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• Published: 06 May 2020

Deforestation and world population sustainability: a quantitative analysis

• Mauro Bologna 1   na1 &
• Gerardo Aquino 2 , 3 , 4   na1  

Scientific Reports volume  10 , Article number:  7631 ( 2020 ) Cite this article

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• Applied mathematics
• Environmental impact
• Population dynamics
• Statistical physics, thermodynamics and nonlinear dynamics

In this paper we afford a quantitative analysis of the sustainability of current world population growth in relation to the parallel deforestation process adopting a statistical point of view. We consider a simplified model based on a stochastic growth process driven by a continuous time random walk, which depicts the technological evolution of human kind, in conjunction with a deterministic generalised logistic model for humans-forest interaction and we evaluate the probability of avoiding the self-destruction of our civilisation. Based on the current resource consumption rates and best estimate of technological rate growth our study shows that we have very low probability, less than 10% in most optimistic estimate, to survive without facing a catastrophic collapse.

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Introduction.

In the last few decades, the debate on climate change has assumed global importance with consequences on national and global policies. Many factors due to human activity are considered as possible responsible of the observed changes: among these water and air contamination (mostly greenhouse effect) and deforestation are the mostly cited. While the extent of human contribution to the greenhouse effect and temperature changes is still a matter of discussion, the deforestation is an undeniable fact. Indeed before the development of human civilisations, our planet was covered by 60 million square kilometres of forest 1 . As a result of deforestation, less than 40 million square kilometres currently remain 2 . In this paper, we focus on the consequence of indiscriminate deforestation.

Trees’ services to our planet range from carbon storage, oxygen production to soil conservation and water cycle regulation. They support natural and human food systems and provide homes for countless species, including us, through building materials. Trees and forests are our best atmosphere cleaners and, due to the key role they play in the terrestrial ecosystem, it is highly unlikely to imagine the survival of many species, including ours, on Earth without them. In this sense, the debate on climate change will be almost obsolete in case of a global deforestation of the planet. Starting from this almost obvious observation, we investigate the problem of the survival of humanity from a statistical point of view. We model the interaction between forests and humans based on a deterministic logistic-like dynamics, while we assume a stochastic model for the technological development of the human civilisation. The former model has already been applied in similar contexts 3 , 4 while the latter is based on data and model of global energy consumption 5 , 6 used as a proxy for the technological development of a society. This gives solidity to our discussion and we show that, keeping the current rate of deforestation, statistically the probability to survive without facing a catastrophic collapse, is very low. We connect such probability to survive to the capability of humankind to spread and exploit the resources of the full solar system. According to Kardashev scale 7 , 8 , which measures a civilisation’s level of technological advancement based on the amount of energy they are able to use, in order to spread through the solar system we need to be able to harness the energy radiated by the Sun at a rate of ≈4 × 10 26 Watt. Our current energy consumption rate is estimated in ≈10 13 Watt 9 . As showed in the subsections “Statistical Model of technological development” and “Numerical results” of the following section, a successful outcome has a well defined threshold and we conclude that the probability of avoiding a catastrophic collapse is very low, less than 10% in the most optimistic estimate.

Model and Results

Deforestation.

The deforestation of the planet is a fact 2 . Between 2000 and 2012, 2.3 million Km 2 of forests around the world were cut down 10 which amounts to 2 × 10 5 Km 2 per year. At this rate all the forests would disappear approximatively in 100–200 years. Clearly it is unrealistic to imagine that the human society would start to be affected by the deforestation only when the last tree would be cut down. The progressive degradation of the environment due to deforestation would heavily affect human society and consequently the human collapse would start much earlier.

Curiously enough, the current situation of our planet has a lot in common with the deforestation of Easter Island as described in 3 . We therefore use the model introduced in that reference to roughly describe the humans-forest interaction. Admittedly, we are not aiming here for an exact exhaustive model. It is probably impossible to build such a model. What we propose and illustrate in the following sections, is a simplified model which nonetheless allows us to extrapolate the time scales of the processes involved: i.e. the deterministic process describing human population and resource (forest) consumption and the stochastic process defining the economic and technological growth of societies. Adopting the model in 3 (see also 11 ) we have for the humans-forest dynamics

where N represent the world population and R the Earth surface covered by forest. β is a positive constant related to the carrying capacity of the planet for human population, r is the growth rate for humans (estimated as r  ~ 0.01 years −1 ) 12 , a 0 may be identified as the technological parameter measuring the rate at which humans can extract the resources from the environment, as a consequence of their reached technological level. r ’ is the renewability parameter representing the capability of the resources to regenerate, (estimated as r ’ ~ 0.001 years −1 ) 13 , R c the resources carrying capacity that in our case may be identified with the initial 60 million square kilometres of forest. A closer look at this simplified model and at the analogy with Easter Island on which is based, shows nonetheless, strong similarities with our current situation. Like the old inhabitants of Easter Island we too, at least for few more decades, cannot leave the planet. The consumption of the natural resources, in particular the forests, is in competition with our technological level. Higher technological level leads to growing population and higher forest consumption (larger a 0 ) but also to a more effective use of resources. With higher technological level we can in principle develop technical solutions to avoid/prevent the ecological collapse of our planet or, as last chance, to rebuild a civilisation in the extraterrestrial space (see section on the Fermi paradox). The dynamics of our model for humans-forest interaction in Eqs. ( 1 , 2 ), is typically characterised by a growing human population until a maximum is reached after which a rapid disastrous collapse in population occurs before eventually reaching a low population steady state or total extinction. We will use this maximum as a reference for reaching a disastrous condition. We call this point in time the “no-return point” because if the deforestation rate is not changed before this time the human population will not be able to sustain itself and a disastrous collapse or even extinction will occur. As a first approximation 3 , since the capability of the resources to regenerate, r ′, is an order of magnitude smaller than the growing rate for humans, r , we may neglect the first term in the right hand-side of Eq. ( 2 ). Therefore, working in a regime of the exploitation of the resources governed essentially by the deforestation, from Eq. ( 2 ) we can derive the rate of tree extinction as

The actual population of the Earth is N  ~ 7.5 × 10 9 inhabitants with a maximum carrying capacity estimated 14 of N c  ~ 10 10 inhabitants. The forest carrying capacity may be taken as 1 R c  ~ 6 × 10 7 Km 2 while the actual surface of forest is \(R\lesssim 4\times {10}^{7}\) Km 2 . Assuming that β is constant, we may estimate this parameter evaluating the equality N c ( t ) =  βR ( t ) at the time when the forests were intact. Here N c ( t ) is the instantaneous human carrying capacity given by Eq. ( 1 ). We obtain β  ~  N c / R c  ~ 170.

In alternative we may evaluate β using actual data of the population growth 15 and inserting it in Eq. ( 1 ). In this case we obtain a range \(700\lesssim \beta \lesssim 900\) that gives a slightly favourable scenario for the human kind (see below and Fig.  4 ). We stress anyway that this second scenario depends on many factors not least the fact that the period examined in 15 is relatively short. On the contrary β  ~ 170 is based on the accepted value for the maximum human carrying capacity. With respect to the value of parameter a 0 , adopting the data relative to years 2000–2012 of ref. 10 ,we have

The time evolution of system ( 1 ) and ( 2 ) is plotted in Figs.  1 and 2 . We note that in Fig.  1 the numerical value of the maximum of the function N ( t ) is N M  ~ 10 10 estimated as the carrying capacity for the Earth population 14 . Again we have to stress that it is unrealistic to think that the decline of the population in a situation of strong environmental degradation would be a non-chaotic and well-ordered decline, that is also way we take the maximum in population and the time at which occurs as the point of reference for the occurrence of an irreversible catastrophic collapse, namely a ‘no-return’ point.

On the left: plot of the solution of Eq. ( 1 ) with the initial condition N 0  = 6 × 10 9 at initial time t  = 2000 A.C. On the right: plot of the solution of Eq. ( 2 ) with the initial condition R 0  = 4 × 10 7 . Here β  = 700 and a 0  = 10 −12 .

On the left: plot of the solution of Eq. ( 1 ) with the initial condition N 0  = 6 × 10 9 at initial time t  = 2000 A.C. On the right: plot of the solution of Eq. ( 2 ) with the initial condition R 0  = 4 × 10 7 . Here β  = 170 and a 0  = 10 −12 .

Statistical model of technological development

According to Kardashev scale 7 , 8 , in order to be able to spread through the solar system, a civilisation must be capable to build a Dyson sphere 16 , i.e. a maximal technological exploitation of most the energy from its local star, which in the case of the Earth with the Sun would correspond to an energy consumption of E D  ≈ 4 × 10 26 Watts, we call this value Dyson limit. Our actual energy consumption is estimated in E c  ≈ 10 13 Watts (Statistical Review of World Energy source) 9 . To describe our technological evolution, we may roughly schematise the development as a dichotomous random process

where T is the level of technological development of human civilisation that we can also identify with the energy consumption. α is a constant parameter describing the technological growth rate (i.e. of T ) and ξ ( t ) a random variable with values 0, 1. We consider therefore, based on data of global energy consumption 5 , 6 an exponential growth with fluctuations mainly reflecting changes in global economy. We therefore consider a modulated exponential growth process where the fluctuations in the growth rate are captured by the variable ξ ( t ). This variable switches between values 0, 1 with waiting times between switches distributed with density ψ ( t ). When ξ ( t ) = 0 the growth stops and resumes when ξ switches to ξ ( t ) = 1. If we consider T more strictly as describing the technological development, ξ ( t ) reflects the fact that investments in research can have interruptions as a consequence of alternation of periods of economic growth and crisis. With the following transformation,

differentiating both sides respect to t and using Eq. ( 5 ), we obtain for the transformed variable W

where \(\bar{\xi }(t)=2[\xi (t)-\langle \xi \rangle ]\) and 〈ξ 〉 is the average of ξ ( t ) so that \(\bar{\xi }(t)\) takes the values ±1.

The above equation has been intensively studied, and a general solution for the probability distribution P ( W , t ) generated by a generic waiting time distribution can be found in literature 17 . Knowing the distribution we may evaluate the first passage time distribution in reaching the necessary level of technology to e.g. live in the extraterrestrial space or develop any other way to sustain population of the planet. This characteristic time has to be compared with the time that it will take to reach the no-return point. Knowing the first passage time distribution 18 we will be able to evaluate the probability to survive for our civilisation.

If the dichotomous process is a Poissonian process with rate γ then the correlation function is an exponential, i.e.

and Eq. ( 7 ) generates for the probability density the well known telegrapher’s equation

We note that the approach that we are following is based on the assumption that at random times, exponentially distributed with rate γ , the dichotomous variable \(\bar{\xi }\) changes its value. With this assumption the solution to Eq. ( 9 ) is

where I n ( z ) are the modified Bessel function of the first kind. Transforming back to the variable T we have

where for sake of compactness we set

In Laplace transform we have

The first passage time distribution, in laplace transform, is evaluated as 19

Inverting the Laplace transform we obtain

which is confirmed (see Fig.  3 ) by numerical simulations. The time average to get the point x for the first time is given by

which interestingly is double the time it would take if a pure exponential growth occurred, depends on the ratio between final and initial value of T and is independent of γ . We also stress that this result depends on parameters directly related to the stage of development of the considered civilisation, namely the starting value T 1 , that we assume to be the energy consumption E c of the fully industrialised stage of the civilisation evolution and the final value T , that we assume to be the Dyson limit E D , and the technological growth rate α . For the latter we may, rather optimistically, choose the value α  = 0.345, following the Moore Law 20 (see next section). Using the data above, relative to our planet’s scenario, we obtain the estimate of 〈 t 〉 ≈ 180 years. From Figs.  1 and 2 we see that the estimate for the no-return time are 130 and 22 years for β  = 700 and β  = 170 respectively, with the latter being the most realistic value. In either case, these estimates based on average values, being less than 180 years, already portend not a favourable outcome for avoiding a catastrophic collapse. Nonetheless, in order to estimate the actual probability for avoiding collapse we cannot rely on average values, but we need to evaluate the single trajectories, and count the ones that manage to reach the Dyson limit before the ‘no-return point’. We implement this numerically as explained in the following.

(Left) Comparison between theoretical prediction of Eq. ( 15 ) (black curve) and numerical simulation of Eq. ( 3 ) (cyan curve) for γ  = 4 (arbitrary units). (Right) Comparison between theoretical prediction of Eq. ( 15 ) (red curve) and numerical simulation of Eq. ( 3 ) (black curve) for γ  = 1/4 (arbitrary units).

(Left panel) Probability p suc of reaching Dyson value before reaching “no-return” point as function of α and a for β  = 170. Parameter a is expressed in Km 2 ys −1 . (Right panel) 2D plot of p suc for a  = 1.5 × 10 −4 Km 2 ys −1 as a function of α . Red line is p suc for β  = 170. Black continuous lines (indistinguishable) are p suc for β  = 300 and 700 respectively (see also Fig.  6 ). Green dashed line indicates the value of α corresponding to Moore’s law.

Numerical results

We run simulations of Eqs. ( 1 ), ( 2 ) and ( 5 ) simultaneously for different values of of parameters a 0 and α for fixed β and we count the number of trajectories that reach Dyson limit before the population level reaches the “no-return point” after which rapid collapse occurs. More precisely, the evolution of T is stochastic due to the dichotomous random process ξ ( t ), so we generate the T ( t ) trajectories and at the same time we follow the evolution of the population and forest density dictated by the dynamics of Eqs. ( 1 ), ( 2 ) 3 until the latter dynamics reaches the no-return point (maximum in population followed by collapse). When this happens, if the trajectory in T ( t ) has reached the Dyson limit we count it as a success, otherwise as failure. This way we determine the probabilities and relative mean times in Figs.  5 , 6 and 7 . Adopting a weak sustainability point of view our model does not specify the technological mechanism by which the successful trajectories are able to find an alternative to forests and avoid collapse, we leave this undefined and link it exclusively and probabilistically to the attainment of the Dyson limit. It is important to notice that we link the technological growth process described by Eq. ( 5 ) to the economic growth and therefore we consider, for both economic and technological growth, a random sequence of growth and stagnation cycles, with mean periods of about 1 and 4 years in accordance with estimates for the driving world economy, i.e. the United States according to the National Bureau of Economic Research 21 .

Average time τ (in years) to reach Dyson value before hitting “no-return” point (success, left) and without meeting Dyson value (failure, right) as function of α and a for β  = 170. Plateau region (left panel) where τ  ≥ 50 corresponds to diverging τ , i.e. Dyson value not being reached before hitting “no-return” point and therefore failure. Plateau region at τ  = 0 (right panel), corresponds to failure not occurring, i.e. success. Parameter a is expressed in Km 2 ys −1 .

Probability p suc of reaching Dyson value before hitting “no-return” point as function of α and a for β  = 300 (left) and 700 (right). Parameter a is expressed in Km 2 ys −1 .

Probability of reaching Dyson value p suc before reaching “no-return” point as function of β and α for a  = 1.5 × 10 −4 Km 2 ys −1 .

In Eq. ( 1 , 2 ) we redefine the variables as N ′ =  N / R W and R ′ =  R / R W with \({R}_{W}\simeq 150\times {10}^{6}\,K{m}^{2}\) the total continental area, and replace parameter a 0 accordingly with a  =  a 0  ×  R W  = 1.5 × 10 −4 Km 2 ys −1 . We run simulations accordingly starting from values \({R{\prime} }_{0}\) and \({N{\prime} }_{0}\) , based respectively on the current forest surface and human population. We take values of a from 10 −5 to 3 × 10 −4 Km 2 ys −1 and for α from 0.01 ys −1 to 4.4 ys −1 . Results are shown in Figs.  4 and 6 . Figure  4 shows a threshold value for the parameter α , the technological growth rate, above which there is a non-zero probability of success. This threshold value increases with the value of the other parameter a . As shown in Fig.  7 this values depends as well on the value of β and higher values of β correspond to a more favourable scenario where the transition to a non-zero probability of success occurs for smaller α , i.e. for smaller, more accessible values, of technological growth rate. More specifically, left panel of Fig.  4 shows that, for the more realistic value β  = 170, a region of parameter values with non-zero probability of avoiding collapse corresponds to values of α larger than 0.5. Even assuming that the technological growth rate be comparable to the value α  = log(2)/2 = 0.345 ys −1 , given by the Moore Law (corresponding to a doubling in size every two years), therefore, it is unlikely in this regime to avoid reaching the the catastrophic ‘no-return point’. When the realistic value of a  = 1.5 × 10 4 Km 2 ys −1 estimated from Eq. ( 4 ), is adopted, in fact, a probability less than 10% is obtained for avoiding collapse with a Moore growth rate, even when adopting the more optimistic scenario corresponding to β  = 700 (black curve in right panel of Fig.  4 ). While an α larger than 1.5 is needed to have a non-zero probability of avoiding collapse when β  = 170 (red curve, same panel). As far as time scales are concerned, right panel of Fig.  5 shows for β  = 170 that even in the range α  > 0.5, corresponding to a non-zero probability of avoiding collapse, collapse is still possible, and when this occurs, the average time to the ‘no-return point’ ranges from 20 to 40 years. Left panel in same figure, shows for the same parameters, that in order to avoid catastrophe, our society has to reach the Dyson’s limit in the same average amount of time of 20–40 years.

In Fig.  7 we show the dependence of the model on the parameter β for a  = 1.5 × 10 −4 .

We run simulations of Eqs. ( 1 ), ( 2 ) and ( 5 ) simultaneously for different values of of parameters a 0 and α depending on β as explained in Methods and Results to generate Figs.  5 , 6 and 7 . Equations ( 1 ), ( 2 ) are integrated via standard Euler method. Eq. ( 5 ) is integrated as well via standard Euler method between the random changes of the variable ξ . The stochastic dichotomous process ξ is generated numerically in the following way: using the random number generator from gsl library we generate the times intervals between the changes of the dichotomous variable ξ  = 0, 1, with an exponential distribution(with mean values of 1 and 4 years respectively), we therefore obtain a time series of 0 and 1 for each trajectory. We then integrate Eq. ( 5 ) in time using this time series and we average over N  = 10000 trajectories. The latter procedure is used to carry out simulations in Figs.  3 and 4 as well in order to evaluate the first passage time probabilities. All simulations are implemented in C++.

Fermi paradox

In this section we briefly discuss a few considerations about the so called Fermi paradox that can be drawn from our model. We may in fact relate the Fermi paradox to the problem of resource consumption and self destruction of a civilisation. The origin of Fermi paradox dates back to a casual conversation about extraterrestrial life that Enrico Fermi had with E. Konopinski, E. Teller and H. York in 1950, during which Fermi asked the famous question: “where is everybody?”, since then become eponymous for the paradox. Starting from the closely related Drake equation 22 , 23 , used to estimate the number of extraterrestrial civilisations in the Milky Way, the debate around this topic has been particularly intense in the past (for a more comprehensive covering we refer to Hart 24 , Freitas 25 and reference therein). Hart’s conclusion is that there are no other advanced or ‘technological’ civilisations in our galaxy as also supported recently by 26 based on a careful reexamination of Drake’s equation. In other words the terrestrial civilisation should be the only one living in the Milk Way. Such conclusions are still debated, but many of Hart’s arguments are undoubtedly still valid while some of them need to be rediscussed or updated. For example, there is also the possibility that avoiding communication might actually be an ‘intelligent’ choice and a possible explanation of the paradox. On several public occasions, in fact, Professor Stephen Hawking suggested human kind should be very cautious about making contact with extraterrestrial life. More precisely when questioned about planet Gliese 832c’s potential for alien life he once said: “One day, we might receive a signal from a planet like this, but we should be wary of answering back”. Human history has in fact been punctuated by clashes between different civilisations and cultures which should serve as caveat. From the relatively soft replacement between Neanderthals and Homo Sapiens (Kolodny 27 ) up to the violent confrontation between native Americans and Europeans, the historical examples of clashes and extinctions of cultures and civilisations have been quite numerous. Looking at human history Hawking’s suggestion appears as a wise warning and we cannot role out the possibility that extraterrestrial societies are following similar advice coming from their best minds.

With the help of new technologies capable of observing extrasolar planetary systems, searching and contacting alien life is becoming a concrete possibility (see for example Grimaldi 28 for a study on the chance of detecting extraterrestrial intelligence), therefore a discussion on the probability of this occurring is an important opportunity to assess also our current situation as a civilisation. Among Hart’s arguments, the self-destruction hypothesis especially needs to be rediscussed at a deeper level. Self-destruction following environmental degradation is becoming more and more an alarming possibility. While violent events, such as global war or natural catastrophic events, are of immediate concern to everyone, a relatively slow consumption of the planetary resources may be not perceived as strongly as a mortal danger for the human civilisation. Modern societies are in fact driven by Economy, and, without giving here a well detailed definition of “economical society”, we may agree that such a kind of society privileges the interest of its components with less or no concern for the whole ecosystem that hosts them (for more details see 29 for a review on Ecological Economics and its criticisms to mainstream Economics). Clear examples of the consequences of this type of societies are the international agreements about Climate Change. The Paris climate agreement 30 , 31 is in fact, just the last example of a weak agreement due to its strong subordination to the economic interests of the single individual countries. In contraposition to this type of society we may have to redefine a different model of society, a “cultural society”, that in some way privileges the interest of the ecosystem above the individual interest of its components, but eventually in accordance with the overall communal interest. This consideration suggests a statistical explanation of Fermi paradox: even if intelligent life forms were very common (in agreement with the mediocrity principle in one of its version 32 : “there is nothing special about the solar system and the planet Earth”) only very few civilisations would be able to reach a sufficient technological level so as to spread in their own solar system before collapsing due to resource consumption.

We are aware that several objections can be raised against this argument and we discuss below the one that we believe to be the most important. The main objection is that we do not know anything about extraterrestrial life. Consequently, we do not know the role that a hypothetical intelligence plays in the ecosystem of the planet. For example not necessarily the planet needs trees (or the equivalent of trees) for its ecosystem. Furthermore the intelligent form of life could be itself the analogous of our trees, so avoiding the problem of the “deforestation” (or its analogous). But if we assume that we are not an exception (mediocrity principle) then independently of the structure of the alien ecosystem, the intelligent life form would exploit every kind of resources, from rocks to organic resources (animal/vegetal/etc), evolving towards a critical situation. Even if we are at the beginning of the extrasolar planetology, we have strong indications that Earth-like planets have the volume magnitude of the order of our planet. In other words, the resources that alien civilisations have at their disposal are, as order of magnitude, the same for all of them, including ourselves. Furthermore the mean time to reach the Dyson limit as derived in Eq.  6 depends only on the ratio between final and initial value of T and therefore would be independent of the size of the planet, if we assume as a proxy for T energy consumption (which scales with the size of the planet), producing a rather general result which can be extended to other civilisations. Along this line of thinking, if we are an exception in the Universe we have a high probability to collapse or become extinct, while if we assume the mediocrity principle we are led to conclude that very few civilisations are able to reach a sufficient technological level so as to spread in their own solar system before the consumption of their planet’s resources triggers a catastrophic population collapse. The mediocrity principle has been questioned (see for example Kukla 33 for a critical discussion about it) but on the other hand the idea that the humankind is in some way “special” in the universe has historically been challenged several times. Starting with the idea of the Earth at the centre of the universe (geocentrism), then of the solar system as centre of the universe (Heliocentrism) and finally our galaxy as centre of the universe. All these beliefs have been denied by the facts. Our discussion, being focused on the resource consumption, shows that whether we assume the mediocrity principle or our “uniqueness” as an intelligent species in the universe, the conclusion does not change. Giving a very broad meaning to the concept of cultural civilisation as a civilisation not strongly ruled by economy, we suggest for avoiding collapse 34 that only civilisations capable of such a switch from an economical society to a sort of “cultural” society in a timely manner, may survive. This discussion leads us to the conclusion that, even assuming the mediocrity principle, the answer to “Where is everybody?” could be a lugubrious “(almost) everyone is dead”.

Conclusions

In conclusion our model shows that a catastrophic collapse in human population, due to resource consumption, is the most likely scenario of the dynamical evolution based on current parameters. Adopting a combined deterministic and stochastic model we conclude from a statistical point of view that the probability that our civilisation survives itself is less than 10% in the most optimistic scenario. Calculations show that, maintaining the actual rate of population growth and resource consumption, in particular forest consumption, we have a few decades left before an irreversible collapse of our civilisation (see Fig.  5 ). Making the situation even worse, we stress once again that it is unrealistic to think that the decline of the population in a situation of strong environmental degradation would be a non-chaotic and well-ordered decline. This consideration leads to an even shorter remaining time. Admittedly, in our analysis, we assume parameters such as population growth and deforestation rate in our model as constant. This is a rough approximation which allows us to predict future scenarios based on current conditions. Nonetheless the resulting mean-times for a catastrophic outcome to occur, which are of the order of 2–4 decades (see Fig.  5 ), make this approximation acceptable, as it is hard to imagine, in absence of very strong collective efforts, big changes of these parameters to occur in such time scale. This interval of time seems to be out of our reach and incompatible with the actual rate of the resource consumption on Earth, although some fluctuations around this trend are possible 35 not only due to unforeseen effects of climate change but also to desirable human-driven reforestation. This scenario offers as well a plausible additional explanation to the fact that no signals from other civilisations are detected. In fact according to Eq. ( 16 ) the mean time to reach Dyson sphere depends on the ratio of the technological level T and therefore, assuming energy consumption (which scales with the size of the planet) as a proxy for T , such ratio is approximately independent of the size of the planet. Based on this observation and on the mediocrity principle, one could extend the results shown in this paper, and conclude that a generic civilisation has approximatively two centuries starting from its fully developed industrial age to reach the capability to spread through its own solar system. In fact, giving a very broad meaning to the concept of cultural civilisation as a civilisation not strongly ruled by economy, we suggest that only civilisations capable of a switch from an economical society to a sort of “cultural” society in a timely manner, may survive.

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Acknowledgements

M.B. and G.A. acknowledge Phy. C.A. for logistical support.

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These authors contributed equally: Mauro Bologna and Gerardo Aquino.

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Departamento de Ingeniería Eléctrica-Electrónica, Universidad de Tarapacá, Arica, Chile

Mauro Bologna

The Alan Turing Institute, London, UK

Gerardo Aquino

University of Surrey, Guildford, UK

Goldsmiths, University of London, London, UK

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M.B. and G.A. equally contributed and reviewed the manuscript.

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Bologna, M., Aquino, G. Deforestation and world population sustainability: a quantitative analysis. Sci Rep 10 , 7631 (2020). https://doi.org/10.1038/s41598-020-63657-6

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A Scientist’s Warning to humanity on human population growth

One needs only to peruse the daily news to be aware that humanity is on a dangerous and challenging trajectory. This essay explores the prospect of adopting a science-based framework for confronting these potentially adverse prospects. It explores a perspective based on relevant ecological and behavioral science. The objective is to involve concerned citizens of the world in this enterprise. The overall objective is to maintain Planet Earth as a favorable home for the future of humanity. Nine ecological principles explain one major aspect of what is happening and provide critical guidelines for appropriate action. Nine social behaviors explore how we might integrate social science insights with those from ecology. Twenty predictions are proposed based on these ecological and social science principles plus existing trends. If these trends are not vigorously and courageously confronted, we will likely be on track for the demise of our civilization. As we examine these challenges, our job will be especially complicated because a major segment of humanity is not prepared to accept evidence based on science, and this generates much resistance to any efforts directed toward effective control of current and future challenges. In these complex circumstances, we must remain as cooperative and optimistic as possible so that we can promote the needed willpower and ingenuity.

This essay has broad support as it is a contribution to the Scientists’ Warning to Humanity Program of the Alliance of World Scientists ( Ripple et al., 2017 ).

1. Introduction

Planet Earth is an absolutely amazing place. An apparent rarity in the universe, it possesses the appropriate physical conditions to support life. As a result it hosts a tremendous variety of living creatures which we recognize and classify as various species. In relatively recent times, human life evolved, and in large part due to our extraordinary intelligence, has become the dominant life form on the planet. With nuclear power technologies, we are now capable of destroying all complex life forms, including ourselves. Our dominance is recognized by the acceptance of the term Anthropocene which proclaims that we have entered a human dominated planetary phase. Our numbers are projected to increase from an estimated 7.6 billion to 10 billion by 2050 ( Baillie and Zhang, 2018 ). Human caused species extinctions have also reached an unprecedented rate such that we are generally viewed as causing the sixth mass extinction episode for the planet. A recent effort to photograph human impacts on land use over the entire globe from 1992 to 2015 documents this rapidly increasing global-scale impact on land areas ( Nowosad et al., 2018 ). The inevitable questions for humanity at this stage in our history are: “Does this matter for our species?” “Does this rapid increase in numbers along with its corresponding expansion of our utilization of the Earth’s land area matter?” What does it mean for us?” Maybe it is merely a signal that we are a very successful species, and we can celebrate our good fortune. On the other hand, perhaps it is a signal that we are over-exploiting the Earth’s resources and we should seriously be preparing for a population crash. Or, are there still other scenarios? In the following two sections of this essay, we will explore these questions from the perspective of ecological science and then again from behavioral science. Subsequently, we will look for lessons learned by considering 20 predictions that emerge from our analysis.

2. Relevant ecological principles

Nine established principles of ecological science that are relevant to the circumstances we face are as follows:

• 1. Population growth in numbers on a finite planet cannot continue indefinitely for any living species, including humans ( Czech, 2013 ; Meadows et al., 2004 ).
• 2. Population growth generates three possible negative forces that collectively increase exponentially and eventually stop growth. These are increasing mortality rates, decreasing birth rates, and increasing rates of emigration relative to immigration. Only the first two of these are applicable with a global perspective. Separately or collectively, these negative processes cause population growth to stop. If access to required resources has been compromised during growth, the population may not only stop growing but decline or even crash ( Lidicker, 2002 ).
• 3. Living systems require energy for their ongoing existence. The proportion of available energy that is required for maintenance of living systems increases as the size and complexity of those systems increase. This means that the proportion of energy available for other desirable activities such as reproduction, individual growth, maintenance of health, and defense against parasites and pathogens will be proportionally and progressively less and less available as numbers increase ( Brown,J.H., Burnside, R. et al., 2011 ).
• 4. For social species such as humans, increasing numbers require additional energy for maintaining the integrity and cohesion of the groups to which they belong, and on which they depend for their livelihood.
• 5. The resources that humans need to support their food and shelter requirements are partly non-renewable and partly renewable. The first requires the extraction of various minerals, water, fuels, and building materials. Over time these resources will decline and become increasingly more difficult to extract. In the case of fresh water, supplies are becoming increasingly polluted. This not only affects us directly, but also all of the non-marine species that constitute the basis for our food supplies, medicinal drugs, other building materials, as well as a myriad of so-called “ecosystem services”. This trend also can influence weather patterns … Recent studies have concluded that our annual supply of renewables is now being used up by about August 1 of any given calendar year. Thus for five months we are deficit spending these resources, and in the process generally doing damage such that the Earth’s capability of generating these renewables becomes diminished ( Wakernagal et al., 2002 ). This human impact on the generation of these essential resources has been dubbed “the human footprint.” Two diverse examples illustrate the major impacts that humanity is making on renewable resources: 1) Mongolian steppe grasslands are heavily degraded because of exploding demands for cashmere wool plus a series of unusually severe winters ( MacLaughlin, 2019 ); 2) Diadromous fish populations in the north Atlantic have declined dramatically from multiple causes ( Limburg and Walden, 2009 ).
• 6. The human enterprise cannot succeed by going it alone ( Crist et al., 2017 ; Heal, 2017 ). Success requires the presence of a rich biota to provide the conditions necessary for our survival. As mentioned, these enabling services have been labeled with the metaphor of “ecosystem services” ( Daily, 1997 ; Norgaard, 2010 ). This concept has been reasonably successful in calling attention to our dependence on the Earth’s biota for humanity’s existence and welfare. These benefits that non-human organisms provide for us include oxygen generation, soil fertility, pollination of crops and other plant food resources, fisheries, air and water purification, pest control, medicines, genetic resources, fuel, building materials, weather moderation, dispersal of seeds and nutrients, partial stabilization of climate, mitigation of floods and droughts, decomposition of wastes, industrial applications, etc. And, this is not to mention the provision of a healthy, aesthetic, and intellectually stimulating environment (Daily1997).
• 7. Cnfounding the Earth’s declining ability to supply a steadily accelerating supply of the resources upon which we depend is that the species of living organisms that are required for production of renewable resources are increasingly facing population declines and risks of extinction because of ongoing fragmentation and degradation of the natural habitats that they need for maintaining healthy populations with long term viability ( Ascensão et al., 2018 ; Tucker,M.A.,K. Bohnning-Gaese et al., 2018 ; Hilty et al., 2019 ; Laurance, 2019 ).
• 8. Fortunately, it is the case that when populations and communities of numerous species are damaged by human activities or unusual forces, they can quite often recover over time if they are suitably protected from subsequent damages. However, we now know that if a community is badly damaged, it can experience a “tipping point” or threshold such that it cannot recover ( Dai et al., 2012 ; Roque et al., 2018 ), and it then becomes a different kind of community that is generally less productive, and much less useful to humans. This illustrates one of the many mechanisms that result in reduced resource availabilities, or expanded human footprints, as populations continue to build.
• 9. As we strive to preserve as much of our natural heritage as possible, we need to be aware of an often neglected feature of highly motile species. This is that individuals of such species often need more than one kind of suitable habitat. For example, there may be different habitat types required for different life history stages. An obvious example is species of frogs that begin life in a freshwater pond, but then metamorphose into adults that live in a forest. Many species are seasonally migratory, utilizing quite different habitats at different seasons. Some may even require particular transit habitats. An interesting case is that of caribou ( Rangifer tarandus ) in eastern Canada. Individuals that spend the winter at greater distances from their summer range survive better, but as a result such individuals will require larger home ranges ( Lafontain et al., 2007 ). A particular hazard for some migratory species is that the travel routes may need to be learned from conspecifics ( Festa-Bianchet, 2018 ; Jesmer et al., 2018 ). This implies that if social groups get too small they may lose all their potential leaders, and hence access to migratory destinations.

3. Relevant social behaviors

The following nine social behaviors can and should be recruited to help humanity respond to the ecological impacts that will surely endanger human civilization if current trends are allowed to continue.

• 1. We must explicitly recognize the need for an appropriate mixture of altruistic and self-promoting social behaviors. The first of these benefit the sociopolitical groups to which we belong (the common good), and the second group of behaviors supports the individual welfare of each of the citizens that constitute those groups ( Reich, 2019 ). Both are essential for our ongoing welfare. This principal emphasizes the necessity of having democratic mechanisms in place that promote true feelings of participation in the crafting of sustainable societies. This spirit of cooperation is essential for encouraging discussions that generate an appropriate mixture of benefits to individuals and to the success of the sociopolitical groups to which they belong ( Reich, 2019 ). Moreover, there will need to be a system of appropriate sanctions for individuals who have overly selfish or parasitic tendencies. This dual-purpose social behavior has a long history going back to our primate ancestors ( de Waal, 2015 ) and is clearly expressed in the U.S. Constitution. An illustrative example of how far we have drifted from this principle is provided by a quote from David Starr Jordan, a famous fish biologist who was a Professor of Zoology and later President of Stanford University, that is preserved in a 1933 8th grade graduation diploma from a school in Hawaii. Jordan’s message includes the statement that “Success means service. The more you serve the cause of others, the greater will be your own success.” In modern societies, this duality of behavioral modes is rarely explicit and increasingly favors individual benefits. When it is discussed, it is often put in terms of pursuit of private wealth (money) versus self-sacrificing altruism. Another unfortunate expression of this duality that has become common in the political dialog in the USA often occurs when behaviors that support the common good are labeled as “socialism.” While technically correct, this term translates for many Americans into “communism” which has widespread negative connotations. On the other hand, the single minded pursuit of money is justified as beneficial capitalism. These unfortunate interpretations of “socialism” make it more difficult to promote democracy which unequivocally requires a balance of the two modes of social behavior ( Lidicker, 2003 ; Reich, 2019 ).
• 2. Sociopolitical groups are hierarchically arranged, and all individuals must be contributing members of one or more groups, preferably including groups at multiple hierarchical levels. For example, an individual may belong to a neighborhood group, a county government, and a professional vocational association. National citizenship is an almost ubiquitous example of group membership.
• 3. Dialog at all levels needs to be respectful of the huge array of world views that currently exist in and among various social groups ( Reich, 2019 ). When serious disagreements arise within a group, it is often appropriate and effective to promote conversations with the observation that differing viewpoints generally will accommodate many objectives or components that are held in common. Hopefully, encouraging this approach will make it possible to address disagreements in a cooperative and compromising manner. An example of such a cultural impediment that needs to be confronted is the almost universal prohibition against including human population growth in relevant discussions ( Bongaarts, O’ Neill et al., 2018 ).
• 4. Appropriately there are often moral issues that need to be discussed or at least acknowledged in any considerations of human population growth. Moral principles are mostly acquired in childhood and as youthful adults. As such they are very difficult to modify. Our deliberations need to respect that reality. Tampering with human population growth is a topic that is loaded with moral issues. Those that accept the relevant scientific evidence are often accused of being genocidal, racist, anti-poor folks, anti-religion, and generally anti-human. These accusations are completely in error. In fact, the position taken in by the scientifically aware is the opposite. Generally, those who accept the scientific imperative feel that they have a moral responsibility to be concerned about the future of mankind. Usually they also are genuinely concerned about the huge inequities in the distribution of resources around the Earth. For many there is also a moral concern for the drift of governments away from democracies and into authoritarian regimes. This trend encourages increasing xenophobia which in turn generates a lack of cooperation among groups, and inevitably increasing negative interactions.
• 5. When engaging in discussion topics that deal with conservation and the future of humanity, it is generally advisable to avoid arguments based largely on aesthetics, love of nature, and related approaches. While these positions are valid in the context of particular world views, they are all susceptible to being characterized as the products of special interest groups, and tend to be divisive. Teachers and leaders at all levels need to appreciate that opinions which humans grow up with are very difficult to change by reasoning and argument alone. The multiple viewpoints can be accommodated by compromises, cooperation, and mutually supported programs and policies.
• 6. Community discussions are much more likely to succeed if the participants have sufficient education so that they can differentiate truth from falsehoods, and know how to think critically. This means that successful societies must provide good public education that is readily available to all children ( Lidicker, 2003 . Reich, 2019 ).
• 7. Related to this last array of social behaviors is the extremely important and yet very difficult social issue of the appropriateness of humans deliberately manipulating their own species numbers. One world view on this is that humans should do what they can to have as many members of their own species living on our planet as they can. This view was ingrained in our genes for almost all of human history, and surely has contributed to our successful survival and expansive distribution. It is also ingrained in many of our cultural behaviors and beliefs. A logical corollary of this viewpoint is that any effort to control population growth is genocide and inherently racist. However, in recent decades other issues relating to population growth have emerged. For example, it is now widely believed that women should be able to determine when and for how many times they should become pregnant. Known outcomes of this view are: smaller families that are less likely to live in poverty, improved education and hence job opportunities, communities with higher average standards of living, less criminal activity, healthier citizens, democratic governing structures, etc. Moreover, it is increasingly apparent that without constraints on population growth there is also the inevitability of genocide of a different type (see ecological principles 1–3) along with the following 20 realistic predictions. Peacefully debating the virtues of these two modes of so-called genocide will be a monumental challenge, but one we must face ( Kopnina and H. Washington, 2016 ; Kopnina and B. Taylor et al., 2018 ; Washington et al., 2019 ). The good news is that there is abundant worldwide evidence that if adults have the tools and understanding needed for controlling their own reproductive output, it will be modest and sustainable.
• 8. An aura of optimism is important. Pessimism leads only to inaction followed by failures and more pessimism ( Lidicker, 2011 ). An encouraging hopeful sign is the recent widespread mobilization of youthful activists in support of numerous progressive causes.
• 9. Conservationists should more aggressively confront the social tendency to minimize or ignore long term consequences of development projects, and take advantage of opportunities to educate the public about the issues involved ( Laurance et al., 2014 ). Litigation also can be a tool for delaying projects long enough for public education to become effective ( Florens and Vincenot., 2018 ).

4. Realistic prospects and problems

Obviously we need to muster all our resources and social skills to prevent continuing in our currently unsustainable trajectory. Equipped now with an ecological and behavioral framework, we can begin to carefully construct guidelines to inform our future efforts. A reasonable place to begin would seem to be an outline of our goals for humanity in the immediate future. Do we accept a fate of massive poverty, massive mortality from wars, terrorism, and disease, and uncontrollable migrations to the places where basic resources are still available? This is our current trajectory ( Brown, 2006 ; Heal 2017 ; Kopnina and Washington (2016) ; Stokstad 2019 ). We can assume, I hope, that we would prefer a future that features a comfortable standard of living with minimal disparity among individuals and social groups, high levels of education, and democratic organizational structures for social groups at all levels of organization. In this way, everyone can feel they have input into decisions being made that likely will affect them. Especially important is respectful coexistence of diverse cultures and world views.

In the recent past, there has been much discussion as to whether our deteriorating situation should be blamed mainly on human population growth or whether affluence and pollution should share as major contributors ( Ehrlich and Holdren 1971 . Actually, these three factors interact in complex ways. For example, while improving the standard of living of people everywhere is clearly a desired objective, this certainly would add to the consumption of renewable and non-renewable resources. On the other hand, if affluence were more equally distributed than it currently is, it would improve the situation so that people in general are more content with their lives and hence are more likely to be cooperative and productive. Pollution of our environment also reduces our standard of living through its negative impacts on our health, and by increasingly deleterious impacts on our agriculture, parks, and natural areas. This in turn reduces the health benefits of natural areas ( Weinstein et al., 2015 ), and diminishes the rate of replenishment of renewable resources.

An often heard argument is that technological advances will allow us to overcome the negative effects of population growth. Technology can and certainly will contribute to a slowing of the current negative trends. However, at this time in our history it is apparent that rapid human population growth along with out-of- control climate change will not only quickly cancel out many of the benefits for humans that technology may contribute, but it will continuously add new challenges as population growth, resource depletion, and climate change continue. Mann (2018) engagingly discusses this dichotomy of prevailing beneficial natural processes dominating our future versus a technology based “green revolution.” Probably some combination of these two survival strategies will prevail. The reality, however, is much more complicated. Superimposed on these two approaches, we face the real possibility that current and future climate changes will force humanity worldwide to confront widespread disruption of human communities and ecosystem services, not to mention negative impacts on biodiversity ( Norgaard, 2010 ; Nolan et al., 2018 ). For example, we can anticipate warming climates increasing crop losses to insect pests, especially at temperate latitudes ( Deutsch et al., 2018 ). Moreover, it is especially important that we plan for anticipated extreme weather events and catastrophic fires. An example of a positive recent research finding is that restoring large grazers to depleted range lands can blunt the impacts of major fires in those situations ( Pennisi, 2018 ).

Hopefully, the negative projections might increase the awareness of the public and governments regarding the necessity to confront the drivers of climate change more vigorously. Inevitably, this will incorporate an increasing focus on slowing of human population growth. Unfortunately, many humans, probably more than half, are opposed to any plan that would involve slowing and eventually stopping human population growth. There are many reasons for this point of view that makes folks unwilling to confront the risks we collectively face. One important reason for this reluctance is that since the late 1970’s, most world cultures have moved toward rewarding individual benefits over supporting the common good. This trend compromises the feeling of cooperation within the social groups to which we all belong and depend on for our survival ( Reich, 2019 ). More troublesome is the realization that, as mentioned, many folks view any efforts to contain population growth as homicide, etc. In reality, efforts to control our runaway population growth are precisely and explicitly the opposite. We want to improve the welfare of people everywhere, and strive to eliminate poverty, racism and other forms of xenophobia. Lastly, we would want to maintain an individual’s freedom to control their own reproductive activities. The only constraint on an individual’s behavior is that it must be compatible with the needs of the social groups to which they belong.

5. Realistic predictions

Realistic predictions can be derived from ecological and sociopolitical knowledge as well as from already existing trends, and can serve to motivate appropriate actions. An example of a well-established existing trend is that of global warming. Scientists have been concerned about this human caused trend at least as far back as 1966 ( Landsberg, 1970 ). Predictions, however, are inherently risky, especially given the power of human ingenuity to address perceived problems. Three examples of failures to predict accurately are: 1) the much faster than predicted sea level rises associated with the deltas of large river systems ( Voosen, 2019 ); 2) The unanticipated huge wave of unusually hot water that beginning five years ago swept across the Pacific Ocean causing widespread havoc with fisheries, seabird populations and whales, and is currently developing again ( Cornwall, 2019 ); and 3) Concentrations of the greenhouse gas methane are increasing in the atmosphere more rapidly than predicted ( Mikaloff Fletcher and Schaefer, 2019 ). In general, modern chaos theory supports the generalization that when dealing with complex systems, longer term predictions are more reliable because they are guided predominately by deterministic processes, while shorter term predictions are less reliably accurate since they often are strongly influenced ly by random processes. In general it will be very difficult to predict the ability of species and he communities of which they are a part to adapt successfully to the rapidly changing conditions in our future ( Bridle and van Rensburg (2020) . In this cautious spirit, the following 20 predictions are offered as potential warnings.

• a) The Earth’s per capita ability to supply basic food resources for humans will decline ( Deutsch et al., 2018 ; Riegler, 2018 ).
• b) Supplies of potable water will decline.
• c) The average standard of living will decline, probably with a continuously increasing unevenness of access to resources.
• d) Human immigration pressures will increase dramatically, mostly directed to those places on the planet that retain the highest levels of access to the remaining resources.
• e) Health maintenance levels and average life expectancies will diminish.
• f) The prevalence of disease outbreaks and pandemics will increase ( Pongsiri et al., 2009 ). In part this will be due to progressive diminution and loss of favorable gut microbiota, especially in urban areas ( Dominguez Bello et al., 2018 ).
• g) The proportion of individuals with debilitating mental illnesses will increase along with a general increase in the proportion of folks unhappy with their living conditions.
• h) Earthquakes will increase in numbers as a result of the proliferation of injection wells ( Goebel et al., 2018 ). These wells generate significantly destructive earthquake activity up to 30 km distance from the wells.
• i) The Earth’s climate will continue to warm into the foreseeable future ( Naff, 2016 ) leading to increasing instances of extreme weather conditions ( Murakami et al., 2018 ).
• j) Saltwater intrusion into coastal communities, sometimes for many kilometers, will endanger coastal forest wetlands ( Gewen, 2018 ), modify greenhouse gas emissions, increase methane production, and jeopardize coastal real estate values ( Worth and Dahl, 2018 ).
• k) Increasing ocean acidification will endanger marine life compromising an extremely important source of food for humans.
• l) Concentrations of methane, a powerful climate altering compound has nearly tripled in the atmosphere since 1800 and is expected to continue increasing driven by many causes, especially by agriculture and use of fossil fuels ( Mikaloff Fletcher and Schaefer, 2019 , Voosen, 2019 , Voosen, 2020 ).
• m) Extinction rates for the Earth’s biota will continue to increase alarmingly Stokstad (2019) .
• n) Insect biomass has declined dramatically in Germany ( Vogel, 2017 ), and this may be a prelude for similar trends elsewhere.
• o) Rapid expansion of infrastructure, such as roads, to support human population growth will generate multiple hazards for humans and the rest of the global biota ( Laurance et al., 2014 ; Laurance, 2019 ).
• p) Criminal activity in general will increase ( Weinstein et al., 2015 ) as well as both domestic and international terrorism.
• q) Governments at all levels will become more authoritarian.
• r) Social groupings above the levels of neighborhoods and small towns will become increasingly xenophobic.
• s) Pressure for recreation will increasingly and negatively impact protected areas.
• t) Support for education and basic research will decline as they are threats to dictatorships.

6. Guidelines

Here are six guidelines for all concerned citizens of this planet that summarize recommended approaches for achieving a sustainable human civilization. In addition, please note that Kopnina et al. (2016) have provided a most welcome list of human behaviors that non-coercively will help to guide us to population stability.

• 1) Pay attention to scientific understanding, and support future research. To make this effective, scientists need to do their part by making the effort to explain their findings in ways that can be understood by educated non-scientists and especially by government leaders.
• 2) Remain as optimistic as the evidence permits. No one wants to contribute time and financial support to lost causes, even if they are presented as important for various reasons. On the other hand, optimism encourages enthusiastic support for even difficult but important programs.
• 3) Maintain respectful dialog with as large a component of the Earth’s citizenry as possible. While local and regional projects are important for improving limited areas, and for education of residents and visitors, in the long term we will not succeed without significant cooperative involvement of all parts of Earth. We need to encourage the recent trends toward eliminating gender, ethnic, and racial biases in all aspects of human civilization. All women of reproductive age should have access to the tools needed to prevent unwanted pregnancies. Relevant to human population growth, it is important to note that when women have control over their reproductive activities, they typically make choices that are appropriate to their social and environmental circumstances. Population numbers then become stabilized. It is also critical for all sex-related decisions that all world citizens have access to education at least through the secondary school level.
• 4) Encourage cooperation and democracy in the organizational structure of social, governmental, and other groupings of various sizes and complexity. The Earth is full of countries with various levels of autocratic governments, and therefore we know that autocrats are not cooperative. Their primary job is maintaining their personal power. The welfare of their citizenry is low on their priorities, and may even be absent. Moreover, maintenance of the natural environment that in the long run supports their government may also be ignored or perhaps be exploited for monetary gain. Unfortunately, many countries, including the USA, are moving in the direction of autocracy, or are already there.
• 5) Be constantly aware of finding the appropriate balance of activities that support both the common good and those that enhance individual fitness. This dual support is essential for survival of social species such as Homo sapiens. However in some countries, including the USA, this duality is rarely mentioned and is certainly not emphasized. When it is mentioned, at least in the United States, it generally is put in terms of making money for the rich and large corporations versus unselfish giving to the poor. These behaviors are justified as appropriate capitalism on the one hand and admirable charity on the other. Recently, another tactic is to give to the financially poor and call it “socialism.” Of course it is socialism but in some countries, such as the US, this name is translated to unacceptable communism. Meaningful support for the financially stressed is threatened or absent, and yet is important to find ways to keep those struggling with financial poverty as contributing members of a democratic society.
• 6) Don’t underestimate the need for rapid progress in confronting the 20 predictions listed above. Cooperative social support is needed now. The current Covid 19 pandemic offers some helpful lessons in social behavior. Cooperation is now widespread and appreciated. Altruism is more and more common. Search for an appropriate vaccine is a worldwide endeavor. Even some corporations are considering giving to the common good. And, as predicted, there are individuals who compromise the cooperative spirit by intentionally not wearing masks when asked to do so, and thereby endangering the larger community in which they are a part. In such a social context, societies would be justified in protecting themselves from such dangers.

A final thought: Nine decades ago, Anne Frank gave us this wisdom: “How wonderful it is that nobody need wait a single moment before starting to improve the world.”

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Open Access

Peer-reviewed

Research Article

Improving wellbeing and reducing future world population

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Ecology, Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, United States of America

Roles Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing

Affiliation Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA, United States of America

Roles Conceptualization, Formal analysis, Methodology, Visualization, Writing – review & editing

Roles Conceptualization, Formal analysis, Project administration, Visualization, Writing – review & editing

• William W. Murdoch, 
• Fang-I Chu, 
• Allan Stewart-Oaten, 
• Mark Q. Wilber

• Published: September 12, 2018
• https://doi.org/10.1371/journal.pone.0202851
• Reader Comments

Almost 80% of the 4 billion projected increase in world population by 2100 comes from 37 Mid-African Countries (MACs), caused mostly by slow declines in Total Fertility Rate (TFR). Historically, TFR has declined in response to increases in wellbeing associated with economic development. We show that, when Infant Survival Rate (ISR, a proxy for wellbeing) has increased, MAC fertility has declined at the same rate, in relation to ISR, as it did in 61 comparable Other Developing Countries (ODCs) whose average fertility is close to replacement level. If MAC ISR were to increase at the historic rate of these ODCs, and TFR declined correspondingly, then the projected world population in 2100 would be decreasing and 1.1 billion lower than currently projected. Such rates of ISR increase, and TFR decrease, are quite feasible and have occurred in comparable ODCs. Increased efforts to improve the wellbeing of poor MAC populations are key.

Citation: Murdoch WW, Chu F-I, Stewart-Oaten A, Wilber MQ (2018) Improving wellbeing and reducing future world population. PLoS ONE 13(9): e0202851. https://doi.org/10.1371/journal.pone.0202851

Editor: Heidi H. EWEN, University of Indianapolis, UNITED STATES

Received: May 3, 2018; Accepted: August 9, 2018; Published: September 12, 2018

Copyright: © 2018 Murdoch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in this article are publicly available. The data are third party data and the authors had no special privileges when accessing these data. The data can be found in at the following locations: United Nations population estimates and projections (available at https://esa.un.org/unpd/wpp/ ), Human Development Index (available at http://hdr.undp.org/en/content/human-development-index-hdi ), country classifications by income (available at http://blogs.worldbank.org/opendata/new-country-classifications-2016 ), per capita GNI data (available at http://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD .), and measures of corruption (available at https://www.transparency.org/news/feature/corruption_perceptions_index_2016 ).

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The UN’s median projected world population in the year 2100 is more than 11 billion and still increasing [ 1 – 3 ]. Just over 3 billion people, or 78.5% of the 3.86 billion projected world population increase from 2015 to 2100, come from the world’s poorest region: a band of 37 high-fertility Mid-African Countries (MACs), which include all African countries with at least 1 million people in 2000, except for the five northern and five southern low-fertility nations. All but Sudan are sub-Saharan. The median UN-projected 2100 MAC population is over 3.97 billion.

The great majority of sub-Saharan (and hence MAC) projected population increase comes from high and slowly declining fertility [ 4 ]. Sub-Saharan fertility decline started about 20 years later than that of other developing nations [ 5 ] and, once begun, is estimated to have been one-fourth as fast as in Asia and Latin America at the equivalent demographic stage [ 6 ].

Historically, fertility has declined in response to increases in wellbeing associated with economic development. We show that, when Infant Survival Rate (ISR, a proxy for wellbeing) has increased, MAC fertility has declined at the same rate, in relation to ISR, as it did in 61 comparable Other Developing Countries (ODCs) whose average fertility is now close to replacement level. We show that if MAC ISR were to increase at the historic rate of these ODCs, and fertility declined correspondingly, then the projected world population in 2100 would be decreasing and 1.1 billion lower than currently projected. Such rates of ISR increase, and fertility decrease, are quite feasible and have occurred in ODCs in conditions comparable to MACs in the present day. Increased efforts to improve the wellbeing of poor MAC populations are key.

Approach and results

Middle-african (macs) and other developing countries (odcs).

We compare MACs with the demographic history of 61 Other Developing Countries (ODCs) which had high fertility (Total Fertility Rate, TFR = 6 or greater in almost all cases) in 1950-55 (the first period for which UN world data are available), and which experienced all or almost all of their fertility decline thereafter ( S1 Text ). We excluded China from the ODCs because of its unique 1-child policy. As in the MACs, all ODCs had more than 1 million people in 2000. The ODCs represent all major geographical regions ( Fig 1 ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Mid-African Countries (MACs) : Eastern Africa: Burundi, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mozambique, Rwanda, Somalia, S. Sudan, Uganda, U.R. Tanzania, Zambia, Zimbabwe; Middle Africa: Angola, Cameroon, Central African Republic, Chad, Congo, D.R. Congo, Gabon; North Africa: Sudan; Western Africa: Benin, Burkina Faso, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo. Other Developing Countries (ODCs) : Northern Africa: Algeria, Egypt, Libya, Morocco, Tunisia; Southern Africa: Botswana, Lesotho, Namibia, South Africa, Swaziland; Eastern Asia: R. Korea, Mongolia; Central Asia: Tajikistan, Turkmenistan, Uzbekistan; Southern Asia: Afghanistan, Bangladesh, India, Iran, Nepal, Pakistan, Sri Lanka; S.E. Asia: Cambodia, Indonesia, Lao P.D.R., Malaysia, Myanmar, Philippines, Singapore, Thailand, Viet Nam; Western Asia: Azerbaijan, Iraq, Jordan, Kuwait, Oman, Saudi Arabia, State of Palestine, Syria, Turkey, U.A. Emirates, Yemen; Southern Europe: Albania; Caribbean: Dominican Republic, Haiti, Jamaica; Central America: Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama; South America: Bolivia, Brazil, Colombia, Ecuador, Paraguay, Peru, Venezuela; Melanesia: Papua New Guinea. The base map is from Natural Earth and is in the public domain under a Creative Commons license.

https://doi.org/10.1371/journal.pone.0202851.g001

The average TFR for MACs in 2015 was 5.19, more than twice both the ultimate replacement rate and the overall world average (2.51) [ 3 ], and almost twice the ODC average (2.66). As seen in the sub-Saharan comparisons discussed above [ 6 ], MAC fertility decline has been later and slower than in the ODCs ( Fig 2A ).

A . Mean Total Fertility Rate (TFR) and B . mean Infant Survival Rate (ISR) with 95% confidence limits for Mid-African Countries (MAC) and Other Developing Countries (ODC), over time since 1950-55. Data from [ 3 ].

https://doi.org/10.1371/journal.pone.0202851.g002

The different MAC and ODC fertility trajectories suggest there may be some major causal differences [ 6 ]. [ 7 ] shows that fertility decline in sub-Saharan Africa began at an unusually low level of economic and social development. He also shows that the higher sub-Saharan African fertility since 1960 is correlated with a higher desired family size (though [ 8 ] note that, recently, desired family size has been declining in the region). However, we suggest that the main drivers of fertility decline operate in the same way in ODCs and MACs: that decrease in fertility is a response to the level and rate of change of the population’s general wellbeing.

Wellbeing and fertility

A vast body of evidence shows that desired family size is determined rationally and, beginning in Europe in the late nineteenth century, has declined largely in response to increased parental socio-economic wellbeing, including associated changes in the costs and benefits of children [ 7 – 11 ]. A diffusion effect, in which fertility decline spreads within a culture may also have operated in some situations [ 7 ]. Note that at the lowest levels of development, fertility often first increases with improved wellbeing (e.g. MACs in Fig 2A ), but thereafter has typically declined steadily.

We next illustrate briefly the relationship between fertility and wellbeing. First, about half (54%) of the variation in TFR among developing countries at one point in time is explained by variation in log(per capita income) ( Fig 3A ). But per capita income misses a major aspect of general wellbeing, namely how widely income and the benefits of development, such as improved health and education, are spread across the population. The UN Human Development Index [ 12 ], HDI, which combines per capita income with scores representing levels of health and education explains about 75% of the variation in TFR ( Fig 3B ).

A . Linear regressions (and 95% confidence bands) of TFR vs log(GNI per capita) (referred to as “per capita income” in the main text) for all developing countries in 2010 as defined by the World Bank [ 13 ]. Income data from World Bank [ 14 ] and TFR data from United Nations [ 3 ]. B . Linear regression (and 95% confidence bands) of TFR on Human Development Index (HDI) in 2010, for all countries in Fig 3 for which an HDI value is available [ 12 ]. C . Linear regression (and 95% confidence bands) of TFR on ISR in 2010. TFR and ISR data from United Nations [ 3 ].

https://doi.org/10.1371/journal.pone.0202851.g003

Second, [ 7 ] conducted a wide-ranging analysis of development and fertility patterns in sub-Saharan Africa. He suggests that fertility has broadly responded to development in sub-Saharan Africa as in 52 other developing countries, with the provisos noted above that sub-Saharan African’s fertility began to decline at a lower level of development, but usually remained higher at each particular level.

Finally, in 25 sub-Saharan countries with DHS (Demographic and Health Surveys) data from multiple time periods, fertility fell significantly faster between consecutive surveys in countries that experienced a greater increase in female education and in those that experienced a greater reduction in infant and child mortality [ 5 ].

Infant survival rate and general wellbeing

We use Infant Survival Rate, ISR = the percent of infants who survive to their first birthday, as our proxy for wellbeing (see S2 Text for other possible measures). It is equivalent to the Infant Mortality Rate (ISR = 100—IMR/10) used by the UN (see [ 3 ]). ISR is likely to indicate not only the health of infants, but also other components of wellbeing such as general health, access to medical care, other services, information, and other goods or opportunities. There is more historical data for ISR than for HDI (described above) and it explains statistically about 70% of the variance in TFR ( Fig 3C ). It is a particularly good indicator of improvement in general population wellbeing, as we explain next.

Data collected during the Demographic and Health Surveys (DHS) illustrate the relationship between national ISR values and general wellbeing in the population. The surveys measure ISR in families that are also classified into five equal-sized relative “wealth” classes. Wealth, a proxy for income, is measured by an index of household conditions and goods [ 15 ].

In developing countries, ISR is of course higher in richer segments of the population: averaged over all available data, ISR increases as we move from the poorest to the richest segments of developing-country populations ( Fig 4A ). ISR has also increased over time in all wealth classes ( Fig 4B ). However, it has increased faster in the poorer than in the richer fractions of the population. The estimated rates of increase per year for the poorest 20%, and the poorest 40% were, respectively, greater than the rates in the richest 20% and the richest 40% (the respective differences between rates being 0.10 (± 0.02 SE) and 0.07 (±0.02 SE)). These differences are statistically different (both p<0.0001 obtained by a linear mixed effects model including random intercepts for 985 observations of 72 countries).

A . The boxplot summarizes the relation between ISR and relative wealth. For each of 72 developing countries, we obtained ISR values from 1990 to 2014 for the 5 wealth classes. For each wealth class, we combined the values across countries for the box plots (the sequence of medians from Lowest to Highest is: 92.3, 93.1, 93.4, 94.3 and 95.6). Using the medians for each country’s wealth classes, a Friedman test that accounts for the within country correlation showed a significant difference among wealth classes ( χ 2 = 161.85 on 4 degrees of freedom, p < 0.0001). Similar results were obtained when using mean ISR instead of median ISR. The outlier had no effect on these results. B . Trends in ISR values between 1990 and 2014 in the five wealth classes. Each thin line is a single country; the thick lines were fitted by local regression.

https://doi.org/10.1371/journal.pone.0202851.g004

Initial (1950-55) ISRs were on average lower in MACs (median, 82%) than in ODCs (median, 85%) and remained so through 2010-15: the highest observed ISR in a MAC is 96.3% (median, 94%) and in ODCs 99.8% (median, 98%) ( Fig 2B ).

Fertility decline vs improvement in wellbeing in MACs and ODCs

UN data show that general wellbeing (as measured by ISR) improved later and more slowly in MACs than in ODCs. Our claim that this caused the later and slower fertility decline in MACs implies that improvements in wellbeing should be associated with similar declines in fertility in the two groups. We use two approaches to examine this.

Between-group analyses : We first compare TFR vs ISR in MACs and ODCs, as a whole, between 1950-55 and 2010-15.

Fertility declined approximately linearly, and at virtually identical speeds, in the shared range 90% ≤ ISR ≤ 96.3%. Linear regressions (black lines) fitted to the two groups over this range have statistically indistinguishable slopes ( Fig 5 ). Reinforcing a conclusion of [ 7 ], MAC TFR was approximately half-a-child higher than ODC TFR at the start of the decline (see also Fig 2 ); because the two fertilities declined at the same speed, the half-child difference persisted. The speed of fertility decline in ODCs increased as ISR approached 100% ( Fig 5 ).

Curves are local regression (LOESS) fits, using span = 1/3 [ 16 ]. Black regression lines are fitted in the approximately linear decline phase using all points with 90.0%≤ISR≤96.3%, indicated by vertical dotted lines. These have slopes -0.365 (ODC, 328 points, SE = 0.041) and -0.351 (MAC, 166 points, SE = 0.036), which are not statistically different. (In the linear model TFR = Mean + Region + ISR + Interaction, with independent, homoscedastic, Normal errors, the test for “no Interaction” gives t = 0.21, p = 0.84. When ODC and MAC error variances are not assumed equal, the slope estimates can be compared by a Welch t-test, which gives t = 0.26 and p = 0.79 on 380 degrees of freedom.) The extreme UN regions in the flat region (ISR ≤ 90%) are indicated by the olive and brown LOESS fits to West Asia and S.E. Asia, respectively. MAC and ODC slopes for 71.5%≤ISR≤87.5% are nearly flat (MAC slope = 0.04; ODC slope = -0.036) but differ statistically (the test for “no Interaction” gives t = 3.63, p = 0.0003). Markers A at 87.5% and B at 93.9% indicate the lower and upper limits for the range of slope overlap used in the single-country slope analyses (see S3 Text for further details).

https://doi.org/10.1371/journal.pone.0202851.g005

Fertility decline began at approximately the same ISR values in MACs and ODCs ( Fig 5 ). Median ISR in the year in which onset occurred (at a TFR 10% lower than the preceding highest value) was 91.18% in MACs and 92.05% in ODCs (MACs range of observations: 84.59%-95.29%; OCD range of observations: 83.46%-97.14%).

MAC and ODC trajectories are significantly different in the pre-decline phase (ISR ≤ 87.5%, point A in Fig 5 ), but the MAC trajectory was well within the large regional variation in ODCs seen in this phase. The slight decrease in ODC overall-group fertility in this phase was caused by differences among ODCs in initial (1950-1955) TFR values, not by fertility declines in individual countries: in the 34 ODCs that had initial ISR ≤ 85%, mean TFR change in this phase was -0.01 (± 0.71 SE).

Single-country analyses : Second, we measure change in fertility in individual countries over an ISR range that most countries in the two groups have experienced, and which also includes the ISR values where most fertility declines began. The range of overlap is 87.5% to 93.9% ISR (points A and B in Fig 5 ). Thirty-six MACs and 46 ODCs, have experienced ISR as low as 87.5% since 1950-55, and 90 of the 98 countries began their fertility declines at or above this threshold. The upper limit, 93.9%, is the median of the highest ISRs observed in MACs by 2010-15 and has been experienced by all but three ODCs.

The speeds of decline of TFR vs ISR over this ISR range are statistically indistinguishable in the two groups (p>0.42 with two sample t-test for unequal variance, sample size for ODC = 60 and MAC = 37), though on average are slightly faster in MACs ( Fig 6 ). The ODC group, which is geographically and culturally more diverse, shows a wider range of slopes.

The black bars in the boxplots give the median slopes. The steepest regression slope for a MAC was -0.62, and for an ODC was -0.91. Mean slopes (± SE), MACs = -0.30 (± 0.15) and ODCs = -0.27 (± 0.28), are not significantly different (two sample t-test with unequal variance: t = −0.76 on 93.93 degrees of freedom, p = 0.45, sample size for ODC = 60 and MAC = 37). The outlier slope for ODC countries is Jamaica. Data from [ 3 ].

https://doi.org/10.1371/journal.pone.0202851.g006

Population projections

We forecast population growth in the MACs by replacing what might be called the current “business as usual” scenario, which gives us the 11 billion projection for 2100, with scenarios where MACs achieve the faster improvements in wellbeing (indicated by ISR) that have been seen and projected in the ODCs, and hence faster declines in fertility. By contrasting these two scenarios we estimate the potential reduction in world population that can be achieved by actively investing in such accelerated improvement in wellbeing in the MACs.

The modeling machinery was that developed by Raftery and colleagues and now used by the UN [ 1 , 17 – 19 ]. Population projections for each future 5-year interval for each country are based on many sequences of age-specific birth and age- and sex-specific death rates generated from probability distributions of these vital rate parameters, following procedures in BayesPopURL [ 20 ]. We used 1000 such sequences. Before replacing MAC rates by ODC rates in these calculations (as described below), we confirmed that using the MAC rates allowed us to replicate UN results: our median projected 2100 total MAC population (3.94 billion) was within 1% of the UN projection (3.97 billion). (See S4 Text for details.)

We then projected MAC populations based on historic and projected rates for 56 of the ODCs. (Following [ 1 ] we excluded from the ODCs the five nations affected by AIDS epidemics.) We first aligned the current (2010-2015) ISR of a given MAC with the matching ISR of each of the 56 ODCs (37 MACs x 56 ODCs = 2,072 MAC-ODC combinations). For example, Angola’s current ISR equals that of Laos in 1990-95. We then kept the first (2015-2020) set of UN-projected MAC rates but replaced the rest by the historic and projected ODC rates, beginning with the matching period. This delay assumes conservatively that even if wellbeing improves immediately, there is a lag of about 5 years before vital rates are affected. For example, we kept Angola’s 1000 sets of 2015-2020 rates (which affect its 2020-2025 population), replaced each set of its projected rates for 2020-2025 to 2040-2045 by Laos’s historic rates for 1990-1995 to 2010-2015, and replaced Angola’s sets of projected rates for 2045-2050 to 2095-2100 by Laos’s sets of projected rates for 2015-2020 to 2065-2070. This pattern—MAC rates for 2015-2020, then historic ODC rates, then projected ODC rates—holds in most cases.

In general, the initially-matched ODC and MAC ISR values will not be equal, so we find the two consecutive ODC periods that straddle the current MAC value, and choose the earlier (lower ISR value) period.

In 2% of 2,072 MAC-ODC combinations even the 1950-55 ODC ISRs are higher than the 2010-15 MAC ISR, and in another 2% the current ODC ISRs are lower than the current MAC ISR. We expand the matching criterion to treat these conservatively (see S4 Text ).

Our projected ODC-based median total MAC population in 2100 is 2.86 billion (2.86B), which is 1.1B (28%) lower than the UN projection ( Fig 7 ). Under these circumstances, world population in 2100 would be 10.1B, and stabilized by 2085. Improving wellbeing quickly is crucial: if MACs were to move earlier to ODC-based trajectories, in 2015-20, the 2100 projected median MAC total would be about 1.5 billion lower than the UN projection.

The intervals show quantiles of the distribution of our MAC population projections. More than 90% of their spread is due to variation in the historic and projected demography among the ODCs. The rest is due to the UN’s probabilistic trajectories. The calculations are described in S4 Text (Method B, median-adjusted). For a given MAC population projection, a value for the world population can be obtained by adding the total of the median UN projections for all other countries (7.24 billion).

https://doi.org/10.1371/journal.pone.0202851.g007

Improving wellbeing in MACs

The results suggest that MAC fertility would fall as fast as it has done in the ODCs if wellbeing, exemplified by infant survival rate, were to increase at the speed it has done in the ODCs in similar demographic circumstances. This seems quite feasible. MACs are mostly poor, inequitable, corrupt and undemocratic; but most ODCs experienced similar conditions during their demographic transition.

Unusually rapid increases in ISR have been achieved at very low incomes and high levels of corruption. For the ODC countries in Fig 5 , we recorded real per capita incomes [ 21 ] in the 5-year period when each first reached ISR ∼ 90%; the median income was $2387 (see S5 Text for details). We then calculated how rapidly (number of percentage points gained per 5-year period) ISR increased to 96% [ 3 ]. Eleven ODCs had increase rates ≥ 1.65%; three of them (Bangladesh, South Korea and Nepal) had among the lowest recorded incomes ($997-$1300) and S. Korea ($1300) had the highest recorded increase rate (2.6%). A fourth, Egypt, had the lowest income and the highest increase rate ($1740, 2.33%) among the five Muslim North African countries. (The other seven ODCs with rapidly increasing ISRs, mainly oil producers and all Muslim, were among the richest; they achieved 90% ISR very late, i.e. at high incomes.)

A focus on the poor has facilitated rapid increases in ISR at low per capita incomes. S. Korea ( c .1960) was one of Asia’s poorest countries but had a famously equitable income distribution and land reform [ 22 , 23 ]. The Bangladesh government explicitly expanded benefits (e.g. micro-credit to women, health care, increased free female schooling, access to contraception services) to poor and, especially, rural portions of the country, also using help from NGOs [ 24 , 25 ]. It has achieved key 2015 UN Millennium Development Goals (MDGs), such as reducing maternal and childhood mortality and the fraction of the population in poverty, at extremely low average income levels. Controlled trials in Matlab, Bangladesh, showed reductions in childhood and maternal mortality and fertility in villages receiving outreach health and family planning services [ 25 ]. Nepal’s government likewise has committed to, and largely achieved, MDG targets. Among MACs, Rwanda, with ISR increase rate 2.0% and income only $1025 at ISR = 90%, exemplifies the effect of explicit government focus on achieving 2015 MDGs in a poor MAC [ 23 ].

Most encouragingly, MACs in general have achieved ISR ∼ 90% at much lower incomes (median = $1,283) than did the ODCs (median = $2,387). Furthermore, the nine MACs with ISR increase rates > 1.5% (1.57% to 2.25%) all had incomes ($344-$1171) below the MAC median.

With regard to corruption, in 2015 22 MACs ranked below (i.e. were worse than) 100 out of 167 countries, but 26 ODCs were also in this range [ 26 ]. Indeed, the rest of these 100 low-ranked countries all have ISR ≥ 98% except for three nations with population < 1M [ 26 ]. Bangladesh and Nepal have dismal histories of corruption. Bangladesh ranked last or second-to-last in the first four years of data (2002-2005), and by 2016 these two nations had escaped the bottom third in only two years [ 26 ].

Perhaps most surprising, in MACs since 1990 there is no correlation between the speed at which ISR has increased and the standard measures of civil conflict: frequency, intensity, and total deaths ( Fig 8 ). Nepal again, is illustrative of this general point. Nepal achieved its relatively rapid increase in ISR even though its Maoist rebellion ended only in 2006, and it experienced almost 10,000 battle deaths between 1996 and 2006, close to the average number in MACs (10,127 deaths).

A . Speed of increase in ISR, between 1990-95 and 2010-15, versus overall intensity of civil conflict between 1990 and 2014 in 36 MACs (no data for S. Sudan). Each dot is a MAC. The slope of the relationship is not significantly different from 0 (slope = 0.008, t = 1.02 on 34 degrees of freedom, p = 0.32). B . Speed of increase in ISR vs log(cumulative number of battle deaths+1) in these countries. The slope of this relationship is not significantly different from 0 (slope = 0.03, t = 1.35 on 34 degrees of freedom, p = 0.19). Change in Rwanda, the point with coordinates (9.1, 2.2), is between 1995-2015 because the 1994 genocide temporarily and severely suppressed ISR to 71.9% in 1990-95. Shown are 95% confidence bands. Data from [ 27 ]. See S6 Text for more details on this figure.

https://doi.org/10.1371/journal.pone.0202851.g008

The UN’s median estimated world population for 2015 is 7.35 billion with 934 million people in the MAC countries. For 2100, its median projected populations are 11.2 billion and 3.97 billion respectively. Our ODC-based median projection for the MACs is 2.86 billion. Using the UN projections for all other countries, our median projected world population is 10.1 billion. Other sets of assumptions also lead to projections lower than the UN’s.

[ 28 ] made alternative population projections for sub-Saharan countries to determine the expected effect on future population size if the observed initial slow fertility decline were to be followed by an accelerated decline (then steady tapering) as seen in some other developing countries. For the projected sub-Saharan fertility patterns, [ 28 ] substituted observed fertility patterns seen in 21 other developing countries covering a range of social and economic contexts (e.g. Bangladesh, China, Peru). The resulting median projected 2100 sub-Saharan population was 770 million below the UN median projection, again emphasizing that significant slowing of growth is consistent with some historical experiences. Using the UN projections for all other countries, the projected world population is 10.4 billion.

[ 29 ] assumed universal implementation of several key UN (2015) Sustainable Development Goals by the target date of 2030: education through secondary school, specific reductions in maternal and infant mortality, and improved reproductive health and family planning. In their highly detailed model, education is the main driver of fertility and also increases survival rates and access to and use of family planning. Achieving these goals results in a boost to development and the demographic transition between 2015 and 2030; thereafter development and demographic changes occur at a more regular speed. Their model applies to all regions of the world, not just MACs or sub-Saharan Africa, and projects a total world population in 2100 of 8.19—8.65 billion.

Our projection is more empirical than that of [ 29 ]: we project on the basis of past experience, that of the ODCs. This experience varied widely, so our ODC-based projections do too: our 5% and 95% quantiles for the 2100 MAC population are 1.75 and 6.23 billion. If the MACs experienced fertility and mortality rate changes following those of S. Korea (the ODC with the fastest rate of increase in infant survival) or of Thailand, the median projected MAC population in 2100 would be only 1.75 billion. Using the UN projections for all other countries, our median projected world population would then be 8.99 billion. Basing future MAC rates on those of Bangladesh, Azerbaijan, Brazil or Myanmar would also lead to world projections which are at least 2 billion below the UN’s median of 11.2 billion.

These special cases are realistic. There is reason to hope that MAC infant survival could increase faster, and hence fertility decrease faster, than predicted by the overall ODC experience. As noted above, ISR reached 90% at a median income of $2,387 in ODCs, but of only $1,283 in MACs. Thus, while the projections of [ 29 ] are optimistic, the necessary demographic changes have been achieved in the past.

Our results and these others suggest that improving widespread wellbeing in MACs, at rates previously achieved by many other developing countries, is likely to lead to future populations much smaller than currently projected. A key step is to intensify current efforts to improve conditions in poor and rural areas, by governments (of MACs and more developed countries), international agencies and NGOs [ 22 , 23 ].

Supporting information

S1 text. details relating to fig 1 : country groups..

https://doi.org/10.1371/journal.pone.0202851.s001

S2 Text. Fertility decline vs improvement in wellbeing in MACs and ODCs: other possible measures.

https://doi.org/10.1371/journal.pone.0202851.s002

S3 Text. Fertility decline vs Infant Mortality Rate.

https://doi.org/10.1371/journal.pone.0202851.s003

S4 Text. Population projection methods.

https://doi.org/10.1371/journal.pone.0202851.s004

S5 Text. Data on per-capita income.

https://doi.org/10.1371/journal.pone.0202851.s005

S6 Text. Data on armed conflicts.

https://doi.org/10.1371/journal.pone.0202851.s006

Acknowledgments

We thank Hana Ševčíková for responsive guidance in our use of BayesPopURL, David Lopez-Carr for helpful comments on the manuscript, and Javier Birchenall for advice on real incomes.

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• Published: 22 February 2020

Human fertility in relation to education, economy, religion, contraception, and family planning programs

• Frank Götmark   ORCID: orcid.org/0000-0003-2604-3298 1 &
• Malte Andersson 1  

BMC Public Health volume  20 , Article number:  265 ( 2020 ) Cite this article

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The world population is expected to increase greatly this century, aggravating current problems related to climate, health, food security, biodiversity, energy and other vital resources. Population growth depends strongly on total fertility rate (TFR), but the relative importance of factors that influence fertility needs more study.

We analyze recent levels of fertility in relation to five factors: education (mean school years for females), economy (Gross Domestic Product, GDP, per capita), religiosity, contraceptive prevalence rate (CPR), and strength of family planning programs. We compare six global regions: E Europe, W Europe and related countries, Latin America and the Caribbean, the Arab States, Sub-Saharan Africa, and Asia. In total, 141 countries are included in the analysis. We estimate the strength of relationships between TFR and the five factors by correlation or regression and present the results graphically.

In decreasing order of strength, fertility (TFR) correlates negatively with education, CPR, and GDP per capita, and positively with religiosity. Europe deviates from other regions in several ways, e.g. TFR increases with education and decreases with religiosity in W Europe. TFR decreases with increasing strength of family planning programs in three regions, but only weakly so in a fourth, Sub-Saharan Africa (the two European regions lacked such programs). Most factors correlated with TFR are also correlated with each other. In particular, education correlates positively with GDP per capita but negatively with religiosity, which is also negatively related to contraception and GDP per capita.

Conclusions

These results help identify factors of likely importance for TFR in global regions and countries. More work is needed to establish causality and relative importance of the factors. Our novel quantitative analysis of TFR suggests that religiosity may counteract the ongoing decline of fertility in some regions and countries.

Peer Review reports

United Nations (UN) projects that the global human population may increase from 7.8 billion in 2020 to 10.9 billion by 2100 (‘medium variant’ [ 1 ];). A 40% population increase would have strong effects on economies, food production, environment and global climate [ 2 , 3 , 4 , 5 ]. Understanding the causes of this extraordinary population growth is critical for many aspects of international and national planning for the future (e.g., [ 6 ]).

The total fertility rate (TFR) is a major determinant of population growth rate [ 1 ]. TFR is the average number of children women would bear, if they survive to the end of reproductive life and have the same probability of child-bearing in each age interval as currently prevails across the population. Based on observations of past and ongoing global decline in TFR, UN [ 1 ] assumes in its medium projection model that TFR in all countries will converge to near replacement level (2.1) during the decades up to 2100. However, such continued decrease in TFR should not be taken for granted [ 7 , 8 ], and altered assumptions markedly change the population projections [ 1 ]. Population policies depend strongly on our limited knowledge and assumptions about how TFR is related to other factors.

The steady decline in global fertility that began about 1965 stalled in many countries from the mid-1990s ([ 9 ]; for Africa, see [ 10 ]). Limitations in contraceptive use, family planning programs [ 11 , 12 ] and education [ 13 ] may be involved in the stall, impeding efforts to reduce population growth. Here we study TFR in six global regions and analyze its relation to five debated factors that are known or assumed to influence fertility: education of girls and women, economy (GDP per capita), religiosity, contraceptive use, and family planning programs. Other factors can also influence TFR [ 9 , 14 ] and may be correlated with the factors analyzed here (see Discussion).

Lutz [ 15 ] suggested a rationale for population policies based on the relation of TFR to education and health. Increased education (school years) of girls and women is associated with declining fertility in many countries. Education can change family relations and childbearing decisions. More and longer education can bring about empowerment of women, later marriage, later onset of childbearing, and smaller family size (e.g. [ 16 , 17 , 18 , 19 ]). There is variation among countries, and the empirical record does “not support the idea that such a simple causal process operates everywhere” [ 20 ]. Nevertheless, fertility differs between more and less educated women in nearly all countries, but the precise mechanism that leads to lower TFR with longer education is not well known [ 21 ].

Reduced family size as nations and economies develop might be due to increasing income per capita, and to trade-off between quantity and quality of children [ 14 , 22 ] (review in [ 23 ]). Technology favors investment in longer education (human capital), implying higher costs of children, and opportunity costs for child-rearing women in job markets (“motherhood wage penalty”). Families are therefore expected to invest in more highly educated but fewer children, and TFR declines.

Based on theoretical models and data from European and other countries, Galor [ 24 ] analyzed four suggested causes of the demographic transition and declining TFR 1851–1915: rising income, reduced child mortality, children as old-age security, and rising demand for education. He rejected the first three (but see [ 25 ]), emphasizing the role of increasing education for fertility decline. Growing economy, industrial production and technology favored higher child quality, and hence smaller families [ 24 ]. In two studies based on countries as units, TFR was more strongly related to education than to GDP per capita [ 19 , 26 ]. TFR had little relation to the level and change of GDP per capita 1960–2010, but GDP changes tended to be increasingly positive for countries at lower TFR level [ 27 ]. Lower TFR may therefore favor economic development, rather than the other way around [ 28 , 29 ].

Faith and religious authority can influence TFR at individual and country levels. For instance, at the UN population conference in Cairo 1994, Vatican and Muslim leaders opposed aspects of family planning, especially abortion and women’s autonomy [ 30 , 31 ]. Increased faith has accompanied population growth in parts of the world [ 32 , 33 ]. Based on the World Values Survey, Norris & Inglehart [ 34 ] ranked 73 countries as “most secular”, “moderate”, or “most religious”. Mean TFR 1970–1975 for the most secular countries was 2.8 children, for moderate 3.3 and for most religious 5.4. The corresponding values 2000–2005 were 1.8, 1.7 and 2.8. Several other studies also suggest that religiosity favors high TFR [ 35 , 36 , 37 ].

Contraceptives and family planning programs

The family planning (FP) movement and FP programs emphasize women’s rights and empowerment, and the imbalance between human numbers and vital resources [ 38 , 39 ]. FP programs spread information, counsel couples and make contraceptives easily available, all of which may reduce TFR. Use of modern contraceptives is important [ 11 , 21 , 40 ], and there is experimental evidence that FP programs increase contraceptive use and reduce TFR [ 41 , 42 , 43 ]. Other factors, such as education and religiosity, also influence contraceptive use (e.g. [ 44 , 45 ]). After the UN Cairo conference in 1994 a concept less clearly linked to family size (“sexual and reproductive health and rights”) spread, and support for family planning declined [ 27 , 31 , 38 , 46 ].

For our analyses, data on contraceptives (and education, economy, religion) were available from six regions, while data on FP programs were available from four regions. FP programs, potentially important in four high fertility regions, are analyzed separately from other factors, but all factors are treated in the Discussion.

Analytical approach

We analyse TFR at regional and country levels. Most studies analyze single factors and groups of countries [ 11 , 14 , 21 , 47 ]. Studies that include both developing and developed countries usually deal with one or two factors (but see [ 19 ]). To our knowledge, TFR and its relations to education, economy, religion, contraception and FP programs have not been analyzed together in the major global regions, our aim here.

Many studies use countries as sample units in statistical analyses and tests, but countries may not be statistically independent units. Neighboring countries can be similar culturally, economically or politically, and also distant countries can have political and economic ties [ 48 ] and similarities in health status and social norms (e.g. [ 49 ]). Some countries may therefore form clusters of similar units, differing markedly from other clusters. Moreover, the number of countries in an area is a partly arbitrary consequence of political events, which may divide a nation into two or more (e.g. former Jugoslavia and Sudan). Countries therefore deviate from requirements of independent sample units in many probabilistic statistical methods. Using countries as units in statistical tests may therefore lead to pseudo-replication, inflated sample size and misleading results as regards probability levels [ 50 , 51 ], a problem that deserves further attention from statisticians.

We therefore avoid statistical testing and multivariate statistical modeling, instead using simple correlation, regression and graphical analysis (e.g. [ 52 ]) for generating hypotheses and identifying factors of likely importance for causal analyses of TFR (see also [ 21 , 53 ]). We do not analyze all countries together but group them into six global regions, forming sets of geographically or otherwise related countries that share similarities, as explained below. Among the regions we examine the extent to which TFR is related to the five factors and how the factors correlate with each other, exploring potential differences between regions.

Regions may differ systematically in unmeasured factors that affect TFR. Compared to analyzing all countries together, analyzing regions separately can then reduce the influence of unmeasured variation in the analysis, increasing the possibility of clarifying differences between the five factors studied as regards their importance for TFR. We complement this approach by analyzing differences and similarities within regions, with countries as units. We use estimates of TFR and the factors from 2005 to 2015 (see below). The results therefore concern the recent situation and help identify factors of likely importance for future causal analyses of TFR.

Regions and countries

We included countries with available data for education, economy, religion and contraception (for FP programs, see below). Russia, China and several other countries could not be included due to lack of data. Based on 141 countries (Table  1 ) we established regions, taking into account geography and culture, as UNESCO [ 55 ] did in categorizing five global regions (Africa, Arab states, Asia and the Pacific, Europe and North America, Latin America and the Caribbean). We also considered shared history and degree of economic and political ties, and differ from UNESCO [ 55 ] mainly in using Eastern Europe as a separate region (motivated by spatial proximity and common history of Soviet influence). The six regions are as follows (see also Table 1 ):

Western Europe and related states (“W Europe” below) . Countries west of the former Soviet Union, and six states with ties to W Europe historically and politically: Israel, Iceland, USA, Canada, Australia and New Zealand. In total 25 countries.

Eastern Europe (“E Europe” below) . Countries in E Europe formerly linked to the Soviet Union, and also Albania, Turkey and Georgia. 20 countries.

Latin America and the Caribbean (“Latin America” below). All American countries S of the US, including four in the Caribbean: Trinidad & Tobago, Jamaica, Dominican Republic and Haiti. 23 countries.

Arab States . Countries in NW, N and NE Africa and in Western Asia, including Iraq and countries in the Arab peninsula but not further east.18 countries.

Sub-Saharan Africa . Countries south of the Sahara (Comoros, Sudan and Mauritania are included in Arab States). 31 countries.

Asia . Countries in central, E and S Asia. This is the most diverse region, with countries that to some extent share cultures and political systems, although these vary markedly. We did not divide Asia into smaller regions as they would contain too few countries for meaningful analyses. 24 countries (small Pacific island nations are excluded).

Table 1 lists countries and TFR for the five-year period June 2010–June 2015 [ 54 ]. Data for female school years are from UNDP [ 56 ] and represent “average number of years of education received by people ages 25 and older, converted from education attainment levels using official durations of each level”. The data are means for 2011–2015. We used data for females, as theory and population policies focus on female education.

Data for GDP (Gross Domestic Product) per capita are World Bank [ 57 ] mean values for 2011–2015, in PPP (Purchasing Power Parity, International dollars, constant 2011 values). Data for religion come from standardized surveys of religiosity by Gallup, Inc. For each country a sample of 1000 respondents is drawn, and weights are assigned so the data reflect the population in terms of gender, age, education, household size, and socioeconomic status. The survey, starting in 2005, has been repeated several times in each country. We use the Gallup question “Is religion an important part of your daily life?” and the proportion of respondents in each country saying yes to this question (“yes” or “no” were the options). We use a compilation of pooled Gallup data for individual countries collected from 2005 to about 2012 ([ 33 ]: Table 1 –6). The average number of respondents per country was 7567 ([ 33 ], p. 12).

As a fourth factor in our analyses of the six regions we used contraceptive prevalence rate (CPR). No data were available for family planning (FP) programs in the two European regions. CPR should bear some relation to FP programs but may not reflect their strength, which we analyze separately (see below). In a global report [ 58 ], CPR is recorded for “generally married or in-union women”, where “a union involves a man and a woman regularly cohabiting in a marriage-like relationship”. CPR is “number of women of reproductive age who are married or in a union and who are currently using a method of contraception”, divided by “number of women of reproductive age who are married or in a union”. We used CPR (%) for the period 2006–2010, for women or couples “using any modern method” (defined as including sterilization, IUD, implant, injectable, pill, condom, vaginal barriers, lactational amenorrhea method, emergency contraception, or other, e.g. contraceptive patch or vaginal ring). A limitation is that sexually active unmarried women and those not in unions, e.g. adolescents, are not included in the UN data (for a study including these categories and also traditional contraceptive methods in 77 countries, see [ 59 ]). We only included modern methods, as they are most effective and emphasized. Three of the 25 W Europe countries lacked CPR data (Cyprus, Iceland, Luxembourg) but had data for school years, GDP per capita and religiosity. They were included in analyses of these factors.

To examine TFR in relation to FP programs in four regions, we used data on FP program strength ([ 60 ] and the website track20.org ). Program-effort scores are given for four components (policy, service, records and evaluation, and contraceptive availability and access) and for a total of 30 measures across components (3–13 measures per component, see [ 60 ]). A total score is calculated from the component scores. The recommended data to use for countries is this score expressed as a percentage of the maximum score [ 60 ]. We used data from 2014, and included only countries that we also used in analyses of the factors above. The number of countries were: Latin America 15, Arab States 9, Sub-Saharan Africa 23 and Asia 12. In a complementary analysis of Sub-Saharan Africa and Asia we used the full sample of countries available at track20.org . We also compared the four regions with respect to their mean program strength in 2014.

Calculations and statistics

Based on countries, we calculated mean TFR (± SD) and mean values of the other factors for each region. All countries have equal weight in the analyses. In graphs we present the mean regional TFR related to each factor, with the linear least square regression line and the coefficient of determination, r 2 , for the relationship (e.g. [ 61 ]). We similarly explored relationships between TFR and the factors within the six regions, using countries as units. We refrain from statistical testing of regression slopes, as explained above. Outliers and countries at opposite ends of the line are indicated in the graphs (maximum five countries).

School years, GDP per capita, religiosity and contraceptive use may be associated with each other. Their pairwise relationships are shown graphically together with the correlation coefficient r . No regression line is shown in these cases, where our purpose is mainly to identify associations among factors, and potential indirect effects on TFR. Estimating influences and dependence/independence among the factors requires other approaches. We summarize this analysis in the Results. Detailed graphs for the six regions, with all countries, are given in Additional file  1 (part 1).

Our main aim here is to analyze variation in TFR. Data on variation in the five factors for all countries are shown in Additional file 1 (part 2).

Levels of TFR and related factors in the six regions

E Europe had the lowest TFR (mean 1.57) and Sub-Saharan Africa the highest (4.95). Arab States had the second highest TFR (3.27), 1.7 less than Sub-Saharan Africa. TFR in E Europe (1.57) and W Europe and related countries (1.73) was well below the approximate global replacement rate of 2.1 children per woman. Latin America and Asia had similar TFR: 2.39 and 2.44, respectively. TFR variation within regions was relatively high in Sub-Saharan Africa, Arab States and Asia, lower in Latin America and W Europe, and very low in E Europe (see SD in Fig.  1 ).

Mean (±SD) Total Fertility Rate (TFR) of Countries in Six Global Regions and its Relationship to Four Factors

The average number of school years for females varied from 4.2 in Sub-Saharan Africa to 11.8 in W Europe (Fig. 1 ). TFR declined with increasing school years among the regions ( r 2  = 0.89). In contrast, TFR increased with religiosity ( r 2  = 0.66, Fig. 1 ). The average proportion of respondents saying yes to “Is religion an important part of your daily life?” varied from 0.44 in W Europe to 0.94 in Sub-Saharan Africa.

The average contraceptive prevalence rate (CPR) varied from 23% in Sub-Saharan Africa to 64% in W Europe. TFR was negatively related to CPR ( r 2  = 0.66) and to GDP per capita ( r 2  = 0.40, Fig. 1 ). GDP per capita varied more than ten-fold; it was lowest in Sub-Saharan Africa and highest in W Europe. E Europe deviated most from the regression lines for TFR versus CPR and TFR versus GDP per capita (Fig. 1 ).

We also used countries within the regions as units for analysis of TFR’s relation to other factors (Figs.  2 , 3 , 4 and 5 ). In five regions TFR decreased with increasing school years (weakest in E Europe, strongest in Sub-Saharan Africa). Surprisingly, it increased with school years in W Europe (Fig.  2 ). In five regions, TFR increased with religiosity (least strongly in Arab States, strongest in Sub-Saharan Africa and Asia). W Europe deviated again, with negative relation between TFR and religiosity (Fig.  3 ).

Total Fertility Rate (TFR) for Countries Within each of Six Global Regions, in Relation to Mean Number of School Years for Females (Note Different Scales on Y-axes)

Total Fertility Rate (TFR) for Countries Within each of Six Global Regions, in Relation to the Proportion of Respondents saying “Yes” to the Question “Is Religion an Important Part of Your Daily Life?” (Note Different Scales on Y- and X-axes)

Total Fertility Rate (TFR) for Countries Within each of Six Global Regions, in Relation to Mean Contraceptive Prevalence Rate (%) (Note Different Scales on Y- and X-axes)

Total Fertility Rate (TFR) for Countries Within each of Six Global Regions, in Relation to GDP Per Capita (international dollars) for the Countries (Note Different Scales on Y- and X-axes)

In five regions, TFR had a negative relation to CPR (weak in E Europe, strong in Sub-Saharan Africa and Asia), but a weak positive relation in W Europe (Fig.  4 ). For GDP per capita, the results were similar: within the regions, TFR decreased with increasing GDP per capita, especially in Sub-Saharan Africa, and also in Latin America, Arab States and Asia, but only weakly so in E Europe, and not at all in W Europe (Fig.  5 ).

Relations between the four factors

In each region separately, we analyzed the degree to which the factors are correlated ( r ) (for graphs with all countries shown, see Additional file 1 , part 1). In W Europe, the three factors associated with TFR decline were positively related (Fig.  6 ). CPR versus GDP per capita had the strongest correlation, followed by school years versus GDP per capita. Religiosity was negatively correlated with the other factors, most strongly with school years and CPR (Fig. 6 ).

Pairwise Correlations (Pearson’s r ) Between the Four Factors Related to TFR, with Circle Size Proportional to r , and Colors indicating Positive or Negative Correlation

In E Europe, Latin America and the Arab States the three factors associated with TFR decline also were positively related (Fig. 6 ), in E Europe most strongly for CPR versus GDP per capita and CPR versus school years, in Latin America and Arab States strongly for school years versus GDP per capita. Religiosity was negatively correlated with the other factors, strongly so for school years. In the Arab states, however, GDP and religiosity were weakly positively related.

In Sub-Saharan Africa the factors were generally more strongly correlated than in other regions (Fig. 6 ). The three factors associated with TFR decline were positively related, with highest r (0.80) between CPR and school years. Religiosity had negative correlations with school years, GDP per capita, and CPR (Fig. 6 ).

Asia followed the same pattern as the other regions (Fig. 6 ): the three factors associated with decline in TFR were positively related, especially school years versus GDP per capita. As in W and E Europe, school years reached a maximum of about 12 years for the most affluent countries (see Additional file 1 , part 1). Religiosity had strong negative correlation especially with school years, and also with GDP per capita (Fig. 6 ).

Table  2 gives mean r values and their range for the six regions. For factors negatively associated with TFR, the highest mean positive correlation was between school years and GDP per capita. For religiosity, the strongest mean negative correlation was between school years and religiosity (Table 2 ). Thus, particularly the number of school years for females was correlated with two major factors: positively with GDP per capita, and negatively with religiosity.

Family planning and TFR

In four regions, we related countries’ TFR to family planning (FP) in 2014. TFR was negatively associated with FP program strength; r 2 ranged from weak (0.07 in Sub-Saharan Africa) to relatively strong relations in the other three regions (0.27–0.40) (Fig.  7 ). In three regions, r 2 is sensitive to outliers. In Arab States, r 2 changes from 0.40 to 0.85 if the outlier Lebanon is removed. In Asia, r 2 changes from 0.34 to 0.65 if Iran is removed. In contrast, in Sub-Saharan Africa a weak relation becomes even weaker if Rwanda is removed ( r 2 changes from 0.07 to 0.02).

Total Fertility Rate (TFR) for Countries Within each of Four Global Regions, in Relation to Strength of Family Planning Programs (FPE index)

In our complementary analysis of Sub-Saharan Africa and Asia, using all countries available at track20.org , the result for Sub-Saharan Africa ( n  = 32) was the same as before ( r 2  = 0.07). For Asia ( n  = 27) the correlation TFR versus FP program strength became weaker ( r 2 dropped from 0.34 to 0.13). There was high variability of TFR at low program strength, mainly due to the addition of Russia, Armenia, and Azerbaijan.

For the four regions we also re-analyzed TFR versus the other four factors, using the countries in the data set for FP program strength (sample in Fig. 7 ) and comparing with the earlier result for the full sample of countries (n-values in Fig. 1 ). For the FP program data set compared to the full sample, r 2 values for the four regions were rather similar for TFR versus school years and CPR, but only about half as large for TFR versus religion and GDP per capita (see Additional file 1 , part 3). We also repeated the analysis including only countries deleted from the full sample (those without FP program data, n  = 38). Analysis of the deleted countries reversed the picture: mean r 2 doubled for TFR versus religion, and TFR versus CPR and GDP per capita also increased markedly, compared to the full sample. For school years, one region had a strongly negative and another region strongly positive relation with TFR (Additional file 1 , part 3).

These contrasting results indicate that the FP program dataset was not representative for the full sample of countries. This was confirmed for Asia, Sub-Saharan Africa and Arab States: the countries absent from the FP program analysis (n = 38) had either low or high TFR (and they were included in the full sample). There was a clearly visible gap among TFR values, and the mean values for these countries with low and high TFR were 1.3 and 2.8 for Asia, 3.1 and 5.2 for Sub-Saharan Africa, and 2.2 and 4.5 for Arab States, respectively. Thus, for three regions, several countries with low or high TFR were lacking in the analysis of TFR versus FP program strength (Fig. 7 ) (Additional file 1 , part 3).

Comparing the four regions, the mean FP program strengths in 2014 (based on countries in Fig. 7 ) were surprisingly similar, ranging only from 49.5 to 54.5%. A complementary analysis with all available countries in track20.org gave an even narrower range, from 48.6 to 52.1% (lowest for Sub-Saharan Africa, highest for Asia). FP program strength therefore was far from a possible 100% maximum value in all four regions.

The broad analysis of six global regions shows associations of TFR with each of the five factors explored (Figs.  1 , 2 , 3 , 4 , 5 , and 7 ). The similarity of results among and within regions suggests that the relationships (negative or positive) are real and fairly general. Intriguing deviations occur in W and E Europe. Moreover, the factors to which TFR is related are themselves related in interesting ways, especially female education, which is positively correlated with GDP per capita and negatively correlated with religiosity.

TFR is strongly associated with education, contraceptive use, and religiosity (r 2  = 0.89, 0.66 and 0.66, respectively). Among regions (Fig. 1 ), TFR decreased with increasing education for females, supporting earlier studies (e.g. [ 18 , 19 , 21 , 62 ]). The number of school years for women increased markedly after 1970 in most regions, but increased less in Africa [ 63 ]. The decrease in TFR might also arise indirectly via school year correlations with improved economy, family planning (FP) programs, and media attention to FP, factors which may also lead to smaller families [ 64 , 65 , 66 , 67 ].

Below, “among regions” refers to comparisons of regions, and “within region” refers to comparisons of countries within regions.

Western Europe and related countries

Within this region, TFR and education were positively associated, in contrast to all other regions (Fig. 2 ). This result is consistent with the reversal of TFR decline between 1975 and 2005 in Western countries at high (and increasing) values of the Human Development Index [ 7 , 68 , 69 ]. Also increasing immigration to W Europe may influence TFR (see [ 70 , 71 ]).

TFR had little or no association with contraceptive prevalence (CPR) or GDP per capita. In contrast to non-European regions, TFR here tended to decline with higher religiosity, partly due to south European countries: among the six countries with highest religiosity, five were in S Europe (Portugal, Italy, Greece, Malta and Cyprus), all with low TFR. But within W European countries, there is evidence that TFR is lowest for religiously unaffiliated or more secular groups [ 72 , 73 ]. Compared to non-European regions, few European countries have strong religiosity.

Eastern Europe

E and W Europe had similar average TFR, school years and religiosity, but E Europe had lower CPR and much lower GDP per capita. In contrast to W Europe, TFR in E Europe had no or weak relation to education (Fig. 2 ). History, post-Soviet economic uncertainty and low GDP per capita may account for higher mean mother’s age at childbirth in E Europe ([ 74 ]; see also [ 75 ]). Note that CPR measures modern contraceptives, whereas E European methods include high prevalence of withdrawal, rhythm method, and abortion [ 76 ]). Contraceptive use in E Europe may therefore be higher than in Fig. 1 , and the relation TFR versus CPR among regions stronger than shown there.

Within E Europe there was no or weak relation between TFR and GDP per capita (Fig. 5 ), but note that TFR varies little. Education had high levels in both E and W Europe. Hilevych & Rusterholz [ 77 ] suggested that female labor force participation and contraceptive use favor small families (low TFR) in both E and W Europe. In addition, countries in these two regions may have gone through a ‘second demographic transition’, with a diversity of union and family types and very low TFR (see [ 78 ], and review in [ 79 ]).

Latin America and the Caribbean

Among regions, Latin America and Asia are intermediate in TFR level and religiosity. Latin America had the second lowest GDP per capita and, perhaps surprisingly, the second highest CPR. In many countries, such as Chile, Colombia, Costa Rica and Mexico, family planning activities, policies or programs started and expanded in the 1960’s and 1970’s. Despite resistance from the Vatican, modern contraception became widespread early [ 30 , 39 , 80 ].

Within Latin America TFR declined with more education, but it declined more strongly with increased CPR and GDP per capita (Figs.  4 , and 5 ), suggesting that these factors may be more important than education for TFR in Latin America. School years and GDP per capita were strongly positively associated, suggesting that economic resources sometimes limit education. CPR on the other hand was weakly related to GDP and education, and may partly be limited by other factors – possibly religiosity, through its negative correlation with education. At higher levels of religiosity in Latin America (proportion > 0.8) there is remarkable variation in school years and CPR among countries. At high levels of religiosity, some countries therefore achieve high levels of female education and CPR, in contrast with others at similarly high level of religiosity. This variation deserves further study, see Additional file 1  (part 1).

Arab states

Arab States had the second highest TFR among the regions, low CPR, and an unusual combination of highest religiosity and second highest GDP per capita among the regions. In some countries, oil resources have led to wealth, but the mean for female school years is low (very low for some countries). Within the region TFR declined strongly with increased education, GDP per capita, and CPR. TFR and religiosity were weakly associated, but note the small variation: almost all countries are highly religious.

The Arab States began implementing FP programs fairly recently, during the 1990’s ([ 81 ]; for exceptions, such as Tunisia and Morocco, see [ 39 ]). Effects of FP efforts may come in the future, unless religiosity hinders TFR decline ([ 63 ], and references therein). As in Latin America, at high levels of religiosity (proportion > 0.9) there is large variation in school years, GDP, and CPR among the countries. Arab State social norms, also associated with religion, generally disfavor female empowerment [ 82 ].

Sub-Saharan Africa

This region stands out with much higher TFR and markedly lower CPR than in the other five regions. The level of religiosity is high, similar to Arab States, but GDP per capita is much lower. Within Sub-Saharan Africa, TFR is strikingly negatively correlated with education, GDP and CPR, which all may affect TFR. Two ‘natural experiments’, involving changes in schooling in Nigeria [ 83 ] and Uganda [ 84 ], support the role of education for TFR. School years, GDP and CPR were strongly positively correlated, particularly CPR and school years, suggesting that education favors contraceptive use.

Religious influence may be one contributing reason for high TFR, and for stalling TFR decline in this region. For the eight countries with religiosity above 0.95, females had on average only 1–5 school years. Religiosity was considered an important determinant of fertility in Sub-Saharan Africa by e.g. Caldwell & Caldwell [ 85 ], Akintunde et al. [ 35 ] and Agadjanian & Yabiku [ 86 ]. A related and probably strong influence is persistent patriarchal social structure and gender inequality (e.g. [ 87 ]). For Burkina Faso, Mali, Niger and Chad, “One of the key barriers to having desired number of children is sociocultural norms, especially the husband’s role as primary decision-maker and the desire for a large family” [ 88 ].

Among regions, Asia resembled Latin America in TFR, GDP per capita and religiosity, though with lower average CPR (Fig. 1 ). Within Asia, lower TFR was associated with longer female education and higher GDP, and especially with higher CPR. As in Latin America, several countries with TFR below replacement level had CPR values above 70% (Thailand, South Korea and Hong Kong). FP programs have been important historically in these and other Asian countries [ 39 ]. In central Asia, however, Pakistan, Tajikistan and Afghanistan had TFR above 3.5 and low levels of CPR. An interesting exception in central Asia is Azerbaijan, with the lowest CPR (Fig. 4 ) but with TFR at 2.1. Many female school years (10.6), low religiosity (proportion 0.5), use of traditional contraception [ 59 ] and economic conditions [ 75 ] may together explain this exception.

The Asian countries show a rather strong positive correlation between education and GDP, and an even stronger negative association between education and religiosity.

Role of different factors

To help clarify factors of likely importance for TFR in different global regions, we studied five potential major agents that could be quantified. Social norms are also important [ 89 ] but often difficult to quantify. For example, large desired family size characterizes Sub-Saharan Africa. Korotayev et al. [ 49 ] related this norm to polygyny, high status of polygynous men, extended families, and child fosterage within kinships. The latter two aspects enable females to carry out traditional hoe agriculture without reducing the number of children, contributing to high TFR. And in modern urban Africa, abolition of postpartum sex taboos reduces birth intervals and may contribute to high TFR when large desired family size persists [ 49 , 90 , 91 ].

To limit the number of factors and relationships we did not analyze infant and child mortality, gender roles and female labor force participation rates, which may all play a role [ 9 , 25 , 92 , 93 , 94 , 95 , 96 ]. These factors seem likely to bear some relation to female education, contraceptive use and GDP per capita. Family planning programs include contraception and education directly related to fertility, and was analyzed in four regions. Lower TFR was associated with stronger FP programs in Asia, Arab States and Latin America, but only weakly so in Sub-Saharan Africa. In a study of 40 countries 2003–2010, TFR levels “were lowest in the presence of both good social settings and strong programs”, but Sub-Saharan Africa was the least successful region ([ 97 ], based on data from track20.org ). Yet, in 2014, the mean values for program strength were similar in all four regions in our study. However, FP programs in Asia and Latin America started earlier, and many of them are considered successful ([ 66 ], and references therein). Duration, change in social norms, institutional support and international funding are important for success of FP programs [ 27 , 40 , 46 ].

Lower TFR was associated with higher FP program strength in three regions. For Sub-Saharan Africa, Arab States and Asia, FP programs were under-represented in low and high TFR countries, compared to our full sample of countries. Incentives for starting FP programs may be lower in countries with relatively low TFR. And such programs might be difficult to start in poor, high-TFR countries with strong religion, corruption or conflicts. Nevertheless, the results in Fig. 7 suggest that FP programs recently have been effective also within relatively narrow TFR ranges in Asia and Arab states, but not in Sub-Saharan Africa.

Among regions, the TFR versus GDP per capita relationship was the weakest of the four (Fig. 1 ). Without Sub-Saharan Africa, the slope of the regression would be near zero. But within four regions, TFR’s negative relation to GDP per capita was strong or relatively strong (Fig. 5 ). So why is TFR not associated with GDP per capita in E and W Europe, in line with economic hypotheses, and despite equally large variation in GDP per capita as in Latin America? And why are school years, potentially improving child ‘quality’, not negatively associated with TFR in E and W Europe? The relation is even reversed, TFR increasing with school years in W Europe.

Evidence for a quantity-quality trade-off, between increased family size and investment in child quality, is mixed ([ 98 ], and references therein). In India, trade-off was strongest in rural areas [ 98 ]. In this study, TFR declined with increasing GDP per capita especially in the three poorest regions (Sub-Saharan Africa, Asia, Latin America). Is there a self-reinforcing loop, where increased wealth motivates higher child quality and other changes that reduce TFR, the reduction feeding back positively on economic development and wealth? According to Canning & Schultz [ 41 ], TFR declines can boost income per capita through reduced youth dependency rates, and may have positive long-term economic effects (see also [ 27 , 28 , 29 ]).

This study is, as far as we know, the first to relate TFR to religiosity together with other major factors in global regions and many countries. Both among the regions (Fig. 1 ) and within two of them (Asia and Sub-Saharan Africa, Fig. 3 ), TFR increased with degree of religiosity. Moreover, stronger religiosity is associated with lower education, CPR and GDP per capita in at least five regions. Among Arab States, effects of the large differences in wealth seem to override effects of the small differences in strength of religiosity.

We quantified religiosity from Gallup surveys, but did not distinguish between religions as regard TFR. There are probably differences [ 37 , 86 , 99 ], but using the same basic measure greatly simplifies regional and global analyses. In a study in the US, religiosity measured as here was more useful than religious affiliation and showed “a substantially positive effect on fertility”, without any gender difference [ 100 ]. Most earlier studies analyzed religious affiliation and TFR. Global TFR 2010–2015 was substantially lower for religiously non-affiliated (1.7) than for affiliated (2.6) [ 36 ].

Why is fertility associated with religiosity? Beside declarations from the Vatican and other religious leaders [ 30 , 31 ], possible reasons are belief in supernatural influence on things we desire, such as “good crops, protection, health and fertility” [ 33 , 101 ], and fatalistic views about fertility, such as children “are up to God” [ 46 , 89 ]. Human sociality and norms, history, type of religion and other conditions influence TFR-religion relationships [ 86 , 99 , 102 ]. Religiosity probably contributes to maintaining high TFR in Sub-Saharan Africa, Arab States and parts of Asia and Latin America, in part by suppressing factors that reduce TFR. Yet FP programs have been successful even in strongly religious countries, as shown by encouraging results in Iran [ 103 ], Tunisia [ 104 ], and Rwanda [ 90 ].

Total fertility rate (TFR) is lower with longer average education for females, higher GDP per capita, higher contraceptive prevalence rate, and stronger family planning programs. These recent relations hold generally among regions, but less so in E Europe, and not at all in W Europe and related countries. In contrast, TFR is higher when religiosity is stronger. Religiosity is also associated with fewer school years, lower GDP per capita and less contraceptive use, in line with several studies of religion, gender aspects, and socioeconomic development ([ 105 , 106 ], but see also [ 30 ]).

To clarify causality, further studies of TFR in relation to these five and other potentially important factors are needed. Longitudinal studies and controlled or ‘natural’ experiments [ 41 ] are valuable, but studies of current conditions are also desirable for TFR policy decisions. More studies are needed of how FP programs started and progressed in different countries and religious settings. The role of media in changing gender norms and contraceptive use also needs further study [ 66 , 107 ].

Human fertility rate has critical consequences for the entire biosphere [ 2 , 6 , 12 , 108 ], but conclusions about the main factors that determine TFR vary markedly between researchers (see for instance [ 8 , 18 , 66 ]). Lack of consensus calls for more research on the importance of e.g. content and quality of education (as pointed out by Cleland, [ 109 ]). Norris & Inglehart [ 34 ] remarked that “the world as a whole is becoming more religious” (see also [ 32 , 33 ]). The role of religiosity therefore needs more study; it might be involved in stalling TFR decline in several countries.

Availability of data and materials

All data analysed during this study are included in published article and Supplementary Information; sources for the data are available to anyone freely on the Internet, or in published books, by Reference list.

Abbreviations

Contraceptive Prevalence Rate

Family Planning

Gross Domestic Product

Total Fertility Rate

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Acknowledgments

We thank Hailemariam Abiy Alemu, Philip Cafaro, Stuart Hurlbert, John McKeown, Thera Mjaaland, Mikko Myrskylä, Bola Lukman Solanke, and especially Jenna Dodson and Patrícia Dérer in The Overpopulation Project ( https://overpopulation-project.com/ ) for valuable comments and suggestions on versions of the manuscript. We also thank Patrícia Dérer for providing important data sets which made this study possible, and Jane O’Sullivan for helpful discussions of TFR. Our research is generously supported by the Global Challenges Foundation, Stockholm.

The research was supported financially by GCF (Global Challenges Foundation, Stockholm), by the Protocol 3/2017. GCF supported the population research, but had no role in the present study, except its relevance to population growth. Open access funding provided by University of Gothenburg.

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F.G. suggested the study, which was much modified over time together with M.A. Data selection, and preparation and Tables and Figures, by F.G.; analyses and writing done together (F.G and M.A.). All authors have read and approved the final version of the manuscript.

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F.G. is a senior professor in ecology and conservation science, M.A. is ecologist and professor emeritus, in the Department of Biology and Environmental Sciences at Gothenburg University, Göteborg, Sweden.

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Götmark, F., Andersson, M. Human fertility in relation to education, economy, religion, contraception, and family planning programs. BMC Public Health 20 , 265 (2020). https://doi.org/10.1186/s12889-020-8331-7

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DOI : https://doi.org/10.1186/s12889-020-8331-7

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Home > CUNY Graduate Center > Dissertations, Theses, and Capstone Projects > 1906

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Overpopulation and the impact on the environment.

Doris Baus , The Graduate Center, City University of New York Follow

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Liberal Studies

Sophia Perdikaris

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Agricultural and Resource Economics | Demography, Population, and Ecology | Economics | Education Policy | Environmental Policy | Environmental Studies | Family, Life Course, and Society | Growth and Development | Health Economics | Health Policy | International and Area Studies | International Relations | Place and Environment | Politics and Social Change | Social and Behavioral Sciences | Urban Studies | Urban Studies and Planning

overpopulation, environmental impact, malthus, population growth, environmental issues, causes of overpopulation

In this research paper, the main focus is on the issue of overpopulation and its impact on the environment. The growing size of the global population is not an issue that appeared within the past couple of decades, but its origins come from the prehistoric time and extend to the very present day. Throughout the history, acknowledged scientists introduced the concept of “overpopulation” and predicted the future consequences if the world follows the same behavioral pattern. According to predictions, scientists invented the birth control pill and set population control through eugenics. Despite that, population continued to increase and fight with constant diseases. Migration was another component that encouraged population rise, which imposes severe threats to the environment. Urbanization destroys natural habitats and reinforces carbon dioxide emissions, which cause climate change and global warming. Species are becoming extinct and humanity is at threat that it set up for itself. Food scarcity and shortage of water as well as lack of job opportunities and inadequate education are the results of global inequality. Uneven distribution of natural resources, financial means, and individual rights give rise to poverty and define the global culture as greedy, despite the aid of international organizations and agencies. Solutions to overpopulation lie in the efforts of national institutions to implement policies that will correspond to the guidelines given by international institutions that work for the best of the global community. Within this global network, individuals act in their best interest, leaving the rest in extreme poverty and shortage. The inequality supports issues that contribute to overpopulation and leads to a humanity’s extinction.

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Baus, Doris, "Overpopulation and the Impact on the Environment" (2017). CUNY Academic Works. https://academicworks.cuny.edu/gc_etds/1906

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