Exergy and Demography: Present Scenarios and Future Projections

Abstract

The study presented in this paper is intended to be a contribution to the practical implementation of the “sustainability” concept, often misunderstood at times and incorrectly applied. The first sections describe a systematic procedure for a rigorous definition of “sustainability” and of “sustainable development” based on thermodynamics. A concept tightly connected with “sustainability” is “resource thriftiness”, i.e., the reduction in the anthropic extraction of irreplaceable supplies of fossil ores and fuels contained in the Earth’ crust and the reduction in the load posed on the environment by discharges, collectively referred to as “environmental conservation”: this is another concept that must be embedded in the definition of sustainability. An environmentally friendly society ought to concentrate on minimising such consumption by implementing an efficient and rational conversion of primary resources to final commodities while maintaining acceptable life standards. A thermodynamics-based approach can help identify the boundaries of the “sustainable region”: if sustainable development depends on a balance between primary input and final consumption, the internal allocation of the latter among citizens becomes a relevant parameter. The study presented in this paper introduces a direct link between demographics and pro capite final exergy use, showing how the age distribution of a society strongly impacts primary consumption. The paper presents some considerations about the quantitative link between the so-called “demographic pyramids” and the exergy budget of a country, with specific examples based on currently available data.

1. Introduction: About the Definition of Sustainability

1.1. How Do We Define “Sustainable Development”?

Let us examine some authoritative definitions of “sustainability”. The original statement formulated by the United Nations Brundtland Commission in 1987 reads “Sustainable development means meeting the needs of the present without compromising the ability of future generations to meet their own needs”. This is a much-cited definition that had the undeniable merit of promoting a worldwide discussion about the “sustainability” issue, but it is also more a principle declaration than an applicable standard. In fact, what are the needs of future generations, and why are we qualified to establish them?

The European Environmental Agency elaborated on the definition in a 2024 document: “Sustainability is about meeting the world’s needs of today and tomorrow by creating systems that allow us to live well and within the limits of our planet”. The question here is, who decides what “living well” means? In other words, who can impose some “globally valid life standards”?

Currently, the crispiest formulation available, although still somewhat vague, can be found in a 2022 report by the University of Alberta, Canada: “Sustainability means meeting our own needs without compromising the ability of future generations to meet their own needs. In addition to natural resources, we also need social and economic resources. Sustainability is not just environmentalism. Embedded in most definitions of sustainability we also find concerns for social equity and economic development”.

1.2. The “Three Pillars of Sustainability”

“Sustainability” and its logical corollary, “sustainable development”, remain open concepts with myriad interpretations and context-specific understandings. A very successful description, first published towards the end of the 1990s [1], provides a visually very effective representation (Figure 1) consisting of three ideal pillars, namely Environmental, economic, and social issues that concur to provide structural support to “sustainability”. Perhaps thanks to its visual appeal, this representation has become very popular in media and lay publications. What is missing here is a fundamental consideration: what is the physical “support” that allows for a society to develop its social and economic structure and to survive? Clearly, the missing item is the “resource base”, which must be sufficient to “feed” the society and to “propel” its progress. The resource base can be analysed in terms of thermodynamic quantities, and one of the goals of this paper is to quantify the interconnection between the total resource consumption of a society and its demographics.

1.3. Resource Cost as a Sustainability Indicator. Exergy

The notion that “human societies thrive on physical resources and not on capital” [2] may seem iconoclastic, but it really represents an extension of our understanding of Nature’s evolution: in fact, the substitution of the attribute “human” with “fungal” or “bacterial” or “insects” or “mammals” makes the above expression perfectly agreeable with scientists and lay people alike. Thus, we shall consider here that the primary exergy input into the country is the “cost” of the survival of the society [3]. (Furthermore, since an accurate and complete exergy budget is available only for some of the countries considered in this study, this input was approximated by an “exergy factor” applied to the total energy input for which reliable data are available; see Section 4).

To properly represent the dynamics of a society, the four relevant parameters are (Figure 2) the rate of renewable and non-renewable resource inflow R_r(t) and R_nr(t), the “output” E_out(t) of the society (goods, services, energy flows, wastes), and its internal irreversibility E_δ(t). Adopting exergy as a quantifier ensures that all terms are homogeneous and provides deeper insight into the type of irreversible losses than a simple energy analysis. Exergy analysis (ExA) has indeed seen several applications in the field of energy conversion systems and sustainability: after the pioneering works by Reistad on the exergy analysis of the US [4] and Wall on the ExA of Ghana, Italy, and Sweden [5], several analyses at the country level were performed, a few examples being the works by Biondi [6] for Italy, Brito [7] and Santos [8] for Portugal, Dai for China [9], Ertesvag for Norway [10], Schaeffer for Brazil [2], and Seckin for Türkiye [11]. The link between exergy and sustainability was explored by Brockway et al. [12], who derived conversion efficiency tables from an exergy analysis of several countries; by Gonzalez Hernandez [13], who used ExA as a tool to quantify resource efficiency and specifically effluent decarbonization; and by Huysmann [14], who presented a detailed and rigorous analysis of the cumulative use of primary exergy in a societal system. Koroneos [15] used an exergy method to derive a series of sustainability indicators; Varbanov [16] derived a sort of exergy cost balance between exergy “profits” and “liabilities” and proposed to use it as a sustainability indicator. Stougie [17] developed a novel formalism for the earlier proposal of a theoretically founded relationship between exergy losses and the environmental impact of technological systems. Yi [18] proposed an exergy-based thermodynamic indicator for industrial processes. More recently, the present author published a historical analysis of the development of the exergy/sustainability approach, showing that there is a coherent and continuous development from the first attempts to establish the rules of ExA and its extension to monetary, environmental, and resource- related issues [19].

The extended knowledge accumulated by the above studies justifies the approach taken in this study: the search for a sustainability indicator starts with an accounting of the physical amount of exergy fed into the country. With reference again to Figure 2, E_R and E_NR are the total input exergy rates and E_S is the rate of final exergy consumption.

Figure 3. Schematic representation of a human society as a thermodynamic system in an input/output perspective.

Figure 3. Schematic representation of a human society as a thermodynamic system in an input/output perspective.

Legend to Figure 3: E_R = renewable exergy flows; E_NR = non-renewable exergy flows; E_out = exergy discharge; E_δ = exergy destruction; E_S = exergy surplus rate. All in [W]. D_S = thermodynamic degree of sustainability.

1.4. The Degree of Sustainability (Interested Readers Are Directed to [20] for a Detailed Discussion of the Degree of Sustainability)

A human society is in a “sustainable state” if the sum of its output E_out + E_P plus its internal irreversibility I (denoted as E_δ in the following) is compensated by an external resource input R. Notice that E_out is the exergy rate discharged to “the environment” at large (re-radiation, material and immaterial effluents), and E_P is the exergy equivalent to the “products” internally available for sustaining the population, maintaining societal services, etc. The flow of resources is quantified by an exergy measure, and for the problem to be well posed, it is mandatory that the mass and energy balances be internally homogeneous and congruent. In an interesting limit case, the balance between internal “consumption” and external “supply” is exact,

R = E_out + E_P +E_δ

(1)

and the system can neither “grow” nor “shrink”. If R > E_out + E_P + E_δ, the system can make use of the extra resources E_S to “accumulate” them and/or increasing its output.

The important concept here is the rate at which resources flow in: as long as R(t) > P(t) + I(t), the system thrives, regardless of the fact that R is “renewable” or “fossil”. But the very definition of “sustainability” demands that all derivations must remain valid for t→∞, and this in turn requires Equation (1) to include only renewable resources.

The above considerations indicate that [20]:

• To properly represent the dynamics of a society, the four relevant parameters are the rate of renewable and non-renewable resource inflow R_r(t) and R_nr(t), the “output” E_out(t) of the society (wastes and internal commodities), and its internal irreversibility E_δ(t).
• As anticipated in the visionary work of Wall [5], life standards, health issues, education, equitable access to resources, and other non-thermodynamic indicators can be built upon a rigorous exergy basis.
• It is clear is that if a society is not “thermodynamically sustainable”, it cannot be classified as “sustainable” by any other indicator.
• Modern human societies offer very few examples of thermodynamically sustainable instantiations: we can thus introduce a “degree of sustainability” to measure the “distance” between the current situation of affairs and our future target. This measure would be (i) standard; (ii) homogeneous for all societies; (iii) varying from country to country and—for the same country—in time.

Following [20], let us then define a Degree of Sustainability:

(a)

In the case of zero growth, E_S = 0, the society simply survives consuming its resource input and the sustainability condition is:

$$E_R+E_{NR}-E_P-E_{out}-E_{\delta }=0$$

(2)

(b)

To “grow” (in an extended sense), E_S must be positive:

$$E_S{=E_R+E_{NR}-E}_P-E_{out}-E_{\delta }>0$$

(3)

The sustainability assumption requires that Equation (3) be valid in the limit t→∞, which implies that only renewable resources must come into play. Two scenarios are possible:

• E_R < $E_S+E_P+E_{out}+E_{\delta }$: for the society to survive, it is necessary to recur to non-renewable resources. The higher the percentage of such non-renewables, the lower the sustainability of the system.
• E_R ≥ $E_S+E_P+E_{out}+E_{\delta }$: the society can evolve sustainably using only renewable resources. The higher the E_S, the higher the margin for sustainable growth.

The “Degree of Sustainability” D_S is defined as:

$$D_S=1-{\displaystyle \frac{max{\langle E}_R,E_S\rangle -min{\langle E}_R,E_S\rangle }{max{\langle E}_R,E_S\rangle }}={\displaystyle \frac{min{\langle E}_R,E_S\rangle }{max{\langle E}_R,E_S\rangle }}$$

(4)

Equation (4) shows that a D_S = 0.75 means that 25% of the resource base is non-renewable. From Equation (2) it follows that D_S can be increased by reducing E_out (minimizing the waste flows, optimizing recycles) and E_δ (increasing the conversion efficiency of the internal sectors). Only for the ideal case of a reversible system with no exergy rejection $D_S=1$. The practical meaning of the above definition is that any degree of fossil input (be it fuels or ores) decreases the degree of sustainability of a system.

Figure 4 provides an illustrative sketch of the behaviour of D_S for an imaginary country in which the ratio E_NR/E_R decreases to zero between 2000 and 2050.

2. Population Pyramids and Their Significance

A population pyramid (PP in the following) is a representation of the demographics of a country that provides a synthetic portrayal of the structure of a population by graphically showing relative numbers of people in different age groups. Population structures differ markedly among different countries and, for the same country, at different times. Pyramids are usually drawn with the % of the male population on the left and of the female population on the right to provide additional quantitative information about birth and death rates as well as life expectancy. The first graphical use of a PP dates back to the 1874 Census Report and was based on 1870 data (Figure 5).

The idea was much older though: already in 1825, Benjamin Gompertz published an essay in which he demonstrated a general formula for the death rate of a population and used tables to illustrate his results: these hand-calculated tables [21] are completely equivalent to population pyramids.

A population pyramid can be constructed for any area, from a whole continent or country to an individual town, city, or village, but its most useful application is at the country level: a survey of several instantiations of PPs led to a classification that takes into account their permanence in time, their general shape, and their possible evolution.

(a)

“Stationary” or constant population pyramids (CPP) describe a situation in which the percentages of population (age and sex) remain approximately constant over time, which implies that (discounting immigration/emigration effects) the numbers of births and deaths roughly balance one another.

(b)

“Expansive” or expanding population pyramids (EPP) are very wide at the bottom (younger age groups) and are characteristic of countries with a high birth rate, usually paired with a high (but percentage-wise lower) death rate. The population is fast-growing, and the size of each birth cohort increases each year. This is a typical pattern for an under-industrialized country.

(c)

“Constrictive” or declining population pyramids (DPP) are narrower at the bottom. The country usually has a long life expectancy, i.e., a low death rate, but also a low birth rate. This is a typical pattern for a highly industrialized country.

EPP and DPP are so “typical” of real situations in different countries that another quite popular classification based on the degree of industrialization (HIC, Highly Industrialized, Figure 6a; MIC, Medium Industrialized, Figure 6b; and LIC, Less Industrialized Countries, Figure 6c) overlaps completely with the former one (all data adapted from [22,23]).

The Qatar situation in Figure 6d is reported only to show how the effects of immigration may distort the shape of a PP. In this case, the age distribution of the small autochthonous population (about 2 million inhabitants) shows a sizeable influx of immigrant workers from India, Egypt, Bangladesh, and the Philippines, mostly males aged 20 to 40.

PP are also providing clues about the economic situation of a country: if young (aged below 15) and elderly groups (aged over 65) have a percent-wise high relevance, the shapes will be wide-based or columnar (barrel-shaped), respectively. Since both youngsters and retirees have a lower productivity but are final energy users, the remainder of the population must make up for this imbalance, both economically and energy-wise. Both situations stress the monetary and the exergy budget of the country.

3. Does Size Matter? Connection Between Demographics and Primary Exergy Consumption

3.1. Pro Capite Exergy Consumption by Age Group

Despite the efforts made by most contemporary societies to reduce the pro capite primary consumption by increasing the efficiency of the primary-to-final exergy conversion, one of the major problems that affect social scientists and policy makers is the steady increase in the world population. If N is the population of a certain country, it is clear that for given life standards, a higher N leads to a higher E_S. The problem is exacerbated by the fact that non-industrialized countries have the highest net birth rates, and the combination of a likely increase in their pro capite final exergy consumption and a corresponding increase in numerosity may lead to catastrophic resource scarcity. The objective of this study is to investigate the problem at a more detailed level: given that not all age groups have the same pro capite final consumption, how does the relative numerosity of different age groups affect the global country exergy consumption? Such an analysis is performed by analysing the link between population pyramids and primary exergy consumption. To do this, let us consider a single age group in a PP, its numerosity being the count of all individuals whose age is between j and j + 1:

$$N_j=count〈j<age<(j+1)〉$$

(5)

Using the exergy budget of the country (As remarked above, for some of the countries analyzed here no accurate exergy budget is available. In these cases, an average exergy factor f_ex has been used to convert the primary energy balance of the country to its equivalent exergy budget. f_ex was calculated by attaching the Szargut–Styrylska exergy factors [25] to the rate of each primary energy source and averaging the resulting sum. See also Section 5.1 below) and consumers data provided by international agencies, it is possible to calculate the “pro-capite consumption curve by age” of final exergy:

$$e_{c,j}={\displaystyle \frac{f_j{E}_c}{N_p}} \hspace{1em}[\text{kWh}/(\text{person}*\text{yr})]$$

(6)

$$E_{c,j}=N_j{e}_{c,j}\hspace{1em}\hspace{1em}[\text{kWh}/\text{yr})]$$

(7)

where E_c is the total exergy consumption of the country (its exergy footprint if we use EEA), N_p the total population, and f_j is the consumption factor by age group. Equation (3) is subject to the two constraints:

$$\sum N_j=N_p;\hspace{1em}\hspace{1em} \sum f_j=1$$

(8)

The pro capite exergy use is calculated for each age echelon by means of an empirical factor f_j (Figure 7): the average exergy consumption of the country is calculated first:

$${\overline{e}}_c={\displaystyle \raisebox{1ex}{$E_c$}\!\left/ \!\raisebox{-1ex}{$N_p$}\right.}$$

(9)

The consumption factor by age group f_j, is then applied to ${\overline{e}}_c$ to obtain the average exergy use by age group (Equation (8)).

Variations in ${\overline{e}}_c$ may occur as a consequence of structural changes in the primary-to-final conversion chain or in an adjustment of the final uses of the society under examination: knowing the demographic distribution N_j at some future time allows us to approximately calculate the total exergy consumption at the country level, E_c, and its allocation among the age cohorts.

3.2. Calculation of the Age Consumption Factor f_j

Studies on the primary energy use by age group are scant. Acceptably accurate data are available for the US in 1990 [26], Italy from 1997–2016 [27], Germany from 2011 to 2014 [28,29], and Japan in 2021 [30]. For non-industrialized countries, there is no available dataset. In the calculations presented here, f_j was derived by two different methods for HIC and LIC.

(a)

High-Income Countries (OECD and BRIC): f_j is calculated by curve-fitting and averaging Inoue’s 2021 [30] and Bardazzi’s 2016 [27] data.

(b)

Lower- and Medium-Income Countries (LMIC): Estiri’s [26] data were heuristically modified as to the skewness among the age groups. In other words, the f_j for LIC and MIC are smaller in the productive- and higher in the retirement age groups than for their HIC counterparts, to reflect the qualitatively known consumption patterns in those types of society.

4. Population and Primary Exergy Use Projections for Selected Countries

The comparative study of the population pyramid and of the exergy input of a society provides some interesting information about future scenarios of the energy system of that country. In this study, a comparison is presented between the currently available data and the 2050 projections. For clarity of interpretation and to ensure reproducibility of results, the steps of the calculations are shown in

Table 1. Logical flowchart of the calculation procedure.

Calculation Step Identification	Description of Action
Identification of country subject of analysis. Status quo	Acquisition of population data, age distribution, total surface, average solar irradiation, material and energy balances. All data from latest available census
Definition of future scenario at 2050	Acquisition of all relevant projections relative to 2050
Classification of the Country	HIC, MIC or LIC depending on official World Bank statistics. For 2050, classification according to projections issued by the country under examination. The working age range is derived from official documentation issued by each country.
Application of the age-dependent exergy consumption factor (Figure 3)	The average final exergy consumption is calculated first. Then each age group is allocated a coneumption equal to the product of the age-exergy factor by the average country consumption.
Calculation of results	Drawing of the population and exergy pyramids for the reference year and 2050; calculation of the % change in population; calculation of the % change in primary exergy consumption (absolute and pro capite).

.

4.1. Industrialized Societies, HIC

4.1.1. Italy

PP and exergy/age profile, Italy 2023 and 2050. Data from [6,23].

Between 2023 and 2050, the Italian population will decrease by 10.7% and age, but the PP will remain of the HIC type (Figure 8). The primary exergy consumption will shift towards higher age cohorts (from 50–65 to 70–80) that are not in the “productive age range”. Primary energy use will increase by 20% and pro capite primary exergy by 34.4%.

4.1.2. France

PP and exergy/age profile, France 2024 and 2050. Data from [23,31].

Between 2024 and 2050, the French population will slightly increase (2.4%) and age, and the PP will remain of the HIC type (Figure 9). The primary exergy consumption will remain well balanced in the age cohorts from 40 to 75. Primary energy use will increase by 20% and pro capite primary exergy by 17.2%.

4.1.3. Romania

PP and exergy/age profile, Romania 2024 and 2050. Data from [23,32].

Between 2024 and 2050, the Romanian population will decrease by a significant 15.2%, and age and the PP will remain of the HIC type (Figure 10). The primary exergy consumption will be shifted from the 45–50 to the 60–75 age cohorts. Primary energy use will decrease by 20.9% and pro capite primary exergy by 6.6%. This seems to be at odds with the projected increase of the GDP, projected to grow by about 10% [33].

4.1.4. Türkiye

PP and exergy/age, Türkiye 2024 and 2050. Data adapted from [11,23,34,35].

Between 2024 and 2050, the Turkish population will increase by a slight 4.1% and substantially age, and the PP (displaying a mixed MIC/HIC type in 2024) will increasingly resemble the HIC type/Figure 11). The primary exergy consumption will be shifted from the 45–50 to the 50–55 age cohorts. Primary energy use will decrease by 9.3% and pro capite primary exergy by 12.8%.

4.2. Medium-Industrialized Societies, MIC

4.2.1. China

PP and exergy/age profile, China 2024 and 2050. Data from [23,24,36].

Between 2024 and 2050, the Chinese population will decrease by a substantial 11% and substantially age so that its PP will gradually morph from an MIC to an HIC type (Figure 12). The primary exergy consumption will be shifted from the 35–40 to the 60–65 age cohorts. Primary energy use will decrease by 6.2% and pro capite primary exergy increase by 4.4%.

4.2.2. India

PP and exergy/age profile, India 2023 and 2050. Data from [23,37,38,39].

Between 2023 and 2050, the Indian population will increase by 16.8% and substantially age and the PP will basically remain of the MIC type (Figure 13). The primary exergy consumption will be shifted from the 20–25 to the 45–50 age cohorts. Primary energy use will increase by 90% and pro capite primary exergy by 16.8%. Since this appears unsustainable, Indian authorities have presented an ambitious “2050 net zero plan” that foresees the “freezing” of the pro capite primary energy use and primary exergy consumption; this seems, though, difficult to reconcile with a modernization of the country’s infrastructures.

4.3. Less-Industrialized Societies, LIC

4.3.1. Bangladesh

PP and Exergy/age profile, Bangladesh 2020 and 2050. Data from [23,40].

Between 2020 and 2050, the Bangladesh population will increase by 29.11% and age substantially, and the PP will start morphing to an MIC type (Figure 14). The primary exergy consumption will be shifted from the 20–25 to the 45–50 age cohorts. Primary energy use will increase by 92.31%, and pro capite primary exergy by 48.95%. The substantial increase in primary energy use is mainly due to a foreseen large import of electrical energy from neighbouring countries.

4.3.2. Congo

PP and exergy/age profile, Congo 2021 and 2050. Data from [23,41,42].

Between 2021 and 2050, the Congolese population will increase by 78.6% but maintain the LIC shape for its PP (Figure 15). The primary exergy consumption will be shifted from the 10–15 to the 15–20 age cohorts. Primary energy use will increase by 180.2% and pro capite primary exergy by 78.6%, but the supply sources are uncertain. The increase in the population almost annihilates the primary energy increase, so that the pro capite energy and exergy use will increase only slightly, which constitutes a serious obstacle on the way to societal modernization.

4.3.3. Kenya

PP and exergy/age profile, Kenya 2020 and 2050. Data from [23,35,42].

Between 2020 and 2050, the Kenyan population will increase by 64% but maintain the LIC shape for its PP in spite of some adjustment towards a more MIC-like shape (Figure 16). The primary exergy consumption will be shifted from the 10–15 to the 20–24 age cohorts. Primary energy use will increase by 490% and pro capite primary exergy by 260%, and the forecasts seem to indicate that a large amount of this increase will still be based on biomass and fossil fuels. The increase in the population counteracts the primary energy increase, but the projected pro capite energy and exergy use increase is substantial.

5. Discussion

5.1. Sensitivity to the Age-Consumption Factor

The conversion from population data to exergy use by age is clearly dependent on the accuracy of the available database. While the population pyramids are published in a standard fashion and based on internationally verified data [23], some problems exist about the primary exergy budget of individual countries. For most industrialized nations (the ones referred to above as “HICs”), an accurate primary energy budget exists, and for some of them, the breakdown of the primary exergy consumption is available. At the time of this study, primary exergy budgets for the reference year were accessible for China [24], Italy [6], Romania [32], and Türkiye [11,34]. Not all budgets are completely reliable because of the different—and at times incomplete—accounting methods used by the authors of each report, but since the scope of this paper is to provide a methodological description and to correlate the results country by country, no attempt was made to modify the original data. For Bangladesh, Congo, France, India, and Kenya, only primary energy data were available; in this case, a conversion to primary exergy was done by first calculating the individual exergy flows per primary source using the Szargut-Styrylska conversion factors [25] and then extracting the weighted average. In all cases where the 2050 projections indicated a substantial shift in the primary energy mix, the calculation was updated accordingly.

The main problem arose with the quantification of the exergy use per age cohort: data are scant and refer to countries with different levels of industrialization. The solution adopted here was to adapt the available curves to the three levels of industrialization considered in this study, HIC, MIC, and LIC, and the results are shown in Figure 7.

To gain some insight about the influence of the exergy-age allocating factor f_j, a comparison was made between the exergy-age pyramids of Italy (HIC) and India (MIC) calculated using the values of Figure 7 and equiallocated, i.e., as if each age cohort were to use the same portion of the primary exergy input. The results are shown in Figure 17 and demonstrate that the f_j factors have indeed a major influence on the allocation to the junior age groups. This calls for additional research on the exergy-age consumption patterns. Anyhow, in a methodological study like the one presented here, it is essential to describe and discuss the procedure. As further data become available, the results (and the analysis) can be updated.

5.2. Policy Implications

How can the model serve as a decision tool? In other words, how can the energy policy of a country be shaped to “improve” the overall exergy consumption? Aside from structural improvements in the primary-to-final conversion chain, does the exergy use by age influence the efficiency and the life standards of a country?

The answer can be found first in the graphs of the change of the pro capite exergy use and the population of each country: projections that show a significant population increase coupled with an increase in the pro capite exergy consumption ought to be viewed with care, because the corresponding increase in the primary exergy consumption does not always go in the direction of a more sustainable scenario. As an example, the Bangladesh increase in its primary budget relies mainly on imported (or fossil-generated) electricity; the picture for Congo and Kenya is similar because there is no guarantee that the enormous increase in the pro capite exergy consumption can be covered by a corresponding increase in hydraulic or, in general, non-fossil primary sources (both countries rely strongly on biomass as a primary source and use it in a way that is not exactly environmentally friendly). For different reasons, the projected India 2050 scenario appears problematic since the doubling of the primary energy inflow cannot be reasonably covered by renewable sources.

There is another interesting consideration about the projected allocation of the exergy over the age cohorts: in most HICs and MICs, the 2050 allocation is shifted towards higher ages, some of them (Italy and Romania, and to a lesser extent China and France) reaching a situation in which the highest consumer echelons are outside of the productive age. On the contrary, Bangladesh, Congo, and Kenya forecast a “virtuous” shifting at the lowest age groups, increasing the pro capite exergy consumption towards ages more compatible with a modern view of the work market.

6. Conclusions

The study presented in this paper illustrates how an exergy analysis (ExA) can be linked to a population dynamics model to offer additional insight on the dynamics of the primary resources of a country. The model establishes a link between the age distribution in a society and the allocation of final exergy to each age cohort and thus provides specific information that is not obtainable by other methods. The results demonstrate that, if a proper population projection is available, a primary resource exploitation scenario can be designed that leads to a more convenient allocation of the final exergy use among the age groups. Alternatively, if a future projection about the latter exists, the total primary resource input can be predicted. Notice that such results can be at odds with projections based on the Gross Domestic Product (GDP): the reason is that the proper allocation of final exergy to the “more productive” age groups is reflected not only in monetary output (increased productivity, growing GDP) but also in a more equitable distribution of resources that may lead to a readjustment of the population pyramid towards a more balanced shape. The literature abounds with studies that remind us that GDP and primary exergy use are two distinct indicators that provide insights into economic performance and sustainability, but too often GDP-based growth models neglect the bounds placed by a resource-conscious approach.

The GDP measures the total monetary value of all goods and services produced within a country’s borders over a specified time frame (usually a year). It is often used as an indicator of economic health and growth: it obviously captures market transactions and reflects the economic activity and productivity of a nation, allows for easy comparison between countries and regions on economic performance, and its variations in time can indicate economic expansion or contraction, helping policymakers evaluate economic strategies.

It does not, however, account for non-market activities such as household labour, volunteer work, and the underground economy, nor for wealth distribution within a population, potentially masking socioeconomic issues. Furthermore, it has long been known that economic growth as measured by GDP has a strong correlation with an increased resource depletion and environmental degradation: monetary corrections to reward less unsustainable consumption and production patterns have led to little improvement.

Primary exergy refers to the maximum useful work obtainable from a given amount of energy, typically representing the natural resources (like fossil fuels, solar, hydraulic, and wind energy, etc.) before they are transformed into other forms. Primary exergy use is the total amount of exergy consumed in energy production and consumption within a given time period. A comparison between primary and final exergy flows provides insights into the efficiency of material and energy conversion and use patterns, which are critical to understanding sustainability. Since exergy analysis (ExA) focuses on the quality of the resources used, primary exergy consumption highlights the ecological impacts of the conversion chains. An ExA (and especially its more recent development, extended exergy accounting, EEA) can help evaluate the sustainability of resource use, promoting more efficient conversion processes from both renewable and non-renewable sources. This makes it a valuable tool for setting the goals of sustainable development and assessing the progress made in that direction.

This paper is intended to demonstrate the possibility of implementing the ExA/PP-based model, and it ought to be considered as a direction paper. To build a decision-making tool based on this model, it is mandatory that a more reliable and expanded database be available, especially for what ExA and EEA are concerned. Only then can the preliminary validations presented in section be refined and applied to the development of less unsustainable scenarios.