The 1972 Model

The original LTG model was designed to simulate the system dynamics of the food supply and resource production needed to keep pace with the needs of a growing world population. This model also attempted to account for the ability of the environment to absorb and degrade the effects of pollution. In the context of a large-scale  system dynamics model, many of the contributing factors were an aggregation of many variables, such that the model should not necessarily be seen as a direct predictive model, but rather as an indicator of possible trends.  As such, the model was used to study a number of possible futures using a wide range of aggregated estimates relating to the total food supply, the total resource base and the role of technology to improve production and efficiency. The original model attempted to account for just major 5 factors:

  1. World Population
  2. Continued Industrialization  
  3. Resulting Pollution
  4. Food Production
  5. Resource Depletion

The authors highlighted that the scope of the initial simulations were limited. Although, in practice, all models are by necessity an over-simplification and therefore an incomplete representation of real-world systems. However, they still believed that the behaviour being extrapolated was so fundamental that they did not expect their broad conclusions to be substantially altered by further revisions – see the  ‘30 Year Update’ and '40 Year Update'. As such, the stated purpose of this model was not necessarily to make specific predictions, but rather to explore how exponential growth might eventually have to be reconciled with finite resources. A generalisation of their original 1972 conclusions might be summarised as follows:

  • If the present growth trends in world population, industrialization, pollution, food production, and resource depletion continue unchanged, the limits to growth will be reached sometime within the next 100 years. The most probable result will be a rather sudden and uncontrollable decline in both population and industrial capacity.

  • It may be possible to alter these growth trends and to establish a condition of ecological and economic stability that is sustainable far into the future. The state of global equilibrium could be designed so that the basic material needs of each person on Earth are satisfied and each person has an equal opportunity.

It is not known whether the authors felt obligated to counter the pessimistic warning within the first bullet with the more optimistic note of the second bullet. However, today in 2014, the second bullet seems to be closer to wishful thinking than an achievable goal, which is not supported by the history of the last 40 years or even necessarily reconcilable with basic human nature,  when considered in terms of evolutionary concepts, e.g. ‘survival of the fittest’. If so, the future, as well as the past, may not hold out much hope for any significant shift towards an equitable system of resource usage. As a consequence, probability may suggest a continuing trend towards an ever-increasing disparity between the rich and poor, where the majority of the population become marginalised in economic poverty or become another statistic of a falling global population. While it is realised that such blunt commentary may not be seen as politically correct, this does not necessarily mean that such a brutal outcome is not more likely, especially when survival is at stake, both on the level of an individual and nation-state.   

But how did the model work in 1972?

Possibly, the interested reader might wish to review Chapter-3 covering the ‘Growth in the World System’ as a good starting point for some of the more technical aspects of the model, which this discussion shall only summarised in outline. Basically, the original model attempted to extrapolate the existing growth curves, as of 1972, for the selected factors in order to try to estimate how much longer certain key resources might last into the future.

Note: Given the short doubling times associated with exponential usage of some resources and the large quantities involved, it should come as no great surprise that some resources were predicted to become  exhausted within 50-100 years. Equally, as the  scarcity of the resources increases, so does the cost of production. This conclusion appears to be borne out by present-day data.

As such, the model attempted to collate and aggregate a large body of data relating to the ‘cause-and-effect’ relationships among the five factors listed above, which might be outlined in principle as follows: 

  • Based on the causal relationships between the five factors selected, and listed above, various feedback mechanisms were identified  and linked to demographics, economics, agronomy, nutrition, geology, and ecology.

  • Each relationship was then quantified, as accurately as possible, using existing global data of the day or extrapolated from other related sources.

  • The model then simulated the effects of concurrent interactions between these relationships as a function of time. Then, by changing the numerical weighting of the key variables, it was also possible to identify the most critical aspects of the system's behaviour.

  • Finally, the model was used to simulate the effects of various policies that might enhance or change the behaviour and outcome of the system, as a whole.

Overall, it was believed that the model would start to converge towards a means of simulating the broad behaviour a system essentially controlled by population and capital. In practice, these behaviours were variables within the system, which changed as a function of time in response to other changes within the system. For example, it has already been pointed out that growth can be constrained by some limiting capacity of the system, e.g. the variable of ‘population’ is dependent of the ‘capacity’ of food production, which we might characterise in terms of the following series of diagrams:

In the first diagram, we see a ‘population’ growing smoothly towards ‘sustainability’ within the limiting ‘capacity’ of the system, such that the exponential growth transitions through linear growth towards zero growth. Of course, in many practical systems, as illustrated in the second and third diagrams, a population might over-shoot the natural capacity of the system, then die back towards sustainability or simply continue to oscillate around the limiting capacity.

However, there is one other permutation that we also need to consider in which the population over-shoot actually changes the natural capacity of the system. In the case of population, the over consumption of resources may then affect food production, e.g. soil erosion or climatic change, such that the original  capacity of the system falls and cannot be restored. So, in this way, the model might be able to test and show possible outcomes of the various interactions, which are described as ‘behavioural modes’ within the aggregated system. However, as pointed out, this initial model was not detailed in this respect, such that the definition of ‘a population’ is only an approximate aggregation of the global population, i.e. it did not account for national demographics or regional birth and death rates.  In a similar fashion, the idea of  pollution was only modelled in terms of long-lived, global pollutants, such as lead, mercury, asbestos, pesticides and radioisotopes, the full effects of which may not be fully understood even today. Likewise, the definition of non-renewable resources was another aggregation, which were all considered necessary in order that the various outcomes of the models might be generally understood by a wider audience. As a result, it was accepted that the model might be limited in detailed information, although we will defer judgement of the accuracy model until comparing the results of the original outcomes against the actual data collated in the 40 years since its original publication.

OK, but what did such a highly aggregated model claim to show?

As has been pointed out, the LTG model was not specifically trying to make accurate predictions, but rather be able to show possible trends based on different interactions of the variables outlined. While, in practice, the authors produced many different output models, one in particular is cited as being representative of the problems now facing the world. This scenario has become known as the ‘standard run’ that models growth proceeding along its historical path for as long as possible without any major course corrections in current global policies. As such, trends in technology, agriculture, industry, and social services basically attempt, but ultimately fail, to maintain exponential growth. The outcome of this scenario has been characterised in many graphs of the following form:

The variables of the model, e.g. population, capital, pollution etc, start with values as determined by the data from 1900. For example, starting in 1900, the global population rises from 1.6 billion to 3.5 billion by 1970, while industrial output, food and services per capita also increase exponentially. However, by 1970, available resources have only fallen to about 95% of the initial value set in 1900.  This said, the overall future trend of this model is one that overshoots the natural limits of the system and collapses, mainly due to the depletion of non-renewable resources, but takes in to consideration the following issues:

  • The growth of the industrialisation process requires an enormous input of natural and non-renewable resources, which eventually leads to their depletion.

  • As resources become more scarce, prices rise as more and more capital must be used to secure these resources, leaving less to be invested in future growth.

  • Eventually, investment cannot keep pace with the cost of resource extraction and the industrial base goes into decline. This in-turn effects both the service and agricultural systems, which have become highly dependent on industrial output in its many forms, e.g. fertilizers and pesticides through to computers and energy.

  • For a while the population continues to rise, but eventually falls, as the death rate increases due to shortages in food production and health services.

However, it is made clear that the exact timeframe for these events is not really being predicted, as the process of aggregation causes obvious uncertainties in the timescales; although the general trend is considered valid. As a way to understand the process of ‘cause and effect’ being acted out in the model, the initial estimates of the available resources in 1900 were doubled, while keeping all other assumptions unchanged. While this change led to industrialization reaching a higher level, as the depletion of resources takes longer, the increased scale of industrialisation releases more pollutants at higher rates. As a consequence, the natural mechanisms of the system to absorb pollutants becomes increasingly saturated, leading to higher death rates plus an associated decline in food production. As a net result, the output of the model still leads to a collapse of the system regardless of the initial doubling of natural resources.

Might developments in technology still refute this model?

Even a cursory examination of the evolution of human civilisation, over the last 5000 years, would suggest that something beyond natural selection has taken place. For it seems clear that developments in science, technology and even economics have all changed our world beyond recognition. In response to this idea, the authors considered another idealised scenario where:

  • nuclear energy basically solves the current energy problems
  • which in-turn leads to a reduction in pollution by a factor of 4
  • resulting in land production yield increasing by a factor of 2.
  • while perfect birth control is practiced voluntarily.

Despite the assumptions of this idealised model, the result still apparently led to an end of growth before the year 2100, because of other cause and effect factors:

  • Overuse of land still leads to soil erosion and food production drops.
  • Resources are still severely depleted by the growth in global prosperity
  • Pollution still accumulates to effect food production and increases the death rate.

As a result, it was concluded that technology alone cannot solve the issue of exponential growth and that even the most optimistic benefits of technology did not seem able to circumvent the eventual decline of population and industry. In fact, the model suggested that technology alone could not postpone the collapse beyond the year 2100. However, while accepting that ‘technology’ can change rapidly, it was also recognised that ‘political and social institutions’ do not. As a generalisation, it might be argued that these institutions almost never change in anticipation of need, but only when forced in response to an unavoidable crisis. Of course, if the problems associated with the ‘limits to growth’ were not even accepted by the powers that be, then the actual outcome that occurred might have been predicted, i.e. no significant change in global policy in the last 40 years.

So were the authors of the model this pessimistic in 1972?

Back in 1972, it was originally hoped that society would do more to consider the implications of technology in advance of its deployment by asking 3 basic questions:

  • What are the side-effects, both physical and social?
  • What social changes will be necessary before it can be safely deployed?
  • How will it change the limits to growth?

In the spirit of the time, and the perception that there was still time, the stated goal was to define a model that would be :

  • sustainable without sudden and uncontrollable collapse
  • capable of satisfying the basic material requirements of all of its people

Again, 40 years of hindsight might suggest that this was simply wishful thinking, although this did not mean that the authors did not recognise the inherent problems. For they clearly recognised that the growth in world population was caused by a positive birth-rate and a dramatic reduction of worldwide mortality. In the language of system dynamics:

“The controlling negative feedback loop has been weakened, allowing the positive loop to operate virtually without constraint. There are only two ways to restore the resulting imbalance. Either the birth rate must be brought down to equal the new, lower death rate, or the death rate must rise again. All of the ‘natural’ constraints to population growth operate in the second way, they raise the death rate. Any society wishing to avoid that result must take deliberate action to control the positive feedback loop, i.e. to reduce the birth rate.”

However, they also recognised that stabilizing the global population, even at that earlier time, would not necessarily prevent the ‘overshoot and collapse’ predicted. The result of stopping population growth, back in 1975, and industrial capital growth, in 1985, with no other changes suggested that that the global population and capital would reach a constant value, but still require a relatively high level of food and industrial output plus services  per person. Even so, resource shortages eventually reduce the industrial output and the system once again declines.

So was decline always the end-game to each scenario modelled?

No, at the time, it was hoped that the model might produce a more favourable outcome, if ‘technological changes’ and other ‘value changes’ could reduce the level of growth in the system. What was exactly meant by ‘value changes’ is not totally clear, but it is assumed that any change to the values of any major variable in the model would actually require political policy change. Based on these original arguments, it was hoped that a stable world population might be achieved that would only be slightly larger than the population in 1972, i.e. ~3.5 billion, not the 6.9 billion of today’s world and certainly not the projected 10 billion of 2050. The basic argument for these changes appears to have been that while resources are still gradually depleted, the rate of depletion would be much slower and therefore provides more time for technology and industry to find realistic and long-term solutions. However, given the state of the world in 2014, there is possibly little to be gain in reviewing all the permutations that might have  possibly worked back in 1972. Clearly, even the authors had some doubts about the probability of what they were suggesting:

“Many people will think that the changes we have introduced into the model to avoid the growth and collapse behaviour mode are not only impossible, but unpleasant, dangerous, even disastrous in themselves. Such policies as reducing the birth rate and diverting capital from production of material goods, by whatever means they might be implemented, seem unnatural and unimaginable, because they have not, in most people’s experience, been tried, or even seriously suggested.”

However, it is equally clear that the author knew, probably better than most, the consequence of not trying or, worst, not even accepting that a problem exists:

“Indeed there would be little point even in discussing such fundamental changes in the functioning of modern society if we felt that the present pattern of unrestricted growth were sustainable into the future. All the evidence available to us, however, suggests that of the three alternatives: 1) unrestricted growth, 2) a self-imposed limitation to growth or 3) a nature-imposed limitation to growth, only the last two are actually possible.”

While agreeing with the sentiment and logic of this position, there is still a distinct between a ‘possibility’ and a ‘probability’. You are left to judge the ‘probability’ of the authors requirements to achieve ‘equilibrium’ in the global system:

  • The capital plant and the population are constant in size. The birth rate equals the death rate and the capital investment rate equals the depreciation rate.

  • All input and output rates - birth, death, investment, and depreciation - are kept to a minimum.

  • The levels of capital and population and the ratio of the two are set in accordance with the values of the society. They may be deliberately revised and slowly adjusted as the advance of technology creates new options.

What is possible, when reading these requirements, is that any government in power would have recognised the near political impossibility of these goals, irrespective of whether they agreed with the logic or not. If so, the only recourse open to those in a position of power may have been to simply dismiss and attack the model as being false, which is exactly what history suggests actually happen. On this note, we will now turn to the update provided by the original authors some 30 years later in 2005.