Bottom-Up Analysis of Energy Consumption and Carbon Emissions, with Particular Emphasis on Human Capital Investment

doi:10.4236/lce.2013.44A001

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Low Carbon Economy, 2013, 4, 1-13

Published Online December 2013 (http://www.scirp.org/journal/lce)

http://dx.doi.org/10.4236/lce.2013.44A001

Open Access LCE

Bottom-Up Analysis of Energy Consumption and Carbon

Emissions, with Particular Emphasis on Human Capital

Investment

Paula Castesana1, Salvador Enrique Puliafito1,2

1Universidad Tecnológica Nacional, Ciudad De Buenos Aires, Argentina; 2Consejo Nacional de Investigaciones Científicas y Técni-

cas, Ciudad De Buenos Aires, Argentina.

Email: pcastesana@gmail.com

Received September 24th, 2013; revised October 22nd, 2013; accepted October 30th, 2013

mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work

is properly cited.

ABSTRACT

Short-term and mid-term projections of energy consumption and carbon emissions raise significant concern about the

availability of the necessary en ergy resources to meet the growing demand an d about the impact of emissions on global

change. Different macroeconomic models address this issue through global variables, such as gross domestic product,

production of goods and services, to tal population and natural resources ex traction. However, the relations among these

variables are neither linear nor simple. In an attempt to base said relation s on a “bottom-up” perspective, the individual

behavior of representative agents of economy, in terms of energy consumption and related carbon emissions, was stud-

ied, with particular emphasis on their investment in human capital. It was found that a higher investment in human

capital (e.g., education, research) was translated into a better distribution of consumption, with a higher level of energy

efficiency and a slight improvement in carbon emissions intensity.

Keywords: Carbon Emissions; Carbonization Index; Economic Growth; Energy Intensity Factor; Human Cap ital

Investment

1. Introduction

Short-term and mid-term projections of energy consump-

tion and carbon emissions are generally assessed on a

global scale, by using macroeconomic and social data

such as gross domestic product (GDP), production of

goods and services, total population, natural resources

extraction and consumption, etc. The relations among

these variables are neither linear nor simple, which g ives

rise to a variety of economic growth theories [1-4]. Be-

sides, the growing rate of energy consumption and its

subsequent carbon emissions has raised multiple con-

cerns about the availability and long-term sustainability

of natural resources and about the impact of CO2 emis-

sions on global climate change. This issue has led to an

increasing interest in th e design of different models in an

attempt to understand the complex relations among the

variables involved, and to develop appropriate mitigation

policies. However, many of these models offer a “top-

down” perspective, based on global relations among the

main variables, with a higher or lower level of detail

[5-8]. These models provide a proper estimation of an-

nual averages, but are less efficient when it comes to

individual consumption (or behavior) per se.

Apart from that, the international financial crisis that

started in 2008 has challeng ed the app licability of curren t

macroeconomic models, giving rise to a new “bottom-

up” paradigm of study [9,10]. This trend has developed

into a new science named Agent-based Computational

Economics (ACE), which is closely related to complex

dynamic systems, where economic processes are mod-

eled after dynamic systems based on agents that interact

among them [11-14].

This paper follows this new paradigm of computa-

tional economic models’ assessment, and has the follow-

ing specific purposes: 1) To analyze the relations be-

tween economic and demographic variables that affect

energy consumption and carbon emissions; 2) To study

the effect of the individual behavior of representative

Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment

agents of the economy on said environmental variables; 3)

To study the existence of any emergent behavior; and 4)

To study the role o f human capital investment in relation

to energy consumption and carbon emissions and their

trends over time.

2. Methodology

From a “top-down” perspective, global consumption of

primary energy may be expressed as a multiplicative

equation as follows [8,15-17]:





exex x



 (1)

Where (e) is the annual consumption of fossil fuel per

capita, (x) is the gross domestic product per capita

(GDP/capita) and (ηe) is the energy intensity factor asso-

ciated to said consumption, i.e., energy use per unit of

GDP. Analogously, carbon emissions per capita (c) re-

sulting from energy consumption may be expressed as a

multiplicative equation following Kaya identity [18],

which allows for the id entification of the main indicators

responsible for such emissions:









cxex cexi



 (2)

Thus, carbon emission s are ex pressed as th e produ ct of

the GDP/capita, the energy intensity factor and the car-

bonization index (ic), which represents the amount of

carbon emissions per unit of consumed energy. By taking

logarithms and then differen tiating Equations (1 ) and (2),

the percentage variations of the factors involved are ob-

tained1:

%% %



 (3)

%% %%

cx i



  (4)

Equations (3) and (4) show that the relative variation

in energy consumption and carbon emissions of each in-

dividual may be explained as the addition of the variation

in their economic production and the variation in tech-

nologic factors used by each individual to achieve said

productio n [19,20].

In order to assess the behavior of representative agents,

this paper provides a detailed analysis of the historical

evolution of the factors involved in the multiplicative

method, based on different variables related to individu-

als’ behavior and development. The analysis was con-

ducted in 57 countries2, for the 1970-2011 period and

portions thereof. Said 57 countries present a wide avail-

ability of data for the period and, nowadays, represent

76.13% of world population, 91.33% of GDP, 90.26% of

energy consumption and 86. 86 % of carbo n emissions from

energy consumption [21-23].

2.1. Economic Growth

Bibliograph y shows th at, within the scope of the study o f

anthropogenic CO2 emissions, the factor related to the

gross domestic product per capita (x) is analyzed from a

macroeconomic approach, as per different output func-

tions based on return to capital [5-7,24]. Barro and Salai-

Martin [25] argue that if a closed economy based on one

sector is considered, the output obtained from the return

to capital may be used for consumption or investment by

the representative agents of the economy. Moreover, they

specify that in said closed economies, households are

their only representative agents3, and that all the capital

stock is owned by their residents. Taking the term capital

in a broad sense, the output of each agent equals the

quantities they have devoted to consumption (cons) and

investment, both in physical capital (ik) and human capi-

tal (ih):

cons ii



 (5)

In order to analyze the economic growth from a per-

spective that considers individual behavior and decision

making by the constituent ag ents of econo my, da ta on the

different types of investments and levels of consumption

of individuals was analyzed, based on their age and edu-

cation level as separate indicators of human capital. To

that end, average ranges of different expenditures were

studied based on the age of the “head of household”, or

of a representative individual of the household, for some

of the countries analyzed. The behavior of economic

variables such as level of investment in human capital,

consumption expenditure and average income of house-

holds were included, based on the education level of the

representative individual of the household. Expenditures

on education and culture were taken as indicators of in-

vestment in human capital. The data represented corre-

sponds to year 1997 for Argentina, and the average data

of the 2002-2011 decade for USA and UK [26-28].

2.2. Definition of Typical Agents

In order to continue with the study from a perspective

that considers individual behavior, it is further argued

that each one of the countries analyzed may be regarded,

in turn, as a group of individuals, symbolized by a single

representative individual of each region, or a typical

agent. In order to characterize these typical agents, val-

ues per capita of the different variables analyzed were

considered as representative values of the historical de-

3The authors argue that working with a model of different companies

and households, or heterogeneity of households, is the same as work-

ing with a model where households are the ones responsible for the

output.

1For example, 1%

dlncdx

dtc dtc

2The list of countries analyzed and the symbols used for each in the

graphics included in this paper are shown in Appendix A.

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Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment 3

velopment of different types of individuals4 with differ-

ent levels of technological development. Thus, for each

year of the period under analysis, each representative

individual would have developed with a given GDP

(equal to the GDP/capita of the country by they repre-

sented), a level of consumption, a level of investment in

both types of capital, and an energy consumption and its

subsequent carbon emissions, related to the values that

said individual (or country) has used for the technologi-

cal indicators under analysis.

For this analysis, the countries were grouped accord-

ing to their level of investment in human capital. To that

end, data for the 1996-20 11 period was used (due to th eir

availability), referring to the percentag e of GDP invested

by countries in research and development (R&D) as a

regional estimator of the level of human capital of the

representative individuals of such countries [23]. Said

groups were labeled based on increasing values of in-

vestment in R&D:

1) Group 1 : Unfavorable human cap ital (investment in

R&D below 0.5% of GDP).

2) Group 2: Medium human capital (investment in R

&D above 0.5% and below 1.5% of GDP).

3) Group 3: Favorable human capital (investment in R

&D above 1.5% and below 2.5% of GDP).

4) Group 4: Very favorable human capital (investment

in R&D above 2.5% of GDP).

Later, for the purposes of assessing different parts of

the period under analysis and draw co mparisons between

them, said classification was extended to the whole 1970-

2011 period, and the countries were grouped under one

of the four categories of investment in R&D, based on

their average investment level for the 1996-2011 period.

Thus, the countries were classified as typical agents with

upward or downward trend towards investment in human

capital.

Secondly, the following categories were defined based

on the efficiency level of the energy intensity factor (ηe)

used by each typical agent [21,23]:

1) Unfavorable in tensity (energy intensity v alues more

than 1.5 times the world average value for 1970).

2) Medium intensity (less than 1.5 and more than 0.75

times the world average value for 1970).

3) Favorable intensity (less than 0.75 and more than

0.50 times the world average value for 1970).

4) Very favorable investment (less than 0.50 times the

world average value for 1970).

Lastly, three categories were created according to the

efficiency level of carbon emissions of each typical agent,

based on the carbonization index (ic) valued used by each

one in each unit of time t (year) [21]:

1) Unfavorable carbonization (carbonization index

values above 90% of the world average for 1 97 0 ).

2) Medium carbonization (below 90% and above 70%

of the world average value for 1970).

3) Favorable carbonization (below 70% of the world

average value for 1970).

These groups and subgroups, together with those de-

scribed in the economic section, present the basic char-

acteristics of typical agents. In other words, each typical

agent will be characterized by a level of investment in

human capital and of consumption, a level of efficiency

in energy consumption and emissions into the atmos-

phere, and age, variables which could change in each

agent dependi n g on the pe ri o d under analysis:

 agents = f (age, human capital investment, consump-

tion level, energy intensity, carbonization).

2.3. Energy Intensity Factor

The energy intensity factor (ηe) represents the amount of

energy used per each dollar generated by the gross do-

mestic product. Both at a global level and for the most

developed regions, historical data shows a constant im-

provement in energy intensity, which translates into en-

ergy savings per unit of production [21,23]. Said im-

provement may be explained by the use of more efficient

technologies, which is directly linked to the levels of

investment in research and development (R&D), or in

terms of this paper, of investment in human capital.

Firstly, the trends of the curves obtained by graphi-

cally representing the energy intensity factor of the

agents of the groups with lower investments in R&D on

the one hand (groups 1 and 2), and the groups with

higher investments on the other hand (groups 3 and 4),

based on the annual household final consumption expen-

diture per capita, as indicator of the annual consumption

of each individual [23]. For both variables, data corre-

sponds to each year of the 1970-2011 period. Moreover,

apart from the trends in the curves, the values taken by

the energy intensity factors of the agents of each group

were analyzed, as compared to the world average value

for year 1970. Secondly, an analysis was conducted on

the frequency distribution of energy intensity values

within each group of investment in R&D. For the pur-

poses of assessing shifts or emerging trends over time,

said analysis was performed on the values for the 1971-

1990 and 1996-2011 periods.

2.4. Carbonization Index

4It is worth mentioning that, even when considering the average values

of each country as sole values related to representative individuals, the

variability of each studied variable in each country is omitted, the

analysis presented in this article is more qualitative-oriented.

Efficiency of carbon emissions into the atmosphere is

related to the carbonization index (ic), which represents

Open Access LCE

Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment

Open Access LCE

3. Results carbon emissions per unit of energy consumed. An

analysis was conducted on the curves obtained from

graphically representing such indicator based on time

[21], for those agents representing the group s with lower

investment in R&D on the one hand (groups 1 and 2),

and the groups with higher investment on the other hand

(groups 3 and 4) , based on ti me.

3.1. Economic Growth

Figure 1(a) shows the curves obtained by representing

education and culture expenditure data (expressed as

weekly expenditure per person) as indicators of house-

hold investment in human capital, based on the age of the

representative individual. On the other hand, part (b) of

the figure shows the curves representing the weekly ex-

penditure per person related to household consumption

(the human capital expenditure being deducted) based on

the age of the representative individual [26-28]. The data

has been expressed relatively to the maximum value

taken by the variable under analysis in each series. Thus,

the values obtained may be read as proportions to the

maximum (equal to 1 in each series), allowing for the

observation of trends in the curves and the making of

comparisons between data from different countries, thus

avoiding the use of different scales. In all cases, the data

represented for Argentina corresponds to year 1997, and

for USA and UK, the average values for the 2002-2011

decade. Figure 2 shows the relative increase (as com-

pared to the value of the group of individuals with a

lower education level) in income, consumption and in-

vestment in human capital of households per capita,

based on the education level of the representative indi-

vidual of the household [27]. Figure 3 shows expendi-

ture of households per capita in education (a), and in

consumption (b), both as percentag es of the total income

of the representative individual, based on their education

level [26,27].

Besides, a further analysis was conducted on the evo-

lution over time of the proportions of the sources of en-

ergy used by a group of countries selected as per the dis-

tribution of their energy matrix [21]. Said countries in-

clude some with strong economies and high percentages

of nuclear (France) and hydraulic (Norway, Brazil) en-

ergy use, countries with emerging economies and high

percentages of use of carbon (China and India), a fuel

with an emission factor above the average of fossil fuels,

countries with a high percentage of gas (Argentina) and

oil consumption (Ecuador), as compared to the world-

wide trend. Graphics were prepared to show the propor-

tions of the sources of energy used, and the historical

curves of the carbonization index related to the represen-

tative agents of said countries, for the 1970-2011 period,

were also included.

Moreover, the values for the carbonization index for

the 1996-2011 period were also analyzed, based on the

percentage of GDP invested in R&D of each agent. Said

data was represented graphically, and 4 divisions were

made on the horizontal scale as per the different ranges

of the human capital indicator, while 3 divisions were

made on the vertical scale, as per the efficiency catego-

ries used by the indicator.

Besides, an analysis was conducted on the frequency

distribution of the carbonization indexes used by the

agents, as per the 4 groups of investment in R&D. As in

the previous section , said an alysis covered the 19 71 -1990

and 1996-2011 period s.

3.2. Energy Intensity

Figure 4 shows energy intensity curves based on con-

sumption, divided into agents with downward trend to-

Figure 1. (a) Investment in human capital of households, and (b) Consumption expenditure per capita as per the age of the

representative individual (Argentina, USA and UK).

Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment 5

Figure 2. Income, consumption and investment in human capital per capita by households as per the education level of the

representative individual (USA).

Figure 3. (a) Education expenditure and (b) Consum ption expenditure per capita, as per education le ve l.

Figure 4. Energy intensity as per the annual expenditure on final consumption of households for investment in R&D below

1.50% of GDP (a), and above 1.50% of GDP (b).

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Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment

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Table 2. Proportions of specific data in each of the catego-

ries of energy efficiency and investment in R&D.

wards investment in human capital (a), and agents with

upward trend towards investment (b). Table 1 shows the

average value of this indicator for each of the 4 groups of

investment in R&D, together with the related category as Human capital Group 1Group 2 Group 3 Group 4

Efficiency level 1971-1990 period Total by efficiency

Unfavorable 2.4%11.4% 0.0% 0.0%13.9%

Medium 11.8%7.2% 11.3% 2.1%32.4%

Favorable 12.3%5.6% 13.5% 4.9%36.3%

Very favorable 6.4%4.3% 3.1% 3.6% 17.5%

Total by human capital32.9%28.5% 27.9% 10.6%100%

Efficiency level 1996-2011 period Total by efficiency

Unfavorable 10.3% 16.4% 0.0% 0.0%26.7%

Medium 11.1% 8.7% 5.0% 0.9% 25.7%

Favorable 8.3%7.7% 6.8% 3.6%26.4%

Very favorable 1.1%5.3% 8.4% 6.4% 21.3%

Total by human capital30.8%38.1% 20.3% 10.8%100%

per the energy efficiency level, for the 1971-1990 and

1996-2011 periods. Moreover, said table also shows the

values obtained for the standard deviation (



), and the

levels of maximum and minimum values taken by the

indicator in each data group for both periods. Table 2

shows the proportions of specific data for each of the

categories determined (level of energy efficiency and

investment in R&D), for both periods. This information

is supplemented by Figure 5, which shows the distribu-

tion in each period of the values taken by the energy in-

tensity factor as per its efficiency level, in each of the

groups of investment in R&D or human capital.

Table 1. Characteristics of energy intensity values for each

group of investment in human capital.

Human capital Group 1 Group 2 Group 3 Group 4

Energy intensity 1971-1990 period

Average ηe (±



) 0.88 (0.67) 1.76 (1.77) 0.74 (0.22) 0.60 (0.22)

Maximum ηe 6.93 9.22 1.39 1.06

Minimum ηe 0.24 0.40 0.33 0.25

Efficiency level medium unfavorable favorable favorable

Energy intensity 1996-2011 period

Average ηe (±



) 1.71 (1.38) 1.97 (2.13) 0.62 (0.25) 0.46 (0.17)

Maximum ηe 7.39 11.21 1.47 1.00

Minimum ηe 0.23 0.25 0.29 0.25

Efficiency level unfavorable unfavorable favorable very favorable

3.3. Carbonization Index

Figure 6 shows the historical trend of the carbonization

index of agents, divided as per their trend towards in-

vestment in R&D. For its part, Figure 7 shows, in over-

lap, the curves fo r the carbonization ind ex and the evolu-

tion over time of the proportions of the sources of energy

used as from 1970 [21], for three sectors with different

types of consumption: energy consumption based on

non-emission sources (France, Norway and Brazil), con-

sumption based on medium-emission sources (Argentina

and Ecuador), and consumption based mainly on “dirty

fuels” (China and India), as compared to the worldwide

trend. Figure 8 shows the values of the carbonization

Figure 5. Distribution of energy intensity values within each group of investment in R&D, for the 1971-1990 (a) and 1996-

2011 (b) periods.

Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment 7

Figure 6. Carbonization index as per time, for countries with investment in R&D below 1.50% of GDP (a), and above 1.50%

of GDP (b).

index corresponding to the representative individuals of

the different countries for the 1996-2011 period, as per

the percentage of GDP invested in R&D, together with

the divisions on the horizontal and vertical scales men-

tioned in the above section. Besides, the average values

of the carbonization index were calculated for the 4

groups divided according to their levels of investment in

R&D. Said values are shown in Table 3, together with

the standard deviation (σ), the levels of maximum and

minimum values, and the categories for the indicator as

per its efficiency level, for the 1971-1990 and 1996-2 011

periods. Table 4 shows the proportions of specific data

for the categories related to the carbonization index and

to the level of investment in R&D, for both periods. In

turn, Figure 9 was prepared based on the analysis of fre-

quency distribution performed for this indicator, and it

shows the distributions of the carbonization index values

for the countries under analysis within each of the groups

classified according to their level of investment in R&D,

for the 1971-1990 and 1996-2 011 periods.

4. Discussion

4.1. Economic Growth

It may be observed that when representing the expendi-

ture in consumption and education of the representative

individuals of the households according to their ages, the

curves obtained show an evolution in the form of an in-

verted bell, with a maximum located at a given time of

each individual’s life. The age corresponding to said

maxima is dependent, in turn, on the type of expenditure,

the period under analysis and the idiosyncrasy of the

represented group of humans. On the other hand, when

representing the economic variables analyzed according

to the education level of individuals, it may be observed

that when such increases, the proportion of the total in-

come invested in human capital shows a significant in-

Table 3. Characteristics of carbonization index values for

each group of investment in human capital.

Human capital Group 1Group 2 Group 3Group 4

Carbonization index1971-1990 period

Average ic (±



) 0.91 (0.10)0.96 (0.16) 0.89 (0.21)0.76 (0.18)

Maximum ic 1.16 1.24 1.12 1.00

Minimum ic 0.72 0.57 0.30 0.40

Efficiency level of

emissions unfavorable unfavorable mediummedium

Carbonization index1996-2011 period

Average ic (±



) 0.86 (0.11) 0.90 (0.16) 0.79 (0.20)0.74 (0.19)

Maximum ic 1.14 1.20 1.07 0.98

Minimum ic 0.58 0.53 0.31 0.36

Efficiency level of

emissions mediumunfavorable mediummedium

Table 4. Proportions of specific data in each of the catego-

ries for carbonization index and investment in R&D.

Human capital Group 1Group 2 Group 3 Group 4

Carbonization index1971-1990 period Total by efficiency

Unfavorable 17.1%26.3% 15.9% 3.7%63.1%

Medium 14.2% 5.9% 3.9% 2.3% 26.3%

Favorable 0.0%3.1% 3.7% 3.8% 10.6%

Total by human capital31.3%35.2% 23.6% 9.8%100%

Carbonization index1996-2011 period Total by efficiency

Unfavorable 9.6%15.5% 7.5% 3.1%35.7%

Medium 19.0% 18.3% 6.2% 3.3%46.8%

Favorable 2.0%4.7% 6.4% 4.4% 17.4%

Total by human capital30.6%38.4% 20.1% 10.8%100%

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Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment

Figure 7. Carbonization index and evolution over time of proportions of energy sources used by different countries.

crease, while the proportion devoted to consumption de-

creases. It may be further observed that the higher the

education level of the representative individuals, the

higher their income. Nevertheless, the relative in crease in

investment in human capital of individuals with higher

education levels, as compared to the individuals with

lower levels, is broadly higher than the relative increase

in income. This means that the proportion of income al-

lotted to consumption or investment in education and

culture is closely related to the level of human capital of

individuals and their trend towards invest in such, and,

indeed, to other socioeconomic and cultural factors be-

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Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment 9

Figure 8. Carbonization index (compared to the world value for 1970) as per investment in R&D as % GDP.

Figure 9. Distribution of carbonization index values within each group of investment in R&D.

yond the scope of this paper. It may be concluded from

this analysis that, in general terms, the greater th e human

capital, the higher the level of investment in human capi-

tal. In turn, those individuals who invest more of their

time in education, or in accumulating human capital,

obtain a higher income and devote a lower proportion of

their production to consumption as compared to other

individuals. Figures 2 and 3 show that the groups with

higher education (despite having higher incomes) pro-

portionately spend more in human capital (>10 times)

than those with a lower education level.

4.2. Energy Intensity Factor

In general terms, it may be observed that energy intensity

significantly decreases for increasing levels of consump-

tion per capita. The trends of the curves representing

those agents less likely to investment in human capital

are not that clear in all cases. Nevertheless, in some cases,

and even more in the curves representing those agents

more inclined to said type of investment, it is possible to

find some emerging patterns. By analyzing said series

separately, 3 types of trends or “sections” of a hypothe-

tical path may be found: increasing trends (section a),

local maxima (section b), and decreasing trends (section

c). In turn, it may be observed that some agents go

through the 3 described sections in their curves, so that

they show, for lower levels of consumption, an increas-

ing trend up to reaching a maximum where their energy

intensity factor starts to decrease, a trend which is main-

tained for increasing values of consumption, whereas

other agents only present, up to date, just 1 or 2 of the

sections described. The level of consumption by itself

does not seem to explain this behavior, since it is not

possible to assign a value to it from which the curves

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Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment

show this or that trend. That is to say, for a given range

of consumption values, it is possible to find increasing or

decreasing curves or even local maxima based on said

consumption; or in other words, there are agents who,

while increasing their consumption, lower its efficiency,

there are others who improve it, and others who shift its

trend, within the same range.

However, if countries are grouped according to their

level of investment in human capital, the trend of the

curves of some of them becomes clearer. In Figure 4(b),

it may be observed that said curves are currently in the

last of the sections described (section c) of the hypo-

thetical path, or in other words, with trends more and

more efficient. Besides, when comparing the vertical

scales of Figure 4(a) with Figure 4(b), it may be ob-

served that the latter shows significantly more efficient

values of energy inten sity.

Apart from that, when analyzing the energy intensity

factors as specific data grouped according to the related

levels of investment in R&D, it may be observ ed that the

averages of the values taken by the indicator in the

groups with higher investment in human capital, and

their variability range, are significantly smaller than

those of the groups with lower investment, both for the

1971-1990 and the 1996-2011 periods (Table 1). Going

into further detail, Table 1 shows that in both periods the

less efficient values of energy intensity are those of the

second group of investment in human capital (group 2).

In turn, it may be observed that the two groups with

higher investment show an improvement in their energy

efficiency over the years, while the two groups with

lower investment show a shift to less favorable values.

When analyzing th e d istribu tio n of the lev els of en ergy

efficiency within each group, it may be observed that for

the 1971-1990 p er iod, g rou ps 1, 3 an d 4 show a Ga ussian

distribution, around favorable efficiency levels for the

last groups and around medium and favorable values for

the first one (Figure 5(a)). It may be further observed

that the curve for group 4 is inclined to more favorable

values than that for group 3. If the fr equency distribution

curves of these same gr oups fo r the 1996- 201 1 per iod ar e

analyzed, it may be observed that they have experienced

a sort of shift. In the curves of the groups with higher

investment in R&D, said shift took place towards more

favorable values, whereas in the group with lower in-

vestment, such shift occurred towards less efficient val-

ues of energy intensity. This event, together with that

described in the previous paragraph, reflects, in turn, an

increase in the existing inequality among the different

groups of agents over time. For its part, group 2 is the

only group which, in both periods, shows its higher pro-

portion of energy intensity values in the unfavorable

category. Moreover, in both periods, such group shows a

decreasing trend in its distribution curves as the effi-

ciency of the indicator under analysis increa ses.

Apart from that, when analyzing Table 2, it may be

observed that for th e 1971-1990 period, th e main propor-

tion of data correspond s to energy intensity valu es of the

favorable category, with a high proportion of medium

values. However, a more homogenous distribution (or a

flatter distribution curve) may be observed for the 1996-

2011 period, with an increase in the proportion of data

corresponding to the level of higher energy efficiency,

and in turn, an increase in the proportion of data corre-

sponding to the level of lower efficiency, as compared to

the previous period. By comparing both periods, it may

be observed that the main contributors to the improve-

ment in the proportion of very favorable values have

been the groups with higher investment in R&D, and, at

the opposite end, the main responsible agents for the un-

favorable data increase have been the groups with lower

investment in human capital. It may be further observed

that for both periods, there is no data corresponding to

the groups with higher investment in human capital that

show inefficient energy intensity values.

Bearing in mind the parallelism observed between

countries and individuals representing such or typical

agents, all the above may be expressed as if the individu-

als who invest the more in human capital are those who

use, on average, more efficient values of energy intensity,

with variability ranges more restricted to higher effi-

ciency values. Nevertheless, the relation between these

two variables is not that linear, since the average energy

intensity of the group of individuals with downward

trend towards investment in human capital is more effi-

cient than that of the following group. A “hypothetical

path” may be again considered, where, rather than the

passage of time or an increase in consumption, it is the

increase in human capital that determines the direction of

development. And said development, in terms of the in-

dicator under analysis, implies consuming, at a very be-

ginning, more and more energy so as to achieve eco-

nomic growth, up to reaching a maximum from which

the reduction of said consumption is possible, without

jeopardizing the growth.

4.3. Carbonization Index

It may be observed that most of the curves obtained by

representing the carbonization index based on time, show

a weak downward trend, despite their slight slopes. By

comparing the graphics obtained after dividing the agents

according to their higher or lower willingness to invest-

ment in human capital, it may be observed that those

with higher investment show more efficient values for

the indicator under analysis. This fact would indicate

again that investment in human capital, whether consid-

Open Access LCE

Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment 11

ered on an individual or collective basis (as indicator of

the technological development reached), helps to im-

prove the efficiency of emissions. However, the response

of this indicator to variations in investment in human

capital is not as fast as in the case of the energy in tensity

factor. By analyzing Figure 8 and its subdivisions, it

may be observed that, even when, in general terms, in-

creases in human capital investment are related to im-

provements in the carbonization index efficiency, a sig-

nificant vertical variability also exists. Said variability

could be the result of the availability of existing energy

sources in each country, or the energy sources used by

the representative individuals of such countries. It may

be observed in the representations of Figure 7 that those

countries with high percentages of nuclear (France) or

hydraulic (Norway and Brazil) energy use show very

favorable values of carbonization index, while those ar-

eas with high percentages of use of carbon (a fuel with an

emission factor above the average of fossil fuels), such as

China and India, show very unfavorable values. It may

be further observed that variations in the energy sources

used are rapidly reflected in the carbonization index,

which shows that said factor is closely related to the

technology used in the energy production. Since that

production is, at a global level, controlled by the large

power plants of carbon used in China and India, with

poor technological efficiencies, the worldwide trend

shows that, currently, said index is slightly increasing.

Other contributing factors are, among others, the age of

many power plants, and the long time elapsed between

design and start up of a new power plant and closing of

the older plant.

Besides, from the frequency distribution analysis per-

formed, it may be observed that even when in the 1971-

1990 period most of the individuals within each group

have experienced a development using unfavorable car-

bonization indexes, as the investment in human capital

increases, the proportion of individuals of each group

who use more efficient carbonization indexes becomes

relevant. Nevertheless, as described for the energy inten-

sity factor, Table 3 shows, again, that it is group 2 that

stands out from the other groups, for having the least

efficient average of values for both periods. Likewise, by

comparing the distributions of both period under analysis,

it may be observed that, over time, all groups show a

shift in the levels of th e indicator toward s more favorable

levels, a fact which is also reflected in the values repre-

sented in Table s 3 an d 4. Th e latter show s that, ov er time,

the groups with lower investment significantly reflect the

improvements in the efficiency of their average carboni-

zation levels. For its part, group 4 had already showed, in

the first analyzed period, a distribution of values much

more efficient than that of the other groups, and over

time the improvement of such distribution has not been

so evident. This event could be translated into the idea

that the carbonization index used by individuals, due to

its strong dependence on the energy sources used, may

be significantly improved up to a given peak, from which

the effort needed to improve it further becomes higher.

5. Conclusions

The main world economic variables affecting energy

consumptions and its subsequent carbon emissions have

been studied in this paper. An analysis was co nducted on

these variables based on the individual behavior of the

representative agents of the economy, with particular

emphasis on the role played by investment in human

capital. It was observed that investment in human capital

has a positive (favorable) effect on each of the analyzed

compounding factors. At an individual level, the accu-

mulation of said capital, translated into high education

levels of the representativ e individuals of the economy, is

reflected in a relative reduction in individual consump-

tion levels, accompanied by upward trends towards in-

vestment in human capital as said accumulation increases.

From an energy point of view, the analysis showed that

the individuals capable of improving the efficiency of

their energy consumption are those who invest the most

in human capital, or those whose time in their lives is

mostly devoted to such accumulation.

If we consider that it is human capital that determines

the direction of the development of societies or groups of

individuals, the promotion of investment in said type of

capital, both at individual and national level, would lead

to societies with behaviors of lower consumption in rela-

tive terms, and of higher efficiency in energy and envi-

ronmental terms.

6. Acknowledgements

The authors acknowledge the funding of research grants

from the National Technological University and the Na-

tional Agency of Scien tific and Technological Promotion

(AGENCIA), and the support and funding of the Buenos

Aires Regional Faculty (UTN-FRBA) and the National

Council Research of Argentina (CONICET).

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Bottom-Up Analysis of Energy Consumption and Carbon Emissions,

with Particular Emphasis on Human Capital Investment 13

Appendix A

List of countries analyzed and legends:

Open Access LCE