Systems practice: Managing sustainability
Systems practice: Managing sustainability

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Systems practice: Managing sustainability

1.1 Limits to growth

Vignette 1

One of the landmark studies that initiated much of the debate that led to the development of the sustainable development concept was that reported in the book Limits to Growth by Meadows et al. published in 1972. Figure 1 shows one of the outputs from the model. It shows the variation in the main variables over time (i.e. through the calculation steps of the model) and, in this example, that economic growth peaks shortly after the year 2000 and the world population peaks around 2050, falling rapidly as food production declines. The authors explored the behaviour of the model under a range of assumptions. For example they found that the collapse in economic growth could be delayed by about ten years if they presumed that the stock of natural resources was twice as large. The model still showed a cessation of growth by 2050 followed by a sharp decline in population, but this time because pollution absorbed a growing proportion of economic output and also reduced life expectancy. The authors introduced technical ‘solutions’ including pollution control, birth control and unlimited natural resources. In all cases the model predicted a cessation of economic growth and dramatic population reductions within the next century. In short the authors claimed that their model convincingly demonstrated that there were substantial limits to the growth of human populations and the global economy. Such views had been expressed before but with considerably less conviction and with less supporting evidence. For example this was the first time that attention was drawn to the increasing carbon dioxide in the atmosphere and how this could affect global climate.

Figure 1 One of the outputs from the model (adapted from Meadows et al. 1972, p. 124)

As may be expected the book gave rise to a great deal of interest and controversy. Specialists in specific areas such as natural resources or food production raised detailed issues on which they disagreed. Economists questioned the whole basis of the model and asserted that it had underestimated the role of technical progress in avoiding the types of catastrophes predicted by the model. They pointed to the similarly dire predictions of Malthus some 200 years earlier that had not been realised. Despite all these objections the idea that human development would be constrained took root. Ultimately there would be constraints imposed on the growth of human population and industrial development because these were taking place on a finite planet. Exactly where and when these constraints would first ‘bite’ was not predictable with any certainty, but that did not mean that the limits did not exist.

One reason why this book created such a fuss was that it challenged the belief in economic growth, advocated by western governments as the main vehicle for solving many problems, including those of poverty and inequality. In a more recent book the authors state:

The predominance of growth in human activity comes as no surprise. In fact most people see it as something to celebrate. Most societies, rich or poor, seek some kind of expansion as a remedy for their most immediate and important problems. In the rich world economic growth is believed to be necessary for employment, social mobility, and technical advance. In the poor world economic growth seems the only way out of poverty. … Until other solutions are found for the legitimate problems of the world, people will cling to the idea that growth is the key to a better future, and they would do all they can to produce more growth.

(Meadows et al., 1992, p. 5)

In their more recent study the authors make use of the same model of the world economy with data updated to take account of the twenty years since the initial study. Their main concern has been to establish whether a sustainable future is possible, and if so at what level of material consumption. Their conclusions are (Meadows et al. 1992, p. xvi):

  1. Human use of many essential resources and generation of many kinds of pollutants have already surpassed rates that are physically sustainable. Without significant reductions in material and energy flows, there will be in the coming decades an uncontrolled decline in per capita food output, energy use, and industrial production.
  2. This decline is not inevitable. To avoid it two changes are necessary. The first is a comprehensive revision of policies and practices that perpetuate growth in material consumption and in population. The second is a rapid, drastic increase in the efficiency with which materials and energy are used.
  3. A sustainable society is still technically and economically possible. It could be much more desirable than a society that tries to solve its problems by constant expansion. The transition to a sustainable society requires a careful balance between long-term and short-term goals and an emphasis on sufficiency, equity, and quality of life rather than on quantity of output. It requires more than productivity and more than technology; it also requires maturity, compassion, and wisdom.

Both books reported the results of a systems dynamics model of the global economy. Systems dynamics is a modelling procedure, which computes changes in stocks and flows of specified variables over time. It was initially developed for modelling flows of materials through industrial processes and is still widely, and successfully, used for this purpose. The MIT (Massachusetts Institute of Technology) group who published Limits to Growth used this modelling procedure to examine the interactions between human population, food production, natural resources, pollution and capital production. The model was designed to represent the global economy, so local detail and differences were not represented. The aim was to explore ways in which the growth of the world economy might be limited, not to discover which nations would grow faster or slower than others. The level of aggregation involved can be gauged from Box 1 below which describes the way that the model calculated human population over time.

Box 1 The population model in Limits to Growth

There are two key feedback loops controlling the growth of population. The first is the number of births per year; this is a positive cycle since the more people there are the more births there will be. The second is the number of deaths per year. This is a negative cycle since the more people there are the more deaths will occur. Each loop is also influenced by outside factors that affect fertility and mortality. This is shown in Figure 2.

The overall model includes sections dealing with resources, pollution, food supply and capital production, all of which will impact the basic model of population growth. For example mortality rates are affected by levels of pollution, quantity of food per capita and the provision of health services, which itself depends upon industrial output and how much of this is diverted to the services sector. Services also influences fertility through education and family planning. There is also a well established relationship between the number of children per family (fertility) and the overall standard of living.

Figure 2 The main feedback loops in the population model

In addition to these links to other sections of the overall model the calculation of population is separated into three age groups, 0–15, 16–45 and over 45. There are separate mortality rates for each age group with different effects from factors such as food and pollution. There are delays which link the numbers of people in each age band. There are also delays between some of the factors and changes in the population parameters; for example there is a delay between an improvement in health services and the corresponding reduction in mortality rates. The complete population calculation model is illustrated in Figure 3. Note the circles denote calculated parameters. Delays are shown by the rectangles divided into four sections. Rates are shown by the rectangles with two triangles, flows by solid arrows and causal relationships by broken arrows. Clouds represent sources or sinks that are not important to the model behaviour.

Figure 3 The complete population model in World 3 (Meadows et al. 1972, p.102-103)

In a ‘run’ of the model each of the parameters and rates is calculated according to the influences shown in the diagram. This is an example of a dynamic model, one that repeats the same calculations many times using the results from the previous calculations as the data for the next set of calculations. The precise nature of the influence, for example the effect of food on life expectancy, has to be established by the modellers and entered into the model as a specific mathematical relationship. The data and curve used to deduce this relationship are shown in Figure 4 below. Each point on this graph represents an individual nation for which suitable statistics were available. The relationship used in the model is shown by the solid curve.

Figure 4 Data and curve used to deduce the relationship between food and life expectancy in the population model in Figure 16 (Meadows et al. 1992, p. 107)

Although the population model shown in Box 1 is clearly a gross simplification of the reality it is already too complex to be understood by simply ‘thinking about it’. Situations considered as systems with feedback loops and delays, are notoriously difficult to both comprehend and control. The example usually used to make this point is the difficulties individuals experience adjusting the temperature of a shower. An adjustment is made but seems inadequate, so a further adjustment is made, which then appears to be too much so a counter adjustment is made. This cycle continues with the temperature oscillating between too hot and too cold until the person waits long enough for the effect of the delay between making the adjustment and the change in water temperature striking their body – usually an annoying minute or so!

A larger scale example of the same problem is the attempt to control inflation by changes in interest rates, a task bequeathed to the Bank of England by the incoming Labour Government in 1997. As the Bank well understood there is a delay of about a year between a change in interest rate and its effect on inflation. So the Bank’s task is doubly difficult; it has to forecast the rate of inflation a year ahead and make adjustments to the interest rate now in order to correct any deviation from the target rate. Needless to say the Bank makes use of models of the economy to assist in this process.

Some time ago the International Institute for Advanced Systems Studies (IIASA) instituted a series of conferences to which different world modellers were invited to present the details of their models and their conclusions to a peer group. By 1980 several well developed world models had been presented in this way to the group – each with their own set of implicit values and beliefs. What emerged from this was that all the models represented a biased representation of the world economy. Despite the fact that all the models were using essentially the same world data they came to very different conclusions – largely as a result of the selection process used in the construction of the overall model.

Where such modelling is carried out to determine the optimum stock levels in a factory or the likely future rate of inflation in an economy both the objectives and necessary data are relatively clear. In the case of ‘world modelling’ the context, objectives and data are all more problematic. Yet the issues involved are of such potential significance that the difficulties and uncertainties in the modelling process are not sufficient reason for either dismissing the conclusions or ceasing this line of inquiry.

SAQ 1 Considering Limits to Growth as a systems study

To what degree would you classify the Limits to Growth study as a systems study? What was systemic, and what was systematic in the approach used?


The authors certainly regard themselves as systems practitioners and claim that theirs is a systems study. It is clearly based upon an epistemological position that regards ‘systems’ as existing out there and capable of having their performance quantitatively modelled. This is consistent with the HS-method and VS-method.

What is systemic about their approach is the explicit concern with the whole, not the parts, the explicit inclusion of feedback, delays and a wider range of influences than would normally be considered.

From the limited information provided about the study you may find it hard to assess the degree to which they were engaging, contextualising, being and managing. From their books it is clear that they were fully engaged with the complexity of their chosen problem domain. Their books also place their work in a very clear context, namely of countering the conventional wisdom of indefinite economic and material growth – a context they have managed with great skill. Their work has undoubtedly affected the understanding and approach to world economics and development. The degree to which they are able to be self-aware is much harder to judge from published material, but the following quotation indicates that this is an aspect they have not neglected.

It is difficult to speak of or to practice love, friendship, generosity, understanding or solidarity within a system whose rules, goals and information are geared for lesser human values. But we try, and we urge you to try.

(Meadows et al. 1992, p. 234)
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