Seneca cliffs of the third kind: how technological progress can generate a faster collapse

19 12 2014

Another guest post by

The image above (from Wikipedia) shows the collapse of the North Atlantic cod stocks. The fishery disaster of the early 1990s was the result of a combination of greed, incompetence, and government support for both. Unfortunately, it is just one of the many examples of how human beings tend to worsen the problems they try to solve. The philosopher Lucius Anneus Seneca had understood this problem already some 2000 years ago, when he said, “It would be some consolation for the feebleness of our selves and our works if all things should perish as slowly as they come into being; but as it is, increases are of sluggish growth, but the way to ruin is rapid.”
 

The collapse of the North Atlantic cod fishery industry gives us a good example of the abrupt collapse in the production of resources – even resources which are theoretically renewable. The shape of the production curve landings shows some similarity with the “Seneca curve“, a general term that I proposed to apply to all cases in which we observe a rapid decline of the production of a non renewable, or slowly renewable, resource. Here is the typical shape of the Seneca Curve:

The similarity with the cod landings curve is only approximate, but clearly, in both cases we have a very rapid decline after a slow growth that, for the cod fishery, had lasted for more than a century. What caused this behavior?

The Seneca curve is a special case of the “Hubbert Curve” which describes the exploitation of a non renewable (or slowly renewable) resource in a free market environment. The Hubbert curve is “bell shaped” and symmetric (and it is the origin of the well known concept of “peak oil). The Seneca curve is similar, but it is skewed forward. In general, the forward skewness can be explained in terms of the attempt of producers to keep producing at all costs a disappearing resource.

There are several mechanisms which can affect the curve. In my first note on this subject, I noted how the Seneca behavior could be generated by growing pollution and, later on, how it could be the result of the application of more capital resources to production as a consequence of increasing market prices. However, in the case of the cod fishery, neither factor seems to be fundamental. Pollution in the form of climate change may have played a role, but it doesn’t explain the upward spike of the 1960s in fish landings. Also, we have no evidence of cod prices increasing sharply during this phase of the production cycle. Instead, there is clear evidence that the spike and the subsequent collapse was generated by technological improvements.

The effect of new and better fishing technologies is clearly described by Hamilton et al. (2003)

Fishing changed as new technology for catching cod and shrimp developed, and boats became larger. A handful of fishermen shifted to trawling or “dragger” gear. The federal government played a decisive role introducing new technology and providing financial resources to fishermen who were willing to take the risk of investing in new gear and larger boats.

Fishermen in open boats and some long-liners continued to fish cod, lobster and seal inshore. Meanwhile draggers  and other long-liners moved onto the open ocean, pursuing cod and shrimp nearly year round. At the height of the boom, dragger captains made $350,000–600,000 a year from cod alone. … The federal government helped finance boat improvements, providing grants covering 30–40% of their cost.
….
By the late 1980s, some fishermen recognized signs of decline. Open boats and long-liners could rarely reach their quotas. To find the remaining cod, fishermen traveled farther north, deployed more gear and intensified their efforts. A few began shifting to alternative species such as crab. Cheating fisheries regulation—by selling unreported catches at night, lining nets with small mesh and dumping bycatch at sea—was said to be commonplace. Large illegal catches on top of too-high legal quotas drew down the resource. Some say they saw trouble coming, but felt powerless to halt it.

So, we don’t really need complicated models (but see below) to understand how human greed and incompetence – and help from the government – generated the cod disaster. Cods were killed faster than they could reproduce and the result was their destruction. Note also that in the case of whaling in the 19th century, the collapse of the fishery was not so abrupt as it was for cods, most likely because, in the 19th century, fishing technology could not “progress” could not be so radical as it was in the 20th century.

The Seneca collapse of the Atlantic cod fishery is just one of the many cases in which humans “push the levers in the wrong directions“, directly generating the problem they try to avoid. If there is some hope that, someday, the cod fishery may recover, the situation is even clearer with fully non-renewable resources, such as oil and most minerals. Also here, technological progress is touted as the way to solve the depletion problems. Nobody seems to worry about the fact that the faster you extract it, the faster you deplete it: that’s the whole concept of the Seneca curve.

So take care: there is a Seneca cliff ahead also for oil!





At last….. relatively good news on CC

2 10 2013

My friend Dave Kimble who has his ear to the ground and whose work I sometimes post here has sent me this by email…….

The IPCC’s AR5 final report from Working Group 1 (still called Final Draft) is available for download,
either all in one giant file of 158 MB (mine was damaged) at http://www.climatechange2013.org/images/uploads/WGIAR5_WGI-12Doc2b_FinalDraft_All.pdf or as lots of files of individual chapters, see http://www.ipcc.ch/report/ar5/wg1/

The RCP2.6 scenario corresponds to Peak Oil, Gas and Coal that peakists would subscribe to.  For reasons that are beyond me, you will have to click on the chart to see it full size…. ipcc.predictions It shows median summer temperatures over land rising to +1.5 C by 2045, and falling very slowly after that.

However the median is only the “most likely” for the whole world, over land, in summer.
The model predicts that the most likely half of all outcomes is in the range +1.0 to +1.8 C.
And the 90% of all outcomes range is +0.2 to +2.6 C.

This of course assumes that we manage to keep producing all the fossil fuels we can, on the downslope of Hubbert’s Curve, which seems very unlikely.

So there you have it.  Only collapse can save us from catastrophic climate change.  Though of course, we might still have fired the Clathrate Gun…..





Mineral resources and the limits to growth

29 09 2013

I thought long and hard about reproducing this remarkable article here…….  It’s rather longer than anything I

Ugo Bardi

Ugo Bardi

usually put up, and I was concerned about copyright, but found nothing on the original website where this was published that says I can’t do it…… and I expect no one at resilience.org objects to ensuring the spread of this important message.

Five years ago, I published a very short item on roughly the same concept.  But I’m no Ugo Bardi…..  So make yourself a good cuppa your favourite poison, and enjoy….

 

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So, ladies and gentleman, let me start with this recent book of mine. It is titled “The Plundered Planet.”  You can surely notice that it is not titled “The Developed Planet” or “The Improved Planet.”  Myself and the co-authors of the book chose to emphasize the concept of “Plundering”; of the fact that we are exploiting the resources of our planet as if they were free for us for the taking; that is, without thinking of the consequences.   And the main consequence, with which we are concerned here is called “depletion,” even though we have to keep in mind the problem of pollution as well.

Now, there have been many studies on the question of depletion, but “The Plundered Planet” has a specific origin, and I can show it to you. Here it is.

It is the rather famous study that was published in 1972 with the title “The Limits to Growth”.  It was one of the first studies that attempted to quantify depletion and its effects on the world’s economic system.  It was a complex study based on the best available data at the time and that used the most sophisticated computers available to study how the interaction of various factors would affect parameters such as industrial production, agricultural production, population and the like.  Here are the main results of the 1972 study, the run that was called the “base case” (or “standard run”).  The calculations were redone in 2004, finding similar results.  

As you can see, the results were not exactly pleasant to behold.  In 1972, the study saw a slowdown of the world’s main economic parameters that would take place within the first two decades of the 21st century.  I am sure that you are comparing, in your minds, these curves with the present economic situation and you may wonder whether these old calculations may be turning out to be incredibly good.  But I would also like to say that these curves are not – and never were – meant to be taken as specific predictions.  No one can predict the future, what we can do is to study tendencies and where these tendencies are leading us.  So, the main result of the Limits to Growth study was to show that the economic system was headed towards a collapse at some moment in the future owing to the combined effect of depletion, pollution, and overpopulation.  Maybe the economic problems we are seeing nowadays are a prelude to the collapse seen by this model, maybe not – maybe the predicted collapse is still far away in the future.  We can’t say right now. 

In any case, the results of the study can be seen at least worrisome.  And a reasonable reaction when the book came out in 1972 would have been to study the problem in greater depth – nobody wants the economy to collapse, of course.  But, as you surely know, the Limits to Growth study was not well received.  It was strongly criticized, accused of having made “mistakes” of all kinds and at times to be part of a worldwide conspiracy to take control of the world and to exterminate most of humankind.  Of course, most of this criticism had political origins.  It was mostly a gut reaction: people didn’t like these results and sought to find ways to demonstrate that the model was wrong (or the data, or the approach, or something else).  If they couldn’t do that, they resorted to demonizing the authors – that’s nothing now; I described it in a book of mine “Revisiting the limits to growth“.

Nevertheless, there was a basic criticism of the “Limits” study that made sense.  Why should one believe in this model?  What are exactly the factors that generate the expected collapse?  Here, I must say, the answer often given in the early times by the authors and by their supporters wasn’t so good.  What the creators of the models said was that the model made sense according to their views and they could show a scheme that was this (from the 1972 Italian edition of the book):

Now, I don’t know what do you think of it; to me it looks more or less like the map of the subway of Tokyo, complete with signs in kanji characters.  Not easy to navigate, to say the least.  So, why did the authors create this spaghetti model?  What was the logic in it?  It turns out that the Limits to Growth model has an internal logic and that it can be explained in thermodynamic terms.  However, it takes some work to describe the whole story.  So, let me start with the ultimate origin of these models:

If you have studied engineering, you surely recognize this object.  It is called a “governor” and it is a device developed in 19th century to regulate the speed of steam engines.  It turns with the engine, and the arms open or close depending on speed.  In so doing, the governor closes or opens the valve that sends steam into the engine.  It is interesting because it is the first self-regulating device of this kind and, at its time, it generated a lot of interest.  James Clerk Maxwell himself studied the behaviour of the governor and, in 1868, he came up with a set of equations describing it. Here is a page from his original article

I am showing you these equations just to let you note how these systems can be described by a set of correlated differential equations.  It is an approach that is still used and today we can solve this kind of equations in real time and control much more complex systems than steam engines.  For instance, drones.

You see here that a drone can be controlled so perfectly that it can hold a glass without spilling the content. And you can have drones playing table tennis with each other and much more.  Of course they are also machines designed for killing people, but let’s not go into that.  The point is that if you can solve a set of differential equations, you can describe – and also control – the behaviour of quite complex systems.

The work of Maxwell so impressed Norbert Wiener, that it led him to develop the concept of “cybernetics”

We don’t use so much the term cybernetics today.  But the ideas that started from the governor study by Maxwell were extremely fecund and gave rise to a whole new field of science.  When you use these equations for controlling mechanical system, you use the term “control theory.”  But when you use the equations for study the behaviour of socio-economic systems, you use the term “system dynamics”

System dynamics is something that was developed mainly by Jay Wright Forrester in the 1950s and 1960s, when there started to exist computers powerful enough to solve sets of coupled differential equations in reasonable times.  That generated a lot of studies, including “The Limits to Growth” of 1972 and today the field is alive and well in many areas.

A point I think is important to make is that these equations describe real world systems and real world systems must obey the laws of thermodynamics.  So, system dynamics must be consistent with thermodynamics. It does.  Let me show you a common example of a system described by system dynamics: practitioners in this field are fond of using a bathub as an example:

On the right you have a representation of the real system, a bathtub partly filled with water.  On the left, its representation using system dynamics.  These models are called “stock and flow”, because you use boxes to represent stocks (the quantity of water in the tub) and you use double edged arrows to indicate flows.  The little butterfly like things indicate valves and single edged arrows indicate relationship.

Note that I used a graphic convention that I like to use for my “mind sized” models.  That is, I have stocks flowing “down”, following the dissipation of thermodynamic potential.  In this case what moves the model is the gravitational potential; it is what makes water flow down, of course.  Ultimately, the process is driven by an increase in entropy and I usually ask to my students where is that entropy increases in this system.  They usually can’t give the right answer.  It is not that easy, indeed – I leave that to you as a little exercise

The model on the left is not simply a drawing of box and arrows, it is made with a software called “Vensim” which actually turns the model “alive” by building the equations and solving them in real time.  And, as you may imagine, it is not so difficult to make a model that describes a bathtub being filled from one side and emptied from the other. But, of course, you can do much more with these models.  So, let me show a model made with Vensim that describes the operation of a governor and of the steam engine.

Before we go on, let me introduce a disclaimer.  This is just a model that I put together for this presentation. It seems to work, in the sense that it describes a behaviour that I think is correct for a governor (you can see the results plotted inside the boxes).  But it doesn’t claim to be a complete model and surely not the only possible way to make a system dynamics model of a governor.  This said, you can give a look to it and notice a few things.  The main one is that we have two “stocks” of energy: one for the large wheel of the steam energy, the other for the small wheel which is the governor.  In order to provide some visual sense of this difference in size, I made the two boxes of different size, but that doesn’t change the equations underlying the model.  Note the “feedback”, the arrows that connect flows and stock sizes.  The concept of feedback is fundamental in these models.

Of course, this is also a model that is compatible with thermodynamics.  Only, in this case we don’t have a gravitational potential that moves the system, but a potential based on temperature differences.  The steam engine works because you have this temperature difference and you know the work of Carnot and the others who described it.  So, I used the same convention here as before; thermodynamic potential are dissipated going “down” in the model’s graphical representation

Now, let me show you another simple model, the simplest version I can think of a model that describes the exploitation of non renewable resources:

It is, again, a model based on thermodynamics and, this time, driven by chemical potentials.  The idea is that the “resources” stock as a high chemical potential in the sense that it may be thought as, for instance, crude oil, which spontaneously combines with oxygen to create energy.  This energy is used by human beings to create what I can call “capital” – the sum of everything you can do with oil; from industries to bureaucracies.

On the right, you can see the results that the model provides in terms of the behaviour as a function of time of the stock of the resources, their production, and the capital stock.  You may easily notice how similar these curves are to those provided by the more complex model of “The Limits to Growth.”  So, we are probably doing something right, even with this simple model.

But the point is that the model works!  When you apply it to real world cases, you see that its results can fit the historical data.  Let me show you an example:

This is the case of whaling in 19th century, when whale oil was used as fuel for lamps, before it became common to use kerosene.  I am showing you this image because it is the first attempt I made to use the model and I was surprised to see that it worked – and it worked remarkably well.  You see, here you have two stocks: one is whales, the other is the capital of the whaling industry that can be measured by means of a proxy that is the total tonnage of the whaling fleet.  And, as I said, the model describes very well how the industry grew on the profit of killing whales, but they killed way too many of them.  Whales are, of course, a renewable resource; in principle.  But, of course, if too many whales are killed, then they don’t have enough time to reproduce and they behave as a non-renewable resource.  Biologists have determined that at the end of this fishing cycle, there were only about 50 females of the species being hunted at that time.  Non renewable, indeed!

So, that is, of course, one of the several cases where we found that the model can work.  Together with my co-workers, we found that it can work also for petroleum extraction, as we describe in a paper published in 2009 (Bardi and Lavacchi).  But let me skip that – the important thing is that the model works in some cases but, as you would expect, not in all. And that is good – because what you don’t want is a “fit-all” model that doesn’t tell you anything about the system you are studying.  Let’s say that the model reproduces what’s called the “Hubbert model” of resource exploitation, which is a purely empirical model that was proposed more than 50 years ago and that remains a basic one in this kind of studies: it is the model that proposes that extraction goes through a “bell-shaped” curve and that the peak of the curve, the “Hubbert peak” is the origin of the concept of “peak oil” which you’ve surely heard about.  Here is the original Hubbert model and you see that it has described reasonably well the production of crude oil in the 48 US lower states.

Now, let’s move on a little.  What I have presented to you is a very simple model that reproduces some of the key elements of the model used for “The Limits to Growth” study but it is of course a very simplified version.  You may have noted that the curves for industrial production of the Limits to Growth tend to be skewed forward and this simple model can’t reproduce that.  So, we must move one step forward and let me show you how it can be done while maintaining the basic idea of a “thermodynamic cascade” that goes from higher potentials to lower potentials.  Here is what I’ve called the “Seneca model”


You see that I added a third stock to the system.   In this case I called it “pollution”; but you might also call it, for instance, “bureaucracy” or may be even “war”.  It is any stock that draws resource from the “Capital” (aka, “the economy”) stock.  And the result is that the capital stock and production collapse rather rapidly; this is what I called “the Seneca effect”; from the roman philosopher Lucius Anneaus Seneca who noted that “Fortune is slow, but ruin is rapid”.

For this model, I can’t show you specific historical cases – we are still working on this idea, but it is not easy to make quantitative fittings because the model is complicated.  But there are cases of simple systems where you see this specific behaviour, highly forward skewed curves – caviar fishing is an example.  But let’s not go there right now.

What I would like to say is that you can move onward with this idea of cascading thermodynamic potentials and build up something that may be considered as a simplified version of the five main stocks taken into account in the “Limits to Growth” calculations.  Here it is

Now, another disclaimer: I am not saying that this model is equivalent to that of the Limits to Growth, nor that it is the only way to arrange stocks and flows in order to produce similar results to the one obtained by the Limits to Growth model.  It is here just to show to you the logic of the model.  And I think you can agree, now, that there is one.  The “Limits” model is not just randomly arranged spaghetti, it is something that has a deep logic based on thermodynamics.  It describes the dissipation of a cascade of thermodynamic potentials.

In the end, all these model, no matter how you arrange their elements, tend to generate similar basic results: the bell shaped curve; the one that Hubbert had already proposed in 1956

The curve may be skewed forward or not, but that changes little on the fact that the downside slope is not so pleasant for those who live it.

Don’t expect this curve to be a physical law; after all it depend on human choices and human choices may be changed.  But, in normal conditions, human beings tend to follow rather predictable patterns, for instance exploiting the “easy” resources (those which are at the highest thermodynamic potential) and then move down to the more difficult ones.  That generates the curve.

Now, I could show you many examples of the tendency of real world systems to follow the bell shape curve.  Let me show you just one; a recent graph recently made by Jean Laherrere.

These are data for the world’s oil production.  As you can see, there are irregularities and oscillations.  But note how, from 2004 to 2013, we have been following the curve: we move on a predictable path.  Already in 2004 we could have predicted what would have been today’s oil production.  But, of course, there are other elements in this system.  In the figure on the right, you can see also the appearance of the so-called “non-conventional” oil resources, which are following their own curve and which are keeping the production of combustible liquids (a concept slightly different from that of “crude oil) rather stable or slightly increasing.  But, you see, the picture is clear and the predictive ability of these models is rather good even though, of course, approximate.

Now, there is another important point I’d like to make.  You see, these models are ultimately based on thermodynamics and there is an embedded thermodynamic parameter in the models that is called EROI (or ERoEI) which is the energy return for the energy invested. It is basically the decline in this parameter that makes, for instance, the extraction of oil gradually producing less energy and, ultimately, becoming pointless when the value of the ERoEI goes below one.  Let me show you an illustration of this concept:

You see?  The data you usually read for petroleum production are just that: how much petroleum is being produced in terms of volume.  There is already a problem with the fact that not all petroleums are the same in the sense of energy per unit volume, but the real question is the NET energy you get by subtracting the energy invested from the energy produced.  And that, as you see, goes down rapidly as you move to more expensive and difficult resources.  For EROEIs under about 20, the problem is significant and below about 10 it becomes serious.  And, as you see, there are many energy resources that have this kind of low EROEI.  So, don’t get impressed by the fact that oil production continues, slowly, to grow.  Net energy is the problem and many things that are happening today in the world seem to be related to the fact that we are producing less and less net energy.  In other words, we are paying more to produce the same.  This appears in terms of high prices in the world market.

Here is an illustration of how prices and production have varied during the past decades from the blog “Early Warning” kept by Stuart Staniford.

And you see that, although we are able to manage a slightly growing production, we can do so only at increasingly high prices.  This is an effect of increasing energy investments in extracting difficult resources – energy costs money, after all.

So, let me show you some data for resources that are not petroleum.  Of course, in this case you can’t speak in terms of ERoEI; because you are not producing energy.  But the problem is the same, since you are using fossil fuels to produce most of the commodities that enter the industrial system, and that is valid also for agriculture. Here are some data.

Food production worldwide is still increasing, but the high costs of fossil fuels are causing this increase in prices.  And that’s a big problem because we all know that the food demand is highly inelastic – in plain words you need to eat or you die.  Several recent events in the world, such as wars and revolutions in North Africa and Middle East have been related to these increases in food prices.

Now, let me go to the general question of mineral production.  Here, we have the same behaviour: most mineral commodities are still growing in terms of extracted quantities, as you can see here (from a paper by Krausmann et al, 2009 http://dx.doi.org/10.1016/j.ecolecon.2009.05.007)

These data go up to 2005 – more recent data show signs of plateauing production, but we don’t see clear evidence of a peak, yet. This is bad, because we are creating a climate disaster. As you see from the most recent data, CO2 are still increasing in a nearly exponential manner

 

But the system is clearly under strain. Here are some data relative to the average price index for aluminium, copper, gold, iron ore, lead, nickel, silver, tin and zinc (adapted from a graphic reported by Bertram et al., Resource Policy, 36(2011)315)

So, you see, there has been this remarkable “bump” in the prices of everything and that correlates well with what I was arguing before: energy costs more and, at the same time, energy requirements are increasing because of ore depletion.  At present, we are still able to keep production stable or even slowly increasing, but this is costing society tremendous sacrifices in terms of reducing social services, health care, pensions and all the rest.  And, in addition, we risk destroying the planetary ecosystem because of climate change.

Now I can summarize what I’ve been saying and get to the take-home point which, I think can be expressed in a single sentence “Mining takes energy

Of course, many people say that we are so smart that we can invent new ways of mining that don’t require so much energy.  Fine, but look at that giant wheel, above, used to extract coal in the mine of Garzweiler in Germany.  Think of how much energy you need to make that wheel; do you think you could use an i-pad, instead?

In the end, energy is the key of everything and if we want to keep mining, and we need to keep mining, we need to be able to keep producing energy.  And we need to obtain that energy without fossil fuels. That’s the concept of the “Energy Transition”

Here, I use the German term “Energiewende” which stands for “Energy Transition”. And I have also slightly modified the words by Stanley Jevons, he was talking about coal, but the general concept of energy is the same.  We need to go through the transition, otherwise, as Jevons said long ago, we’ll be forced to return to the “laborious poverty” of older times.

That doesn’t mean that the times of low cost mineral commodities will ever return but we should be able to maintain a reasonable flux of mineral commodities into the industrial system and keep it going.  But we’ll have to adapt to less opulent and wasteful life as the society of “developed” countries has been accustomed to so far.  I think it is not impossible, if we don’t ask too much:

h/t ms. Ruza Jankovich – the car shown here is an old Fiat “500” that was produced in the 1960s and it would move people around without the need of SUVs

____________________________________________

Acknowledgement:

The Club of Rome team

Daphne Davies
Ian Johnson
Linda Schenk
Alexander Stefes
Joséphine von Mitschke-Collande
Karl Wagner

And the coauthors of the book “Plundering the Planet”

Philippe Bihouix
Colin Campbell
Stefano Caporali
Partick Dery
Luis De Souza
Michael Dittmar
Ian Dunlop
Toufic El Asmar
Rolf Jakobi
Jutta Gutberlet
Rui Rosa
Iorg Schindler
Emilia Suomalainen
Marco Pagani
Karl Wagner
Werner Zittel

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400 parts per million for the first time in 3 million years

28 04 2013

The world’s CO2 levels are on the cusp of 400 parts per million (ppm) in the atmosphere for the first time in some 3 million years……

The daily CO2 concentration at the Mauna Loa Observatory in Hawaii, was measured at

David Keeling pointing at Keeling Curve charts

David Keeling pointing at Keeling Curve charts

399.72 ppm last Thursday.  A few hourly readings had already gone over 400 ppm. ”I wish it weren’t true, but it looks like the world is going to blow through the 400 ppm level without losing a beat,” said Ralph Keeling, son of Charles David Keeling.  Keeling was the first to confirm the rise of atmospheric carbon dioxide by very precise measurements producing a data set now known widely as the “Keeling Curve.”  Ralph is a geochemist at the US Scripps Institution of Oceanography, which operates the Hawaiian observatory. ”At this pace we’ll hit 450 ppm within a few decades” he said…

The 450 ppm level is considered the point where the world has a 50% chance of avoiding catastrophic climate change.  Any higher, and the odds of avoiding searing temperature rises of four or five degrees by 2100 become prohibitively risky.

The last time CO2 reached the symbolic milestone of 400 ppm in the atmosphere – in the Pliocene era – temperatures were three to four degrees higher than today, and sea levels were between five and 40 metres higher. Carbon dioxide levels have been rising since the measurements began at the observatory in 1958, and recorded 317 ppm.

It comes as Australia’s Climate Commission will release a report today (April 29 2013) on global action to reduce emissions. The US and, particularly, China are moving into leadership positions on greenhouse gas cuts, according to this report, “The Critical Decade: Global Action Building on Climate Change”.

Growth (ie, not consumption!) in coal use in China had declined and renewable energy had expanded on a massive scale.  Chinese wind power generation had increased almost 50-fold between 2005 and 2012, and solar power capacity rose by 75 % in 2012.

Australia doubled its renewable energy capacity between 2001 and 2012 (from a base of virtually zero), but is at risk of being left behind by other nations, according to the head of the Climate Commission, Tim Flannery. ”We are the 15th largest emitter in the world, larger than 180 other countries,” Professor Flannery said. ”We are more influential than most of us think.”