I’ll be heading off to Stockholm in August to present a paper at the 2nd International Conference on Information and Communication Technologies for Sustainability (ICT4S’2014). The theme of the conference this year is “ICT and transformational change”, which got me thinking about how we think about change, and especially whether we equip students in computing with the right conceptual toolkit to think about change. I ended up writing a long critique of Computational Thinking, which has become popular lately as a way of describing what we teach in computing undergrad programs. I don’t think there’s anything wrong with computational thinking in small doses. But when an entire university program teaches nothing but computational thinking, we turn out generations of computing professionals who are ill-equipped to think about complex societal issues. This then makes them particularly vulnerable to technological solutionism. I hope the paper will provoke some interesting discussion!

Here’s the abstract for my paper (click here for the full paper):

From Computational Thinking to Systems Thinking: A conceptual toolkit for sustainability computing

Steve Easterbrook, University of Toronto

If information and communication technologies (ICT) are to bring about a transformational change to a sustainable society, then we need to transform our thinking. Computer professionals already have a conceptual toolkit for problem solving, sometimes known as computational thinking. However, computational thinking tends to see the world in terms a series of problems (or problem types) that have computational solutions (or solution types). Sustainability, on the other hand, demands a more systemic approach, to avoid technological solutionism, and to acknowledge that technology, human behaviour and environmental impacts are tightly inter-related. In this paper, I argue that systems thinking provides the necessary bridge from computational thinking to sustainability practice, as it provides a domain ontology for reasoning about sustainability, a conceptual basis for reasoning about transformational change, and a set of methods for critical thinking about the social and environmental impacts of technology. I end the paper with a set of suggestions for how to build these ideas into the undergraduate curriculum for computer and information sciences.

Yesterday I talked about three re-inforcing feedback loops in the earth system, each of which has the potential to accelerate a warming trend once it has started. I also suggested there are other similar feedback loops, some of which are known, and others perhaps yet to be discovered. For example, a paper published last month suggested a new feedback loop, to do with ocean acidification. In a nutshell, as the ocean absorbs more CO2, it becomes more acidic, which inhibits the growth of phytoplankton. These plankton are a major source of sulphur compounds that end up as aerosols in the atmosphere, which seeds the formation of clouds. Less clouds mean lower albedo, which means more warming. Whether this feedback loop is important remains to be seen, but we do know that clouds have an important role to play in climate change.

I didn’t include clouds on my diagrams yet, because clouds deserve a special treatment, in part because they are involved in two major feedback loops that have opposite effects:

Two opposing cloud feedback loops

Two opposing cloud feedback loops. An increase in temperature leads to an increase in moisture in the atmosphere. This leads to two new loops…

As the earth warms, we get more moisture in the atmosphere (simply because there is more evaporation from the surface, and warmer air can hold more moisture). Water vapour is a powerful greenhouse gas, so the more there is in the atmosphere, the more warming we get (greenhouse gases reduce the outgoing radiation). So this sets up a reinforcing feedback loop: more moisture causes more warming causes more moisture.

However, if there is more moisture in the atmosphere, there’s also likely to be more cloud formation. Clouds raise the albedo of the planet and reflect sunlight back into space before it can reach the surface. Hence, there is also a balancing loop: by blocking more sunlight, extra clouds will help to put the brakes on any warming. Note that I phrased this carefully: this balancing loop can slow a warming trend, but it does not create a cooling trend. Balancing loops tend to stop a change from occurring, but they do not create a change in the opposite direction. For example, if enough clouds form to completely counteract the warming, they also remove the mechanism (i.e. warming!) that causes growth in cloud cover in the first place. If we did end up with so many extra clouds that it cooled the planet, the cooling would then remove the extra clouds, so we’d be back where we started. In fact, this loop is nowhere near that strong anyway. [Note that under some circumstances, balancing loops can lead to oscillations, rather than gently converging on an equilibrium point, and the first wave of a very slow oscillation might be mistaken for a cooling trend. We have to be careful with our assumptions and timescales here!].

So now we have two new loops that set up opposite effects – one tends to accelerate warming, and the other tends to decelerate it. You can experience both these effects directly: cloudy days tend to be cooler than sunny days, because the clouds reflect away some of the sunlight. But cloudy nights tend to be warmer than clear nights because the water vapour traps more of the escaping heat from the surface. In the daytime, both effects are operating, and the cooling effect tends to dominate. During the night, there is no sunlight to block, so only the warming effect works.

If we average out the effects of these loops over many days, months, or years, which of the effects dominate? (i.e. which loop is stronger?) Does the extra moisture mean more warming or less warming? This is clearly an area where building a computer model and experimenting with it might help, as we need to quantify the effects to understand them better. We can build good computer models of how clouds form at the small scale, by simulating the interaction of dust and water vapour. But running such a model for the whole planet is not feasible with today’s computers.

To make things a little more complicated, these two feedback loops interact with other things. For example, another likely feedback loop comes from a change in the vertical temperature profile of the atmosphere. Current models indicate that, at least in the tropics, the upper atmosphere will warm faster than the surface (in technical terms, it will reduce the lapse rate – the rate at which temperature drops as you climb higher). This then increases the outgoing radiation, because it’s from the upper atmosphere that the earth loses its heat to space. This creates another (small) balancing feedback:

The lapse rate feedback - if the upper troposphere warms faster than the surface (i.e. a lower lapse rate), this increases outgoing radiation from the planet.

The lapse rate feedback – if the upper troposphere warms faster than the surface (i.e. a lower lapse rate), this increases outgoing radiation from the planet.

Note that this lapse rate feedback operates in the same way as the main energy balance loop – the two ‘-‘ links have the same effect as the existing ‘+’ link from temperature to outgoing infra-red radiation. In other words this new loop just strengthens the effect of the existing loop – for convenience we could just fold both paths into the one link.

However, water vapour feedback can interact with this new feedback loop, because the warmer upper atmosphere will hold more water vapour in exactly the place where it’s most effective as a greenhouse gas. Not only that, but clouds themselves can change the vertical temperature profile, depending on their height. I said it was complicated!

The difficulty of simulating all these different interactions of clouds accurately leads to one of the biggest uncertainties in climate science. In 1979, the Charney report calculated that all these cloud and water vapour feedback loops roughly cancel out, but pointed out that there was a large uncertainty bound on this estimate. More than thirty years later, we understand much more about the how cloud formation and distribution are altered in a warming world, but our margins of error for calculating cloud effects have barely reduced, because of the difficulty of simulating them on a global scale. Our best guess is now that the (reinforcing) water vapour feedback loop is slightly stronger than than the (balancing) cloud albedo and lapse rate loops. So the net effect of these three loops is an amplifying effect on the warming.

Other posts in this series, so far:

At the start of this series, I argued that Climate Science is inherently a Systems Discipline. To develop that idea, I described two important systems as feedback loops: the earth’s temperature equilibrium loop and economic growth and energy consumption, and then we put these two systems together.

The basic climate system now looks like this (leaving out, for now, the dynamics that drive economic development and energy use):

The basic planetary energy balancing loop, with the burning of fossil fuels forcing the temperature to change

The basic planetary energy balancing loop, with the burning of fossil fuels forcing the temperature to change

Recall that the balancing loop (marked with a ‘B’) ensures that for each change to the input forcings (in this case greenhouse gases and aerosols in the atmosphere), the earth system will settle down to a new equilibrium point: a temperature at which the incoming and outgoing energy flows are balanced again. Each time we increase the concentration of greenhouse gases in the atmosphere, we can expect the earth to warm, slowly, until it reaches this new equilibrium. The economy-energy system (not shown above) is ensuring that we keep on adding more greenhouse gases, so we’re continually pushing the system further and further out of balance. That means we’re continually increasing the eventual temperature rise that the earth will experience before it reaches a new equilibrium.

Meanwhile, the aerosols provide a slight cooling effect, but they wash out of the atmosphere fairly quickly, so their overall concentration isn’t rising much. Carbon dioxide does not wash out quickly – it can remain in the atmosphere for thousands of years. Hence the warming effect dominates.

Now, if that was the whole picture, climate change would be very predictable, using basic thermodynamic principles. Unfortunately, there are other feedback loops that we haven’t considered yet. Here’s one:

The basic climate system with the ice albedo feedback

The basic climate system with the ice albedo feedback

As the temperature rises, the ice sheets start to melt and shrink. These include the Arctic sea ice, glaciers on Greenland and the Antarctic, and mountain glaciers across the world. When sea ice melts, it leaves more sea exposed, which is much darker than the ice. When land ice melts, it uncovers rocks, soils, and (eventually) plants, all of which are also darker than ice. Because of this, loss of ice leads to a lower albedo for the planet. A lower albedo means less of the incoming sunlight is reflected straight back into space, so more reaches the surface. In other words, less albedo means more incoming solar radiation. And, as we already know, this leads to more energy retained and more warming. In other words, it is a re-inforcing feedback loop.

As a quick check, we can use the rule of thumb that reinforcing loops have an even number of ‘-‘ links. Trace the path of this loop to check:

Ice albedo feedback loop on its own

Ice albedo feedback loop on its own

Because this is a reinforcing loop, it can modify the behaviour of the basic energy balancing loop. If a warming process starts, this loop can accelerate it, and cause more warming than we’d expect from just the main balancing loop. In extreme cases, a reinforcing loop can completely destabilize a system that is normally dominated by balancing loops. However, all reinforcing loops also must have limits (remember: nothing can grow forever). In this case, there is clearly a limit once all the ice sheets on the planet have melted. The loop can no longer function at that point.

Here’s another reinforcing loop:

Climate system with permafrost feedback

Climate system with permafrost feedback

In this loop, as the temperature rises, it melts the permafrost across Northern Canada and Russia. This releases the methane from the frozen soils. Methane is a greenhouse gas, so this loop also accelerates the warming. Again, it’s a re-inforcing loop, and again, there’s a limit: the loop must stop once all the permafrost has melted.

Here’s another:

Climate system with carbon sinks feedback

Climate system with carbon sinks feedback

This loop occurs because the more greenhouse gases we put into the atmosphere, the more work the carbon sinks have to do. Carbon sinks include the ocean and soils – they slowly remove carbon dioxide from the atmosphere. But the more carbon they have to absorb, the less effective they are at taking more. There’s an additional effect for the ocean, because a warmer ocean is less able to absorb CO2. Some model studies even suggest that after a few degrees of warming, the ocean might stop being a carbon sink and start being a source.

So, put that altogether and we have three re-inforcing loops working to destabilize the main energy balance loop. The main loop tends to limit the amount of warming we might expect, and the reinforcing loops all tend to increase it:

All three reinforcing loops working together

All three reinforcing loops working together

Remember, all three re-inforcing loops might operate at once. More likely, each will kick in at different times as the planet warms. Predicting when that might occur is hard, as is calculating the likely size of the effect. We can calculate absolute limits to each of these reinforcing loops, but there are likely to be other reasons why the loop stops working before reaching these absolute limits.

One of the goals of climate modelling is to capture these kinds of feedbacks in a computational model, to attempt to quantify the effects, so that we can understand them better. We can use both basic physics and empirical observations to put numbers on each of the relationships in the diagram, and we can experiment with the model to test how sensitive it is to different kinds of perturbation, especially in areas where it’s hard to be sure about the numbers.

However, there’s also the possibility that we missed some important feedback loops. In the model above, we have missed an important one, to do with clouds. We’ll meet that in the next post…

Other posts in this series, so far:

The story so far: First, I argued that Climate Science is inherently a Systems Discipline. To develop that idea, I described two important systems as feedback loops: the earth’s temperature equilibrium loop and economic growth and energy consumption. Now it’s time to put those two systems together…

First, we’ll need to capture the unintended consequences of burning fossil fuels for energy, in the form of two distinct kinds of pollution:

Effect of two different kinds of pollutant

Effect of two different kinds of pollutant

Aerosols are tiny particles (smoke, dust, etc) produced when dirtier fossil fuels are burnt, particularly, sulphur dioxide. Coal is the worst for producing these, but oil produces them as well, especially from poorly tuned gasoline and diesel engines. The effect of aerosols is easy to understand, because we can see them. They hang around in the air and block out the light. They contribute to the clouds of smog that hang over our cities in the summer, and they react with water vapour to create sulphuric acid, leading to acid rain. It’s possible to greatly reduce the amount of aerosols produced when we burn fossil fuels, by processing the fuels first to remove the impurities that otherwise would end up as aerosols. For example, low-sulphur coal is much “cleaner” than regular coal, because it produces very few aerosols when you burn it. That’s good for our air quality.

Greenhouse gases include carbon dioxide, methane, water vapour, and a number of other gases such as Chlorofluorocarbons (CFCs). By volume, CO2 is by far the most common byproduct from fossil fuels, although some of the rarer gases actually have a larger “greenhouse effect”. Some greenhouse gases are “short-lived”, because they are chemically unstable, and break down fairly rapidly (for example, carbon monoxide). Others are “long-lived” because they are very stable. For example, carbon dioxide stays in the atmosphere for thousands of years. Unfortunately, we can’t remove these compounds before we burn fossil fuels, because fossil fuels are primarily made of carbon, and it is the carbon that makes them useful as fuels. So, unlike sulphur, you can’t “clean up” the fuel first. When the coal industry talks about “clean coal” these days, they don’t mean the coal itself is clean; they mean they’re working on technology to capture the CO2 after it is produced, but before it disappears up the chimney. Whether this can work cost-effectively on a large scale is an open question.

These two pollutants have opposite effects on the climate system, because each blocks a different part of the spectrum. Aerosols block visible light, and hence reduce the incoming sunlight (like adding a sunshade). Greenhouse gases block infrared radiation, and hence reduce the outgoing radiation from the planet (like adding an extra blanket):

The effect of these two different kinds of pollutant

The effect of these two different kinds of pollutant

Now when we look at these two effects in the context of all the feedback loops we’ve explored so far, we get the following:

The energy system interacting with the basic climate system

The energy system interacting with the basic climate system

So aerosols reduce the net radiative forcing (causing cooling), and greenhouse gases increase it (causing warming). The earth’s energy balance loop means that each time the concentrations of aerosols and greenhouse gases in the atmosphere change, the earth will change its temperature until all the forces balance out again. Unfortunately, the reinforcing loop that drives energy consumption means that the concentrations of these pollutants are continually changing, and they’re changing at a rate that’s faster than the earth’s balancing loop can cope with. We already noted that the earth’s balancing loop can take several decades to find a new equilibrium. If we were able to “turn off the tap”, so that we’re not adding any more of these pollutants (but we leave the ones that are already in the atmosphere), we’d find the earth’s temperature continues to change.

Which one is winning? Satellites allow us to measure the different effects fairly accurately, and observations from the pre-satellite era allow us to extrapolate backwards, so we can estimate the total effect of each from pre-industrial times to the present. Here’s a chart summarizing the effects:

Total Radiative Forcing from different sources for the period 1750 to 2005. From the IPCC Fourth Assessment Report (AR4). Click for bigger version and original IPCC caption.

 

Note that aerosols have two different effects. The direct effect is the one we described in the system diagram above – it blocks incoming sunlight. The indirect effect is because aerosols also interact with clouds. We’ll explore the indirect effect in a future post. However we look at it, the greenhouse gases are winning, by a large margin. That should mean the planet is warming. And it is:

Land-surface temperatures from 1750-2005, from the Berkeley Earth Surface Temperature project (click for original source)

Note the steep rise from the 1980s onwards, and compare it to the exponential curve of greenhouse gas emissions we saw earlier. More interestingly note the slight fall in the immediate postwar period (1940s to 1970s). One hypothesis for this is that during this period the sulphate aerosols were winning. There’s some uncertainty about the exact size of the aerosol effect during this period (note the size of the ‘uncertainty whiskers’ on the bar graph above). However, it’s true that concern about acid rain led legislation and international agreements in the 1980s to reduce sulphate emissions from fossil fuels.

The fact that sulphate aerosols have a cooling effect that can counteract the warming effect from greenhouse gases leads to an interesting proposal. If we can’t reduce the amount of greenhouse gases we emit, maybe instead we can increase the amount of sulphate aerosols. This has been studied as a serious geo-engineering proposal, and could be done quite cheaply, although we’d have to keep pumping up more of the stuff, as it washes out of the atmosphere fairly quickly. Alan Robock has identified 20 reasons why this is a bad idea. But really, we only need to know one reason why it’s a silly idea, and that comes directly from our analysis of the feedback loops in the economic growth and energy consumption system. As long as that loop is producing an exponential growth in greenhouse gas emissions, any attempt to counter-act them would also have to grow exponentially to keep up. The dimming effect from sulphate aerosols will affect many things on earth, including crop production. Committing ourselves to a path of exponential growth in sulphate aerosols in the stratosphere is quite clearly ridiculous. So if we ever do try this, it can only ever be a short-term solution, perhaps to buy us a few years to get the growth in greenhouse gases under control.

One other comment about the system diagrams we’ve created so far. Energy is mentioned twice in the diagrams: once in the loop describing economic growth, and once in the earth’s energy balance loop. We can compare these two. In the top loop, the current worldwide energy consumption by humans is about 16 terawatts. In the bottom loop, the current amount of energy being added to the earth due to greenhouse gases is about 300 terawatts. So the earth is currently gaining about 18 times the amount of energy that the entire human race is actually using. (Here’s how this is calculated)

Finally, note that although the diagram contains four different feedback loops, none of these are what climate scientists mean when they talk about feedbacks in the climate system. To understand why, we have to make a distinction between the basic operation of the system I’ve described so far (which drives global warming), and additional chains of cause-and-effect that respond to changes in the basic system. If you start warming the planet, using the system we’ve described so far, there are many other consequences. Some of those consequences can come back to bite you as reinforcing feedbacks. We’ll start looking at these in the next post.

Other posts in this series, so far:

In part 1, I described the central equilibrium loop that controls the earth’s temperature. Now it’s time to look at other loops that interact with central loop, and tend to push it out of balance. The most important of these is the use of fossil fuels that produce greenhouse gases, which change the composition of the atmosphere. Let’s first have a look at energy consumption on its own. Here’s the basic loop:

Core economic growth and energy consumption loop

Core economic growth and energy consumption loop

This reinforcing loop has driven the growth in the economy and energy use since the beginning of the industrial era. As we might expect from a reinforcing loop, this dynamic creates an exponential curve – in both the size of the global economy and the consumption of fossil fuels. For example, here’s the curve for carbon produced per year from fossil fuels (data from CDIAC):

Global Greenhouse gas emissions per year

The exponential rise in global carbon emissions (click for bigger version)

For the first century, the curve looks flat, but it’s not zero. In 1751 the world was producing about 3 tonnes of carbon per year, and this rises to about 50 tonnes per year by 1851. The growth really gets going in the postwar period. There are dips for global recession in the 1930s and 1980s, but these barely dent the overall rise. (For a slightly more detailed exploration of the dynamics that drive this exponential growth, see the Postcarbon Institute’s 300 years of fossil fuels in 300 seconds).

Exponential growth cannot go on forever, so there must be a balancing loop somewhere that (eventually) brings this growth to a halt. The world’s supply of fossil fuels is finite, so if we keep climbing the exponential curve, we must eventually run out. But long before that happens, prices start to rise because of scarcity. So the actual balancing loop looks like this:

The "peak oil" balancing loop for economic growth

I call the left hand loop the “peak oil” balancing loop for economic growth

In this new loop, each link inverts the direction of change: as consumption of fossil fuels rises, the remaining reserves fall. As reserves fall, the price tends to go up. As the price goes up, the rate of consumption falls. It’s a balancing loop because (once it starts operating) each rise in fossil fuel prices should cause a price rise that then damps down further consumption.

If these two loops operate on their own, we might expect an initial exponential curve in the use of fossil fuels, until there are signs that reserves are being depleted, and then a gradual phasing out of fossil fuels, producing the classic bell-shaped curve of Hubbert’s peak theory. In the process, economic development would also grind to a halt, unless we manage to decouple the first loop fairly quickly, by switching to renewable sources of energy. In theory, the rising price of fossil fuels should cause this switch to happen gracefully, once prices start rising – a properly functioning economic system should guarantee this. Unfortunately, the switch is not easy, because we’ve build a massive energy infrastructure that is based exclusively on fossil fuels, and this locks us in to a dependency on fossil fuels. This lock-in, along with the exponential growth in demand, means that we cannot just switch to alternative energy as the prices rise – a more likely outcome is an overshoot, where the rate of consumption is stuck in an upwards trend, causing prices to shoot up, while the balancing loop is unable to do its stuff.

However, there’s another complication. Conventional sources aren’t the only way to get fossil fuels. As the price rises, other sources become viable. The classic example is the Alberta oil sands. Twenty years ago, nobody could extract these because it was too expensive, compared to the price of oil. Today, the price of oil is high enough that exploiting the oil sands becomes profitable. So there’s another loop:

I call this new loop the "tar sands" balancing loop

I call this new loop the “tar sands” balancing loop

This new loop balances the rising prices from the middle loop when reserves start to fall. So now we have a system that could keep the economy functioning well beyond the point at which we exhaust conventional sources of fossil fuels. At each new price point, there’s a stimulus to start tapping new sources of these fuels, and as these new sources come on stream, they allow the global economy to keep growing, and the consumption of fossil fuels to keep rising. To someone who just studies economics of energy, everything looks okay for the foreseeable future (except that cheap oil is never coming back). To someone who predicts doom because of peak oil, it complicates the picture (except that the resource depletion predictions were basically correct). But to someone who studies climate, it means the challenge just got harder…

In the next post, I’ll link this system with the basic climate system.

Other posts in this series, so far:

I wrote earlier this week that we should incorporate more of the key ideas from systems thinking into discussions about climate change and sustainability. Here’s an example: I think it’s very helpful to think about the climate as a set of interacting feedback loops. If you understand how those feedback loops work, you’ve captured the main reasons why climate change is such a massive challenge for humanity. So, this is the first in a series of posts where I attempt to substantiate my argument. I’ll describe the global climate in terms of a set of balancing and reinforcing feedback loops. (Note: This is a very elementary introduction. If you prefer a detailed mathematical treatment of feedbacks in the climate system, try this paper by Gerard Roe)

Before we start, we need some basic concepts. The first is the idea of a feedback loop. We’re used to thinking in terms of linear sequences of cause and effect: A causes B, which causes C, and so on. However, our interactions with the world are rarely like this. More often, change tends to feed back on itself. For example, we identify a problem that needs solving, we take some action to solve it, and that action ends up changing our perception of the problem. The feedback usually comes in one of two forms. The first is a balancing feedback: The more you try to change something, the more the world pushes back and makes it harder. Take dieting for example: if we manage to lose a few pounds, the sense of achievement can make us complacent, and then we put the weight all back on again. The second form is a reinforcing feedback. This is where success feeds on itself. For example, perhaps we try a new exercise regime, and it makes us feel energized, so we end up exercising even more, and so on.

In physics and engineering, these are usually called ‘positive’ and ‘negative’ feedback loops. I prefer to call them ‘reinforcing’ and ‘balancing’ loops, because it’s a better description of what they do. People tend to think ‘positive’ means good and ‘negative’ means bad. In fact, both types of loop can be good or bad, depending on what you think the system ought to be doing. A reinforcing loop is good when you want to achieve a change (e.g. your protest movement goes viral), but is certainly not good when it’s driving a change you don’t want (a forest fire spreading towards your town, for example). Similarly, a balancing loop is good when it keeps a system stable that you depend on (prices in the marketplace, perhaps), but is bad when it defeats your attempts to bring about change (as in the dieting example above). Of course, what’s good to one person might be bad to someone else, so we’ll set aside such value judgements for the moment, and just focus on how the loops work in the climate system.

It helps to draw pictures. Here’s an example of how both types of loop affect a tech company trying to sell a new product (say, the iPhone):

The action of reinforcing and balancing feedback loops in selling iPhones

The action of reinforcing and balancing feedback loops in selling iPhones

You can read the arrows labelled “+” as “more of A tends to cause more of B than there otherwise would have been, while less of A tends to cause less of B than there otherwise would have been”. The arrows labelled “-” mean “more of A tends to cause less of B, and less of A tends to cause more of B”. [Note: there are some subtleties to this interpretation, but we can ignore them for now.]

On the left, we have a reinforcing loop (labelled with an ‘R’): the effect of word of mouth. The more iPhones we sell, the more people there are to spread the word, which in turn means more get sold. This tends to create an exponential growth in sales figures. However, this cannot go on forever. Sooner or later, the balancing loop on the right starts to matter (labelled with a ‘B’). The more iPhones sold, the fewer there are people left without one – we start to saturate the market. The more the market is saturated, the fewer iPhones we can sell. The growth in sales slows, and may even stop altogether. The resulting graph of sales over time might look like this:

How the sales of iPhones might look over time

How the sales of iPhones might look over time

When the reinforcing loop dominates, sales grow exponentially. When the balancing loop dominates, sales stagnate. In this case, the natural limit is when everyone who might ever want an iPhone has one. Of course, in real life, the curves are never this smooth – other feedback loops (that we haven’t mentioned yet) kick in, and temporarily push sales up or down. However, we could hypothesize that these two loops do explain most of the dynamic behaviour of the sales of a new product, and everything else is just noise. In many cases this is true – diffusion of innovation studies frequently reveal this type of S-shaped curve.

The structure of these two loops and the S-shaped curve they produce describe many real world phenomena: the spread of disease, growth of a population, the growth of a firm, the spread of a forest fire. In each case, there may well be other feedback loops that complicate the picture. But the underlying story about growth and its limits still captures a basic truth: exponential growth occurs when there is a reinforcing feedback loop, and as nothing can grow exponentially forever, there must always be a balancing loop somewhere that provides a limit to growth.

Okay, that’s enough background. Time to look at the first feedback loop in the global climate system. We’ll start with the global climate system in its equilibrium state – i.e. when the climate is not changing. The climate has been remarkably stable for the last 10,000 years, since the end of the last ice age. Over that time, it has varied only within less than 1°C. That stability suggests there are likely to be balancing feedback loops keeping it stable. The most important of these is the basic energy balance loop:

The Earth's energy balance as a balancing loop

The Earth’s energy balance as a balancing loop

The temperature of the planet is determined primarily by the balance between the incoming energy from the sun and the outgoing energy lost back into space. The incoming energy is in the form of shortwave radiation from the sun, and the amount we get is determined by the solar constant (which, of course, is not really constant, although the variations were too small to measure before the satellite era). The incoming energy from the sun, averaged out over the surface of the earth, is about 340 watts per square meter. If this is greater than the outgoing energy, the imbalance causes the earth to retain more energy, and so the temperature rises. As a warmer planet loses energy faster, this increases the outgoing radiation, which in turn reduces the imbalance again (i.e. this is a balancing loop).

Imagine there’s an overshoot – i.e. the outgoing radiation rises, but goes a little too far, so that it’s now more than the incoming solar radiation. This reduces the net radiative forcing so far that it becomes negative. But a decrease in net radiative forcing tends to cause a decrease in energy retained, which causes a decrease in temperature, which causes a decrease in outgoing radiation again. So the balancing loop also cancels out any overshoot sooner or later. In other words, the structure of this loop always pushes the planet to find a (roughly) stable equilibrium: essentially, if the incoming and outgoing energy ever get out of balance, the temperature of the planet rises or falls until they are balanced again.

Note that we could tell this is a balancing loop, without tracing the effects, just by counting up the number of “-” links. If it’s an odd number, it’s a balancing loop; if it’s even (or zero), it’s a reinforcing loop. In my systems thinking class, we play a game that simulates different kinds of loop, with each person acting as one link (some are “+” links, some are “-” links). The students usually find it hard to predict how loops of different structure will behave, but once we’ve played it a few times, everyone has a good intuition for the difference between reinforcing loops and balancing loops.

There is one more complication for this loop. The net radiative forcing determines the rate at which energy is retained, rather than the total amount. If the net forcing is positive, the earth keeps on retaining energy. So although this leads to an increase temperature, and, if you follow the loop around, a decrease in the net radiative forcing, it will reduce the rate at which energy is retained (and hence the rate of warming), it won’t actually stop the warming until the net radiative balance falls to zero. And then, when the warming stops, it doesn’t cool off again – the loop ensures the planet stays at this new temperature. It’s a slow process because it takes time for the planet to warm up. For example, the oceans can absorb a huge amount of energy before you’ll notice any increase in temperature. This means the loop operates slowly. We know from simulations (and from studies of the distant past) that it can take many decades for the planet to find a new balance in response to a change in net radiative forcing.

There are, of course, other feedback loops to complicate the picture, and some of them are reinforcing loops. I’ll describe some of these in my next post. But from an understanding of this one loop, we can gain a number of insights:

  1. This loop, on its own, cannot produce a runaway global warming (or cooling) – the earth will eventually find a new equilibrium in response to a change in net radiative forcing. More precisely, for a runaway warming to occur, some other reinforcing loop must dominate this one. As I said, there are some reinforcing loops, and they complicate the picture, but nobody has managed to demonstrate that any of them are strong enough to overcome the balancing effect of this loop.
  2. The balancing loop has a delay, because it takes a lot of energy to warm the oceans. Hence, once a change starts in this loop, it takes many decades for the balancing effect to kick in. That’s the main reason why we have to take action on climate change many decades before we see the full effect. On human timescales, the earth’s natural balancing mechanism is a very slow process.
  3. If we make a one-time change to the radiative balance, the earth will slowly change its temperature until it reaches a new balance point, and then will stay there, because the balancing loop keeps it there. However, if there is some other force that keeps changing the radiative balance, despite this loop’s attempts to adjust, then the temperature will keep on changing. Our current dilemma with respect to climate change isn’t that we’ve made a one-time change to the amount of greenhouse gases in the atmosphere – the dilemma is that we’re continually changing them. This balancing loop only really helps once we stop changing the atmosphere.

Other posts in this series, so far:

Early in my career I trained as a systems analyst. My PhD was about the ability to identify and make use of multiple perspectives on a system when understanding people’s needs, and designing new information systems to meet them. I became a “systems thinker”, although I didn’t encounter the term until later.

I also didn’t really appreciate until recently how much systems thinking changes everything about how you perceive the world. Perhaps the best analogy is the scene in The Matrix, when Morpheus offers Neo the choice of the red pill or the blue pill. One of these choices will allow him to step outside of the system and see it in a new way. Once he has done that he can never go back to seeing the world the way he used to (although there’s an interesting subplot in the movie where one of the characters tries to do exactly that).

When I think about climate change, I approach it as a systems thinker. I look for parts of the problem that I can characterize as a system: where are the inputs and outputs, boundaries and control mechanisms, positive and negative feedbacks, interactions with other systems? I want to build systems dynamics models that capture a system as a set of stocks and flows, and explore how cycles and delays affect the overall behaviour of the system. And of course, I’m always looking out for emergent properties: things that arise as a result of interactions across a system but that cannot be studied through reductionism.

It’s not surprising then, that I’m fascinated by Earth System Models (ESMs). These capture some of the most complex systems interactions ever described in a computational model – on a planetary scale! ESMs can be used to explore how processes at small scales give rise to emergent properties on a global scale. They provide a test-bed for what-if questions, to explore whether our understanding of the physical systems makes sense. And fundamentally, they’re used to probe questions of stability of the system: the relationship between the size of a “forcing” (which tends to push the system out of equilibrium) and the size of its “effect” (e.g. how sensitive is the global average temperature to a doubling of CO2?). To connect the two, you have to explore the positive and negative feedbacks that amplify (or dampen) the effects. And of course, we’d like to understand the nature of tipping points, thresholds beyond which positive feedbacks can push the system towards entirely different equilibrium points.

People who don’t understand climate change tend to lack a grasp of how complex systems work, and that’s unfortunate because for any system of sufficient complexity, most of its behaviour is counter-intuitive. People ask how a gas that forms such a tiny fraction of the atmosphere can have such a large effect, because they don’t understand that the earth constantly receives and emits huge amounts of energy into space, and that it only takes a tiny imbalance between the input and output to disrupt the planet’s equilibrium. People assume the climate system will always tend to revert to the stable pattern it has exhibited in the past, because they don’t understand positive feedbacks and exponential change. People assume we can wait to fix the climate system once we’ve seen how bad it might get, because they don’t understand the ideas of inertia and overshoot when a system has a delayed response to a stimulus. And people wonder how we can predict anything at all about climate dynamics, because they confuse chaos with randomness.

Climate science (and especially climate modeling) is inherently a systems discipline. However, climate scientists tend to hail from the physical sciences, and hence sometimes seem to miss an important aspect of systems analysis. In the physical sciences, you learn how to observe and experiment with physical systems in order to understand and explain them. But you’re not trained to re-design them to work better – that’s generally left to the engineers. Unfortunately, most engineering disciplines don’t cover systems thinking either. They concern themselves with the properties of families of devices (e.g. electrical circuits), and how such devices can be applied to solve problems. Engineers are not usually trained to re-conceptualize systems in entirely new ways, to understand how they can be changed. (Systems Engineering would be the exception here, but it’s a very young discipline).

So systems thinkers are quite rare, both across the physical sciences and the engineering disciplines. You actually encounter more of them in the social sciences, because social systems tend to defy attempts at understanding them through reductionism, and because social scientists are often more comfortable with constructivism: the idea that the systems we describe as existing in the world are really only mental constructs, arrived at through social processes. My favourite definition of a system, from Gerald Weinberg is “a way of looking at the world”. In a sense, systems aren’t “out there” in the world, waiting to be studied. Systems are a convenient mental tool for making sense of how things in the world interact with one another. This means there’s no such thing as the “climate system”, just lots of interacting thermodynamic and chemical processes. That we choose to call it a ‘system’, name its parts, and treat it as a whole, is a convenience. But it’s a very useful one, because it offers rich insights for understanding, for example, how human activities alter the climate. Modelling the climate as a system means that we have to decide which clusters of things in the world to include in the models, and where we might usefully draw system boundaries. And if we’re doing this right, we ought to acknowledge that there are other ways of viewing these systems – no decision about where to draw system boundaries can ever be ‘correct’, but some decisions lead to more insights than others (compare with Box’s famous saying about models: “All models are wrong, but some are useful”).

While traditional branches of science offer tools and methods for understanding each of the pieces of the climate system, the study of the climate system as a whole requires a different approach. It is a trans-disciplinary field, because the interactions that matter include physical, chemical, biological, geographic, social, and economic processes. It goes beyond traditional methodologies of the physical sciences because it is anti-reductionist: it must grapple with understanding holistic properties of systems, even when the detailed behaviour of those systems is not sufficiently understood. In other words it’s a systems science, and climate modellers have to be systems thinkers.

All this leads me to argue that we should incorporate more of the key ideas from systems thinking into discussions about climate change and sustainability. I think that a better understanding of systems dynamics would help a lot in giving people the right intuitions about climate change. And I think a better understanding of critical systems approaches would give people a better understanding of how to improve collective decision-making around climate policy.

Note: This is the first of a series of posts exploring the systems dynamics of climate change. Here’s the rest of the series, so far:

Next year, I’ll be teaching a new undergraduate course, as part of an initiative by the Faculty of Arts and Science known as Big Ideas courses. The idea is to offer trans-disciplinary courses, team taught by professors from across the physical sciences, social sciences, and humanities, that will probe important ideas about the world from different disciplinary perspectives. For the coming year, U of T is launching three Big Ideas courses:

  • BIG100: “The end of the world as we know it”;
  • BIG101: “Energy: From Fire to the Future”;
  • BIG102: “The Internet: Saving Civilization or Trashing the Planet?”

I’m delighted to be teaming up with Prof Miriam Diamond from Earth Sciences and Prof Pamela Klassen from Study of Religion to teach BIG102. Our aim is to give students some understanding of how the technologies that drive the internet work, and then to explore how the internet has reshaped the way we use information, our knowledge and beliefs about the world, and the impact that creating (and disposing of) internet technologies has on the environment, on the economy, and on the dynamics of innovation. A key goal is to foster critical thinking and information literacy skills, and especially to be able to think about and analyze a complex system-of-systems from different perspectives.

For the first term, we’re planning to cover a broad set of provocative questions, to get students thinking about the internet from different perspectives:

  1. What is a big idea? (A course introduction, and a primer on trans-disciplinary thinking)
  2. Who invented the internet? (Myths about the internet, and why they stick)
  3. How does the internet work? (An introduction to some of the key technologies)
  4. How new is the internet? (A short history of communications technologies, to put the internet in its historical context)
  5. Has the internet changed us? (We’ll explore in particular, how the internet is transforming universities and learning)
  6. What is the environmental footprint of the internet? (An initial assessment of energy consumption, resource extraction, and waste disposal)
  7. Does the internet make us smarter? (An exploration of how internet search works, and how it affects our approaches to problem-solving)
  8. Is the internet a time-saver or time-waster? (How the internet offers endless distractions, blurs distinctions between work and leisure, and its overall effect on productivity)
  9. Can you be anonymous on the internet? (The idea of your information footprint – who’s keeping track of data about you, how they do it, and why)
  10. Is the Internet a Cheater’s Paradise? (From plagiarism to adultery – how the internet facilitates cheating, new ways of discovering it, and virtual vigilante justice)
  11. Who’s Not Online? (The idea of the digital divide, and the demographic and socio-economic factors that limit people’s access)
  12. Gadgets as Gifts? (Just in time for the Christmas break, we’ll explore the environmental impact of our love of new gadgets, and whether there are sustainable alternatives)

In the second term, we plan to pick three themes to explore in more detail, so that we can explore inter-connections between some of these questions, and get the students engaged in independent research projects that synthesize what they’re learning:

  1. The Internet and the Innovation Imperative.
    • Is the Internet Innovative? How Moore’s law has driven innovation; the dotcom boom and bust; and the current hype around new technologies such as 3D printing, sensor networks, and the semantic web.
    • What are the Resource Implications of the Internet? We’ll use material flow analysis to explore extraction and disposal and likely shortages of strategic minerals, and the geo-political implications of attempting to feed an exponential growth in demand.
    • The Environmental and Human Health Burden of the Internet. Building on the discussion of resource implications, we’ll look at the health implications of mineral extraction and e-waste disposal, and the burden this places on people and ecosystems, especially in poorer countries.
    • What is the Opportunity Cost of the Internet? Does investment in internet innovation mean we’re underinvesting in other things (eg clean energy, transport, social innovation). Have we developed an over-optimistic belief that IT technologies can solve all problems?
  2. The Internet, Democracy, and Security.
    • Censorship & Internet Governance. How much power do governments have to control what happens on the internet? Does the internet enhance or undermine democracy?
    • The Underbelly of the Internet: Hackers, Espionage, and Trolls. How internet systems can be exploited by different groups, for example by crime syndicates who break into secure systems, by political groups who use a web presence to spread misinformation, and by internet trolls who violate social norms to disrupt and intimidate online discussions.
    • Does the Internet make us a more open society? The open source movement and its successors (open government, creative commons, etc) are based on the idea that if everyone has access to the inner workings of systems, this removes barriers to participation, fosters creativity, and makes those systems better for everyone. But does it work?
    • Transnational Jurisdiction: Legal boundaries and the Internet. We’ll wrap up this theme with a question about who should police the internet.
  3. The Internet, Communities, and Interpersonal Relationships
    • Does your Google-Brain make you forget? How has instant access to vast amounts of information changed our memories and our perceptions of ourselves? For example, does GPS route-finding mean we lose our ability to navigate and our sense of place? And what are the implications of the kind of personal digital archives that technologies such as Google Glass might allow us to create?
    • Can you find love on the Internet? An exploration of how the internet changes personal relationships, from the role of dating sites and virtual social networks, to the way that online porn affects our perceptions of gender roles and body image.
    • Can you find God on the Internet? How the internet affects religious communities, tolerance of different worldviews, and the very nature of faith.

Of course, this outline is still a draft – we’ll refine it over the next few months as we prepare for the first group of students in September.

We’re still exploring which textbooks to use, and even whether ‘books’ makes sense for a course like this – we’re hoping to make this a constructivist learning experience by using a variety of different internet-based media and information access tools throughout the course.  However, we’re currently evaluating these books:

Feel free to suggest other books and material!

We’re taking the kids to see their favourite band: Muse are playing in Toronto tonight. I’m hoping they play my favourite track:

I find this song fascinating, partly because of the weird mix of progressive rock and dubstep. But more for the lyrics:

All natural and technological processes proceed in such a way that the availability of the remaining energy decreases. In all energy exchanges, if no energy enters or leaves an isolated system, the entropy of that system increases. Energy continuously flows from being concentrated to becoming dispersed, spread out, wasted and useless. New energy cannot be created and high grade energy is destroyed. An economy based on endless growth is unsustainable. The fundamental laws of thermodynamics will place fixed limits on technological innovation and human advancement. In an isolated system, the entropy can only increase. A species set on endless growth is unsustainable.

This summarizes, perhaps a little too succinctly, the core of the critique of our current economy, first articulated clearly in 1972 by the Club of Rome in the Limits to Growth Study. Unfortunately, that study was widely dismissed by economists and policymakers. As Jorgen Randers points out in a 2012 paper, the criticism of the Limits to Growth study was largely based on misunderstandings, and the key lessons are absolutely crucial to understanding the state of the global economy today, and the trends that are likely over the next few decades. In a nutshell, humans exceeded the carrying capacity of the planet sometime in the latter part of the 20th century. We’re now in the overshoot portion, where it’s only possible to feed the world and provide energy for economic growth by consuming irreplaceable resources and using up environmental capital. This cannot be sustained.

In general systems terms, there are three conditions for sustainability (I believe it was Herman Daly who first set them out in this way):

  1. We cannot use renewable resources faster than they can be replenished.
  2. We cannot generate wastes faster than they can be absorbed by the environment.
  3. We cannot use up any non-renewable resource.

We can and do violate all of these conditions all the time. Indeed, modern economic growth is based on systematically violating all three of them, but especially #3, as we rely on cheap fossil fuel energy. But any system that violates these rules cannot be sustained indefinitely, unless it is also able to import resources and export wastes to other (external) systems. The key problem for the 21st century is that we’re now violating all three conditions on a global scale, and there are no longer other systems that we can rely on to provide a cushion – the planet as a whole is an isolated system. There are really only two paths forward: either we figure out how to re-structure the global economy to meet Daly’s three conditions, or we face a global collapse (for an understanding of the latter, see GrahamTurner’s 2012 paper).

A species set on endless growth is unsustainable.

The second speaker at our Workshop on City Science was Andrew Wisdom from Arup, talking about Cities as Systems of Systems. Andrew began with the observation that cities are increasingly under pressure, as the urban population continues to grow, and cities struggle to provide adequate infrastructure for their populations to thrive. But a central part of his message is that the way we think about things tends to create the way they are, and this is especially so with how we think about our cities.

As an exercise, he first presented a continuum of worldviews, from Technocentric at one end, to Ecocentric at the other end:

  • In the Techno-centric view, humans are dissociated from the earth. Nature has no inherent value, and we can solve everything with ingenuity and technology. This worldview tends to view the earth as an inert machine to be exploited.
  • In the Eco-centric view, the earth is alive and central to the web of life. Humans are an intrinsic part of nature, but human activity is already exceeding the limits of what the planet can support, to the point that environmental problems are potentially catastrophic. Hence, we need to get rid of materialism, eliminate growth, and work to restore balance.
  • Somewhere in the middle is a Sustain-centric view, which accepts that the earth provides an essential life support system, and that nature has some intrinsic value. This view accepts that limits are being reached, that environmental problems tend to take decades to solve, and that more growth is not automatically good. Humans can replace some but not all natural processes, and we have to focus more on quality of life as a measure of success.

As an exercise, Andrew asked the audience to imagine this continuum spread along one wall of the room, and asked us each to go and stand where we felt we fit on the spectrum. Many of the workshop participants positioned themselves somewhere between the eco-centric and sustain-centric views, with a small cluster at the extreme eco-centric end, and another cluster just to the techno-centric side of sustain-centric. Nobody stood at the extreme techno-centric end of the room!

Then, he asked us to move to where we think the city of Toronto sits, and then where we think Canada sits, and finally where we feel the world sits. For the first two of these, everyone shifted a long way towards the technocentric end of the spectrum (and some discussion ensued to the effect that both our mayor and our prime minister are a long way off the chart altogether – they are both well known for strong anti-environmentalist views). For the whole world, people didn’t move much from the “Canada” perspective. An immediate insight was that we (workshop attendees) are far more towards the ecocentric end of the spectrum than either our current city or federal governments, and perhaps the world in general. So if our governments (and by extension the voters who elect them) are out of step with our own worldviews, what are the implications? Should we, as researchers, be aiming to shift people’s perspectives?

One problem that arises from one’s worldview is how people understand messages about environmental problems. For example, people with a technocentric perspective tend to view discussions of sustainability as being about sacrifice – ‘wearing a hair shirt’, consume less, etc. Which then leads to a waning interest in these topics. For example, analysis of google trends on terms like global warming and climate change show spikes in 2007 around the release of Al Gore’s movie and the IPCC assessment, but declining interest since then.

Jeb Brugmann, the previous speaker, talked about the idea of a Consumptive city versus a Generative city, which is a change in perspective that alters how we view cities, changes what we choose to measure, and hence affects the way our cities evolve.

Changes in the indices we pay attention to can have a dramatic impact. For example, a study in Melbourne created that VAMPIRE index (Vulnerability Assessment for Mortgage, Petroleum and Inflation Risks and Expenses), which shows the relative degree of socio-economic stress in suburbs in Brisbane, Sydney, Melbourne, Adelaide and Perth. The pattern that emerges is that in the Western suburbs of Melbourne, there are few jobs, and many people paying off mortgages, all having to commute and hour and a half to the east of the city for work.

Our view of a city tend to create structures that compartmentalize different systems into silos, and then we attempt to optimize within these silos. For example, zoning laws create chunks of land with particular prescribed purposes, and then we end up trying to optimize within each zone. When zoning laws create the kind of problem indicated by the Melbourne VAMPIRE index, there’s little the city can do about it if they continue to think in terms of zoning. The structure of these silos has become fossilized into the organizational structure of government. Take transport, for example. We tend to look at existing roads, and ask how to widen them to handle growth in traffic; we rarely attempt to solve traffic issues by asking bigger questions about why people choose to drive. Hence, we miss the opportunity to solve traffic problems by changing the relationship between where people live and where they work. Re-designing a city to provide more employment opportunities in neighbourhoods that are suffering socio-economic stress is far more likely to help than improving the transport corridors between those neighbourhoods and other parts of the city.

Healthcare is another example. The outcome metrics typically used for hospital use include average length of stay, 30-day unplanned readmission rate, cost of readmission, etc. Again, these metrics create a narrow view of the system – a silo – that we then try to optimize within. However, if you compare European and American healthcare systems, there are major structural difference. The US system is based on formula funding, in which ‘clients’ are classified in terms of type of illness, standard interventions for that illness, and associated costs. Funding is then allocated to service providers based on this classification scheme. In Europe, service provides are funded directly, and are able to decide at the local level how best to allocate that funding to serve the needs of the population they care for. The European model is a much more flexible system that treats patients real needs, rather than trying to fit each patient into a pre-defined category. In the US, the medical catalogue of disorders becomes an accounting scheme for allocating funds, and the result is that in the US, medical care costs going up faster than any other country. If you plot life expectancy against health spending, the US is falling far behind:

The problem is that the US health system views illness as a problem to be solved. If you think in terms of wellbeing rather than illness, you broaden the set of approaches you can use. For example, there are significant health benefits to pet ownership, providing green space within cities, and so on, but these are not fundable with the US system. There are obvious connections between body mass index and the availability of healthy foods, the walkability of neighbourhoods, and so on, but these don’t fit into a healthcare paradigm that allocates resources according to disease diagnosis.

Andrew then illustrated the power of re-thinking cities as systems-of-systems through several Arup case studies:

  • Dongtan eco-city. This city was designed from the ground up to be food positive, and energy positive (ie. intended to generate more food and more clean energy than it uses). The design makes it more preferable to walk or bike than to drive a car. A key design tool was the use of an integrated model that captures the interactions of different systems within the city. [Dongtan is, incidentally, a classic example of how the media alternately overhypes and then trashtalks major sustainability initiatives, when the real story is so much more interesting].
  • Low2No, Helsinki, a more modest project that aims to work within the existing city to create carbon negative buildings and energy efficient neighbourhoods step by step.
  • Werribee, a suburb of Melbourne, which is mainly an agricultural town, particularly known for its broccoli farming. But with fluctuating prices, farmers have had difficulty selling their broccoli. In an innovative solution that turns this problem into an opportunity, Arup developed a new vision that uses local renewable energy, water and waste re-processing to build a self-sufficient hothouse food production and research facility that provides employment and education along with food and energy.

In conclusion, we have to understand how our views of these systems constrain us to particular pathways, and we have to understand the connections between multiple systems if we want to understand the important issues. In many cases, we don’t do well at recognizing good outcomes, because our worldviews lead us to the wrong measures of success, and then we use these measures to create silos, attempting to optimize within them, rather than seeing the big picture. Understanding the systems, and understanding how these systems shape our thinking is crucial. However, the real challenges then lie in using this understanding to frame effective policy and create effective action.

After Andrew’s talk, we moved into a hands-on workshop activity, using a set of cards developed by Arup called Drivers of Change. The cards are fascinating – there are 189 cards in the deck, each of which summarizes a key issue (e.g. urban migration, homelessness, clean water, climate change, etc), and on the back, distills some key facts and figures. Our exercise was to find connections between the cards – each person had to pick one card that interested him or her, and then team up with two other people to identify how their three cards are related. It was a fascinating and thought-provoking exercise, that really got us thinking about systems-of-systems. I’m now a big fan of the cards and plan to use them in the classroom. (I bought a deck at Indigo for $45, although I note that, bizarrely, Amazon has them selling for over $1000!).

I’ve been following a heated discussion on twitter this past week about a planned protest on Sunday in the UK, in which environmentalists plan to destroy a crop of genetically modified wheat being grown as part of a scientific experiment at Rothamsted, in Hertfordshire (which is, incidentally, close to where I grew up). Many scientists I follow on twitter are incensed, calling the protest anti-science. And some worry that it’s part of a larger anti-science trend in which the science on issues such as climate change gets ignored too. In return, the protesters are adamant that the experiment should not be happening, no matter what potential benefits the research might bring.

I’m fascinated by the debate, because it seems to be a classic example of the principle of complementarity in action, with each group describing things in terms of different systems, and rejecting the others’ position because it makes no sense within their own worldview. So, it should make a great case study for applying boundary critique, in which we identify the system that each group is seeing, and then explore where they’ve chosen to draw the boundaries of that system, and why. I think this will make a great case study for my course next month.

I’ve identified eight different systems that people have talked about in the debate. This is still something of a work in progress (and I hope my students can extend the analysis). So here they are, and for each some initial comments towards a boundary critique:

  1. A system of scientists doing research. Many scientists see the protests as nothing more than irrational destruction of research. The system that motivates this view is a system of scientific experimentation, in which expert researchers choose problems to work on, based on their expectation that the results will be interesting and useful in some way. In this case, the GM trials are applied research – there is an expectation that the modified wheat might lead to agricultural improvements (e.g. through improved yield, or reduced need for fertilizer or pesticide). Within this system, science is seen as a neutral pursuit of knowledge, and therefore, attempts to disrupt experiments must be “anti-knowledge”, or “anti-science”. People who operate within this system tend to frame the discussion in terms of an attack on a particular group of researchers (on twitter, they’ve been using the hashtag #dontdestroyresearch), and they ask, pointedly, whether green politicians and groups condone or condemn the destruction. (The irony here is that the latter question itself is, itself, unscientific – it’s a rhetorical device used in wedge politics – but few of the people using it acknowledge this). Questions about whether certain kinds of research are ethical, or who might yield the benefits from this research lie outside the boundary of this system, and so are not considered. It is assumed that the researchers themselves, as experts, have made those judgments well, and that the research itself is not, and cannot be, a political act.
  2. A system of research ethics and risk management. If we expand the boundaries of system 1 a little, we see a system of processes by which scientific experiments are assessed for how they manage the risks they pose to be public. Scientific fields differ in their sophistication for how they arrange this system. In the physical sciences, the question often doesn’t arise, because the the research itself carries no risk. But in medical and social sciences, processes have arisen for making this judgement, sometimes in response to a disaster or a scandal. Most research institutions have set up Internal Review Boards (IRBs) who must approve (or prevent) research studies that poses a risk to people or ecosystems. My own research often strays into behavioural science, so I frequently have to go though our ethics approval process. The approvals process is usually frustrating, and I’m often surprised at some of the modifications the ethics board asks me to make, because my assessment of the risk is different to theirs. However, if I take a step back, I can see that both the process and the restrictions it places on me are necessary, and that I’m definitely not the right person to make judgements about the risks I might impose on others in my research. The central question is usually one of beneficence: does the value of the knowledge gained outweigh any potential risk to participants or others affected by the study? Some research clearly should not happen, because the argument for beneficence is too weak. In this view, the Rothamsted protest is really about democratic control of the risk assessment process. If all stakeholders aren’t included, and the potential impact on them is not taken seriously, they lose faith in the scientific enterprise itself. In the case of GMOs, there’s a widespread public perception (in the UK) that the interests of large corporations who stand to profit from this research are being allowed to drive the approvals process, and that the researchers themselves are unable to see this because they’re stuck in system 1. I’ve no idea how true this is for GMO research, but there’s plenty of evidence that’s it’s become a huge problem in pharmaceutical research. Medical research organizations have, in the last few years, taken significant steps to reduce the problem, e.g by creating registers of trials to ensure negative results don’t get hidden. The biotech research community appear to be way behind on this, and much research still gets done behind the veil of corporate secrecy. (The irony here is that the Rothamsted trials are publicly funded, and results will be publicly available, making it perhaps the least troublesome biotech research with respect to corporate control. However, that visibility makes it an easy target, and hence, within this system, the protest is really an objection to how the government ran the risk assessment and approval process for this experiment).
  3. A system of ecosystems and contaminants that weaken them. Some of the protesters are focused more specifically on the threat that this and similar experiments might pose on neighbouring ecosystems. In this view, GMOs are a potential contaminant, which, once released into the wild cannot ever be recalled. Ecosystems are complex systems, and we still don’t understand all the interactions that take place within them, and how changing conditions can damage them. Previous experimentation (e.g. the introduction of non-native species, culls of species regarded as pests, etc), have often been disastrous, because of unanticipated system interactions. Within this system, scientists releasing GMOs into the wild are potentially repeating these mistakes of the past, but on a grander scale, because a GMO represents a bigger step change within the system than, say, selective breeding. Because these ecosystems have non-linear dynamics, bigger step changes aren’t just a little more risky than small step changes; they risk hitting a threshold and causing ecosystem collapse. People who see this system tend to frame the discussion in terms of the likelihood of cross-contamination by the GMO, and hence worry that no set of safeguards by the researchers is sufficient to guarantee the GMO won’t escape. Hence, they object to the field trials on principle. This trial is therefore, potentially, the thin end of the wedge, a step towards lifting the wider ban on such trials. If this trial is allowed to go ahead, then others will surely follow, and sooner or later, various GMOs will escape with largely unpredictable consequences for ecosystems. As the GMOs are supposed to have a competitive advantage of other related species, once they’ve escaped, they’re likely to spread, in the same way that invasive species did. So, although the researchers in this experiment may have taken extensive precautions to prevent cross-contamination, such measures will never be sufficient to guarantee protection, and indeed, there’s already a systematic pattern of researchers underestimating the potential spread of GMO seeds (e.g. through birds and insects), and of course, they routinely underestimate the likelihood of human error. Part of the problem here is that the researchers themselves are biased in at least two ways: they designed the protection measures themselves, so they tend to overestimate their effectiveness, and they believe their GMOs are likely to be beneficial (otherwise they wouldn’t be working on them), so they downplay the risk to ecosystems if they do escape. Within this system, halting this trial is equivalent to protecting the ecosystems from risky contamination. (The irony here is that a bunch of protesters marching into the field to destroy the crop is likely to spread the contamination anyway. The protesters might rationalize it by saying this particular trial is more symbolic, because the risk from any one trial is rather low; instead the aim is to make it impossible for future trials to go ahead)
  4. A system of intellectual property rights and the corresponding privatization of public goods. Some see GMO research as part of a growing system of intellectual property rights, in which large corporations gain control of who can grow which seeds and when. In Canada, this issue became salient when Monsanto tried suing farmers who were found to have their genetically modified corn planted in their fields, despite the fact that those farmers had never planted them (it turned out the seeds were the result of cross-contamination from other fields, something that Monsanto officially denies is possible). By requiring farmers to pay a licence fee each year to re-plant their proprietary seeds, these companies create a financial dependency that didn’t exist when farmers were able to save seeds to be replanted. Across developing countries, there is growing concern that agribusiness is gaining too much control of local agriculture, creating a market in which only their proprietary seeds can be planted, and hence causing a net outflow of wealth from countries that can least afford it to large multi-national corporations. I don’t see this view playing a major role in the UK protests this week, although it does come up in the literature from the protest groups, and is implicit in the name of the protest group: Take The Flour Back.
  5. An economic system in which investment in R&D is expected to boost the economy. This is the basic capitalist system. Companies that have the capital invest in research into new technologies (GMOs) that can potentially bring big returns on investment for biotech corporations. This is almost certainly the UK government’s perspective on the trials at Rothamsted – the research should be good for the economy. It’s also perhaps the system that motivates some of the protesters, especially where they see this system exacerbating current inequalities (big corporations get richer, everyone else pays more for their food). Certainly, economic analysis of the winners and losers from GM technology demonstrate that large corporations gain, and small-scale farmers lose out.
  6. A system of global food supply and demand, in which a growing global population, and a fundamental limit on the land available for agriculture, place serious challenges on how to achieve a better match of food consumption to food production. In the past, we solved this problem through two means: expanding the amount of land under cultivation, and through the green revolution, in which agricultural yields were increased by industrialization of the agricultural system and the wide-scale use of artificial fertilizers. GMOs are (depending on who you ask) either the magic bullet that will allow us to feed 9 billion people by mid-century, or, more modestly, one of many possible solutions that we should investigate. In this system, the research at Rothamsted is seen as a valuable step towards solving world hunger, and so protesting against it is irrational. The irony here is that improving agricultural yields is probably the least important part of the challenge of feeding 9 billion people: there is much more leverage to be had in solving problems of food distribution, reducing wastage, and reducing the amount of agricultural land devoted to non-foods.
  7. A system of potential threats to human health and well-being. Some see GMOs as a health issue. Potential human health effects include allergies, and cross-species genetic transfer, although scientists dismiss both, citing a lack of evidence. While there is some (disputed) evidence of such health risks already occurring, on balance this is more a concern about unpredictable future impacts, rather than what has already happened, which means an insistence on providing evidence is irrelevant: a bad outcome doesn’t have to have already occurred for us to take the risk seriously. If we rely on ever more GMOs to drive the global agricultural system, sooner or later we will encounter such health problems, most likely through increased allergic reaction. Allergies themselves have interesting systemic properties – they arise when the body’s normal immune system, doing it’s normal thing, ends up over-reacting to a stimulus (e.g. new proteins) that is otherwise harmless. The concern here, then, is that the reinforcing feedback loop of ever more GM plant variants means that, sooner or later, we will cross a threshold where there is an impact on human health. People who worry about this system tend to frame the discussion using terms such as “Frankenfoods“, a term that is widely derided by biotech scientists. The irony here is that by dismissing such risks entirely, the scientists reduce their credibility in the eyes of the general public, and end up seeming even more like Dr Frankenstein, oblivious to their own folly.
  8. A system of sustainable agriculture, with long time horizons. In this system, short term improvements in agricultural yield are largely irrelevant, unless the improvement can be demonstrated to be sustainable indefinitely without further substantial inputs to the system. In general, most technological fixes fail this test. The green revolution was brought about by a massive reliance on artificial fertilizer, derived from fossil fuels. As we hit peak oil, this approach cannot be sustained. Additionally, the approach has brought its own problems, including a massive nitrogen pollution of lakes and coastal waters, and poorer quality soils, and of course, the resulting climate change from the heavy use of fossil fuels. In this sense, technological fixes provide short term gains in exchange for a long term debt that must be paid by future generations. In this view, GMOs are seen as an even bigger step in the wrong direction, as they replace an existing diversity in seed-stocks and farming methods with industrialized mono-cultures, and divert attention away from the need for soil conservation, and long-term sustainable farming practices. In this system, small scale organic farming is seen as the best way of improving the resilience of the global food production. While organic farming sometime (but not always!) means lower yields, it reduces dependency on external inputs (e.g. artificial fertilizers and pesticides), and increases diversity. Systems with more diverse structures tend to be more resilient in the face of new threats, and the changing climates over the next few decades will severely test the resilience of our farming methods in many regions of the world.  The people who worry about this system point to failures of GMOs to maintain their resistance to pests. Here, you get a reinforcing feedback loop in which you need ever more advances in GMO technology to keep pace with the growth of resistance within the ecosystem, and with each such advance, you make it harder for non-GMO food varieties to survive. So while most proponents of GMOs see them as technological saviours, in the long term it’s likely they actually reduce the ability of the global agricultural system to survive the shocks of climate change.

Systems theory leads us to expect that these systems will interact in interesting ways, and indeed they do. For example, systems 6 and 8 can easily be confused as having the same goal, but in fact, because the systems have very different temporal scales, they can end up being in conflict: short-term improvements to agricultural yield can lead to long term reduction of sustainability and resilience. Systems 6 and 7 can also interfere – it’s been argued that the green revolution reduced world starvation and replaced it with widespread malnutrition, as industrialization of food production gives us fewer healthy food choices. Systems 1 and 4 are often in conflict, and are leading to ever more heated debates over open access to research results. And of course, one of the biggest worries of some of the protest groups is the interaction between systems 2 and 5: the existence of a large profit motive tends to weaken good risk management practices in biotech research.

Perhaps the most telling interaction is the opportunity cost. While governments and corporations, focusing on systems 5 & 6, pour funding and effort into research into GMOs, other, better solutions to long term sustainability and resilience, required in system 8, become under-invested. More simply: if we’re asking the wrong question about the benefit of GMOs, we’ll make poor decisions about whether to pursue them. We should be asking different questions about how to feed the world, and resources put into publicly funded GMO research tend to push us even further in the wrong direction.

So where does that leave the proposed protests? Should the trials at Rothamsted be allowed to continue, or do the protesters have the right to force an end to the experiment, by wilful destruction if necessary? My personal take is that the experiment should be halted immediately, preferably by Rothamsted itself, on the basis that it hasn’t yet passed the test for beneficence in a number of systems. The knowledge gain from this one trial is too small to justify creating this level of societal conflict. I’m sure some of my colleague will label me anti-science for this position, but in fact, I would argue that my position here is strongly pro-science: an act of humility by scientists is far more likely to improve the level of trust that the public has in the scientific community. Proceeding with the trial puts public trust in scientists further at risk.

Let’s return to that question of whether there’s an analogy between people attacking the biotech scientists and people attacking climate scientists. If you operate purely within system 1, the analogy seems compelling. However, it breaks down as soon as you move to system 2, because the risks have opposite signs. In the case of GMO food trials, the research itself creates a risk; choosing not to do the research at all (or destroying it if someone else tries it) is an attempt to reduce risk. In the case of climate science, the biggest risks are on the business-as-usual scenario. Choosing to do the research itself poses no additional risk, and indeed reduces it, because we come to understand more about how the climate system works.

The closest analogy in climate science I can think of is the debate over geo-engineering. Many climate scientists objected to any research being done on geo-engineering for many years, for exactly the reason many people object to GMO research – because it diverts attention away from more important things we should be doing, such as reducing greenhouse gas emissions. A few years back, the climate science community seems to have shifted perspective, towards the view that geo-engineering is a desperate measure that might buy us more time  to get emissions under control, and hence research is necessary to find out how well it works. A few geo-engineering field trials have already happened. As these start to gain more public attention, I would expect the protests to start in earnest, along with threats to destroy the research. And it will be for all the same reasons that people want to destroy the GM wheat trials at Rothamsted. And, unless we all become better systems thinkers, we’ll have all the same misunderstandings.

Update (May 29, 2012): I ought to collect links to thought provoking articles on this. Here are some:

Sometime in May, I’ll be running a new graduate course, DGC 2003 Systems Thinking for Global Problems. The course will be part of the Dynamics of Global Change graduate program, a cross-disciplinary program run by the Munk School of Global Affairs.

Here’s my draft description of the course:

The dynamics of global change are complex, and demand new ways of conceptualizing and analyzing inter-relationships between multiple global systems. In this course, we will explore the role of systems thinking as a conceptual toolkit for studying the inter-relationships between problems such as globalization, climate change, energy, health & wellbeing, and food security. The course will explore the roots of systems thinking, for example in General Systems Theory, developed by Karl Bertalanffy to study biological systems, and in Cybernetics, developed by Norbert Wiener to explore feedback and control in living organisms, machines, and organizations. We will trace this intellectual history to recent efforts to understand planetary boundaries, tipping points in the behaviour of global dynamics, and societal resilience. We will explore the philosophical roots of systems thinking as a counterpoint to the reductionism used widely across the natural sciences, and look at how well it supports multiple perspectives, trans-disciplinary synthesis, and computational modeling of global dynamics. Throughout the course, we will use global climate change as a central case study, and apply systems thinking to study how climate change interacts with many other pressing global challenges.

I’m planning to get the students to think about issues such as the principle of complementarity, and second-order cybernetics, and of course, how to understand the dynamics of non-linear systems, and the idea of leverage points. We’ll take a quick look at how earth system models work, but not in any detail, because it’s not intended to be physics or computing course; I’m expecting most of the students to be from political science, education, etc.

The hard part will be picking a good core text. I’m leaning towards Donnella Meadows’s book, Thinking in Systems, although I just received my copy of the awesome book Systems Thinkers, by Magnus Ramage and Karen Shipp (I’m proud to report that Magnus was once a student of mine!).

Anyway, suggestions for material to cover, books & papers to include, etc are most welcome.

One of the things that strikes me about discussions of climate change, especially from those who dismiss it as relatively harmless, is a widespread lack of understanding on how non-linear systems behave. Indeed, this seems to be one of the key characteristics that separate those who are alarmed at the prospect of a warming climate from those who are not.

At the AGU meeting this month, Kerry Emanuel presented a great example of this in his talk on “Hurricanes in a Warming Climate”. I only caught his talk by chance, as I was slipping out of the session in the next room, but I’m glad I did, because he made an important point about how we think about the impacts of climate change, and in particular, showed two graphs that illustrate the point beautifully.

Kerry’s talk was an overview of a new study that estimates changes in damage from tropical cyclones with climate change, using a new integrated assessment model. The results are reported in detail in a working paper at the World Bank. The report points out that the link between hurricanes and climate change remains controversial. So, while Atlantic hurricane power has more than doubled over the last 30 years, and model forecasts show an increase in the average intensity of hurricanes in a warmer world, there is still no clear statistical evidence of a trend in damages caused by these storms, and hence a great deal of uncertainty about future trends.

The analysis is complicated by several factors:

  • Increasing insurance claims from hurricane damage in the US have a lot to do with growing economic activity in vulnerable regions. Indeed, expected economic development in the regions subject to tropical storm damage means that there’s certain to be big increases in damage even if there were no warming at all.
  • The damage is determined more by when and where each storm makes landfall than it is by the intensity of the storm.
  • There simply isn’t enough data to detect trends. More than half of the economic damage due to hurricanes in the US since 1870 was caused by just 8 storms.

The new study by Emanuel and colleagues overcomes some of these difficulties by simulating large numbers of storms. They took the outputs of four different Global Climate Models, using the A1B emissions scenario, and fed them into a cyclone generator model to simulate thousands of storms, comparing the characteristics of these storms with those that have caused damage in the US in the last few decades, and then adjusting the damage estimates according to anticipated changes in population and economic activity in the areas impacted (for details, see the report).

The first thing to note is that the models forecast only a small change in hurricanes, typically a slight decrease in medium-strength storms and a slight increase in more intense storms. For example, at first sight, the MIROC model indicates almost no difference:

Probability density for storm damage on the US East Coast, generated from the MIROC model for current vs. year 2100, under the A1B scenario, for which this model forecasts a global average temperature increase of around 4.5C. Note that x axis is a logarithmic scale: 8 means $100 million, 9 means $1 billion, 10 means $10 billion, etc (source: Figure 9 in Mendelsohn et al, 2011)

Note particularly that at the peak of the graph, the model shows a very slight reduction in the number of storms (consistent with a slight decrease in the overall frequency of hurricanes), while on the upper tail, the model shows a very slight increase (consistent with a forecast that there’ll be more of the most intense storms). The other three models show slightly bigger changes by the year 2100, but overall, the graphs seem very comforting. It looks like we don’t have much to worry about (at least as far as hurricane damage from climate change is concerned). Right?

The problem is that the long tail is where all the action is. The good news is that there appears to be a fundamental limit on storm intensity, so the tail doesn’t really get much longer. But the problem is that it only takes a few more of these very intense storms to make a big difference in the amount of damage caused. Here’s what you get if you multiply the probability by the damage in the above graph:

Changing risk of hurricane damage due to climate change. Calculated as probability times impact. (Source: courtesy of K. Emanuel, from his AGU 2011 talk)

That tiny change in the long tail generates a massive change in the risk, because the system is non-linear. If most of the damage is done by a few very intense storms, then you only need a few more of them to greatly increase the damage. Note in particular, what happens at 12 on the damage scale – these are trillion dollar storms. [Update: Kerry points out that the total hurricane damage is proportional to the area under the curves of the second graph].

The key observation here is that the things that matter most to people (e.g. storm damage) do not change linearly as the climate changes. That’s why people who understand non-linear systems tend to worry much more about climate change than people who do not.

I’ll be giving a talk to the Toronto section of the IEEE Systems Council on December 1st, in which I plan to draw together several of the ideas I’ve been writing about recently on systems thinking and leverage points, and apply them to the problem of planetary boundaries. Come and join in the discussion if you’re around:

Who’s flying this ship? Systems Engineering for Planet Earth

Thurs, Dec 1, 2011, 12:00 p.m. – 1:00 p.m, Ryerson University (details and free registration here)

At the beginning of this month, the human population reached 7 billion people. The impact of humanity on the planet is vast: we use nearly 40% of the earth’s land surface to grow food, we’re driving other species to extinction at a rate not seen since the last ice age, and we’ve altered the planet’s energy balance by changing the atmosphere. In short, we’ve entered a new geological age, the Anthropocene, in which our collective actions will dramatically alter the inhabitability of the planet. We face an urgent task: we have to learn how to manage the earth as a giant system of systems, before we do irreparable damage. In this talk, I will describe some of the key systems that are relevant to this task, including climate change, agriculture, trade, energy production, and the global financial system. I will explore some of the interactions between these systems, and characterize the feedback cycles that alter their dynamics and affect their stability. This will lead us to an initial attempt to identify planetary boundaries for some of these systems, which together define a safe operating space for humanity. I will end the talk by offering a framework for thinking about the leverage points that may allow us to manage these systems to keep them within the safe operating limits.

What unites both the climate crisis and the financial crisis? What is it that has driven scientists and environmentalists to risk arrest in protests across the world? What is it that’s driven people from all walks of life to show up in their thousands to occupy their cities? In both cases, there’s a growing sense that the system is fundamentally broken, and that our current political elites are unable (rather than just unwilling) to fix them. And in both cases, it’s becoming increasingly apparent that our current political system is a major cause of the problems. Which therefore makes it even harder to discover solutions.

So how do we make progress? If we’re going to take seriously the problems that have led people to take to the streets, then we have to understand the processes that are steadily driving us in the wrong direction when it comes to things we care about – a clean environment, a stable climate, secure jobs, a stable economy. In other words, we have to understand the underlying systems, understand the dynamics within those systems, and we have to find the right leverage points that would allow us to change those dynamics to work the way we would like.

Failure to take a systems view is evident throughout discussions of climate change, and now, more recently, throughout mainstream media discussions about the Occupy protests. Suggestions for what needs fixing tend to focus on superficial aspects of the systems that matter, mainly by tinkering with parameters (emissions targets, stabilization wedges, the size of the debt, the bank interest rate, etc). If the system itself is broken, you can’t fix it by adjusting its current parameters – you have to look at the underlying dynamics and change the structure of the system that gave rise to the problem in the first place. Most people are focusing on the wrong leverage points. Even worse, in some cases, they are pushing in the wrong direction on some of the leverage points…

Perhaps the best analysis of this I’ve ever seen is Donella Meadows’ essay on leverage points. [If you’re not already familiar with it, I highly recommend reading it before tackling the rest of this post]. Meadows has written some wonderfully accessible material on systems thinking, but only gives a very brief overview in this particular essay, because she’s focussing here on how to identify leverage points that allow one to alter a system. She identifies twelve places to look, and orders them, roughly, from the least effective to the most effective.

To illustrate the point, I’ll begin with a much simpler system than the ones we really want to fix. My example is the controllability of water temperature in a shower. The particular shower I have in mind is in a small hotel in Paris, and is a little antiquated, the result of old-fashioned plumbing. It takes time for the hot water to reach the shower head from the hot water tank, and there’s enough of a delay between the taps that control the hot and cold water and the temperature response, that you’re forever trying to adjust it to get a good temperature. It’s too cold, so you turn up the hot tap. The temperature barely seems to change, so you crank it up a lot. After a few minutes the water heats up so much it’s scalding. So you crank up the cold tap. Again, the temperature responds slowly until you realise it’s now too cold. You turn down the cold tap, and soon find it’s too hot again. And so on.

Does this remind you of the economy? Or, for that matter, the way the physical climate system works over the course of tens of thousands of years? More worryingly, it’s the outcome I expect if we ever try to geo-engineer our way out of extreme climate change. Right now, the human race is cranking up the hot tap. But the system responds very slowly. And by the time we’ve realized the heat has built up, we’ll have overshot our comfort zones. We’ll slam on the brakes and end up overcompensating. Because the system is just as hard to control (actually, a damn sight harder!) than that annoying shower in Paris.

Let’s look at how Meadows’ twelve leverage points might help us analyze the Parisian shower. At #12, we have what is usually the least effective place to seek change:

#12 Changes in constants, parameters, numbers. Example: Change the set point on the water tank thermostat. In general, such adjustments make no difference to the controllability of the shower. [There is an interesting exception, when we’re prepared to make really big adjustments. For example, if we crank the thermostat on the hot water tank all the way down to ‘pleasantly warm’ we’ll never have to balance the hot and cold taps again, we can just use the “hot” tap. Usually, such really big adjustments are unlikely to be made, for other reasons].

#11. Change the sizes of buffers and stocks relative to their flows. Example: Get a bigger hot water tank. This will make the energy bills bigger, but still won’t make the shower any more controllable.

#10. Change the structure of material stocks and flows. Example: Replace the water pipes with smaller diameter pipes. This might help, as it reduces the thermal mass of the pipes, and hence, may affect the lag between the water tank and the shower, leading to more responsive shower controls.

#9. Change the length of delays, relative to rate of system change. Example: Relocate the hot water tank closer to the bathroom; or Wait a little longer for temperature response to settle before touching the taps again. Such changes might be hard to achieve (hence they’re high up on the list), but very effective if we could do them.

#8. Increase the strength of negative feedback loops relative to the impacts they try to correct against. Example: Take a deep breath and calm down – you’re the negative feedback trying to keep the system stable. If you’re less impulsive on the taps, you’ll help to dampen the temperature fluctuations. If the system is changing too quickly, or is subject to instability, identifying the negative feedbacks, and working to strengthen them can often yield simple and effective leverage points. But when you’re in the shower getting scalded, it might be hard to remember this.

#7. Reduce the gain around positive feedback loops. Example: Replace the taps with ones that offer a finer level of control. This reduces the big temperature fluctuations when we turn the taps too quickly, and hence reduces the positive feedback loop that leads to temperature overshoot.

#6. Change the structure of information flows, to alter who does (or does not) have access to information. Example: Put an adjustable marker on the shower dial to record a preferred setting. Changing the flows of information about a system is generally much easier and cheaper than changing any other aspect of a system, hence, it’s often a more powerful leverage point than any of the above. For the shower, this one tiny fix may entirely cure the temperature fluctuation problem.

#5. Change the rules of the system (incentives, punishments, constraints). Example: Set limits on amount of time you can shower for. This might reduce the incentive to spend time fiddling with the temperature controls. But who will enforce the constraint?

#4. Nurture the power to add, change, evolve or self-organize system structure. Example: Teach yourself to tolerate a wider range of shower temperatures. or: Design a new automated temperature controller.

#3. Change the goal of the system. Example: Focus on getting clean quickly rather than getting the water to exactly the desired temperature. Of course, changing the goal of the system is hard, because it means changing people’s perceptions of the system itself.

#2. Change the mindset or paradigm out of which the system arises. Example: Is cleanliness over-rated? or: why stay in these antiquated hotels in Paris anyway? Paradigm shifts are hard to achieve, but when they happen, they have a dramatic transformative effect on the systems that arose in the previous paradigm.

#1. The power to transcend paradigms. Example: Learn systems thinking and gain the ability to understand a system from multiple perspectives; Realise that system structure and behaviour arises from the dominant paradigm; Explore how our own perspectives shape our interactions with the system.

Note that Meadows emphasizes the point that all twelve types of leverage point can be effective for changing systems, if you have a good understanding of how the system works, and can make good choices for where to make changes. However, in a self-perpectuating system, the dynamics that created the problem you’re trying to solve will also tend to defeat most kinds of change, unless they really do alter those dynamics in an important way.

Note that for many of these examples, I’ve chosen to include the person in the shower as part of my ‘shower system’. More importantly, some of my suggestions refer to how the person in the shower understands the shower system, and how her understanding of the system affects the system’s behaviour. This is to emphasize a mistake we often make when thinking about both the climate and the economy. In both cases, we have to understand the role that people play within these systems, and especially how our expectations and cultural norms themselves form part of the system. If people, in general, have the wrong mental model of how the systems work, it’s significantly more challenging to figure out how to fix things when they go wrong.

Let’s look at how the list of leverage points applies to the climate system and the financial system.

#12 Changes in Constants, parameters, numbers.

  • Climate System: tighten pollution standards, negotiate stronger version of the Kyoto protocol, increase fuel taxes, etc.
  • Financial System: change the interest base rates, increase size of stimulus spending, increase taxes, cut government spending, put caps on campaign contributions, increase the minimum wage, vote for the other party.

While changing the parameters of the existing system can make a difference, it’s rare that it does. Systems tend to operate in regions where small parameter adjustments make no difference to the overall stability of the system. If there’s a systemic effect that is pushing a system in the wrong direction (dependence on fossil fuels, financial instability, poverty, etc), then adjusting the system’s parameters is unlikely to make much difference, if you don’t also change the structure of the system.

None of these examples are likely to make much difference to the underlying problems. To understand this point, you have to understand the system you’re dealing with. For example, the whole problem of climate change itself might appear to be the result of a small parameter change – a small increase in radiative forcing, caused by a small increase (measured in parts per million!) in atmospheric concentrations of certain gases. But that’s not the real cause. The real cause is a systemic change in human activity that traces back to the industrial revolution: a new source of energy was harnessed, which then kicked off mutually reinforcing positive feedback loops in human population growth and energy use. A few more parts per million of CO2 in the atmosphere is not the problem; the problem is a new exponential trend that did not exist previously.

However, remember there’s sometimes an exception, if you make very large adjustments. For the climate system, you could increase fuel taxes so that gas (petrol) costs, say, ten times as much as it does today. Such an adjustment would be guaranteed to change the system (but not perhaps, when the mobs are done with you, in the way you intended). Here’s an interesting rule of thumb: if you change any parameter in a system by an order of magnitude or more, what you get is an entirely different type of system. Try it: a twenty-storey building is fundamentally different from a 2-storey house. A ten-lane freeway is fundamentally different from a single lane road. A salary of $1 million is fundamentally different from a salary of $100K.

#11. Change the sizes of buffers and stocks relative to their flows

  • Climate System: Plant more forests to create bigger carbon sinks. Ocean fertilization and/or artificial trees to soak up carbon, etc.
  • Financial System: Increase the federal reserve, require banks to hold larger reserves, increase debt ceiling limits.

In many systems these are hard to change, as they require large investments in infrastructure (the canonical example is a large dam to create a buffer in the water supply). This also means it can be hard to make frequent, fine-grained adjustments. More importantly, they tie up resources – keeping a large stock means that the stock isn’t working for you: your bigger water tank will make your energy bills much higher (and won’t affect your shower adjustment problem anyway). Making banks keep larger reserves will make them much less dynamic, and will reduce the funds available for lending.

All of these things might ease the problem a little, but none of them will make any significant difference to the cause of the problem. No matter how big you make the reserves, you’ll quickly be defeated by the exponential growth curves that you didn’t tackle.

#10. Change the structure of material stocks and flows

  • Climate System: Carbon Capture and Storage (diverts emissions at the point they are generated, so they don’t enter the atmosphere).
  • Financial System: Create a Tobin tax, which diverts a small percentage of each financial transaction to create a new pool of money to fix problems. Create new kinds of super-tax on the very rich. Separate the high street banks from their gambling investment operations.

Physical structure is also, usually, very hard to change, once the system is operating, although it’s sometimes easier to change how things flow than it is to create new buffers. However, both types of change tend to have limited impact, because the stocks and flows arise from the nature of the system – which means the system itself will find ways of defeating your efforts, for the same reason it ended up like it is now.

For example, separating high street banks from investment firms won’t really achieve much. People will find other ways to gamble bank money on foolish investments, if you haven’t actually addressed the reasons why such investments are made in the first place. Similarly for carbon capture and storage — diverting some percentage of the carbon that would go into the atmosphere via (expensive) CCS won’t help if our use of fossil fuels continues to grow at the rate it has done in the past. The fundamental problem of exponential growth in fossil fuel use will always outstrip our attempts to sequester some of it. There’s also the problem that on the timescales that matter (decades to centuries), it’s not clear the carbon will stay put. Oh, and CCS is only ever likely to be feasible on large, static sites like big power plants, so won’t make any different to emissions from transport, aviation, agriculture, etc.

#9. Change the length of delays, relative to rate of system change

  • Climate System: Speed up widescale deployment of clean energy technologies. Speed up the legislative process for climate policy. Speed up implementation of new standards for emissions. Use a faster ramp up on carbon pricing. Lengthen the approval process for new fossil fuel plants, oil pipelines, etc.
  • Financial System: Lengthen the approval process for risky loans, mergers, etc. Speed up implementation of government jobs programs. Slow down economic growth (to remove the boom and bust cycles).

Often, these kinds of change can be very powerful leverage points, but many of the delays in large systems are impossible to shorten – things take as long as they take. Also, as Meadows points out, most people try to shift things in the wrong direction, as many of these changes are counter-intuitive. For example, reducing the delay in money transfer times just increases chance of wild gyrations in the markets. Governments around the world usually seek to maximize economic growth, when often they should be trying to dampen it.

#8. Increase the strength of negative feedback loops relative to the impacts they try to correct against.

  • Climate System: Make the price of all goods reflect their true environmental cost. Remove perverse subsidies to fossil fuel companies (the cost of extraction & processing should be a negative feedback loop on dependence on fossil fuels); introduce better monitoring and data collection for global carbon fluxs, to more quickly assess impacts of different actions.
  • Financial System: More transparent democracy to allow people to vote out corrupt politicians more quickly; Remove subsidies, bailouts, etc (these distort the negative feedbacks that keep the financial system stable); protection for whistleblowers; more scrutiny of boardroom pay rises by unions and shareholders.

Negative feedbacks are what tend to keep a system stable. If the system is changing too quickly, or is subject to instability, identifying the negative feedbacks, and working to strengthen them, can often yield simple and effective leverage points. All of the examples here a likely to be more effective at fixing the respective systems than anything we’ve mentioned so far. Some of them rely on people acting as negative feedbacks. For example, by offering better protection for whistleblowers, you create a culture in which the people are less likely yield to corrupting influences.

#7. Reduce the gain around positive feedback loops

  • Climate System: Higher energy efficiency standards (this dampens the growth in energy demand); Green development – mechanisms that allow people to improve their quality of life without needing to increase their use of fossil fuels; wider use of birth control to curb population increases.
  • Financial System: Punish bankers who make reckless investment decisions (discourages others from following suit). Use a more progressive tax structure and introduce very high inheritance taxes (these prevent the rich from getting ever richer). High quality & free public education (to prevent the rich from forming privileged elites). Forgive all student loans on graduation (ends the cycle of individual indebtedness)
  • Both Systems: Slower economic growth (above, I described this as a “length of delay” issue – as slower growth can allow other processes, such as clean energy technology to keep up; but more importantly, economic growth is itself a positive feedback loop that drives ever more resource consumption, financial fluctuations and environmental degradation.

Runaway feedback loops inevitably destroy a system, unless some negative feedback loops kick in. In the long run, a negative feedback always kicks in: we use up all the resources, we kill off most of the population, we bankrupt everyone. But by that time the system has already been destroyed. The trick is to dampen the positive feedbacks long before too much damage is done, and this is often much more effective than trying to boost the strength of countervailing negative feedbacks. For example, if we want to address inequality, using tax structures that stop the rich getting ever richer is much more effective than creating anti-poverty programs aimed at mopping up the resulting inequalities.

Economic growth is an important example here. Remember that economic growth is a measure of the change in GDP over time. And GDP itself is a measure of the volume of financial transactions, or in other words, how fast money is flowing through the system. Accelerating these money flows makes all the instabilities in the financial system much worse. Worse still, one of the primary ways that GDP grows is by ever accelerating consumption of resources, so you get a self-reinforcing positive feedback loop between over-consumption and economic growth.

#6. Change the structure of information flows, to alter who does (or does not) have access to information

  • Climate System: Get IPCC assessment results out to the public faster, and in more accessible formats. Put journalists in touch with climate scientists. Label products with lifecycle carbon footprint data. Put meters in cars showing the total cost of each journey.
  • Financial System: Publish pay and benefit rates for all employees of private companies. Publish details of all political donations. Increase government oversight of financial transactions.
  • Both Systems: Open access to data, e.g. on campaign financing, carbon emissions, etc; Require all lobbyists and think tanks to publish full details on funding sources.

Changing the flows of information about a system is generally much easier and cheaper than changing any other aspect of a system, which means these can be very powerful leverage points. Many of these examples are powerful enough to cause significant changes to the underlying behaviour in the system, because they expose problems to the people that shape the behaviour of the system.

Providing people with full information about the true cost of things at the point they use them is a very powerful inducement to change behaviour. Meadows gives an example of electricity meters in the front hall of a home, rather than in the basement. Another one that bugs me is the information imbalance between different transportation choices. We always know how exactly how much a trip will cost by public transit, but the cost of driving is largely invisible (paying to fill up, paying the insurance and maintenance bills, etc are too far removed from the actual per-journey decisions). Some potential fixes are very simple: Google maps could show the cost, as well as the distance & time, when doing journey planning.

#5. Change the rules of the system (incentives, punishments, constraints)

  • Climate System: Government grants for energy efficiency projects. Free public transit. Jail-time for executives whose companies break carbon emissions rules. Mandatory science comprehension testing for anyone standing for public office.
  • Financial System: Give workers the right to vote on boardroom pay rates. New regulations on what banks can and cannot do with investors’ money. Remove immunity from prosecution for politicians. Ban all private funding of political campaigns.
  • Both Systems: Strict limits on ownership of media outlets. New ethics rules for journalists and advertisers.

These changes tend to impact the behaviour of the system immediately (as long as they’re actually enforced), and hence can have very high leverage. Unfortunately, one of the problems with fixing both the climate and financial systems is that our systems for changing the rules (e.g. legislative processes) are themselves broken. Large corporations (especially fossil fuel companies) have, over a period of many years, deliberately co-opted legislative processes to meet their own goals. Where once it might have been possible for governments to pass new laws to address climate change, or to change the way governments allocate resources, now they cannot, because these processes no longer act in the interest of the people. Similarly, the mainstream media has been co-opted by the same vested interests, so that people are fed little more than propaganda about how great the system is.

#4. Nurture the power to add, change, evolve or self-organize system structure

  • Climate System: Evidence-based policymaking. Resilient communities such as Transition Towns (these empower individual communities to manage their own process of ending dependence on fossil fuels).
  • Financial System: Switch from private companies to credit unions and worker-owned cooperatives.
  • Both Systems: Change to proportional representation for elections (this gives a more diverse set of political parties access to power, and helps voters feel their vote counts). Celebrate social diversity and give greater access to political power for minorities (this removes the tendency to have one dominant culture, and hence helps build resilience).

Systems gain the ability to evolve because of diversity within them. In biology, we understand this well – the diversity of an ecosystem is essential for evolutionary processes to work. Unfortunately, few people understand this matters just as much for social systems. Societies with a single dominant culture tend to find it very hard to change, while societies that encourage and promote diversity are also laying the foundations for new ideas and new forms of self-organization to emerge.

The political culture in the US is case in point. US politics is dominated by two parties that share an almost identical set of cultural assumptions, especially to do with the capitalist system, the role of markets, and the correct way to manage the economy. This makes the US particularly resistant to change, no matter how much the evidence accumulates that the system isn’t working.

#3. Change the goal of the system

  • Climate System: Don’t focus on emission reduction, focus on eliminating each and every dependency on fossil fuels. Don’t focus on international negotiations towards a treaty, focus on zero-carbon strategies for each city or region.
  • Financial System: Stop chasing short-term corporate profits as the primary goal of the economy. Corporations should aim for sustainability rather than growth. Instead of measuring GDP, measure gross national happiness.
  • Both Systems: Recognize and challenge the hidden goals of the current system, such as: the tendency for large corporations to maximize their power and influence over national governments; the desire to control access to information via media conglomerates; the desire of ruling elites to perpetuate their control.

Of course, changing the goal of the system is hard, because it means changing people’s perceptions of the system itself. It requires people to be able to step outside the system and see it from a fresh perspective, to identify how the dominant goals of the system shape its structure and operation. In short, it requires people to be systems thinkers.

There’s one kind of goal change related to the climate system that troubles me: instead of trying to prevent climate change, we could instead focus on survival strategies for living on a hotter planet. Given what we understand of the impacts on food, water and habitable regions, this would only be possible for a much smaller human population, and so it entails giving up trying to save as many people as possible. But it’s a very simple leverage point. The problem is that there are both ethical and practical reasons to reject this approach. The ethical reasons are well understood. The practical problem is that humans are very effective at fighting like crazy over diminishing resources, so it’s hard to see how this approach would work in the face of growing waves of climate refugees.

#2. Change the mindset or paradigm out of which the system arises

  • Climate and Financial Systems: Triple-bottom line accounting (forces companies to balance social and environmental impact with profitability). A shift in mindset from consumerism to living in harmony with the environment. A shift from material wealth as a measure of success to (say) social connectedness. A shift in mindset from individualism to community. A shift from individual greed to egalitarianism.

Paradigm shifts are hard to achieve, but when they happen, they transform the systems that arose in the previous paradigm. Much of the root cause of current problems with climate change and financial instability are due to the dominant paradigm of the last thirty years: blind faith in the free market to fix everything, along with accumulation of wealth and material assets as a virtue.

#1. The power to transcend paradigms

  • Climate and Financial Systems: Learn systems thinking and gain the ability to understand a system from multiple perspectives; Realise that system structure and behaviour arises from dominant paradigm. Explore how our own perspectives shape our interactions with the system…
  • … And then take to the streets.

Postscript: Notice that as we proceed down the list, and look at more fundamental changes to the systems, the solutions for climate change and the financial crisis start to merge. Also notice that in both cases, many of my examples aren’t what climate scientists or economists generally talk about. We need to broaden the conversation.