIGI Chair in Global Health

4:30 – 5:00 Wrap-up
Dan Scott and Simon Dalby

Some thoughts

Though I’m playing a designated role in this workshop—the “mathematician”—I don’t think it makes sense to focus on mathematical models of climate change, or the math projects I’m working on now.

I will probably seem strange and “mathematical” enough just saying what I think about climate change! Most of the other people come from fields quite different than mine: they seem much more in tune with the nitty-gritty details of politics and economics. So, perhaps my proper role is to mention some facts and numbers that they probably know already, to remind them of the magnitude, scope and urgency of the problem.

It may also be useful to emphasize that with very high probability, we won’t do enough soon enough, so we need to study a series of fallback positions, not just an ‘optimal’ response to climate change. And these fallback positions should go as far as thinking about what happens if we burn all the available carbon. What to do then?

When I talked about this workshop with the mathematician Sasha Beilinson, he wickedly suggested that the best solution to global warming might be a global economic collapse… and he asked if anyone was looking into this.

Of course this solution comes along with huge problems, and anyone who actually advocates it is branded as a nut and excluded from the ‘serious’ discourse on global warming. But the funny thing is, a global economic collapse could be just as probable as some more optimistic scenarios, for example those that require a massive outbreak of altruism worldwide.

So it’s worth thinking about economic collapse scenarios, and ‘burn carbon until there’s none left’ scenarios, even if we don’t want them. And these are the sort of things that only the mathematician in the room may be brave—or foolish—enough to mention.

What else?

Autocatalysis in Reaction Networks

guest post by Manoj Gopalkrishnan

Since this is my first time writing a blog post here, let me start with a word of introduction. I am a computer scientist at the Tata Institute of Fundamental Research, broadly interested in connections between Biology and Computer Science, with a particular interest in reaction networks. I first started thinking about them during my Ph.D. at the Laboratory for Molecular Science. My fascination with them has been predominantly mathematical. As a graduate student, I encountered an area with rich connections between combinatorics and dynamics, and surprisingly easy-to-state and compelling unsolved conjectures, and got hooked.

There is a story about Richard Feynman that he used to take bets with mathematicians. If any mathematician could make Feynman understand a mathematical statement, then Feynman would guess whether or not the statement was true. Of course, Feynman was in a habit of winning these bets, which allowed him to make the boast that mathematics, especially in its obsession for proof, was essentially irrelevant, since a relative novice like himself could after a moment’s thought guess at the truth of these mathematical statements. I have always felt Feynman’s claim to be unjust, but have often wondered what mathematical statement I would put to him so that his chances of winning were no better than random.

Today I want to tell you of a result about reaction networks that I have recently discovered with Abhishek Deshpande. The statement seems like a fine candidate to throw at Feynman because until we proved it, I would not have bet either way about its truth. Even after we obtained a short and elementary proof, I do not completely ‘see’ why it must be true. I am hoping some of you will be able to demystify it for me. So, I’m just going to introduce enough terms to be able to make the statement of our result, and let you think about how to prove it.

John and his colleagues have been talking about reaction networks as Petri nets in the network theory series on this blog. As discussed in part 2 of that series, a Petri net is a diagram like this:

Following John’s terminology, I will call the aqua squares ‘transitions’ and the yellow circles ‘species’. If we have some number #rabbit of rabbits and some number #wolf of wolves, we draw #rabbit many black dots called ‘tokens’ inside the yellow circle for rabbit, and #wolf tokens inside the yellow circle for wolf, like this:

Here #rabbit = 4 and #wolf = 3. The predation transition consumes one ‘rabbit’ token and one ‘wolf’ token, and produces two ‘wolf’ tokens, taking us here:

John explained in parts 2 and 3 how one can put rates on different transitions. For today I am only going to be concerned with ‘reachability:’ what token states are reachable from what other token states. John talked about this idea in part 25.

By a complex I will mean a population vector: a snapshot of the number of tokens in each species. In the example above, (#rabbit, #wolf) is a complex. If are two complexes, then we write

if we can get from to by a single transition in our Petri net. For example, we just saw that

via the predation transition.

Reachability, denoted , is the transitive closure of the relation . So (read is reachable from ) iff there are complexes

such that

For example, here by repeated predation.

I am very interested in switches. After all, a computer is essentially a box of switches! You can build computers by connecting switches together. In fact, that’s how early computers like the Z3 were built. The CMOS gates at the heart of modern computers are essentially switches. By analogy, the study of switches in reaction networks may help us understand biochemical circuits.

A siphon is a set of species that is ‘switch-offable’. That is, if there are no tokens in the siphon states, then they will remain absent in future. Equivalently, the only reactions that can produce tokens in the siphon states are those that require tokens from the siphon states before they can fire. Note that no matter how many rabbits there are, if there are no wolves, there will continue to be no wolves. So {wolf} is a siphon. Similarly, {rabbit} is a siphon, as is the union {rabbit, wolf}. However, when Hydrogen and Oxygen form Water, {Water} is not a siphon.

For another example, consider this Petri net:

The set {HCl, NaCl} is a siphon. However, there is a conservation law: whenever an HCl token is destroyed, an NaCl token is created, so that #HCl + #NaCl is invariant. If both HCl and NaCl were present to begin with, the complexes where both are absent are not reachable. In this sense, this siphon is not ‘really’ switch-offable. As a first pass at capturing this idea, we will introduce the notion of ‘critical set’.

A conservation law is a linear expression involving numbers of tokens that is invariant under every transition in the Petri net. A conservation law is positive if all the coefficients are non-negative. A critical set of states is a set that does not contain the support of a positive conservation law.

For example, the support of the positive conservation law #HCl + #NaCl is {HCl, NaCl}, and hence no set containing this set is critical. Thus {HCl, NaCl} is a siphon, but not critical. On the other hand, the set {NaCl} is critical but not a siphon. {HCl} is a critical siphon. And in our other example, {Wolf, Rabbit} is a critical siphon.

Of particular interest to us will be minimal critical siphons, the minimal sets among critical siphons. Consider this example:

Here we have two transitions:

and

The set is a critical siphon. But so is the smaller set So, is not minimal.

We define a self-replicable set to be a set of species such that there exist complexes and with such that for all we have

So, there are transitions that accomplish the job of creating more tokens for all the species in In other words: these species can ‘replicate themselves’.

We define a drainable set by changing the to a . So, there are transitions that accomplish the job of reducing the number of tokens for all the species in These species can ‘drain away’.

Now here comes the statement:

Every minimal critical siphon is either drainable or self-replicable!

We prove it in this paper:

• Abhishek Deshpande and Manoj Gopalkrishnan, Autocatalysis in reaction networks.

But first note that the statement becomes false if the critical siphon is not minimal. Look at this example again:

The set is a critical siphon. However is neither self-replicable (since every reaction destroys ) nor drainable (since every reaction produces ). But we’ve already seen that is not minimal. It has a critical subsiphon, namely This one is minimal—and it obeys our theorem, because it is drainable.

Checking these statements is a good way to make sure you understand the concepts! I know I’ve introduced a lot of terminology here, and it takes a while to absorb.

Anyway: our proof that every minimal critical siphon is either drainable or self-replicable makes use of a fun result about matrices. Consider a real square matrix with a sign pattern like this:

If the matrix is full-rank then there is a positive linear combination of the rows of the matrix so that all the entries are nonzero and have the same sign. In fact, we prove something stronger in Theorem 5.9 of our paper. At first, we thought this statement about matrices should be equivalent to one of the many well-known alternative statements of Farkas’ lemma, like Gordan’s theorem.

However, we could not find a way to make this work, so we ended up proving it by a different technique. Later, my colleague Jaikumar Radhakrishnan came up with a clever proof that uses Farkas’ lemma twice. However, so far we have not obtained the stronger result in Theorem 5.9 with this proof technique.

My interest in the result that every minimal critical siphon is either drainable or self-replicable is not purely aesthetic (though aesthetics is a big part of it). There is a research community of folks who are thinking of reaction networks as a programming language, and synthesizing molecular systems that exhibit sophisticated dynamical behavior as per specification:

• International Conference on DNA Computing and Molecular Programming.

• Foundations of Nanoscience: Self-Assembled Architectures and Devices.

• Molecular Programming Architectures, Abstractions, Algorithms and Applications.

Networks that exhibit some kind of catalytic behavior are a recurring theme among such systems, and even more so in biochemical circuits.

Here is an example of catalytic behavior:

The ‘catalyst’ helps transform to In the absence of the reaction is turned off. Hence, catalysts are switches in chemical circuits! From this point of view, it is hardly surprising that they are required for the synthesis of complex behaviors.

In information processing, one needs amplification to make sure that a signal can propagate through a circuit without being overwhelmed by errors. Here is a chemical counterpart to such amplification:

Here the catalyst catalyzes its own production: it is an ‘autocatalyst’, or a self-replicating species. By analogy, autocatalysis is key for scaling synthetic molecular systems.

Our work deals with these notions on a network level. We generalize the notion of catalysis in two ways. First, we allow a catalyst to be a set of species instead of a single species; second, its absence can turn off a reaction pathway instead of a single reaction. We propose the notion of self-replicable siphons as a generalization of the notion of autocatalysis. In particular, ‘weakly reversible’ networks have critical siphons precisely when they exhibit autocatalytic behavior. I was led to this work when I noticed the manifestation of this last statement in many examples.

Another hope I have is that perhaps one can study the dynamics of each minimal critical siphon of a reaction network separately, and then somehow be able to answer interesting questions about the dynamics of the entire network, by stitching together what we know for each minimal critical siphon. On the synthesis side, perhaps this could lead to a programming language to synthesize a reaction network that will achieve a specified dynamics. If any of this works out, it would be really cool! I think of how abelian group theory (and more broadly, the theory of abelian categories, which includes categories of vector bundles) benefits from a fundamental theorem that lets you break a finite abelian group into parts that are easy to study—or how number theory benefits from a special case, the fundamental theorem of arithmetic. John has also pointed out that reaction networks are really presentations of symmetric monoidal categories, so perhaps this could point the way to a Fundamental Theorem for Symmetric Monoidal Categories.

And then there is the Global Attractor Conjecture, a
long-standing open problem concerning the long-term behavior of solutions to the rate equations. Now that is a whole story by itself, and will have to wait for another day.

Levels of Excellence

 

Over on Google+, a computer scientist at McGill named Artem Kaznatcheev passed on this great description of what it’s like to learn math, written by someone who calls himself ‘man after midnight’:

The way it was described to me when I was in high school was in terms of ‘levels’.

Sometimes, in your mathematics career, you find that your slow progress, and careful accumulation of tools and ideas, has suddenly allowed you to do a bunch of new things that you couldn’t possibly do before. Even though you were learning things that were useless by themselves, when they’ve all become second nature, a whole new world of possibility appears. You have “leveled up”, if you will. Something clicks, but now there are new challenges, and now, things you were barely able to think about before suddenly become critically important.

It’s usually obvious when you’re talking to somebody a level above you, because they see lots of things instantly when those things take considerable work for you to figure out. These are good people to learn from, because they remember what it’s like to struggle in the place where you’re struggling, but the things they do still make sense from your perspective (you just couldn’t do them yourself).

Talking to somebody two or levels above you is a different story. They’re barely speaking the same language, and it’s almost impossible to imagine that you could ever know what they know. You can still learn from them, if you don’t get discouraged, but the things they want to teach you seem really philosophical, and you don’t think they’ll help you—but for some reason, they do.

Somebody three levels above is actually speaking a different language. They probably seem less impressive to you than the person two levels above, because most of what they’re thinking about is completely invisible to you. From where you are, it is not possible to imagine what they think about, or why. You might think you can, but this is only because they know how to tell entertaining stories. Any one of these stories probably contains enough wisdom to get you halfway to your next level if you put in enough time thinking about it.

What follows is my rough opinion on how this looks in a typical path towards a Ph.D. in math. Obviously this is rather subjective, and makes math look too linear, but I think it’s a useful thought experiment.

Consider the change that a person undergoes in first mastering elementary algebra. Let’s say that that’s one level. This student is now comfortable with algebraic manipulation and the idea of variables.

The next level may come somewhere during a first calculus course. The student now understands the concept of the infinitely small, of slope at a point, and can reason about areas, physical motion, and optimization.

Many stop here, believing that they have finally learned math. Those who do not stop, might proceed through multivariable calculus and perhaps a basic linear algebra course with the tools they currently possess. Their next level comes when they find themselves suffering through an abstract algebra course, and have to once again reshape their whole thought process just to squeak by with a C.

Once this student masters all of that, the rest of the undergraduate curriculum at their university might be a breeze. But not so with graduate school. They gain a level their first year. They gain another their third year. And they are horrified to discover that they are expected to gain a third level before they graduate. This level is the hardest of them all, because it is the first one that consists in mastering material that has been created largely by the student.

I don’t know how many levels there are after that. At least three.

So, the bad news is, you never do see the whole picture (though you see the old picture shrink down to a tiny point), and you can’t really explain what you do see. But the good news is that the world of mathematics is so rich and exciting and wonderful that even your wildest dreams about it cannot possibly compare. It is not like seeing the Matrix—it is like seeing the Matrix within the Matrix within the Matrix within the Matrix within the Matrix.

As he points out, this talk of ‘levels’ is too linear. You can be much better at algebraic geometry than your friend, but way behind them in probability theory. Or even within a field like algebraic geometry, you might be able to understand sheaf cohomology better than your friend, yet still way behind in some classical topic like elliptic curves.

To have worthwhile conversations with someone who is not evenly matched with you in some subject, it’s often good for one of you to play ‘student’ while the other plays ‘teacher’. Playing teacher is an ego boost, and it helps organize your thoughts – but playing student is a great way to amass knowledge and practice humility… and a good student can help the teacher think about things in new ways.

Taking turns between who is teacher and who is student helps keep things from becoming unbalanced. And it’s especially fun when some subject can only be understood with the combined knowledge of both players.

I have a feeling good mathematicians spend a lot of time playing these games—we often hear of famous teams like Atiyah, Bott and Singer, or even bigger ones like the French collective called ‘Bourbaki’. For about a decade, I played teacher/student games with James Dolan, and it was really productive. I should probably find a new partner to learn the new kinds of math I’m working on now. Trying to learn things by yourself is a huge disadvantage if you want to quickly rise to higher ‘levels’.

If we took things a bit more seriously and talked about them more, maybe a lot of us could get better at things faster.

 

 

Indeed, after I passed on these remarks, T.A. Abinandanan, a professor of materials science in Bangalore, pointed out this study on excellence in swimming:

• Daniel Chambliss, The mundanity of excellence.

Chambliss emphasizes that in swimming there really are discrete levels of excellence, because there are different kinds of swimming competitions, each with their own different ethos. Here are some of his other main points:

1) Excellence comes from qualitative changes in behavior, not just quantitative ones. More time practicing is not good enough. Nor is simply moving your arms faster! A low-level breaststroke swimmer does very different things than a top-ranked one. The low-level swimmer tends to pull her arms far back beneath her, kick the legs out very wide without bringing them together at the finish, lift herself high out of the water on the turn, and fail to go underwater for a long ways after the turn. The top-ranked one sculls her arms out to the side and sweeps back in, kicks narrowly with the feet finishing together, stays low on the turns, and goes underwater for a long distance after the turn. They’re completely different!

2) The different levels of excellence in swimming are like different worlds, with different rules. People can move up or down within a level by putting in more or less effort, but going up a level requires something very different—see point 1).

3) Excellence is not the product of socially deviant personalities. The best swimmers aren’t “oddballs,” nor are they loners—kids who have given up “the normal teenage life”.

4) Excellence does not come from some mystical inner quality of the athlete. Rather, it comes from learning how to do lots of things right.

5) The best swimmers are more disciplined. They’re more likely to be strict with their training, come to workouts on time, watch what they eat, sleep regular hours, do proper warmups before a meet, and the like.

6) Features of the sport that low-level swimmers find unpleasant, excellent swimmers enjoy. What others see as boring – swimming back and forth over a black line for two hours, say – the best swimmers find peaceful, even meditative, or challenging, or therapeutic. They enjoy hard practices, look forward to difficult competitions, and try to set difficult goals.

7) The best swimmers don’t spend a lot of time dreaming about big goals like winning the Olympics. They concentrate on “small wins”: clearly defined minor achievements that can be rather easily done, but produce real effects.

8) The best swimmers don’t “choke”. Faced with what seems to be a tremendous challenge or a strikingly unusual event such as the Olympic Games, they take it as a normal, manageable situation. One way they do this is by sticking to the same routines. Chambliss calls this the “mundanity of excellence”.

I’ve just paraphrased chunks of the paper. The whole thing is worth reading! I can’t help wondering how much these lessons apply to other areas. He gives an example that could easily apply to mathematics—a

more personal example of failing to maintain a sense of mundanity, from the world of academia: the inability to finish the doctoral thesis, the hopeless struggle for the magnum opus. Upon my arrival to graduate school some 12 years ago, I was introduced to an advanced student we will call Michael. Michael was very bright, very well thought of by his professors, and very hard working, claiming (apparently truthfully) to log a minimum of twelve hours a day at his studies. Senior scholars sought out his comments on their manuscripts, and their acknowledgements always mentioned him by name. All the signs pointed to a successful career. Yet seven years later, when I left the university, Michael was still there-still working 12 hours a day, only a bit less well thought of. At last report, there he remains, toiling away: “finishing up,” in the common expression.

In our terms, Michael could not maintain his sense of mundanity. He never accepted that a dissertation is a mundane piece of work, nothing more than some words which one person writes and a few other people read. He hasn’t learned that the real exams, the true tests (such as the dissertation requirement) in graduate school are really designed to discover whether at some point one is willing just to turn the damn thing in.

The EU’s Biggest Renewable Energy Source

Puzzle. The European Union has a goal of producing 20% of all its energy from renewable sources by 2020. Right now, which source of renewable energy does the EU use most?

1) wind
2) solar
3) hydropower
4) tides
5) geothermal
6) trash
7) wood
8) bureaucrats in hamster wheels
9) trolls

Think about it a bit before reading further!

The Economist writes:

Which source of renewable energy is most important to the European Union? Solar power, perhaps? (Europe has three-quarters of the world’s total installed capacity of solar photovoltaic energy.) Or wind? (Germany trebled its wind-power capacity in the past decade.) The answer is neither. By far the largest so-called renewable fuel used in Europe is wood.

In its various forms, from sticks to pellets to sawdust, wood (or to use its fashionable name, biomass) accounts for about half of Europe’s renewable-energy consumption. In some countries, such as Poland and Finland, wood meets more than 80% of renewable-energy demand. Even in Germany, home of the Energiewende (energy transformation) which has poured huge subsidies into wind and solar power, 38% of non-fossil fuel consumption comes from the stuff.

I haven’t yet found confirmation of this on the EU’s own websites, but this page:

• Eurostat, Renewable energy statistics.

says that in 2010, 67.6% of primary renewable energy production in the EU came from “biomass and waste”. This is at least compatible with The Economist‘s claims. Hydropower accounted for 18.9%, wind for 7.7%, geothermal for 3.5% and solar for just 2.2%.

It seems that because wood counts as renewable energy in the EU, and there are big incentives to increase the use of renewable energy, demand for wood is booming. According to the Economist, imports of wood pellets into the EU rose by 50% in 2010 alone. They say that thanks to Chinese as well as EU demand, global trade in these pellets could rise five- or sixfold from 10-12 million tonnes a year now to 60 million tonnes by 2020.

Wood from tree farms may be approximately carbon-neutral, but turning it into pellets takes energy… and importing wood pellets takes more. The EU may be making a mistake here.

Or maybe not.

Either way, it’s interesting that we always hear about the rising use of wind and solar in the EU, but not about wood.

Can you find more statistics or well-informed discussions about wood as a renewable energy source?

Here’s the article:

• Wood: the fuel of the future, The Economist, 6 April 2013.

If its facts are wrong, I’d like to know.

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P.S. – This is the 400th post on this blog!