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Climate Change, Population Growth, and Population Pressure

We develop a novel method for assessing the effect of constraints imposed by spatially-fixed natural resources on aggregate economic output. We apply it to estimate and compare the projected effects of climate change and population growth over the course of the 21st century, by country and globally. We find that standard population growth projections imply larger reductions in income than even the most extreme widely-adopted climate change scenario (RCP8.5). Climate and population impacts are correlated across countries: climate change and population growth will have their most damaging effects in similar places. Relative to previous work on macro climate impacts, our approach has the advantages of being disciplined by a simple macro growth model that allows for adaptation and of assessing impacts via a large set of climate moments, not just annual average temperature and precipitation. Further, our estimated effects of climate are by construction independent of country-level factors such as institutions.

We are grateful to Lint Barrage, Greg Casey, Maureen Cropper, Eric Galbraith, and Zeina Hasna for helpful advice; to Lucy Li, Frankie Fan, William Yang, and Raymond Yeo for research assistance; to David Anthoff, Brian Prest, and Lisa Rennels for access to data and code; and to seminar audiences at the Bank of Italy, University of Bologna, Université Catholique de Louvain, University of Chicago, University of Chile, University of Connecticut, ETH Zurich, IIASA, Korea University, Lahore School of Economics, University of Manchester, NBER Summer Institute, NYU Abu Dhabi, Osaka University, Oxford University, RIDGE forum on Sustainable Growth, Schumpeter Seminar (Humboldt University), Sungkyunkwan University, University of Tokyo, and the World Bank for useful feedback. Research was supported by the Population Studies and Training Center at Brown University through the generosity of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2C HD041020 and T32 HD007338).}} The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

In the past three years I have received significant research funding from the World Bank, the International Growth Centre and the U.S. Department of Transportation.

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Demographic delusions: world population growth is exceeding most projections and jeopardising scenarios for sustainable futures.

1. Introduction

2. the treatment of population in future scenarios, 3. the united nations projections, 4. the world population is growing faster than we are told, 5. projecting fertility decline, 6. alternative projections of global population.

• The population component of the Shared Socioeconomic Pathways mentioned above, developed for climate change modelling under the IPCC [ 50 ]. These projections were developed by the Wittgenstein Centre for Demography and Global Human Capital in Austria. The SSP projections originally had a base year of 2010, while the 2018 revision (“version 2”) has a base year of 2015 [ 51 ].
• The Institute for Health Metrics and Evaluation (IHME), based at the University of Washington. Their projections were developed as part of their Global Burden of Disease project [ 52 ].
• The Earth4All project, including members of the Potsdam Institute for Climate Impact Research, Stockholm Resilience Centre and the BI Norwegian Business School [ 53 ]. The population modelling is part of a larger exercise in mapping out a sustainable future for humanity. The project is sponsored by the Club of Rome, as a follow-up to its famous 1972 Limits to Growth study, featuring MIT’s then-groundbreaking Earth3 model [ 54 ]. Earth4All is a creative extension of Earth4, intended to be Earth3’s successor.

7. Drivers of Fertility Decline

8. living sustainably with dignity for all, 9. conclusions, data availability statement, acknowledgments, conflicts of interest.

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O’Sullivan, J.N. Demographic Delusions: World Population Growth Is Exceeding Most Projections and Jeopardising Scenarios for Sustainable Futures. World 2023 , 4 , 545-568. https://doi.org/10.3390/world4030034

O’Sullivan JN. Demographic Delusions: World Population Growth Is Exceeding Most Projections and Jeopardising Scenarios for Sustainable Futures. World . 2023; 4(3):545-568. https://doi.org/10.3390/world4030034

O’Sullivan, Jane N. 2023. "Demographic Delusions: World Population Growth Is Exceeding Most Projections and Jeopardising Scenarios for Sustainable Futures" World 4, no. 3: 545-568. https://doi.org/10.3390/world4030034

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How does population growth affect economic growth and vice versa? An empirical analysis

Review of Economics and Political Science

ISSN : 2631-3561

Article publication date: 31 January 2024

Issue publication date: 18 June 2024

• Supplementary Material

Our paper studies a central issue with a long history in economics: the relationship between population and economic growth. We analyze the joint dynamics of economic and demographic growth in 111 countries during the period 1960–2019.

Design/methodology/approach

Using the concept of economic regime, the paper introduces the notion of distance between the dynamical paths of different countries. Then, a minimal spanning tree (MST) and a hierarchical tree (HT) are constructed to detect groups of countries sharing similar dynamic performance.

The methodology confirms the existence of three country clubs, each of which exhibits a different dynamic behavior pattern. The analysis also shows that the clusters clearly differ with respect to the evolution of other fundamental variables not previously considered [gross domestic product (GDP) per capita, human capital and life expectancy, among others].

Practical implications

Our results indirectly suggest the existence of dynamic interdependence in the trajectories of economic growth and population change between countries. It also provides evidence against single-model approaches to explain the interdependence between demographic change and economic growth.

Originality/value

We introduce a methodology that allows for a model-free topological and hierarchical description of the interplay between economic growth and population.

• Time series analysis
• Non parametric analysis
• Minimal spanning tree

Hierarchical tree

Population dynamics

• Economic growth

Brida, J.G. , Alvarez, E. , Cayssials, G. and Mednik, M. (2024), "How does population growth affect economic growth and vice versa? An empirical analysis", Review of Economics and Political Science , Vol. 9 No. 3, pp. 265-297. https://doi.org/10.1108/REPS-11-2022-0093

Emerald Publishing Limited

Copyright © 2024, Juan Gabriel Brida, Emiliano Alvarez, Gaston Cayssials and Matias Mednik

Published in Review of Economics and Political Science . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

A persistent topic in the public discourse revolves around the interplay between population and its impact on economic growth [1] . According to the latest United Nations [2] reports, population growth projections indicate a sustained deceleration followed by successive stability. Nevertheless, the process has been far from consistent across different economies. Vast regions of the globe continue to experience high population growth rates, while contrasting areas are facing stagnant or even declining demographic trends. However, even if these projections materialize, there is no reason to believe that migratory movements will cease, nor for asserting that the population’s composition and magnitude will remain static. Until economies achieve a stable population in terms of composition and size, they will encounter potential repercussions on both economic growth and welfare. Said potential effects, along with their magnitude and the mechanisms through which they operate, remain largely uncertain. This paper contributes to the existing empirical literature concerning a crucial issue with a rich history within economics: the complex relationship between population and economic growth.

The study of the relations between economic and population growth has a significant history within the field of economics. Population has held a central position in the exploration of economic growth, a role that can be traced back at least to Adam Smith’s assertion ( Smith (1776) 2010, p. 12) that a nation’s wealth should be measured in terms of per capita income rather than aggregate income. Shortly after, Malthus (2018) 2018 proposed his “Population Principle”, which postulates that population and aggregate income dynamics are inextricably related in a bidirectional causal relationship. His vision became highly influential in the development of economic theory, and since then, no influential economist studied economic growth without considering population dynamics. Nonetheless, no consensus has emerged on whether population growth is beneficial, neutral, or detrimental to economic growth. Similarly, there is no unanimous agreement on the effects of economic growth on population dynamics. However, this particular aspect or direction of causation has not been extensively explored in the existing literature.

Modern growth theories treat population differently from the classics. Broadly, standard growth models abstract out the role of population by assuming it to be an exogenous variable that expands at a fixed rate.

Solow’s model ( 1956 ) establishes a connection between population dynamics and economic growth via the population growth rate. The model predicts a negative correlation between population growth rate and per capita income. Over the long term, a higher population growth rate leads to a lower steady-state per capita output. In the short term, a higher population growth rate results in a reduced growth of per capita output during the transition to the new steady-state equilibrium. The model does not differentiate population from labor force, implicitly assuming that both grow at the same rate, or in another manner that keeps the population structure stable. In this setting, the assumption of decreasing marginal returns results in a stable or fixated per capita output. Sustained growth can only be achieved through continuous technological progress.

Certain endogenous economic growth models ( Romer, 1986 , 1990 ), posit a positive relation between population and economic growth. In these models, population is not merely a proxy for the labor force, but the source of scientists and innovators. The greater their number, the more technological progress. At the same time, a larger population generates a higher demand for innovative goods, which in turn alters the human capital endowment, resulting in higher productivity ( Kuznets, 1967 ; Kremer, 1993 ; Simon, 1989 ). This approach diverges from previous efforts to model economic growth by incorporating controversial “scale effects”.

Other theoretical approaches adopt the classic’s approach of considering population as an endogenously determined variable. Hansen and Prescott (2002) , Irmen (2004) , Mierau and Turnovsky (2014) , Corchón (2016) , and more recently, Bucci et al. (2019) , among others, have developed models in which the relation between population growth and economic growth is nonmonotonic, with effects that vary in size, sign and direction.

When delving into the empirical literature on the interplay between economic growth and demographic change, there is a pronounced emphasis on both testing for cointegration between these two variables and studying their causal relations. To contextualize our research, the subsequent section offers a representative and updated review of this literature. The primary objective is to highlight the lack of consensus and the extensive range of results, which, in certain instances, present contradictions.

The present work analyzes the hierarchical structure and the dynamic relations between economic and population growth for a large group of countries using a nonparametric approach. The main advantage of this technique is that it allowed us to study and compare the interplay of population growth and economic growth without a predetermined model. The predominant approach of the vast majority of surveyed studies, characterized as ex ante , is to begin with a theoretical model (primarily Solow’s model) that predicts the influence of population growth on economic growth and then assess this empirically. In contrast, our proposed model will take an ex post perspective, employing an inductive approach. In this sense, cluster analysis allows us to categorize the countries within the sample according to the resemblance of their dynamic behaviors.

Our study is limited to considering only the interrelationship between population and economic growth. We did not take into account other relevant variables that affect the relationship, as documented by the analyses of Magazzino and Cerulli (2019) , who analyze the links between economic growth, urban population and CO2 emissions or studies on the impact of aging (associated with slower population growth) on productivity and growth ( Cristea et al. , 2020 ; Maestra et al. , 2023 ).

The originality of this research derives from its multifaceted objectives. Firstly, it introduces a methodology that facilitates a model-free, topological and hierarchical portrayal of the interplay between economic growth and population. To the best of our knowledge, no prior endeavors in the literature have relied upon this methodology. Secondly, while we refrain from delving into the underlying mechanisms (causes, effects and propagation mechanisms), the proposed procedure indirectly implies the presence of dynamic interdependence in the trajectories of economic growth and population change between countries. Moreover, it offers evidence against approaches centered on singular models for explaining the interdependence between demographic change and economic growth. Furthermore, it provides evidence in support of conceptualizing this relationship as nonlinear and nonmonotonic. This finding has strong implications for policy recommendations. If the relationship is nonlinear, and the sign and direction of the causal relationships change over time, it is necessary to evaluate policies in terms of timeliness and efficiency in order to adapt them to these changes.

This paper is organized as follows. In the next section, we provide a brief review of the empirical literature concerning the relations between economic growth and demographic change. The third section introduces the data and a set of tools that enabled us to conduct the empirical analysis of comparative economic growth without imposing an ex ante model. Then, the methodology employed to construct minimum spanning trees (MSTs) and hierarchical trees (HTs) is detailed. We also introduce the concept of regime, analyze symbolic time series and define a distance within this space to measure the degree of similarity among countries. With these tools at our disposal, we then proceed to detect and analyze the global structure, taxonomy and hierarchy within our sample of countries in the fourth section. Lastly, the fifth section presents our concluding remarks.

Population dynamics and economic growth: a review of the empirical literature

The initial research efforts aimed to empirically assess the influence of population change on economic growth were based on correlation analysis. An example of this is provided by Coale and Hover (1958) , who studied India and identified a negative relationship between the variables. The authors concluded that rapid population growth constituted an obstacle to economic growth in that country. Interestingly, their conclusions were reversed when studying Mexico between 1955 and 1975 ( Coale, 1977 ). By analyzing six-year periods across 86 different countries, Barlow (1994) found no discernible correlation between the two variables. When incorporating fertility rates into the analysis, the authors were able to uncover a significant adverse relationship between population change and economic growth. The same analysis was subsequently conducted separating countries by income levels. This examination revealed that while the correlation remained negative for both low and high-income countries, it retained statistical significance solely for the former. Additionally, a positive correlation emerged between fertility lagged by one generation and economic growth.

Starting in the late 1960s, a considerable share of the empirical studies consists in cross-country section analysis regressions. The analyses conducted by Kuznets (1967) , Thirlwall (1972) , Simon (1989) and Crenshaw et al. (1997) , among others, did not find evidence of a negative relationship. Their estimations resulted in positive coefficients, although they were not statistically significant. Utilizing fixed-effects modeling (FEM) and random-effects modeling (REM), Kelley and Schmidt (1995) studied 89 countries with populations exceeding one million inhabitants across three different periods: 1960–1970, 1970–1980 and 1980–1990. Their estimation models included population-related variables –such as education and density– as well as economic variables like savings and investment. Their analysis did not uncover evidence supporting a significant impact of population on per capita income during the 1960 and 1970s.

The publication of the Penn tables from the Maddison Project (particularly Maddison, 1995 ), marked a significant milestone in this field of study. By providing standardized per capita gross domestic product (GDP) statistics across countries, it greatly facilitated comparative analyses of the intricate relationship between population and economic growth. Table 1 summarizes the surveyed empirical literature that has analyzed the connections amongst demographic and economic growth. This summary includes detailed information on the analysis period, sample, method used and main findings.

p ⇒ y, unidirectional causality, population growth stimulates economic growth: Darrat et al. (1999) , Yao et al. (2007) , Liu et al. (2013) , Ali et al. (2013) , Furuoka (2013) , Musa (2015) and Sebikabu et al. (2020) .

y ⇒ p , unidirectional causality, economic growth stimulates population to grow: Nakibulla (1998) .

p ⇔ y, bidirectional causality, population growth stimulates and is stimulated by economic growth: Garza-Rodriguez et al. (2016) , Alvarez-Diaz et al. (2018) and Furuoka (2018) .

Noncausality, population growth neither stimulates nor is stimulated by economic growth: Dawson and Tiffin (1998) , Thornton (2001) and Mulok et al. (2011) .

Mixed results: Jung and Quddus (1986) , Kapuria-Foreman (1995) , Tsen and Furuoka (2005) and Chang et al. (2017) .

The reviewed regression analyses, particularly those involving cointegration testing, often presuppose a linear model. This assumption is partly rooted in their utilization of the underlying model (usually Solow’s model), which postulates a linear relationship. The goal of these studies is to examine the existence of a linear long-term relation between population and per capita output growth rates. However, a smaller subset of studies employs nonparametric approaches to investigate the dynamic interplay between demographic change and economic growth, often revealing evidence of a nonlinear causal relationship between said variables. Some examples of such studies include An and Jeon (2006) and Azomahou and Mishra (2008) . The former examines the data from 25 Organization for Economic Co-operation and Development (OECD) countries over the period 1960–2000 [3] .

Their findings depict a dynamic relationship between the two variables that undergoes changes over time. Initially, demographic change exerts a positive impact on economic growth, yet the magnitude of the effect decreases over time and eventually becomes negative towards the end of the period. In other words, the relationship between the variables follows the form of an inverted U-shaped curve. The authors explain this phenomenon by attributing it to the three stages of demographic transitions: (1) high fertility/high mortality, (2) high fertility/low mortality and (3) low fertility/low mortality.

Azomahou and Mishra (2008) present other example of a nonparametric approach. They analyze the same period (1960–2000), but covering a broader range of countries. Their panel includes 110 countries: 24 OECD members and 86 developing countries. Their estimations reveal evidence of a nonlinear relationship between the two variables, as well as “direct” and “feedback” effects of population structure on growth. Furthermore, they affirm that a highly nonlinear demographic structure characterizes age-structured populations and their economic growth, with this nonlinearity potentially acting as a source of growth fluctuations in both OECD and nonOECD countries.

Most of the empirical literature reviewed consists of linear regression models coupled with Granger causality tests ( Engle and Granger, 1987 ). The linearity assumption is rarely discussed and Granger causality tests are frequently misinterpreted. Granger causality analysis is useful for forecasting, but the conclusions that can be drawn about the causal mechanism are limited. The Granger test should serve as a starting point for a more in-depth analysis of the causal relationships between economic and population growth. The capacity to derive conclusions about the causal mechanism, extending beyond temporal precedence, as well as the possibilities for manipulation through political actions, is indeed constrained. On the other hand, the substantial disparities observed in the outcomes of multiple empirical studies focused on the same country, despite the utilization of similar econometric techniques, suggest the presence of a potentially nonlinear underlying cointegration relationship, an aspect not possible to capture through Granger analysis. Among the studies resorting to panel data models, a notable proportion fails to check for homogeneity in the impact of explanatory variables across the different countries. Zooming out from the details, the picture that emerges points to the inadequacy of a single model to explain the dynamic relations between demographic change and economic growth across all countries and/or over long periods. This picture is the starting point of our work. We seek to explore a novel path within the empirical strand of the literature that studies the dynamic relations between demographic change and economic growth, without imposing constraints on the form of these relations or assuming homogeneity in the effects across countries. More specifically, we intend to examine the possibility of multiple patterns in the dynamic relations between these two coexistent variables. With this objective in mind, our pursuit is to identify groups of countries that exhibit internal homogeneity in terms of dynamic relations between demographic change and economic growth, while also maintaining clear distinctions from other groups.

Data and methodology

In this study, population and economic growth dynamics are represented by the evolution of the growth rates of population growth and per capita GDP, respectively. Annual data of per capita GDP (in 2011 constant dollars, PPP [12] adjusted), population and their corresponding growth rates, were obtained from the Penn World Table 10.0 database ( Feenstra et al. , 2015 , available for download at www.ggdc.net/pwt ), considered the standard data source when it comes to comparative economic growth. The dataset includes annual data for 111 countries during the period 1960–2019. We sought to find a balance between including as many countries as possible, while covering a period long enough to ensure the robustness of our methods.

Throughout the period of analysis, aggregate world population exhibits a clear trend. As depicted in Figure 1 , the total world population grows at a decreasing rate: slow evolution marked by a consistent trend, with minimal fluctuations in its growth rate. This observation is consistent with the established patterns of the demographic transition. Still, this trend averages out significant disparities between countries in terms of the timing of their demographic transitions and the pace at which each stage passes. These disparities serve as the central focus of this study.

The average growth rates of population and per capita GDP over the analyzed period are remarkably similar: 1.8 and 2.01% respectively, but the similarities end there. Average GDP per capita growth does not show any discernible trend (as seen in Figure 2 ). Its standard deviation is six times larger than that of the population growth rate, and it exhibits pronounced volatility in the short term. Additionally, its mean inter-annual variation exceeds that of the population growth rate by more than 40 times.

Table 2 provides the most relevant descriptive statistics for the considered variables.

Methodology

This section outlines the methodology applied to compare and analyze the behavioral patterns of different countries from the sample in relation to the variables of economic growth and demographic change. Our approach involved initially studying each variable independently and subsequently repeating the analysis for both variables considered in conjunction. At each step, we obtained a taxonomy and established a hierarchical order among countries, enabling us to assess the degree of similarity in their trajectories. In order to build the taxonomy, we relied on the nearest neighbor clustering procedure, which categorizes time series based on their proximity as determined by a distance function. Two different metrics were used. When analyzing each variable in isolation, we utilized a distance metric introduced by Mantegna (1999) , which is founded on a transformation of the Pearson correlation coefficient between two time series, Yi and Yj. For the joint analysis of demographic change and economic performance, we used a metric specifically suited for symbolic sequences.

Except for the metric used to construct the distance matrix, the procedure for grouping and classifying the countries in our sample remains consistent. We followed the same series of steps to investigate the dynamics involving: (1) demographic change, (2) economic growth and (3) both demographic change and economic growth. These steps are as follows: compute the distance matrix, construct the MST, calculate the subdominant ultrametric distance matrix, create the HT and apply a hierarchical clustering stopping rule to determine the optimal number of clusters in the sample.

We started the procedure by building the distance matrix. NxN matrix D, where N is the number of countries and the d i j element is the distance between country i and country j . The second step was to use Kruskal’s algorithm to find the MST ( Kruskal, 1956 ). In this regard, we began by sorting all edges (pairs of countries) in the distance matrix according to their weight (distance). Next, we selected the smallest edge and examined whether it formed a cycle with the spanning tree we had built so far. If no cycle was formed, we incorporated the edge into said spanning tree. On the other hand, if a cycle was in fact detected, we discarded the edge. We repeated this process of selecting the smallest edge and checking for cycles until the spanning tree reached V – 1 edges. The result of this process was an MST: a connected edge-weighted graph of the 111 countries within the sample, which highlighted the 110 most pertinent distances and helped us identify which countries had more similar and dissimilar dynamics in terms of one or more variables.

The MST offers an arrangement of countries based on the most relevant connections among each constituent within the group of countries. Any pair of countries is directly connected through one or more vertices, which represent the paths of minimum distance between them.

The third step involves obtaining the clusters. From the MST we obtained the subdominant ultrametric distance matrix D * ( Rammal et al. , 1986 ), whose elements d * i j are defined as the longest step (maximal distance between connected countries) in the shortest path that connects countries i and j in the MST. Formally, d * i j  =  max ( d k l ) , in colloquial language “where k and l stand for all nodes connecting i and j (including i and j ) in the corresponding MST”. Once the values of d * i j were calculated for every pair of countries, we had all the elements to build the HT.

The HT illustrates how to group countries for a given number of groups. That is, if the objective is to partition the sample of countries into eight groups, the HT determines the allocation of countries into each of these eight groups. To determine the most statistically relevant number of groups –which is the optimal number– we used the pseudo – T 2 ( Duda and Hart, 1973 ) and the C-Kalinski ( Calinski and Harabasz, 1974 ) stopping rules.

The exercise ends with an analysis of group dynamics. To study their evolution, we divide the period of analysis into 27 moving windows of 30 years amplitude. For each window, we repeat the previous exercise, which allows us to study the stability in terms of the composition of each group and to visualize the convergence-divergence between them.

Empirical analysis

This section is divided into two parts. The first part reports the results of the analysis of each variable separately. The second part presents the outcomes derived from analyzing demographic change and economic growth simultaneously.

First exploratory analysis

For the analysis of each variable on its own, we used the distance introduced by Mantegna (1999) , which defines the distance based on the Pearson correlation coefficient between two time series, Y i   and Y j   . (1) ρ i j = 〈 Y i   Y j 〉 − 〈 Y i   〉 〈 Y j 〉 ( 〈 Y i 2 〉 − 〈 Y i 〉 2 )   ( 〈 Y j 2 〉 − 〈 Y j 〉 2 ) it defines the distance, (2) d ( i , j ) = 2 ( 1 − ρ ij )

This metric, first introduced by Gower (1966) , provided us with a distance between two unidimensional temporal series, where closeness is defined in terms of their co-movements [4] . Applied to our scenario, two countries have similar dynamics in terms of population change when the movements or shifts in their population growth rates resemble each other. For instance, if we have three countries with the following sequences of population growth rates g A = ( 0 . 02 , 0 . 03 , 0 . 01 )   g B = ( 0 . 04 , 0 . 06 , 0 . 02 )   g C = ( 0 . 02 , 0 . 01 , 0 . 0166 ) , then  d ( A , B ) = 0   and   d ( A , C ) = 2 .

The purpose of this first exercise is to compare the taxonomies of countries arising from the analysis of their population dynamics with those emerging from the analysis of their economic performance. As we delve into our findings, it becomes evident that the results from these to analyses are both qualitatively and quantitatively different.

In order to compare and analyze the behavior of the countries within the sample in terms of demographic change, we used equation (2) to construct the distance matrix. Then, we applied Kruskal’s algorithm to obtain the MST. Figure 3 shows the MST that corresponds to the population growth rate, while Appendix 1 indicates the corresponding country for each code.

Once we calculated the MST, the next step was to build the HT (see Figure). For this purpose, we computed the subdominant ultrametric distance matrix ( D * ). The final step was to apply a stopping rule to determine the number of clusters in the sample. Table VII (see Appendix 1 ) shows the grouping that emerged from this procedure.

Group 1, the largest cluster, comprises countries from all continents. Group 2 is composed of eight European countries and New Zealand. Group 3 includes several African developing countries.

Growth dynamics

We repeated the same exercise for GDP per capita growth. Refer to Figure 4 for the corresponding MST visualization.

A comparative analysis revealed a few interesting results. Firstly, countries exhibit greater similarity in terms of their population dynamics than in relation to their economic growth. This is due to the fact that the global distance (sum of all the distances in the MST [5] ) is smaller for the population than for GDP per capita. This observation aligns with our earlier comment regarding the substantial disparity in the behavior of the two series. The population demonstrates a slower pace of change, a distinct long-term trend and less volatility compared to GDP per capita.

Secondly, there is little correlation in terms of country closeness between the two dimensions. That is, a similar population dynamic between two countries does not necessarily translate into similar economic performance. This can be appreciated in Tables 3 and 4 .

Comparing the ten smallest distances in each of the MSTs revealed a notable absence of coincidences, meaning there is minimal overlap among the country-dyads. Furthermore, when considering population, these distances are significantly smaller.

The substantial disparity between the MST and the resultant groupings in each of the preceding exercises serves as an indication of interdependence between these variables. At the same time, this contrast suggests that the functional relationship between them is not unique. In the next section, we repeated the previous exercise considering population and economic growth simultaneously. Our goal was to establish a hierarchical organization and a taxonomy of countries that would enable us to measure the degree of similarity between countries in terms of the co-evolution in time of their population and output per capita. In this joint analysis, our expectation was to find groupings of countries with the same conditions. However, the distance function used so far is limited to univariate time series. Hence, we needed an alternative distance function capable of handling bivariate time series ( ( g p , g y ) ) . To address this, we introduced the notion of regime, which allowed us to define a distance between the dynamic trajectories –in our case bivariate– of different countries.

Symbolic series and regimes

In order to describe the qualitative behavior of the joint evolution of economic and demographic growth, we introduced the notion of regime ( Brida et al. , 2003 ; Brida and Punzo, 2003 ). A regime consists of a range of conditions characterizing the behavior of a system, particularly for the purpose of our research, one that describes the joint dynamics of population and per capita output. These conditions then divide the state space of population and per capita production into regions, each corresponding to a different regime. Each regime represents an explanatory model of the joint performance of population and economic growth distinct from the others. We defined two conditions: one sets a threshold for yearly population change, while the other defines a threshold for the annual change in the rate of per capita GDP growth. As a result, the state space is divided into four regions (refer to Figure 5 ). If each region corresponds to a different relationship between demographic change and economic performance (a different regime), then a country moving from one region to another implies a structural change in the way population and output per capita relate to each other in that country (a regime switch). It is possible to distinguish two types of dynamics: one within each regime and the other during transitions between regimes. In our analysis, we focused on the dynamics of regimes, aiming to qualitatively describe the evolution of performance in terms of population growth and economic growth as economies progress through successive regimes over the analyzed period. Our interest lies in the sequence of regimes that a country transitions during a certain period of time.

We evaluated the advantages and disadvantages associated with utilizing different thresholds. The evaluation considered the annual average across all nations, the historical average for each country and the overall average for all countries. Nevertheless, it is pertinent to mention that each of these options is accompanied by its own set of drawbacks. Using different thresholds for each country could appear sensitive at first. Still, the most straightforward operationalization –taking the country’s average rate during the period of analysis– would imply forcing every country to transition across all four regimes. Similarly, adopting varying thresholds for each year could be sensitive to fluctuations in global economic conditions, yet it would artificially necessitate having countries on both sides of the thresholds every year. We finally opted for the average change in per capita income and in population during the period of analysis for all countries [6] . The result was the following four-region partition of the state space: (3) R 1 = { ( g p , g y ) : g p ≥ μ p , g y ≤ μ y }

Region 1 is characterized by low (below average) economic growth and high (above average) population growth, which could be associated with economies locked in what are colloquially referred to as “poverty traps”, as observed in countries such as Senegal or Kenya. (4) R 2 = { ( g p , g y ) : g p ≥ μ p , g y ≥ μ y }

In Region 2 we find a virtuous relation between population and economic growth, with both variables surpassing the average growth rates. This pattern is identified as the “demographic dividend capture” regime, exemplified by countries like Egypt. (5) R 3 = { ( g p , g y ) : g p ≤ μ p , g y ≥ μ y }

Regime 3 is marked by a population growth rate that unfolds at a slow pace, accompanied with GDP per capita growth that exceeds the average. For instance, this can be observed in a country such as China. (6)   R 4 = { ( g p , g y ) : g p ≤ μ p , g y ≤ μ y }

Finally, Regime 4 corresponds to an economy where both population and per capita production grow slowly, falling below the average. This scenario is exemplified by countries like Japan.

Figure 6 illustrates, in the space of states, the regimes experienced by Algeria, Mexico, Pakistan and Sweden. As portrayed, there are notable distinctions in the dynamics of these regimes. Algeria and Mexico traverse all four regimes, whereas Pakistan’s trajectory includes only regimes 1 and 2 and Sweden encompasses regimes 3 and 4. To account for the short term variations in global economic conditions and minimize the noise characteristic of macroeconomic times series such as output, we filtered the per capita GDP series to smooth its movements.

By framing the problem in the context of multiple regimes that countries transition over time, we gained the flexibility to consider different sequences of dynamic interactions between population and economic performance. An important regime sequence to keep in mind is R 1   →   R 2   →   R 3   →   R 4 , which captures the stylized facts of the demographic transition theory. In this ideal sequence, countries are able to capture the demographic dividend [7] . Additionally, by capturing the demographic transition theory as a particular case of regime sequences, our framework allowed us to assess the degree to which countries adhere to this stylized pattern.

Table 5 below offers an initial approximation to the characterization of regime dynamics. It shows the percentage of time each country or economy spends in each regime during the period of analysis.

An initial observation reveals a diverse range of behaviors among the countries in our sample, both in terms of the regimes they encounter and the duration they spend within each. Some of them alternated between regimes R 3 and R 4 and never visited R 1 or R 2 . Others did the opposite, alternating between regimes R 1 and R 2 and never visiting R 3 or R 4 . Another group of countries transitioned through all four regimes. In short, there is not a single pattern but a myriad of them. This first approximation to regime dynamics possesses an important limitation: it leaves aside the order in which countries undergo different regimes, a factor that provides valuable insights into regime dynamics. In particular, this approach overlooks all aspects related to regime transitions. To address this problem we used symbolic series to represent regime dynamics, reducing the information space of the issue but without sacrificing valuable information. If we label each regime R i with the symbol j , we can substitute the original bivariate time series { ( g 1 p , g 1 y ) , ( g 2 p , g 2 y ) , . . . , ( g T p , g T y ) } for a sequence of symbols { s 1 , s 2 , . . . , s T   } such that s t = j if and only if ( g p , g y ) belongs to R j . This Symbolic Series summarizes the most relevant qualitative information on the dynamics of a country’s regime [8] .

To categorize the 111 countries in terms of their distinct economic-demographic performance, we used the same nonparametric approach applied in the previous section: calculating the distance matrix, constructing the MST, computing the subdominant ultrametric distance matrix, building the HT and applying a hierarchical clustering stopping rule to determine the number of clusters in the sample. As explained in said section, a combined analysis of demographic change and economic performance requires a different metric than the one used to study each of the variables separately. Here, we were addressing regime dynamics represented by symbolic sequences, therefore we needed to measure distances between symbolic sequences.

The distance function we used is simple. Given two countries, we first measured the distance between them every year. There are two possible values for yearly distances: zero if the countries coincide on the same regime or one if they are on different ones. The second step required to get the square root of the sum of all the yearly distances to get the overall distance between the two countries during the entire period.

Given two symbolic series { s i t } t = 1 t = T and { s j t } t = 1 t = T , corresponding to countries i and j , we define the following distance: (7) d ( i , j ) = ∑ t = 1 T f ( s i t , s j t ) where (8) f ( s i t , s j t ) = { 1   i f   s i t ≠ s j t   0   i f   s i t = s j t   ∀   i ≠ j , ∀   t .

Intuitively, the more coincidences two countries have in the same regime, the smaller their distance. When two countries exhibit the exact same sequence of regimes, they reach the minimum possible distance, which is zero. The maximum possible distance is ( √ T ) occurring when two countries never coincide on the same regime in any year.

To construct the MST we used Kruskal’s algorithm. With its 111 vertices and 110 edges, the resulting weighted graph highlights the most relevant distances for each country. The shortest distance in the MST is d ( A u s t r i a , P o r t u g a l ) = 2.24 , implying that Austria and Portugal had the most similar trajectories in the sample. The second shortest distance is between Belgium and Germany: d ( B e l g i u m , G e r m a n y ) = 2.45 .

The tree is obtained by joining Austria and Portugal (the shortest distance), then Belgium and Germany (the second shortest distance) and so on. The process continues until all 111 countries are included. Thus constructed, the MST offers an arrangement of the countries where the most relevant connections are taken from each country in the sample. The connections between two countries represent the shortest path between them. Figure 7 shows the resulting tree.

The MST and the matrix D * allowed us to compute the subdominant ultrametric distance matrix, which is the prerequisite to build the HT. Figure 8 shows the dendrogram that represents the HT obtained.

The HT demonstrates the process of categorizing countries into a specified number of groups. For instance, if the goal is to partition the country sample into eight distinct groups, the HT allocates each country to one of these eight groups. The concluding action involved the application of a hierarchical clustering stopping rule to find the optimal number of groups. The utilization of the C-Kalisky rule resulted in three well-differentiated clusters containing 87 of the 111 countries (approximately 80% of the countries in the sample).

Empirical results

The first group – mature economies – contains 32 countries, and it stands out as the most homogeneous of the three. The sum of this group distances in the MST is the smallest one. It includes all 24 of the initial members of the OECD, except for Turkey [9] . NonOECD countries in the group (Argentina, Barbados, Malta, Mauritius, Trinidad and Tobago, Rumania and Uruguay) are currently classified as upper income or upper-middle income countries. Regarding regime dynamics, the common denominator in this group is their nearly exclusive pattern of alternating between regimes R 3 and R 4 during the entire period of analysis. Other countries –such as Canada, Chile, or Trinidad and Tobago– have a short initial phase alternating between regimes R 1 and R 2 (but concentrated in R 2 ). This alternating pattern lasts for the first decade and a half of the analysis period [10] at most. In brief, this group comprises countries that transitioned from high to low population prior to the period of analysis, with a few cases occurring at the beginning of said period (before the mid-1970s).

Figure 9 shows a plot of the symbolic series of the countries in the first group.

To illustrate this, we calculated the symbolic series for an average country within the group, referred to as the centroid, whose trajectory of regimes can be observed in Figure 10 .

Containing 28 countries, the second group – young economies – is the most heterogeneous of the three that we obtained. It includes 22 Sub-Saharan African countries, three middle eastern countries (Egypt, Jordan and Syria), two Central American countries (Guatemala and Honduras) and Pakistan. Continuing with the pattern observed in the previous cluster, the distinguishing feature of the countries within this group is their near-exclusive alternation between regimes R 1 and R 2 throughout the analysis period, mirroring the dynamics of the mature economies cluster. Of the 28 countries in this group, 16 of them never visited regimes R 3 and R 4 . Mauritania, Mozambique and Syria, are the cases where it would be possible to talk about a short phase in R 3 and R 4 : Mauritania experienced this during the 1960s, Mozambique witnessed it in the 1980s and Syria, more recently, within the last decade. The Syrian anomaly has to do with the population displacement resulting from the civil war that started in 2011.

Figure 11 shows a plot of the symbolic series of the countries in the second group.

The trajectory of an average country within the group can be visualized in Figure 12 .

Broadly speaking, countries in group 3 – transition economies – exhibit two distinct phases. In the first phase, countries alternate between regimes R 1 and R 2 , while in the second phase, they shift to alternating between regimes R 3 and R 4 . There is variation in terms of the moment when countries switch between phases. The two extreme cases are Korea, which transitioned to the second phase as early as the late 1970s and Philippines, which did not switch phases until the mid-2000s.

There is also variation concerning the proportion of years with above-average economic growth within each phase. To exemplify, during the first phase, the rate is markedly low for Namibia, Venezuela and Ecuador, while Taiwan and Korea boast notably high proportions. What binds the 26 countries forming this cluster is their transition from high to low population growth throughout the analysis period. A substantial portion of these nations managed to harness the demographic dividend over the study’s course, a phenomenon seemingly reflected by their time spent in regions R 2 and R 3 .

Figure 13 presents a plot of the symbolic series of the countries in the third group.

The trajectory of an average country within the group can be visualized in Figure 14 .

Let’s briefly discuss the 25 countries that deviate from the three primary groups and are excluded from the classification. Among them, five constitute two smaller clusters (BOL, IND, LSO, HTI and NIC). The remaining 20 countries, however, do not form any distinct group. Regarding regime dynamics, these countries visit all four of them. Based on the sequence of the two distinct phases identified in group 3, we can distinguish three sub-groups within this default category. The first sub-group comprises 13 countries, distinguished by the absence of clearly distinct phases in which countries alternate across different partitions of the state space. Within this context, a second sub-group emerges, encompassing four countries that exhibit the same dual phases observed in transition economies , albeit in reverse order. During the first phase, countries alternate between regimes R 3 and R 4 , while in the second phase they switch between regimes R 1 and R 2 . Finally, an additional (third) sub-group of eight countries exhibit the same distinct phases as group 3, in the same order. That is, given the way we characterized the three main groups, the dynamic behavior of these eight countries is indistinguishable from the group of transition economies .

To conclude this section, we proceed to characterize the three groups with respect to a set of variables closely associated with the two dimensions of our regime dynamics analysis. More precisely, these variables are intertwined within the dynamic system alongside our analysis dimensions. The variables are life expectancy, fecundity, per capita GDP and the human capital index. For instance, both life expectancy and fecundity contribute to determining population growth. Moreover, the literature suggests that these variables are influenced by per capita GDP levels, which, in turn, are shaped by historical growth rates in per capita GDP. Figures 17, 18, 19 and 20 (see Appendix 2 ) illustrate that the three identified groups can be clearly differentiated based on these supplementary variables. Notably, the figures highlight minimal overlap within the range of variation for these variables and a discernible order across the groups. The predominant commonality among the three groups lies in their shared temporal trend, particularly noticeable in groups 1 and 2: fertility decreases, life expectancy and human capital increases.

In summary, we grouped countries based on their regime dynamics, as captured by symbolic series constructed from considering only population growth rates and per capita GDP. Interestingly, we found that these groups also exhibit distinct patterns in relation to other variables that were not initially included in the symbolization process, but are considered relevant in the literature, and in certain instances, even fundamental. The implication here is that the symbolization of two variables reduced enormously the level of complexity of a dynamic system involving several variables, while retaining valuable information that enables us to characterize the entire system.

Cluster dynamics, global distance and convergence

In the previous analysis, we discovered information about the dynamics over the entire period. As mentioned earlier, the dynamics of the clusters throughout the considered period are clearly distinct from each other (see Figures 10, 12 and 14 ). It is possible to observe significant qualitative differences among them.

We conducted an analysis of cluster evolution. Our focus was directed towards investigating the stability of both the quantity and composition within each cluster. Additionally, we aimed to discern whether a trend of convergence could be identified among them, indicating similar dynamics, or conversely, if distinct patterns emerged. To achieve this, we partitioned the analysis period into 30-year timeframes. For each of the 27 windows, we replicated the preceding analysis.

To study whether the countries within the analyzed sample move closer or farther apart over the analysis period, a metric for global distance becomes imperative. Following the methodology employed by Onnela et al. (2002) , the summation of all MST distances establishes the diameter of each MST, providing insight into the proximity of the countries within the set. The evolution of this global distance in each tree of every time window is depicted in Figure 15 , revealing a subtle trend of diminishing distances among the sample countries. This trend suggests an inclination towards increased similarity in their dynamics.

Regarding the stability of group composition, our findings indicate that within the mature economies (group 2), all but eight countries (Argentina, Australia, Chile, Mauritius, New Zealand, Romania, Sweden, Trinidad and Tobago and Uruguay) out of the 28 comprising the group, have consistently maintained their positions throughout the analysis period. Notably, these countries primarily encompass European countries and the USA. Argentina, Australia, Chile and Uruguay exhibit similar behavior, consistently moving in tandem and in more recent timeframes, transitioning to the group of economies in transition .

In the group of young economies (group 3), the behavior has been similar. Its composition has been the most stable, and it is possible to identify 24 countries (out of the 28 that make up the group) that remained together in 25 out of the 27 windows. The country that has stayed the least within the group is Egypt, which has moved away from the group in almost half of the windows. In no case were any of the countries this group part of either of the other two groups, and they tended to move away.

The group of transition economies initially includes a small set of countries that remain united throughout the period: Taiwan, Korea, Hong Kong and Thailand. The remaining economies in this group are part of the young economies group during the first half of the period and are then added to the transition economies group in the second half. In no case do they become part of the mature economies group.

The behaviors of Mexico and the Philippines stand out, as in the last eight windows, they tend to move away from the group without joining either of the other two groups. Considering the dynamic behavior of an average country from each cluster, we analyzed the evolution of the distance between them. As portrayed in Figure 16 , the results show that the groups have exhibited opposite behaviors. The cluster of transition economies is gradually distancing itself from the group of young economies and edging closer towards the category of mature economies . Concurrently, the gap between young economies and mature economies remains constant.

Results discussion

The most prominent feature of the partition achieved here is the influence of demographic transition. The clustering that emerges from symbolizing population change and per capita output displays a substantial alignment with the timing of the demographic transition. Mature economies encompass countries that had concluded their demographic transition before the analysis period, transition economies are those that underwent demographic transition during the analysis period (with the majority experiencing this shift during the final 2 decades of the twentieth century) and young economies consist of countries that have yet to undergo a demographic transition.

Interestingly, the taxonomy derived from analyzing population change alone contrasts significantly with the classification derived from the joint dynamics of population and per capita output. In essence, a demographic transition grouping does not arise solely from the consideration of population change; it necessitates the inclusion of the interplay between population change and per capita output dynamics. This aligns logically with the understanding that demographic transition encompasses more than just changes in population figures. There are numerous potential explanations for this product.

The groupings derived from the similarity in the trajectories of regime change reveal some remarkable facts. First, in addition to the interactions between these variables, the different patterns of behavior reveal functional relationships that vary in sign and magnitude across groups and over time.

This allows us to make some conjectures to explain the diverse and contradictory results found in previous studies. If the relationship between population and economic growth is not monotonic, case studies of a particular economy could reveal causal relationships with different signs if the time period or the amplitude of the same do not coincide. To illustrate this point, Yao et al . (2013) and Rahman et al. (2017) analyze the case of China. The former considers the period 1952–2007 and the latter 1960–2013. Both find evidence of a unidirectional causal relationship from population to economic growth, but they differ in the sign. The same observation can be made when looking at the studies by Azam et al. (2020) , Dawson and Tiffin (1998) and Kapuria-Foreman (1995) on India, or by Aksoy et al. (2019) and Lianos et al. (2022) , who focus on OECD countries. Our results allow us to qualify these differences, if the relationship is not linear, the results may differ and will be sensitive to the period of analysis.

At the same time, the marked differences between the groups, both in terms of their regime dynamics and their behavior with respect to human capital or per capita GDP [11] , provide an additional explanation for the results reported in the literature. A result that depends on a single model, as is done in standard analyses, has difficulties and obstacles that are difficult to overcome. This idea is reinforced by the analysis of stability, composition and distance between groups. The persistent gap between the group of mature economies (high-income and upper-middle-income countries) and the group of young, low-income, high-growth economies is particularly relevant.

Our results provide evidence of a dynamic interdependence between population and economic growth that is not linear. In terms of causality, the sign, magnitude and direction vary over time and across countries. This has important implications for policy recommendations, design and evaluation. The population control policies pursued in most developing countries may no longer be advisable. In the absence of more in-depth studies of this complex relationship, there is a need for periodic review of these policies, which may become inefficient and have undesirable effects.

Concluding remarks

The study of the interplay between economic and population growth holds a rich historical lineage within the field of economics. However, from a theoretical point of view, there is still no agreement about the scope and channels through which population and economic growth affect each other. Empirical evidence does little to resolve the controversy. Despite the extensive body of studies addressing this topic, no unanimous conclusions have emerged. On the contrary, the results are often contradictory. Given the wide range of findings found within the literature, we have opted to conduct a descriptive and exploratory analysis of the connections between economic and population growth.

In this paper, we have presented a methodology that allowed a model-independent, topological and hierarchical exposition of the intricate relationship between economic growth and population.

By applying clustering techniques and building upon the introduced notion of regime, our objective was to identify groups of countries, each internally homogeneous in terms of the dynamic relations between demographic change and economic growth, while also maintaining clear distinction from the other groups.

Our results show evidence of multiple patterns in the dynamic relations between these two coexistent variables. We identified three distinct groups of countries, each demonstrating a unique dynamic pattern. These countries were classified as mature economies , economies in transition and young economies . The first group comprises mainly OECD countries, characterized by low population growth and robust economic performance, boasting above-average per capita GDP growth rates. In contrast, the young economies group, primarily from central Africa, experiences above-average population growth coupled with sluggish economic development. On the other hand, the economies in transition group display a distinct pattern set apart from the other two. Initially, its population growth exceeded the average during the first half of the period, only to decline below the average in the latter half. Despite this, its economic performance remains generally above the average.

The methodology enabled the inclusion of additional variables in the analysis, such as life expectancy, fertility, per capita GDP and human capital. This allowed us to compare the impact of these variables on the formation of clusters based on performance changes. The analysis revealed distinct differences among clusters in terms of the trajectories of these variables, thus providing a form of validation for the earlier analysis.

Upon a global examination of the dynamics across all countries in the sample, a subtle tendency towards converging trajectories was observed. Analyzed individually, the dynamics of the three main clusters show that the groups of young and mature economies are stable in terms of composition.

The in transition group initially consisted of a reduced subset of countries, to which those originally part of the young economies group were added in the latter half of the period. Towards the end of said period, certain countries from the in transition economies group showed a tendency to align with the mature economies group, although not vice versa. Analyzing the evolution of the distance between clusters, we observed contrasting dynamics. The economies in transition cluster demonstrated a tendency to converge with the mature economies , whereas the young economies cluster moved away from both the transition economies and the mature economies .

The evidence provided by our results on interdependence, the variety of ways in which economic growth and population are linked across countries and the changes that occur over time have strong policy implications, especially in terms of their design and evaluation.

Lastly, it is important to highlight certain limitations inherent in the analysis and provide directions for future research. During our investigation, the distinction between natural population growth and the effect of net immigration was not made. This is relevant in light of the fact that the dynamic effects of these two sources of population change exert on output. Incorporating this differentiation stands as a key avenue for future research. Again, the study is exploratory and descriptive; while it provides evidence of interdependence between economic growth and population, it does not allow conclusions to be drawn about causal relationships, nor about the sign or magnitude of possible effects. Another avenue for future research involves conducting a cointegration and causality analysis on the groups derived from countries exhibiting similar dynamics in population and economic growth. This analysis will be based on panel data. Prior to this, an examination of the linearity hypothesis will be undertaken, followed by a comparison of the results with findings from the existing empirical literature.

Population growth rate

Average GDP per capita growth

Minimum spanning tree – population

Minimum spanning tree – GDP per capita growth

Data partition in the state space for the set of 111 countries (population growth rate, growth rate GDP per capita)

Dynamics of regimes in Algeria, Mexico, Pakistan and Sweden, in the period 1960–2019

Regime dynamic group 1, mature economies

Regime dynamic for an average country of the group 1, mature economies

Regime dynamic group 2, young economies

Regime dynamic for an average country of the group 2, young economies

Regime dynamic group 3, transition economies

Regime dynamic for an average country of the group 3, transition economies

Evolution of the diameter of the MST for windows of 30 years

Distance between clusters

Empirical literature surveyed

Autor	Period	Sample	Estimation method	Findings
	1950–1980	44 countries	Granger Causality test	p ⇒+ y p ⇒− y y ⇒+ p y ⇒− p Non causality
	1960–1970 1970–1980 1980–1990	86 countries	FEM REM	No impact p to y
	1961–1991 1961–1990 1953–1989 1951–1990 1953–1989 1961–1991 1949–1991 1952–1991 1961–1990 1961–1990 1951–1990 1958–1990 1961–1990 1952–1990 1948–1986	Nepal India China Ghana Sri Lanka Bolivia Philippines Guatemala Syria Peru Thailand Turkey Chile Argentina Mexico	Granger Causality test	p ⇒+ y p + ⇔−∗∗y p − ⇔+ y y ⇒−p y ⇒− p Noncausality p ⇒+ y y ⇒− p y ⇒− p Noncausality p − ⇔+ y p − ⇔+ y Noncausality p ⇒+ y
	1960–1990	Bangladesh	VAR
	1950–1993	India	Cointegration (Johansen	Noncausality
	1950–1996	20 countries	Cointegration VEC	p ⇒+ y
(2000)	1965–1990	70 countries	OLS	p ⇒ y
	1900–1994 1925–1994 1921–1994 1913–1994	Argentina, Brazil Chile, Venezuela Colombia Mexico Peru	Granger Test VAR	Noncausality
	1978–1998	China	VI – GMM	p ⇒− y
	1961–2003	Thailand	Cointegration (Johansen) VEC	p ⇒ y
	1952–1998	China	VAR VEC	y ⇒− p
	1961–2003	China India Pakistan	OLS	Effect positive (growth differential pop of working age – total pop) 46% 39% 25%
(2011)	1960–2009	Malaysia	Cointegration (Johansen) VAR, Toda-Yamamoto	Noncausality
(2013)	1952–2007	China	Cointegration, VECM	p ⇒− y
	1983–2008	provinces China (panel)	OLS	p ⇒− y
	1980–2013	India	Cointegration (Johansen) VEC	p ⇒+ y
	1961–2014	China	ARDL	p ⇔ y
	1950–2000	Japan, Korea, Thailand China, Singapore, Philippines Honk Kong, Malaysia Taiwan, Indonesia	Cointegration (Johansen) VAR	p ⇔ y p ⇒ y y ⇒ p Noncausality
	1960–2000	25 OCDE countries	cross-country regression nonparametric kernel	relation inverted U-shape
(2006)	1950–2000	125 countries	OLS (logy) (logy)2	Africa–Asia U-shape inverted Europe: y ⇒− p
(2007)	1954–2005	Taiwan	Cointegration (Johansen) VAR, Toda-Yamamoto	until 2000 p ⇒+ y until 2005 insignificant
	1960–2000	110 countries	GAM non parametric
	1950–2001	Pakistan	OLS	Negative effect (p ⇒ y)
	1980–2007	Panel 90 countries	simultaneous ADL	p ⇒− y
	1965–2009	13 countries Asia	OLS	Negative effect (p ⇒ y)
(2013)	1975–2008	Pakistan	ARDL	p ⇒+ y
(2017)	1870–2013	Finland, France, Portugal Sweden Canada, Germany Japan Norway Switzerland Austria, Italy Belgium, Denmark, Netherlands UK, US New Zealand	Panel Granger Causality Test	p ⇒+ y y ⇒− p p ⇔ y Noncausality
(2016)	1962–2012	Mexico	VEC	p ⇔ y
	1990–2013	7 countries Southeast Asia	Panel regression model Structural Equation Model	p ⇔ y
(2017)	1960–2013	USA, UK, Canada China, India, Brazil	Panel cointegration VEC	p ⇒+ y
	1970–2015 ADL	Zambia	Cointegration (Johansen)	p ⇔ y
(2019)	1970–2014	21 OECD countries	Panel VAR	p ⇒+ y
(2020)	1980–2018	57 Islamic countries	Cointegration (Johansen) VEC	p ⇒+ y
(2020)	1974–2013	Rwanda	ARDL	Positive effect (p ⇒ y)
(2020)	1996–2016	10 Middle East countries	OLS	Positive effect (p ⇒ y)
	1987–2017	Ghana	ARDL	p ⇒- y
(2020)	1980–2020	India	ARDL	p ⇒+ y
	1980–2019	Ethiopia	ARDL	p ⇔ y (positive)
(2022)	1820–1938 1950–2016	USA, UK Germany France, Italy	Toda-Yamamoto, Granger, Sims Causality test	p ⇒+ y p ⇔ y y ⇒+ p

The table summarizes the results found in the literature review. In the results column, y ⇒ p indicates a unidirectional causal relationship (Granger causality), where per capita income causes population, p ⇒ y indicates population causes per capita income and p ⇔ y indicates a bidirectional causal relationship. The signs + or − and (*), indicate the sign and significance when reported

Authors’ own elaboration

Growth rate	Mean	SD	Min	X	X	X	Max
Population	1.80%	1.17%	−22.02%	0.93%	1.89%	2.65%	11.76%
GDP per capita	2.01%	6.20%	−59.27%	−0.13%	2.29%	4.59%	42.58%

The mean, the variance, the quartiles and the maximum and minimum values of the variables

Authors' own calculations based on PTW 10.0

Source(s): Authors’ own elaboration

Biswar, S. (1 may, 2023). Most populous nation: Should India rejoice or panic? BBC . Available at: https://www.bbc.com/news/world-asia-india-65322706

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In the case of Germany the period of analysis is 1970–2000.

See Mantegna and Stanley (1999) , chap. 13 for a proof that the function satisfies the distance properties.

Following the methodology proposed by Onnela et al . (2002) , where the sum of the distances, also known as the tree diameter, provides a general measure of the distance between all countries in the sample.

The results we got are contingent on the specific thresholds we relied upon. For future research, it would be interesting to explore alternative partitions of the state space and compare the results with the ones obtained here.

Given that our framework considers overall population growth without differentiating the effects of birth rates and mortality rates, it’s not possible to ascribe the demographic dividend to a single specific regime. That said, in a regime sequence of the type R1 → R2 → R3 → R4, the demographic dividend would be captured somewhere between R2 → R3

See Brida et al. (2003) for a more detailed exposition of regime dynamics and its symbolic representation. In Brida et al. (2011) can be found an empirical analysis on convergence clubs that apply the same approach as the one used in our paper.

By initial members, we mean the countries that joined the organization in its first decade or so of existence.

Three countries in the group, Australia, Ireland and Luxembourg have some years alternating between R1 and R2 in the final 15 years of the analysis. One possible explanation: the relatively high influx of immigrants during those years. In fact, as a percentage of their population, these are the countries that received the most immigrants in the group during the last 2 decades.

To some extent, this is a sign of the robustness of our results.

Purchasing Power Parities

The supplementary material for this article can be found online.

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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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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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Drivers of future population growth in six most populous countries: Effect of demographic components on the population growth using decomposition analysis

Jayachandran A A Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation John Stover Roles: Supervision, Writing – Review & Editing

This article is included in the International Conference on Family Planning gateway.

Decomposition of population growth, population projections, population momentum, world population prospects

The United Nations Department of Economic & Social Affairs (UN DESA) of the United Nations Secretariate released the twenty-seventh edition of World Population Prospects (WPP) on 11 July 2022. The official estimates of the United Nations for the past years starting from 1950 till present and projected numbers under ten deterministic projection scenarios until 2100 are available for 237 countries or areas in the WPP 2022 report 1 . The 10 projection scenarios include i) five fertility level assumptions, ii) a constant mortality scenario, iii) a zero-migration scenario, iv) an instant replacement zero-migration scenario, v) a no-change scenario and vi) a “momentum” scenario to measure the impact of age-structure on long-term changes in population 2 . Framing such scenarios helps us to measure the impact of varying assumptions in terms of demographic components from the “ medium ” variant among other benefits.

According to 2022 UN population estimates, the global population has crossed eight billion on 15 November 2022, suggesting a new set of challenges and opportunities. It took 12 years to add a billion people to the world population and will take about 15 years to add the next billion to the total population signaling slowing down of population growth. The latest projection estimates suggest that the Global population is growing at its slowest pace since 1950 as fertility has fallen markedly for several countries in recent decades and the same slowdown trend is likely to continue in the coming years. The landmark news of a population of 8 billion triggers fears among different sections of media, which is evident from the recently published report by the UNFPA while discussing many myths and facts of the population growth 3 .

The ten most populous countries, namely, China, India, USA, Indonesia, Pakistan, Nigeria, Brazil, Bangladesh, Russia, and Mexico account more than 57 percent of the Global population in 2022. It would be interesting to know what demographic factors drive future population growth in this century. This paper tries to find and estimate the impacts of demographic factors – fertility, mortality, migration, and current age structure, popularly known as population momentum – on the future of the world’s population and in the six most populous countries.

The main objective of this paper is to estimate the proportion of demographic components that cause changes in projected population of selected six most populous countries and the world population in this century using the 2022 WPP data.

Literature review

The basic principle of UN population estimates and projections is the cohort-component method for projecting population (CCMPP) 2 . Three demographic factors that influence the future population growth due to a typical consequence of demographic transition are: future fertility and mortality patterns, and migration trends. The fourth demographic factor called population momentum was conceptualized by Keyfitz N in 1971 4 . Momentum measures the contribution of age structure to a population’s future growth or decline 5 . These four demographic components can have a significant impact – positive or negative and in different permutation and combinations depending on which demographic transition phase a particular country passing through – on future population growth.

Researchers have tried to decompose future population growth from time to time using different datasets. Prominent among these, a methodological report published by Andreev et al. 6 in which authors have conceptualised a framework to break-down future population growth into current age structure, change in fertility and mortality and trends in net-migration using 2010 WPP data for countries and regions. In earlier studies Bongaarts 7 and Bongaarts and Bulatao 8 mentioned such analyses would help in understanding the relative weight of key factors that drive population growth and can inform policies and programmes aimed at balancing impending changes and social, economic, health and environmental objectives. Influences of population growth factors depend on how soon countries pass through demographic transition. Blue and Espenshade 5 provide insight into the trends in population momentum in non-stationery age structures and its influence on crude birth and death rates amid demographic transition. In long-term projections, this illustration is useful as age structure changes more rapidly when fertility and mortality likely to undergo major changes.

More recently, Kulkarni P.M. 9 advocated policies that respond to anticipated changes in population size and structure based on decomposition of India and state population growth from 2021 to 2101. In his study, among four demographic factors, migration factor is not considered while measuring the impact of population growth, but he did examine fertility, mortality, and age structure.

Study design

This paper estimates the relative contributions of four demographic components—fertility, mortality, migration, and the current age structure of population— on future population growths of selected countries. The analysis is based on the data compiled from the 2022 Revision of World Population Prospects 10 , which is available online under the file type “standard projections estimates and projection scenarios”. The study has been carried out for the six most populous countries and the global population as of 1 January of projected years.

World population prospects data

The UN projection used cohort-component method for projecting population (CCMP), which relies on information about fertility by age of mother to determine the number of births taking place each year; mortality by sex and age to determine the number of deaths; and net international migration by sex and age to determine the levels and patterns of population shifts across international borders 2 . Methodologically, several updates have been incorporated in the 2022 Revision of WPP and some of the important modifications are: ten deterministic projection scenarios were constructed to illustrate the impact of differing assumptions from the medium scenario, five-year age groups by five-year periods (5X5 matrix) into single-year age groups by one-year periods (1X1 matrix), population reference dates changed from 1 July to 1 January to help align the calendar year. For more details about the methodology adopted in the 2022 Revision of WPP, please refer to the comprehensive note on methodology published by the UN in 2022 2 .

To calculate the relative effects of fertility, mortality, migration, and population momentum, on population changes from base year 2022, we use the standard decomposition technique as explained hereunder. The input data to estimate effects of four demographic factors are four variants of 2022 Revision of WPP projection results, viz., i) medium variant (base values), ii) zero-migration variant (natural change in fertility and mortality), iii) instant replacement fertility (fertility set to instant replacement as of 2022 and other parameters are equivalent to medium variant), and iv) momentum variant (fertility set to instant replacement as of 2022, mortality set to 2022 level and zero migration from 2022). Projected results as of 1 January are used.

The population change from base year takes the summative form of population changes caused by fertility, mortality, migration, and age structure which can be written as,

where ΔP is change in population between two time points

P Mom – Population change due to momentum during the same period

P Fer – Population change due to Fertility during the same period

P Mor – Population change due to Mortality during the same period

P Mig – Population change due to Migration during the same period

1. Population changes due to population momentum

Population momentum ( P Mom ) refers to an inherent driving force for population growth resulted from the existing age structure 1 . Effect due to population momentum is calculated by subtracting the population projected figure under Momentum variant from the base year (here 2022).

2. Population changes due to fertility

Fertility component contributes to population growth positively or negatively depending on whether fertility rates are above or below replacement level fertility. Replacement level of fertility is an important concept in demographic transition and considered to be it’s total fertility rate (TFR) value around 2.1 while considering survival chances of females through their reproductive lifespan. Difference of population projected figures based on instant replacement scenario from figures under zero-migration scenario attributed to population changes due to fertility. This is calculated on a year-on-year basis.

3. Population changes due to mortality

Mortality pattern changes the contours of future population growth in either way. Usually gains in mortality situation cause spurts in population growth whereas the recent Covid-19 pandemic marginally impacted population decline in many countries. The difference in total population between the instant replacement and Momentum variants indicates the effect of anticipated change in mortality on future population size.

4. Population changes due to migration

Future trends in net-migration influences positively or negatively the population size of a country. Since migration is closed for the global population, its impact is not calculated however, computed for countries by subtracting projected figures under the zero-migration scenario from the figures of median variant.

Finally, calculated figures have been checked for the consistency as per Equation 1 by summing these above-mentioned four figures with the population differences computed from base year under median variant for projected years. The above methodology is largely based on a seminal technical paper by Andreev K et al. published in 2013 by the Population Division of UN DESA which looked at the demographic components of future population growth 6 .

5. Selection of six countries

The six most populous countries based on 2022 population data have been selected for the analysis and it is interesting to note that they represent different continents (none belong to Europe and Latin America though), passing through different stages of demographic transition, financially different economic categories, and follow different geo-political systems. In this analysis, population projection figures refer to 1 January unless otherwise specified.

A: Trend in the Projected Population for selected countries and Global Total

The analysis started by looking at the positional changes of 10 most populous countries in different decades based on projected population figures. According to the medium variant 2022 Revision WPP, the 10 most populous countries are ordered and plotted in the Figure 1 below for period 2022 – 2100. Six of the top ten countries in 2022 appear in the list until 2100 with minor positional changes. India is likely to overtake China by May 2023 and continues to lead the list with China in the second spot until 2100 (projection period ends). The projected population shows that India will be the only country with a population of over one billion after 2080 as China’s population continues to decline and falls below 1 billion by 2080.

Figure 1. Position of top 10 countries according to projected population sizes 2022–2100.

Note: ISO-3166 Country Codes - BGD: Bangladesh, BRA: Brazil, CHN: China, COD: Democratic Republic of the Congo, EGY: Egypt, ETH: Ethiopia, IDN: Indonesia, IND: India, MEX: Mexico, NGA: Nigeria, PAK: Pakistan, RUS: Russia, TZA: Tanzania, and USA: United States of America

In 2030, Mexico is edged out by Ethiopia in the top 10 list while other countries have stuck to their positions except that Russia moved to tenth from ninth in the list. Two changes have been noticed in the 2040 list – i) DR Congo entered the list by edging Russia out of top 10 and ii) Pakistan and Nigeria moved up to take fourth and fifth positions respectively while Indonesia pushed to sixth from fourth position. The 2050 list is marked by a few more reshufflings – i) Nigeria edges past Pakistan to occupy fourth position, ii) Bangladesh is pushed to tenth from eighth position and iii) DR Congo and Ethiopia moved up in the ladder placed in eighth and ninth positions respectively.

The next two decades, i.e., 2060–70 did not observe any dramatic positional changes except that Brazil moved to ninth position from seventh. Tanzania entered the list of ten populous countries in 2080 by edging out Bangladesh while DR Congo replaced Indonesia to occupy sixth position. The last decade of this century will see Egypt entering the list and Brazil out of the list of ten most populous countries. DR Congo sneak past the USA to occupy the fifth position; finally bringing the tally of four countries from Asia, five countries from Africa and one from the Americas.

Figure 2 presents the trend of projected population for the total, China and India under medium variant scenario provides an overview of the future population size. The world population is likely to stabilise in 2086 at 10.43 billion, which is twenty-two years after India’s population expected to stabilise at 1.70 billion in 2064. China’s population is already showing declining trend in 2022 suggesting that its population has achieved population stabilisation. Interestingly, the projected population of China shows that by 2079 it will likely leave India alone in the one billion plus club as its projected population dropped to 989.8 million from 1.01 billion in this year – after its population crossed the 1 billion mark nearly 100 years ago in 1982 .

Figure 2. Trend in projected population for China, India, and Global Total, 2022–2100.

To provide clear picture of population age-sex composition and future trends, population pyramids are created for the six countries and for the World population and displayed below ( Figure 3a to Figure 3g ).

Figure 3a. Age-sex population pyramid, China, 2022.

Figure 3b. age-sex population pyramid, usa, 2022., figure 3c. age-sex population pyramid, india, 2022., figure 3d. age-sex population pyramid, indonesia, 2022., figure 3e. age-sex population pyramid, pakistan, 2022., figure 3f. age-sex population pyramid, nigeria, 2022., figure 3g. age-sex population pyramid, world, 2022..

2022 age-sex population pyramid of China suggests likelihood of declining population due to rapid decline in fertility below replacement which is expected to stay well below the replacement level for nearly three decades. However, the USA’s population pyramid for the year 2022 portrays an interesting shape signifying a nearly stable population – meaning considerably slower growth in the future.

Population pyramids of India and Indonesia represent an “ expanding slowly ” scenario in which age structure, improved mortality situations and net-migration are likely to drive future population growth with fertility transition being completed.

Population pyramids of both Nigeria and Pakistan represent scenario of “ expanding rapidly ” implying both fertility transition is yet to occur and help population growth more than other demographic factors.

The World’s 2022 age-sex population pyramid represents early signs of “ expanding slowly ” as fertility is getting closer to replacement level in the near future.

Figure 4 provides the details of trends in TFR forecasted by the 2022 WPP for six countries considered for the analysis to show how fertility transition is envisaged in these countries. Fertility rates in Nigeria and Pakistan are well above 3 in 2022 and beginning to converge with other countries towards the end of this century. Fertility transition in other four countries has already been completed in the base year 2022. China’s fertility is forecast to increase from just above 1.19 in 2022 to reach 1.48 by the end of this century.

Figure 4. Future trends in TFRs ( medium variant ) of six most populous countries.

Source: 2022 WPP, UNDESA.

B. Decomposition of demographic indicators in future population growth of six most populous countries and the world population

1) Factors affecting World Population Growth

Since the migration factor is closed for the world population, the contributions of the other three factors – fertility, mortality, and age structure - are calculated and presented in the form of horizontal bars on primary axis in Figure 5 below. The figure also presented the changes in population from 2022 in the form of two lines representing additions and subtractions to the population due to changes in fertility, mortality, and age structure on the secondary axis.

Figure 5. Factors affecting Global population growth and changes in population size (in ‘000s).

During 2022–30, the world’s population is likely to increase by 570 million of which age structure or population momentum of past growth contributes 78.1 percent while 12.5 percent is driven by the gain in mortality conditions and the marginally above replacement level of fertility attributed the rest 9.4 percent. Globally, 1.75 billion people will be added to the 2022 population level during the 2022–2050 period and two-thirds (66 percent) of this growth would be due to the population momentum, gains in mortality accounts for one-fourth (25 percent) increase and the contribution of above replacement level fertility is estimated at 8.5 percent.

The global population continues to grow at a slower pace in the second half of the century with the contributions from mortality decline touching 50 percent; now the leading cause of population growth by overtaking the contributions from age structure (46.6 percent) during 2022–80 period. In this period the population is likely to grow by 2.47 billion as fertility accounts for only 3.3 percent of the population increase. Finally, the last decade of this century shows decline in population growth at the global level with the gains in mortality contributing nearly two-thirds (65.4 percent) of the 2.41 billion population projected to be added between 2022-2100. Since fertility stays below replacement level ensuring 259 million fewer in the total population.

2) Factors affecting China’s population change

Contributions of factors affecting changes in China’s projected population are provided in Figure 6 below and tell different story. Impact of population momentum on growth in the population disappear in the next three decades – from 95 percent in 2022–30 to no impact in the 2022–60 period and negative impact thereafter – signifying fast changing population age structure resultant of fertility remaining below replacement level for a longer period in the past. The projected population of China shows negative population growth starting between 2022–30 with 9 million less population in this decade. Net-migration (4 to 5.8 percent) and fertility below replacement level (94 to 95 percent) are likely to contribute to the negative population growth until mid-century. During this period, both age structure and gains in mortality enhance the moderate population growth. Impacts of age structure turned out to be influencing negative population growth starting 2060 onwards for the country by leaving gains in mortality the lone influencer of population growth. By the end of the century, China expected to cutdown 0.65 billion in population size compared to its 2022 population count under the medium scenario assumptions.

Figure 6. Factors affecting China’s population growth and changes in population size (in ‘000s).

3) Factors affecting India’s future population growth

India’s population growth story is presented in Figure 7 below. The projected population of India shows an increase of 115 million people due to population momentum and gains in mortality while a decrease of 18 million due to fertility below replacement level and net outmigration resulting in net addition of 97 million during 2022–30 period. Age structure contributes more than 80 percent of 115 million increase and the remaining 20 percent contributed by the gains in mortality situations. More than three-fourth (76.3 percent) of the 18 million less population impacted by the below replacement of fertility levels and 23.7 percent due to outmigration during the same period.

Figure 7. Factors affecting India’s population growth and changes in population size (in ‘000s).

The same trend continues until the end of this century as population momentum and longer expectation of life driving population gains while fertility below replacement levels and net outmigration are likely to reduce the population with the impact in mortality gains overtaking population momentum in the period 2022–2080 as the major contributor in share of population increase.

4) Factors affecting Nigeria’s future population growth

Nigeria starts as the sixth populous country before moving up to third populous country by 2060 signifying a typical population growth story. The population of Nigeria is expected to double from 216 million in 2022 to 436 million in 40 years making it one of the fastest growing countries in the world. Figure 8 presents the factors affecting Nigeria’s population growth over the next few decades. Fertility above replacement level of fertility (ranging from 55 to 65 percent) contributes significantly to the population growth followed by the population momentum (ranging from 14 to 40 percent) and marginally by mortality gains (ranges between less than 1 percent initially and 23 percent by the end of the century) as shown in the figure below.

Figure 8. Factors affecting Nigeria’s population growth and changes in population size (in ‘000s).

5) Factors affecting USA’s future population growth

Population of the USA is projected to grow at a very slow pace from 337.5 million in 2022 to 394 million in 2100; an addition of 56.5 million in 72 years under the medium scenario . The analysis shows that growth of population in the US is contributed by age structure, mortality gains, and net in-migration whereas, fertility levels below replacement contribute to decrease in population in the coming years and decades. Figure 9 below provides the details. Between 2022–30, the population is projected to increase by nearly 30 million people due to age structure (31.6 percent), mortality gains (39.3 percent) and net in-migration (29 percent) while lower fertility rates contributed to decrease of 16 million in the same period resulting in a net addition of 13.8 million people to the total population of the country. Factors propelling population increase between 2022–50 are net in-migration (36.3 percent), mortality (54.5 percent) and age structure (9.2 percent) of the 99.7 million population while lower fertility rates below replacement contributes to 62.1 million less population resulting in 37.6 million net increases in the population in the same period.

Figure 9. Factors affecting USA’s population growth and changes in population size (in ‘000s).

6) Factors affecting Indonesia’s future population growth

Under medium projection scenario, presently the fourth populous country, Indonesia, is projected to grow moderately from 274.6 million in 2022 to 319.4 million in 2060 before population decline starts. At the end of this century in 2100, Indonesia’s population is projected to fall below the 300 million mark and likely to be the eighth populous country. Figure 10 shows the contributions of factors affecting the population change on primary axis and changes in population sizes during different periods on the secondary axis.

Figure 10. Factors affecting Indonesia’s population growth and changes in population size (in ‘000s).

Both mortality and age structure propelling population increase while net out-migration and lower fertility levels below replacement fertility contribute to the population decrease throughout this century for the country. Population momentum contributes more than 78 percent and improvements in mortality conditions cause 21 percent of the 17 million population increase for the 2022–30 period. Analysis shows that net outmigration (72 percent) and low birth rates (28 percent) bring down population by 0.61 million in the same period. During the 2022–60 period, Indonesia is likely to add 44.8 million people to its total before stabilising the population with 62 percent of population increase (59.6 million) contributed by population momentum and 38 percent of increase due to improved mortality conditions in the same period. Below replacement of fertility levels (83 percent) and net outmigration (17 percent) bring down the 14.8 million population between 2022 and 2060 in Indonesia. The trends in contributions of factors affecting population changes continue until the end of the projection period as fertility contributes to 92 percent and net outmigration covers 8 percent of population decrease while shares of mortality gains 63 percent and population momentum 37 percent of population increase estimated for the country.

7) Factors affecting Pakistan’s future population growth

Like Nigeria, Pakistan too follows a rapid population growth trajectory as its population is projected to double in 40 years from 215.9 million in 2022 to 431.4 million in 2061 ( Figure 11 below). The decomposition analysis shows that mortality (8.6 percent) and net out-migration (91.4 percent) account for nominal population decline of 3.9 million during 2022–40 while fertility higher than replacement level fertility (38 percent) and age structure (62 percent) drive population increase of 90.6 million resulting net addition of 86.7 million from the 2022 base population in Pakistan.

Figure 11. Factors affecting Pakistan’s population growth and changes in population size (in ‘000s).

Since then, three factors – fertility, mortality, and age structure – drive population growth with net out-migration lone contributor of population decrease for the rest of the projection period in the country. The shares of age structure declined from 62 percent to 39 percent, gains in mortality increased from 0.9 percent to 14.6 percent and higher fertility above replacement level from 37.1 percent to 46.7 percent in population size increase from 2022 to the end of different decades.

Population momentum

Population momentum, a force that drives future population growth resulting from the existing age structure with constant levels of mortality and net zero migration continues to grow even when fertility still is constant at the replacement level, which turns out to be a large young population accumulated due to high fertility in the past. Population momentum can also trigger negative population growth if the existing age structure is old thus, could cause positive or negative population growth depending on young or old age structure, respectively.

The 2022 UNDESA medium scenario population projection results suggest that the global population continues to grow from 7.94 billion in 2022 to 10.43 billion until 2086 before it starts declining. Age structure is found to be the major driver (66 percent) of the 1.75 billion population increase at least for the next three decades from 2022. This result corroborates results from a previously conducted study using 2019 UNDESA data 11 . Population momentum is the only common factor among four demographic factors driving population increase in six most populous countries considered for the analysis at least until 2050.

Population growth caused by population momentum is inevitable unless significant changes in mortality and migration (at country level) parameters impacted on population. WPP 2022 rightly summarised that further actions by Governments aimed at reducing fertility would not have a major impact on the pace of growth between now and mid-century, beyond the gradual slowdown anticipated by the projections 12 . Population momentum is a natural demographic transition and has already been in motion fuelling population growth. The effect of population momentum on China’s population is found to be declining and likely to have a negative impact starting from 2060 onwards. China’s population growth pattern provides a clue to an argument that cumulative effect of lower fertility, if continued over several decades, could result in a more substantial reduction of population growth in later decades. When we compare the results from 2010 WPP data by Andreev et al. 6 , the impact of age structure on population growth in the year 2022 significantly reduced for both China and India. In absolute numbers, 447 million people would have projected to add between 2010 and 2100 due to young age structure in India significantly reduced to 174 million people between 2022 and 2100 whereas, China would have added 146 million between 2010 and 2100 now 142 million fewer people than 2022 population as per the 2022 WPP analysis.

Population growth patterns of other five countries are different than China’s as the population momentum continues to influence population growth with varying degrees in these five countries during the projection period.

Fertility component

Fertility rates above replacement level of fertility influences the population growth. During demographic transition, fertility rates play important roles in prospects of population growth of any country. In the present global population scenario, fertility is still a deciding factor influencing positive population growth at least until 2050. The population of Nigeria and Pakistan too are positively influenced by their higher levels of fertility rates however, in the other four countries where fertility rates have reached below replacement level of fertility so it is no more a deciding factor shaping population growth in future. Decades of sustained much lower fertility than replacement level of fertility in China greatly changed the country’s population growth trajectory as more than 94 percent of the decline in population was attributed to the fertility factor alone during 2022–2050. The one-child policy adopted by the country during the 1980–2015 period (which was reversed to a two-child policy in January 2016 and further to a three-child policy in May 2021 13 substantially lowered the fertility rates and curbs the population growth. Decline in population due to consistently lower fertility rates than replacement level in China was calculated at 425 million between 2010 and 2100 6 against the 621 million between 2022 and 2100 in this study.

On the other hand, prolonged higher fertility rates along with population momentum will have significant impact on the population growths of Nigeria and Pakistan. Both the countries will see a surge in population in the coming several decades with a doubling time of 40 years and are likely to be the third and fourth most populous countries by the second half of this century.

Impact of fertility on curtailing population growths of India, Indonesia and USA is evident due to the sustained lower than replacement fertility rates observed in these countries. Once fertility was synonymous with the population growth in many developing countries since 1950s but now with the wider use of family planning methods it is no longer influencing population growth rather restricting population.

Mortality component

Globally, better healthcare services, improved living conditions and hygiene practices in recent decades help people live longer, which is found to be one of the driving forces of future population growth. Gains in mortality conditions in five of the six countries (except Pakistan) analysed here show a positive impact on population growth until 2050 and going forward will contribute to the population increase by accounting for a major share (more than 50 percent). Pakistan’s case was found to be exceptional due to the upward adjustment of mortality estimates accounting for excess Covid-19 deaths pushing negative growth of population in 2022 WPP projections until 2040. This upward adjustment of mortality estimates was carried out for all the countries however, its negative impact was visible only for one or two years in the projection period for other five countries analysed here. Gains in mortality and population momentum started clearly offsetting the decline in fertility rates while positively impacting population growth after 2040 in five countries apart from China where the share of age structure shifted to groups of factors (fertility and migration) that influences negative population growth since 2040.

Migration component

Net migration irrespective of whether in-migration or out-migration is likely to change the population growth of many developed countries (USA in this analysis) where natural population growth is nearly zero as noted by a previous report 6 . The results suggest that population growth in the US is characterized by cumulative sum of age structure, gains in mortality and net in-migration of which the last two factors contribute nearly 91 percent of population growth during the 2022–50 period. The situation is different in developing countries like India and Indonesia where more than one-fifth (21.1 percent) of the negative population growth for India (21.1 percent) and Indonesia (21.4 percent) attributed to net out-migration in the same period whereas migration did not show much impact on population growth of China, Nigeria, and Pakistan (at least volume-wise). International migration is considered to be an important factor in propelling population growth (negative or positive) in the coming years, it has been largely regulated by geo-political and policy environment. Thus, policies related to the international relations are likely to change the assumptions made regarding the future migration trends carried out under population projections.

Comparison with the results of similar study using 2010 WPP data reveals an interesting pattern where overall trends in influences of different demographic factors on population growth are the same however, levels vary significantly due to fast changes in fertility and mortality behaviours. A 2022 study by Dai, Shen and Cheng 14 evaluated nine population projections available for China (UN, IHME, IIASA, etc.) and concluded that the slowing of the country’s actual population growth rates from 2017 is earlier than most datasets projected. Therefore, the turning point of China’s population decline probably comes rapidly before these datasets expected between 2024 and 2034 providing guidance to modellers and forecasters to rigorously verify assumptions regarding fertility, mortality, and migration to improve the accuracy of population projections.

Long-term population projections published by the UN provide extremely useful implications on population trends and patterns. Analysis of population projections helps to identify far reaching causes and consequences of population growth.

Population momentum because of current young age structure significantly shapes the future population growth of the global population and six most populous countries analysed at least until mid-century. One of the key conclusions of this study is population growth caused by population momentum of global population and many countries is inevitable at least for the next three decades.

Sustained low fertility below replacement level for a long period in China helped to achieve population stabilisation much faster and subsequently triggered negative population growth starting 2022. Policies and programmes may be framed to specifically address long-term implications of negative population growth in China.

Both India’s and Indonesia’s population growths are greatly influenced by the age structure and gains in mortality which are moderately offset by fertility below replacement of fertility rates and net outmigration. Such countries could closely observe contours of fertility patterns, reap advantages of demographic dividend, and pay attention to consequences of brain-drain due to outmigration.

Net in-migration, age structure and gains in mortality combinedly propel the US population at a slow pace in this century. The contribution of migration could be substantial, especially for the developed countries who may have specific tasks in framing policies and programmes addressing migrants’ needs.

Nigeria and Pakistan are two of the fastest growing populations and their population increase is substantially influenced by high fertility, age structure, and improvements in mortality conditions through the century.

The study suggests conducting such studies using the latest available data and compared with the results of previously conducted studies from time to time helps to accurately measure the impacts of these demographic factors on population growth and recommends suitable policies.

Finally, population policies and programmes based on implications of such long-term projections help countries to guide and adopt appropriate strategies to realistically achieve SDGs in a time-bound manner.

Data availability

Underlying data.

United Nations, Department of Economic and Social Affairs (DESA), Population Division (2022). World Population Prospects 2022, Online Edition. Under a Creative Commons license CC BY 3.0 IGO. Access here:

https://population.un.org/wpp/Download/Standard/MostUsed/

Figshare: Raw data and calculations in Excel format. DOI: 10.6084/m9.figshare.23577831 15 .

This project contains the following underlying data:

Global Calculation File-AAJ-JS-14741.xlsx

CHN Calculation File-AAJ-JS-14741.xlsx

IDN Calculation File-AAJ-JS-14741.xlsx

IND Calculation File-AAJ-JS-14741.xlsx

NGA Calculation File-AAJ-JS-14741.xlsx

PAK Calculation File-AAJ-JS-14741.xlsx

USA Calculation File-AAJ-JS-14741.xlsx

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

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Rapid Growth of the World Population and Its Socioeconomic Results

Rahim sadigov.

Baku Business University, 88a H. Zardabi St., Baku AZ1122, Azerbaijan

Associated Data

(1) Information of Table 1 (world historical and predicted population by regions) was obtained from the following source: World historical and predicted populations, https://p2k.unkris.ac.id/IT/en/3065-2962/seven-billion-people-alive_22650_p2k-unkris.html2 . Information of Diagram 1 (population by regions in 1800) was obtained from Table 1.3). Information of Table 2 (regional population growth forecast for the world by 2100) was obtained from the following source: World Population Perspectives: 2017, Table S.2. Total population by country and region, 1950, 2017, 2030, 2050, and 2100 (medium variant) https://population.un.org/wpp/Publications/Files/WPP2017_KeyFindings.pdf 4 . Information of Diagram 3 (world population growth dynamics until 2095) was obtained from the following 2 sources: (1) Author's application of mathematical-statistical extrapolation method. (2) World population, https://en.wikipedia.org/wiki/World_population#cite_note-UN-115 . (5) Information of Table 3 (statistical table of world population for the last five years) was obtained from the source. The table was prepared by the author on the basis of the table: World Population by Year^ https://www.worldometers.info/world-population/world-population-by-year). (6) Information of Figure 1 (matrix of factors influencing population growth) was prepared by the author. (7) Information of Table 4 (forest area by regions in the world) was obtained from the source: Global Forest Resources Assessment 2015, Desk Reference, Download FRA 2015 results, https://www.fao.org/forest-resources-assessment/past-assessments/fra-2015/en/ . (8) Information of Table 5 (GDP per capita in the regions of the world) was obtained from the following sources: (1) Statistic times, List of continents by GDP, https://statisticstimes.com/economy/continents-by-gdp.php . (2) World historical and predicted populations, https://p2k.unkris.ac.id/IT/en/3065-2962/seven-billion-people-alive_22650_p2k-unkris.html .

This article is mainly devoted to the study of socioeconomic opportunities and problems that may arise from the growth of the world's population. The article identifies the reasons for the increase in world population and analyzes the factors influencing on the process. The article examines the impact of changes on the world's demographics on socioeconomic development. As a result, the characteristics of possible problems were investigated and evaluated. The study analyzes the issues of demographic change in the world population, the current situation, and opportunities of the world economy in accordance with population statistics and its growth rate. The main purpose of the study is to determine the causes of world population growth, analyze the current demographic situation, and determine and assess the forecast of future growth dynamics. The study discusses, analyzes, and evaluates the problems that can be caused by the growth of the world's population. The main problem we raise in the study is the mismatch between the rapid growth of the world's population and the socioeconomic security of the people. That is, if the issue of socioeconomic security is not resolved, the growth of the world's population would be a global social problem.

1. Introduction

The history of mankind covers a period of more than 3 million years. During this time, more than 80 billion people came to Earth, and today 10 percent of them live. According to statistics [ 1 ], the world's population is more than 7 billion 884 million people.

At present, the dynamics of world population growth [ 2 ] is very high. Such a rapid growth rate is already worrying humanity. Not only is the population growth in the world rapid, but this growth far exceeds the level of production and improvement of people's social needs. The content of the article is mainly related to the study of the future disproportion between population growth and the socioeconomic security of the people, as well as the problems it will cause because failure of meeting the socioeconomic needs of the population at the required level in the future will create certain problems in their lifestyle and social life. We can even note that the degree of social problems will be different in different regions.

In the article, we have classified the negative factors by regions into 2 groups. We grouped them into natural, economic, and social factors. These factors include shortages of drinkable water, deforestation, soil corrosion, declining arable land, GDP production, food shortages, wars, and diseases. If the existence or development of these factors takes place in the context of rapid population growth, then it will pose great threat to human's life and health. We conclude that most of these problems will be more prevalent, mainly in Africa and Asia.

More than 7.884 billion people live in five of the six continents in the world [ 1 ]. The world's population growth rate has been higher in the last two centuries. In the history of mankind, the world's population reached 1 billion in 1800. The second billion was reached in 130 years (1930), the third billion in 1960, that is, in 30 years, the fourth billion in 1974, the fifth billion in 1987, the sixth billion in 1999, and the last seventh billion in 2011, that is, in only 12 years. In the twentieth century, the world's population rose from 1.65 billion to 6 billion.

Although the world's population has begun to grow rapidly in the last two centuries, it has grown more in the twentieth century. Opinions about the consequences of rapid population growth vary widely in the world community. This quote from an article on the principle of population [ 3 ] expresses an important point of Malthus: The population is growing geometrically, but food production is growing arithmetically. According to Ermisch (1983), population growth can create unemployment, as well as increase conflict and crime in society. Imam [ 4 ] claimed that the birth rate in Japan was low, arguing that this decline had a negative impact on various areas of life in Japan, especially the economic sector.

In this article, we explore the methodology of the issue and assess the challenges that humanity will face terms of population growth and its future consequences. These problems can eventually lead to certain difficulties in meeting people's material and spiritual needs, as well as living standards. Meeting human material and spiritual needs is related to nature, economy, education, health, and social factors. Of course, the dynamic changes in the population must be accompanied by changes in these areas.

2. Purposes

The purpose of the article is to study the rate of population growth in the regions and to identify future socioeconomic problems.

3. Results and Discussions

The article notes that more than 7.884 billion people live on five of the world's six continents. About 77% of the world's population lives on two continents: Asia (60%) and Africa (17%). The remaining 23% settled on 3 continents. However, in Antarctica, there is no symbol of life due to severe low temperatures. Table 1 shows the world's population by regions since 1800.

World historical and predicted populations by regions (figures in million people).

Source: [ 5 ], https://p2k.unkris.ac.id/IT/en/3065–2962/sevenbillion-people-alive_22650_p2k-unkris.html .

As can be seen from Table 1 , the world's population grew steadily from 1800 to 2020 and now stands at 7.794 billion.

If we look at Figure 1 , we see that in 1800, more than half of the world's population lived in Asia. Particularly, 65% of the population lives in Asia, 21% in Europe, 11% in Africa, and the remaining 3% in North America, South America, and Oceania. However, a very small part of the world's population lived on the continents of America and Oceania. This was due to the fact that at that time the migration of people to these regions was still weak. But mass migration continued to those regions from Europe and Asia.

Population by regions in 1800 (source: the diagram was prepared by the author on the basis of Table 1 ).

The population increased 52.7 times in North America, 27.2 times in South America, 21.5 times in Oceania, 12.5 times in Africa, 7.3 times in Asia, and 3.7 times in Europe. If in 1800 the population of North America, South America and Oceania was 3% of the world's population, in 2020 this figure would increase 4.7 times to 14%.

Migration from other continents has played a role in population growth in North and South America. Thus, after the discovery of America as a continent in the 15th century, there was a mass influx of Europeans in the 17th century. This influx has led to an increase in the population of the American continent.

As it can be seen from Table 1 , the highest regional growth rates since 1900 have been recorded in Africa. During this period, the number of people in Africa has increased more than 10 times. At present, in countries where statistical analysis is considered a demographic explosion [ 6 ], the average number of children per woman is 3–4, while in African countries this figure reaches 5.

However, in many Eastern European and CIS countries, population growth has slowed and even declined in recent years.

Sociologists have determined that man, as a biological being, can live 100–140 years. However, in fact, as a result of various socioeconomic and environmental factors, a person lives less than this age. Recently, there is a tendency to prolong human life. According to world statistics, the average life expectancy in 1950 was 50 years, but in 2004 it increased to 66.7 years.

4. Forecast of World Population Growth by Regions

Recent changes in the world's population and the socioeconomic problems they will create in society require research and analysis in this area [ 1 ] According to a number of forecast sources, the world's population will exceed 11 billion by 2100 [ 2 ].

According to the forecast, the world's population will exceed 8.5 billion in 2030, 9.7 billion in 2050, and 11.1 billion by 2100. As it can be seen from Table 2 , the population growth rate in Africa is expected to be much higher. The continent with the highest growth dynamics in the last century was Africa. With half of the population under the age of 18, Africa's population is expected to increase by more than 3 billion by 2100. Figure 2 shows the dynamics of world population growth by regions.

Dynamics of world's population growth forecast for 2100 by regions (source: the diagram was prepared by the author, on the basis of Table 2 ) (figures in million people).

Regional population growth forecast for the world by 2100 (figures in million people).

Source: [ 7 ], Table S.2. Total population by country and region, 1950, 2017, 2030, 2050, and 2100 (medium variant); https://population.un.org/wpp/Publications/Files/WPP2017_KeyFindings.pdf .

According to the forecast, if currently more than 60 percent of the world's population lives in Asia and 11 percent in Africa, the number of people living on both continents will be approximately equal during the forecast period.

One of the factors proving that the world's population growth rate will be high in the near future is that about half of the world's population is now under 25 years old. More than 3.2 billion people in the world are under the age of 15. In other words, the large number of people under the age of adolescence means that the population will grow rapidly in the near future. However, the current high growth rate of the world's population gives us reason to say that the population will increase even more for the XXI century. That is, we assume population growth as follows: taking into account the current dynamics of world population growth and applying the method of extrapolation forecasting, if the material and social needs of the growing population of the world are met at the required level.

In the forecast, the growth dynamics of the last three billion (5, 6, and 7 billion) was recorded at the frequencies of 13 and 12 years. If the dynamics of growth is estimated at this rate, the world's population could reach 14 billion by 2095. Our forecast for world's population growth by 2100 is about $3 billion higher than that of the United Nations, the Department of Economic and Social Affairs.

We applied the extrapolation method with a graphical model in Figure 3 . Now, let us apply the extrapolation method of forecasting the world's population for 2100 with a trend model. In Table 3 , we list the world's population for the last five years.

World's population growth dynamics until 2095 (the figures in the horizontal direction show the years) (Source: the table is based on two sources: author's application of mathematical-statistical extrapolation method [ 1 ] and https://en.wikipedia.org/wiki/World_population#cite_note-UN-115 ) (figures in billion people).

Statistical table of world population for the last five years.

Source . The table was prepared by the author on a basis of the table in [ 8 ]; https://www.worldometers.info/world-population/world-population-by-year .

According to the trend model of the extrapolation method, the forecast for a certain period is determined by the following formula.

Formula ( 1 ). Trend model of the extrapolation method.

(Source of formula ( 1 ): [ 9 ]) As shown in Table 3 ,

n  = 5 years (from 2016 to 2020). t  = 85 years (from 2016 to 2100).

According to the Trend model, for the forecast period, that is, in 2100, the world's population will exceed 14.743 billion people.

Such a growth rate of the world's population must be consistent with the growth of certain macroeconomic factors. Otherwise, the world's population will face a number of socioeconomic problems, which we will discuss in the following section.

5. Global Problems That Society Will Face as Its Population Grows

The rapid growth of the world's population creates tasks and responsibilities for mankind. Because as a result of this growth, people may face many economic and social problems in the future. Overcoming these problems, as well as providing the resources necessary for human life, is one of the most important issues facing humanity.

In our study, the problems that may occur as a result of population growth and the reasons for them are reflected in the following matrix. This matrix is illustrated in Figure 4 .

Matrix of factors influencing population growth. Figure is prepared by the author.

The study of such factors, the analysis of the current situation, the assessment of relevant opportunities, and the assessment of their impact on the future level of social life form the methodological basis of our research. Based on the research, we have analyzed these factors as follows.

One of the biggest natural problems in the world is the problem of “drinking water.” Currently, 771 million people in the world, or one in nine people, do not have access to safe drinking water (Water Is the Way, https://water.org ). At least 2 billion people worldwide use a source of water contaminated with feces. By 2025, half of the world's population will live in water-scarce areas.

Assuming that the least developed countries are located in Africa, Asia, and Latin America, 22% of these countries do not have water services, 21% do not have sewerage services, and 22% do not have waste management services. Even in Asia and Latin America, where population growth is expected to increase over the next 80 years, the problem of drinking water will worsen. However, in Africa, where the population is expected to increase by 3.128 billion people, or 3.3 times over that period, it is expected to have serious consequences in this area.

The next natural problem is the rapid deforestation. The reduction of forest areas in the world will have a negative impact on climate change, as well as a shortage of timber as a natural national resource. This will create serious problems in meeting people's demand for wood products. Table 4 shows the change in the forest area of the regions over the years. This allows us to observe how much the forest area in different regions increased and decreased in 1900–2015.

Forest area by regions in the world (by 1000 hectares).

Source: [ 10 ], Desk Reference, Download FRA 2015 results, https://www.fao.org/forest-resources-assessment/past-assessments/fra-%202015/en/ .

As can be seen from Table 4 , the regions with the largest forest area in the world are Europe and the Americas. The world's forest area has decreased by 129,1 million hectares over 25 years. This means that over 25 years, the world's forest area has decreased by 3.1%. As can be seen from Figure 5 , the rapid dynamics of decline was observed in 1990–2000. The figures in the horizontal direction show the years, and the figures in the vertical direction show the forest area in billion hectares.

Territorial dynamics of the forest area (source: the figure was prepared by the author on the basis of Table 4 ).

The decline is mainly in Africa and Latin America. Between 1990 and 2015, forest area decreased by 81.6 million hectares in Africa, 88.8 million hectares in South America, 1.8 million hectares in North America, and 3.3 million hectares in Oceania. If the rate of deforestation continues as in the last five years, by 2100, up to 8% of the world's forest cover will be destroyed. However, 21.2 million hectares of forests have increased in Europe and 5.1 million in Asia.

By 2100, we forecast that the population of Africa will increase by 255.7%, and forest cover will decrease by 39% (241.1 million hectares). If currently there are 496.9 thousand hectares of forest per capita in Africa, in 2100 this figure will be 85.7 thousand hectares. In other words, per capita forest area in Africa will decrease by 6.8 times by the end of the century. The sharp decline will have a negative impact on Africa's climate, forestry, wood processing, and the food industry, which is one of the most important sectors for human life. So, we can conclude from the above that the changes that may occur in the forest sector may cause serious problems for Africa, Latin America, and Oceania in the near future.

Soil erosion is also among the most likely natural problems. Soil erosion has been around for millennia as part of geological processes and climate change. Globally, corroded land covers 200,000 hectares of arable land and pastures. It covers an area larger than the United States and Mexico. More than 55% of this problem is caused by water erosion and about 33% by wind erosion.

Soil erosion affects the reduction of arable land. Every year, soil erosion and other soil degradation destroy 5–7 million hectares of arable land around the world. Every year, 25,000 million tons of topsoil are washed away. China's Huang River alone discharges 1,600 million tons of soil into the sea each year. The United States has lost about a third of its topsoil since agriculture began. So far, soil erosion has endangered the lives of about 1 billion people worldwide.

The abovementioned problems will lead to a reduction in the area under agricultural crops. This, in turn, will create significant restrictions on the production of agricultural products for the world's population. By 2100, the number of people whose lives will be at risk as a result of this problem will exceed 3 billion.

The next economic problem that we consider to be for the world's population, which is expected to increase, is the production of GDP. If there is a mismatch between world's population growth and GDP production, this problem can have serious socioeconomic consequences in those regions. Some experts do not consider the increase in the number of people to be a problem. According to Simon [ 11 ], “the benefit of population growth is the increase in the supply of intelligence and knowledge. In his view, the mind is as economically important as the hands or mouth.”

But production consists of 3 main resources.

• Fixed assets (equipment, machines, etc.)
• Current assets (raw materials, materials, fuel, and energy)
• Workforce (human resources).

Human resources produce products using other resources. Man, as a social being, has intellect, thinking, and reason. But in order to produce a product, it is necessary to have equipment and technology, as well as raw materials and material resources.

In Table 5 , we systematize the GDP per capita in specific regions of the world. According to statistics, the GDP per capita in Africa is $1788,61 and in South America $4360,06. Table 2 shows population growth forecasts in the African and South American regions. In case of population growth, it is necessary to increase the GDP in those regions in order to meet their material and moral needs at the current level.

GDP per capita in the regions of the world.

Source . The table was prepared by the author on a basis of two sources 1.[ 12 ], List of continents by GDP, https://statisticstimes.com/economy/continents-by-gdp.php . 2.[ 5 ], https://p2k.unkris.ac.id/IT/en/3065–-2962/seven%20-billion-people-alive_22650_p2k-unkris.html .

If we look at the current state of GDP and the dynamics of world population growth, we see that in 2020, the African continent will produce 2.8% of world GDP. However, if by 2100 Africa's population will increase 3.3 times and make up 40% of the world's population, then the socioeconomic situation on that continent could reach a very catastrophic level. By 2100, population growth is predicted to be 1.67 times in Oceania and 1.4 times in North America. This increase will not worsen the economic situation in Oceania, which has a GDP per capita of $37,246.7. If the balance between population growth and output growth in Latin America is broken, the socioeconomic situation in the region is expected to deteriorate.

Population growth for the forecast period will not worsen the socioeconomic situation in the Asian region. But the regions with the highest per capita GDP are North America ($65371,73) and Europe ($27990,22). If the population of North America increases by 1.4 times, we expect that the socioeconomic situation in the region will remain high. Today, the North American region produces more than 28 percent of the world's GDP, more than six times the world's GDP per capita. In Europe, which has strong economic relations, although the population is expected to decline, the region's GDP is expected to increase.

There are enough energy resources in the world. According to statistics, oil reserves exceed 1,500 billion barrels. Carbon-hydrogen energy resources are already being replaced by cheap and easily available resources in the industry, such as solar energy, hydropower, and wind energy. The current availability of energy resources and the increase in opportunities for alternative energy sources give us reason to say that no problems are expected in this area in the last 100 years.

One of the most dangerous problems predicted for the end of the century is food. The food crisis has existed in certain regions of human history. However, the worst food crisis in the world since 1974 occurred in 2007 and 2008. One of the reasons for the crisis was the high prices [ 13 ] for food products (especially wheat, rice, soybeans, and corn) on the world market. This led to an increase in the level of hunger. Despite the moderate decline in prices since 2008, the number of hungry people continued to rise in 2009. This food crisis has brought the fight against hunger to the international agenda.

Some of the problems listed above can affect the food problem. These include declining arable land, soil erosion, declining agricultural water resources, and others. We have discussed these problems above, and we have assessed the level at which they will occur.

One of the reasons for the food problem is that more than 1.3 billion tons of all food products, or 1/3, are wasted. We say this according to the World Food Program. To produce this wasted food, Russia needs as much water as the annual flow of the Volga River and as much arable land as Canada (The Top 10 Causes of Global Hunger, https://www.concernusa.org/story/causes-of-global-hunger ).

6. Conclusions

The study discusses the problems that can be caused by the growth of the world's population, and we have come to the following conclusions.

• (6.1) According to the Trend model, we have set a forecast for the world's population in 2100.
• (6.2) Global negative changes in natural resources and events will create socioeconomic problems at a time when the world's population is growing rapidly.
• (6.3) Population growth in the world can result in various social problems in the some regions. Social problems can manifest themselves in various social spheres of society. Population growth in Latin America over the forecast period will create problems in meeting the material and spiritual needs of the population in that region. However, rapid population growth in Africa could result in very serious socioeconomic consequences.
• (6.4) Although the population of Europe is expected to decrease, socioeconomic development is predicted in the region.
• (6.5) Population growth in North America and Oceania will lead to an increase labor force and, consequently, will play a positive role in economic development in the regions.

Acknowledgments

The author received funding for this research from Baku Business University, Azerbaijan.

Data Availability

By personally contributing to this work, the author has analyzed and evaluated the future consequences of the rapid growth of the world's population. The author personally contributed to this work. “Recent research and analysis of publications.” The article is based on the materials of “The Department of Economic and Social Affairs in United Nations” and “International Programs Center at the U.S. Census Bureau, Population Division” sources for 2017–2019. In addition, a number of literatures, articles, and websites were used in writing the article. The information was obtained from these sources on the number of people in the world and in the regions, as well as the forecast indicators of their growth. The article can be used in research on the demographic problems of the regions.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Historical environmental Kuznets curve for the USA and the UK: cyclical environmental Kuznets curve evidence

• Published: 17 September 2024

Cite this article

• Tolga Omay   ORCID: orcid.org/0000-0003-0263-2258 1 ,
• Julide Yildirim   ORCID: orcid.org/0000-0002-4739-6028 2 &
• Nazmiye Balta-Ozkan   ORCID: orcid.org/0000-0002-2848-5535 3  

Human activities, including population growth, industrialization, and urbanization, have increasingly impacted the environment. Despite the benefits of economic growth to individual welfare, its negative environmental consequences necessitate a thorough assessment. The environmental Kuznets curve (EKC), positing an inverted U-shaped relationship between income per capita and environmental degradation, has been extensively studied since its proposition by Grossman and Krueger (Environmental impacts of a North American free trade agreement, National Bureau of Economic Research working paper, 1991. https://doi.org/10.3386/w3914 ). However, empirical evidence on the validity and shape of the EKC varies due to methodological differences, country-specific dynamics, and other factors. Examining the historical growth paths of individual countries helps explain the mixed findings in empirical EKC research. Long-term data allow researchers to determine the EKC's shape and turning points, aiding policymakers in devising appropriate environmental policies for each economic growth cycle within the framework of global environmental governance. Accordingly, this study contributes to the literature by taking a historical perspective on the EKC, focusing specifically on the United States and the United Kingdom. Drawing on data spanning from 1850, we employ advanced econometric techniques, including fractional frequency flexible Fourier form Dickey–Fuller-type unit root tests and structural breaks unit root tests, to overcome limitations of traditional linearized EKC estimations. Moreover, the classical polynomial regression approach is employed to model the long-term cycles based on the scatterplot inspection of per capita carbon dioxide (CO 2 ) and per capita GNP series. Contrary to conventional expectations, our empirical findings do not support the existence of a clear inverted U-shaped EKC relationship between CO 2 emissions and economic growth for either country. Instead, our analysis reveals the presence of multiple regimes, indicating a cyclical pattern where economic growth affects environmental quality with varying severity over time. Furthermore, we demonstrate proper modeling techniques for the EKC, highlighting the importance of identification and misspecification tests. Our study identifies cyclical EKC patterns for both the UK and the USA, with the UK exhibiting two cycles and the USA exhibiting three, shaped by varying economic, social, and technological contexts. By revealing the nuances of the economic growth-environmental degradation nexus for these early developer countries, our study provides valuable insights for policymakers seeking to devise evidence-based and environmentally sustainable growth policies within the framework of global environmental governance. These findings underscore the importance of considering historical context and structural changes when analyzing the EKC, providing valuable insights for policymakers aiming to design adaptive and sustainable economic growth strategies.

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Omay, T., Yildirim, J. & Balta-Ozkan, N. Historical environmental Kuznets curve for the USA and the UK: cyclical environmental Kuznets curve evidence. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05320-y

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DOI : https://doi.org/10.1007/s10668-024-05320-y

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• Cyclical environmental Kuznets curve
• Environmental degradation
• Economic growth
• Fourier filter
• Smooth structural breaks unit root test

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