Challenges in Sustainability | 2013 | Volume 1 | Issue 2 | Pages 94–103
DOI: 10.12924/cis2013.01020094
Challenges in
Research Article
Carbon Intensities of Economies from the Perspective of
Learning Curves
Henrique Pacini* and Semida Silveira
Department of Energy Technology, KTH Royal Institute of Technology, Brinellv
agen 68, Stockholm, Sweden;
E-Mails: (HP), (SS)
* Corresponding author: E-Mail:; Tel.: +41 789490827
Submitted: 25 September 2013 | In revised form: 3 February 2014 | Accepted: 9 February 2014 |
Published: 26 March 2014
Abstract: While some countries have achieved considerable development, many others still lack access
to the goods and services considered standard in the modern society. As CO
emissions and develop-
ment are often correlated, this paper employs the theoretical background of the Environmental Kuznets
Curve (EKC) and the learning curves toolkit to analyze how carbon intensities have changed as countries
move towards higher development (and cumulative wealth) levels. The EKC concept is then tested with
the methodology of learning curves for the period between 1971 and 2010, so as to capture a dynamic
picture of emissions trends and development. Results of both analyses reveal that empirical data fails
to provide direct evidence of an EKC for emissions and development. The data does show, however,
an interesting pattern in the dispersion of emissions levels for countries within the same HDI categories.
While data does not show that countries grow more polluting during intermediary development stages, it
does provide evidence that countries become more heterogeneous in their emission intensities as they
develop, later re-converging to lower emission intensities at higher HDI levels. Learning rates also indicate
heterogeneity among developing countries and relative convergence among developed countries. Given
the heterogeneity of development paths among countries, the experiences of those which are managing
to develop at low carbon intensities can prove valuable examples for ongoing efforts in climate change
mitigation, especially in the developing world.
Keywords: carbon emissions; development; EKC; learning curves
1. Introduction
Higher income levels have been traditionally correlated
with increased energy consumption and higher carbon
emissions in industrialized and developing countries alike.
As climate change awareness grew during the 2000s, in-
terest in using alternative, renewable energy sources in or-
der to reduce dependence on fossil hydrocarbons rose.
While efforts have been made to de-link energy from
carbon emissions, the bulk of energy production in the
world continues to be linked to carbon-emitting sources
[1, 2]. As initially presented by Kaya [3], the endur-
ing prevalence of fossil fuels in the global energy mix
binds together economic activity, energy usage and carbon
emissions which continue exacerbating the risks for cli-
mate change. The problem is compounded since interna-
tional negotiations towards a more widely-reaching climate
agreement than Kyoto have been beset by slow progress,
particularly in the last COP meetings in Copenhagen, Can-
cun, Durban, Doha and Warsaw [4, 5]. In an escalating
blame-game, countries criticize each other for being lax in
their pollution (and emissions) controls, with some even
threatening to retaliate in international trade—in the ab-
sence of better multilateral solutions—with the introduction
2014 by the authors; licensee Librello, Switzerland. This open access article was published
under a Creative Commons Attribution License (
of border carbon adjustments or similar mechanisms [6].
As of 2013, countries differ not only in their level of de-
velopment, but also in terms of their share of renewable en-
ergy, the energy and carbon intensities of their economies,
and policies applied to enhance environmental protection
and sustainability. Stepping aside from the debate around
international climate negotiations, one of the key environ-
mental issues—CO
emissions in the atmosphere due to
energy production—profits from good available statistics
which allow it to be measured and correlated with macroe-
conomic indicators.
While the correlation between economic growth and
carbon emissions is nothing new to environmental prac-
titioners, this paper contributes to the international emis-
sions debate by examining the carbon intensities of the
major global economies employing the alternative optic of
learning curves. While traditionally used to assess cost
reductions as specific technologies are adopted, learning
curves can also be used in socio-economic fields, such
as examining the evolution of labour intensities of GDP
through time [7, 8]. Based on data from IEA [9], this paper
uses economic performance and CO
emission statistics
to look at countries as if they were industries which would
be expected to reduce their carbon intensities throughout
time. Specifically, the paper estimates how fast major eco-
nomic regions, as well as the world as a whole, have re-
duced emissions based on cumulative economic output
between 1971 and 2010. This examination is followed by
a discussion of the possible reasons behind the different
learning rates for reduction of carbon intensities found for
different world regions.
2. Heterogeneous Development Paths
Back in the 1960s economists found an apparent corre-
lation between income levels and inequality in national
economies [10]. Observations showed that inequality ap-
peared to rise with economic growth, particularly in the
early stages of a country’s development, up to a point when
it started to decline. The shape of this correlation has been
known as the inverted “U” curve, or simply the Kuznets
Curve [11].
More recently, the same concept has been extrapolated
to environmental economics and named the Environmen-
tal Kuznets Curve or EKC [12]. Analogous to the original
concept, the EKC asserts that pollution increases with de-
velopment up to a certain level, after which it declines ([13],
p. 2). The existence of EKC relations, however, have been
a matter of scientific debate. An overview of proponents
and critics of the EKC has been made by Stern [14]. One
of the main criticisms is outlined by Arrow et al. (1995, [15])
who criticize the EKC’s inherent assumption that there ex-
ists a sustainable system in which environmental damage
is not captured so as to reduce economic activity, income
and, eventually also the growth process. Others argue that
EKC relationships might be only expressions of the effects
of trade, different shares of services in national economies,
and the distribution of polluting industries between coun-
tries [16]. Brajer et al. (2008, [17]) also noticed that the
appearance of an inverted U shape configuration in the
EKC is highly dependent on which indicators are chosen
to describe environmental degradation.
As such, the EKC concept has obvious shortcomings
and has not been verified for all sorts of environmental
degradation. While the concept is intuitive and elegant, it
can be misleading, causing policy makers to think that the
solution for climate change is simply to “get rich” and over-
come emission-intensive transition stages once higher de-
velopment levels have been achieved [18]. A plot of carbon
intensities of the economies of 138 countries compared to
their Human Development Indexes (HDI) is presented in
Figure 1.
It becomes evident that the poor fitting of the logarith-
mic trend line (R
= 0.18) puts into question the validity
of an inverted U-shaped pattern for the relationship be-
tween HDI and CO
emissions per dollar of output. In other
words, many countries have seen an increase in quality of
life without a corresponding increase in the carbon intensi-
ties of their economies. Thus, human development alone
is no guaranteed solution for the climate problem. In the-
ory, increased HDI could actually be harmful for the climate
system since many developing countries are found in the
high end of Figure 1 with no guarantee that their trajectory
will take them down to the right end of the curve. At the
same time, it is worth observing that many countries are
managing to increase their welfare (towards higher HDI) at
lower emission intensities as indicated by the large number
of dots on the lower end of the curve [19, 20].
Countries on the right lower end of Figure 1 suggest
that as countries get richer, they can invest in environmen-
tal improvements and reduce their emissions. However,
the large dispersion among developing countries among
which “high learners” figure close to “bad performers”,
leave room for some discussion. Since, in the develop-
ing world, countries are delivering similar standards of liv-
ing to their citizens at different levels of emissions, they
apparently do so at different levels of environmental costs
(measured in GHG emissions).
There are just too many exceptions to the idea that de-
velopment leads to low emission intensities. This prevents
a generalization based on the traditional view of the EKC
rule on development paths.
Overall, countries have achieved substantial progress
measured by improvements in their HDI during the last
decades, and this can be seen in the latest reports on the
millennium development goals [21]. Virtually all countries
in the world advanced their HDI rankings, albeit not at the
same pace. A similar pattern, however, could not be ob-
served in their emission reductions per unit of economic
output. Figure 2 adds a dynamic component to the plot
made in Figure 1, in the sense of analyzing what happened
between 1980 and 2007, in the form of a 138-country inter-
temporal plot of HDI against emissions per unit of GDP.
The plot focuses on three select groups: (1) The world av-
erage, (2) BRICs [22] and (3) Scandinavian countries (as
a proxy of advanced economies).
While Scandinavia experienced dramatic reductions in
carbon intensities, the overall world figures indicate only a
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Emissionsper dollar of GDP(kg/CO2eq)
HDI level
Figure 1. Emissions per dollar of GDP (2010) plotted against HDI for a sample of 138 countries. Sources: developed
by the authors based on data from the United Nations Development Programme and the International Energy Agency.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Emmissionsperunitof GDP,accordingtoHDI levels. Regionalandworld
Figure 2. Emissions per unit of GDP plotted against HDI levels, 1980–2007. Plotted are 138 countries during 8 sampled
time periods. Regional and world weighted averages according to respective populations. Based on data from IEA and
United Nations Development Programme (2007).
modest decrease in this parameter. This is especially evi-
dent in the BRICs, which had emissions figures above the
world average during the period analyzed (1980–2007).
Previous literature suggests that countries cannot rely
on development alone to drive down CO
emissions. Sim-
ilar to propositions by Tierney (2009, [18]), the answer lies
in two underlying aspects of development: that develop-
ment paths can present mutual-dependency between dif-
ferent countries; and overall system constraints.
2.1. Trade and Mutual Dependency between Countries
Mutual dependency means that the very driver of glob-
alization—specialization in comparative advantages ex-
pressed by international trade—can lead to more rigid
emission patterns for some countries than others. At the
same time, the movement of goods between countries can
be key for some economies to be able to reduce their emis-
sion intensities. As noted by Suri and Chapman (1998,
[13]), exporting countries can increase their emission in-
tensities while importing countries can reduce their emis-
sion intensity. Hamilton and Turton (2002, [23]) noticed that
by having a larger share of the service sector in the overall
economy, some countries can manage to outsource emis-
sions while still retaining profitable economic activities in-
side their markets. It is important to bear this in mind, as
the low emissions per unit of GDP in Scandinavia may be
not only the result of shifts towards renewable energy or
higher energy efficiency, but also of emigrated emission-
intensive to other regions of the world [24, 25].
2.2. System Constraints
Similarly to what has been proposed by [26], environmen-
tal systems constraints determine the operating space for
humanity. This implies a maximum amount of emissions
that can be absorbed by natural sinks without triggering
costly climate change. Thus, even in the absence of the
mutual dependency issue discussed above, it would not be
an option to simply wait for emissions per unit of GDP to
go down to a “safe” threshold. According to data from the
International Energy Agency [27], developing and least de-
veloped countries (HDI < 0.89) comprise 84% of the world
population. The global carbon budget will have long since
expired as the economies of these nations approach Scan-
dinavian emission levels. In other words, in a business-as-
usual trajectory, HDI values will most likely retrocede if a
fossil-intensive path is pursued by the populous develop-
ing world [19].
Finally, even with the positive indication illustrated in
Figure 2 that emissions per dollar of GDP are falling for
the world as a whole, this will not be enough to hedge
against climate risks because two other factors cancel out
the gains of reduced global carbon intensity by a large de-
gree. Figure 3 shows that climate damage has four dimen-
sions: while carbon and energy intensities are on average
decreasing, total world GDP and population are growing
steadily [28]. Average emissions per unit of GDP have
fallen 24% between 1971 and 2008 (from 0.92 to 0.70 kg
eq/dollar of GDP) while the world population has prac-
tically doubled from 3.4 billion to 6.6 billion in the same
period. Global wealth followed the same trend (323% in-
crease since 1971). So while the world economy makes
cleaner dollars today, it makes so many of them that the
aggregate level of emissions has grown exponentially.
This can be illustrated by the relationship proposed by
Kaya (1997, [3] ), which is a useful tool to understand the
human impacts on the climate system. The Kaya equa-
tion incorporates indicators which declined over the period
between 1971 and 2008 (energy and carbon intensities),
comparing the overall impact of these efficiency gains with
the growth in wealth (GDP) and population during the same
period. Although simple, the relation is a good representa-
tion of the magnitude of human emissions on the climate
system. The Kaya identity focuses on CO
emissions from
anthropogenic sources and is expressed as follows:
F = P ×
F = P × g × e × f (2)
where F is global CO
emissions from human sources, P
is global population, G is world GDP and E is global pri-
mary energy consumption. Then, g =
is the global
per-capita GDP, e =
is the energy intensity of world
GDP and f =
is the carbon intensity of energy.
The Kaya identity suggests that damage to the climate
system is directly proportional to the global population (P ),
the wealth of these individuals (g), the amount of energy
used to run each unit of the economy (e) and the carbon-
footprint associated to every unit of energy produced (f ).
With growing population and wealth, emissions increase
when material flows in the economy are enabled by en-
ergy sources that emit carbon (thus creating an impact on
the environment). Figure 3 uses data from IEA to provide
empirical illustration to the Kaya identity, showing that any
efficiency gains in emissions and energy usage have been
clearly offset by growing populations and wealth at a global
With evidence indicating a growing human impact on
the climate system, more attention should be given to
countries entering more intensive development stages.
Given their large populations, developing countries will
have to play a major role in an eventual reduction of overall
emissions and stabilization of greenhouse gas concentra-
tions affecting the climate system. As previously discussed
and demonstrated in Figure 1, GDP and HDI indicators do
not correlate to confirm an EKC relationship, and thus de-
velopment as pursued in past decades will not lead us to
a safe trajectory. In the next section, we will try an alter-
native approach in search of a pattern between emissions
and development. In order to capture some of the dynamic
effects which occurred between 1971 and 2008, we this
time use the concept of learning curves, calculating the
learning rates (the rate at which reductions in the carbon
intensity occurred) for each individual country.
3. Learning Rates for Carbon Intensities of World
The concept of learning curves—also known as experi-
ence curves—is conventionally used to represent an im-
provement in technology, such as production costs or ef-
ficiencies, along with associated experience or cumulative
output [29, 30, 31]. Learning curves provide a graphical
representation of changing rates of learning over time for a
given activity. The concept is often used for specific indus-
tries, as costs of innovative technologies tend to decrease
as experience is accumulated [32].
Examples of learning curve analysis often include spe-
cific technological sectors [33]. Studies have been made
for photovoltaics, where the cost of solar electricity has
been shown to decline as a function of the cumulative num-
ber of photovoltaic panels installed [30]. Another known ex-
ample concerns biofuels, where Goldemberg (2004, [34])
showed a strong historical downward trend in Brazilian
ethanol prices following the rapid increase in ethanol pro-
duction and use in the country, which eventually improved
the competitiveness of ethanol in relation to gasoline. The
plot of learning curves often encompasses logarithmic
The use of learning curves for the analysis of techno-
logical learning paths is subject to shortfalls as the curves
usually fail to differentiate among the full range of compo-
nents that contribute to a given technological solution [35].
World population World per capitaGDP Energy intensity GDP
Carbon intensity of energy Kaya(Million tonsCO2)
Figure 3. The calculated Kaya index (total anthropogenic CO
emissions in the climate system) and its components.
While carbon and energy intensities have fallen since 1971, both total wealth and population are increasing, cancelling
out the benefits of lower carbon intensity in the global economy. Source: calculated by the authors based on IEA [27].
Each component may follow a different learning path over
time, thus affecting the overall path of the total solution.
Another difficulty is to capture variations of learning rates
over time [32]. Still, learning curves can be useful tools
for strategic planning and for the analysis of technological
performance or price variations over time.
In this section, we apply the concept of learning curves
to analyze the development of carbon intensities in na-
tional economies. While a component-learning approach
of sub-sectors of national economies would be the ideal
approach, data limitations impose some analytical simpli-
fications. Here, countries are considered to be production
units with their output expressed in units of gross domestic
product (GDP). Costs are considered to be the carbon in-
tensities of national GDP, which can be interpreted as the
environmental cost of generating each unit of GDP.
The theory behind the EKC suggests that the least
developed countries may experience “negative learn-
ing” as their emissions are—still according to the EKC
logic—expected to increase per unit of GDP produced; de-
veloping countries are expected to have low learning rates;
and developed countries should display positive learning,
indicated by a downward slope in their emissions per dol-
lar (see Figure 4). Instead of a static analysis of the car-
bon intensities of countries for a single year, this section
uses recent IEA data spanning from 1971 to 2010 to rep-
resent the learning process. Observations between 1971
and 2010 are used to calculate whether learning rates jus-
tify the hypothesis derived from the Environmental Kuznets
Based on Ferioli et al. (2009, [32]), the expression rep-
resenting learning curves can be written as:
C(x) = C(x
where x is the cumulative output, x
is the initial out-
put, C(x) is the carbon intensity at the cumulative output,
C(x0) is the carbon intensity at the initial output and L
is the learning parameter. As the inclination of learning
curves are based on learning rates (LR), these are ex-
pressed as:
LR = 1 2
where LR is the learning rate, which expresses the rate
of change in emissions per dollar of GDP from the first
observation (1971) to the most recent observation avail-
able (2010), based on data from the International Energy
Agency (IEA) published in 2010 [36]. For calculation pur-
poses, (1) and (2) are combined in the final working ex-
LR = 1 2 ×
While learning rates could be calculated for each of
the 131 countries sampled, in this note we calculate the
rates for the main macro-regions under the IEA classifica-
tion [37]. Learning rates consider all intermediate years
between 1971 and 2010, as a regression is made for the
entire data set. A sample data plot for the world average is
made on a double-log scale in Figure 5.
The results of the learning rates of decarbonization are
shown in Figure 6.
developing countries
no learning
Negative learning
(emissions increasing)
Positive learning
(emmissions decreasing)
developed countries
Figure 4. Hypotheses based on the EKC concept for the learning rate of decarbonization of economies.
Contrary to the hypothesis implied by the Kuznets logic,
it is not evident from the data that least developed countries
are linked to negative learning rates, developing countries
with learning rates close to zero and positive learning rates
for developed countries. Figures 5 and 6 indicate that al-
though the world as a whole experienced a reduction in its
carbon emissions per unit of GDP (1971 0.88 kg CO
per USD of GDP; against 0.59 kg in 2010), a number of in-
dividual countries have experienced negative learning. In
other words, many countries increased their emissions per
dollar during the period between 1971 and 2010. Coun-
tries which figure close to the range of zero learning in-
clude Brazil, Costa Rica, India, Tunisia and Mexico.
In line with the aforementioned hypothesis, countries
which had negative learning generally belong to lower
HDI classes, but also include exceptions such as wealthy
oil-producing states, one EU country (Greece) and New
Zealand. These exceptions indicate that the learning
rates of decarbonization might be highly dependent of
which sectors emerge as central in each national econ-
omy (e.g. mining, oil exploration), as well as how much
of those emission-intensive resources are exported when
compared to domestic consumption. This is relevant be-
cause even in the presence of international trade, cur-
rent emissions statistics are bound to the country of oc-
currence, not to countries which import high emissions-
intensive products or energy [38].
Finally, countries which had positive learning—those
which effectively reduced the carbon intensity of their
economies between 1971 and 2008—are the most diffi-
cult to interpret. As expected, most of the leading world
economies figure among the “positive learners”, such as
the USA, most European countries and Japan. However,
the top positive learner is not a state with a high HDI, but
instead China, which reduced its carbon intensities from
5.43 kg per dollar in 1971 to 1.79 in 2010. While a carbon
intensity of 1.79 was still higher than the average world car-
bon intensity of 0.59 kg CO
, China’s significant re-
duction in carbon intensity could be due to transformations
in the national energy system (mostly based on coal use),
but also due to factors beyond low-carbon policies, such
as exchange rate dynamics between the Chinese renmibi
and the US dollar [39].
4. Discussion
The rate of learning of reductions in the carbon intensities
of economies depends on the starting point of each nation.
For a country which started with high carbon intensities in
1971, it will be comparatively easier to reduce its emissions
by 2010 than for another country which already had low
carbon intensities in the initial period. This follows a similar
logic to the catch-up effect in development economics, as
proposed by Abramovitz (1986, [40]). For countries which
already had low carbon intensities (<1.0 kg CO
further reductions are likely to be increasingly more diffi-
cult and more costly—supposing the existence of decreas-
ing returns—if no structural change occurs. Obviously, if
energy is increasingly sourced from low-carbon or carbon-
free sources, further reductions in carbon intensities may
nonetheless be feasible. Interestingly, the Latin American
region managed to achieve an average carbon intensity of
0.58 kg CO
at USD 32 trillion in cumulative output,
while OECD North America took USD 320 trillion in prod-
uct to achieve the same carbon intensity levels.
Although the initial analysis of learning curves of macro
regions apparently offers a stronger basis for an EKC in-
terpretation, the strong variability in national carbon inten-
sities between the years 1971 and 2008 makes trend lines
of different regions difficult to compare. Factors such as
the oil shocks in 1973 and 1979, the collapse of the USSR
in 1990—1991, and varying levels of GDP growth over the
years have contributed to this variability in emissions [41].
Some developing countries have shown progress to-
wards low-carbon development paths. Examples include
Mozambique, China and Colombia, all of which have im-
plemented national policies aimed at the exploitation of
bioenergy, hydropower and other potential sources which
may have contributed to lowering their carbon intensities.
This diversity in development paths among developing
countries provides rich ground for further investigations.
While the inverted “U” curve pattern is weak for direct at-
R² =0.9054
4069 16276 65104 260416 1041664
KgCO2/USDofGDP log
Worldcummulated GDP19712010(inbillion USDof 2005) log
Figure 5. Learning curve for reductions in carbon intensity based on cummulative economic output. Aggregated data
for the World. Source: authors calculations based on IEA [9].
tempts to represent an EKC (Figure 1), the measure of
variability of carbon intensities per HDI group implies a
more pronounced inverted ”U” shape for the same data.
This indicates a higher level of dispersion—here called het-
erogeneity—for developing countries. Figure 7 illustrates
this, highlighting that values vary the most for countries
in the HDI interval between 0.6 and 0.9, converging after-
The existence of high dispersion in both carbon intensi-
ties and learning rates among developing countries hints at
the existence of a plurality of development paths. As sug-
gested by Burke (2012, [42]), this makes the case for pol-
icy studies among the developing countries with the lowest
carbon intensities, as a way to better understand why some
countries seem push ahead with their development with a
relative decouple from carbon emissions.
Drawing lessons from successful cases of low car-
bon development paths is an urgent necessity for cli-
mate change mitigation efforts, which would enrich the
toolkit of options available to strengthen—and facili-
tate—international cooperation related to climate change
5. Conclusion
The last three decades were characterized by substan-
tial improvements in human development, but this was
achieved at a high environmental cost. The emergence of
large countries such as China and India has put the future
growth trajectories of the developing world in the global
spotlight. It is now evident that emerging economies can-
not follow the same carbon-intensive paths which current
advanced economies once did, as this would most likely
trigger negative environmental externalities that could can-
cel out gains in human development.
By using the theoretical background of the Environmen-
tal Kuznets Curve (EKC), we have explored whether em-
pirical data supports an EKC relationship between devel-
opment and emissions intensities of economies. The re-
sults indicated a weak correlation with the EKC considering
carbon intensities and human development indexes (HDI).
This indicates that there is no rule for dirty development in
emerging countries, as the EKC fails to show a clear trend
of increased emissions for countries undergoing interme-
diate development stages.
The discussion of the EKC for HDI and carbon inten-
sities represents only a static view of development based
on data from 2010. In order to obtain a glimpse of the dy-
namic effects of carbon intensity changes between 1971
and 2008, we have employed the instrument of learning
curves. When applying the learning curves methodology
to measure the speed on how countries reduce their emis-
sions intensities as their cumulative GDPs double, this pa-
per was able to show that there is also no empirical backing
for an EKC relationship in learning. An EKC relationship in
learning implies a hypothesis of negative learning (increas-
ing carbon intensities) for the least developed countries,
near-zero learning rates for developing countries, and pos-
itive learning rates (reduction in carbon intensities) for de-
veloped countries. The data, however, has challenged this
hypothesis. For example, learning rates of economic de-
carbonization have been especially high for China (mean-
ing emissions per dollar fell strongly for each doubling of
GDP in the period analyzed). Negative learning has been
observed, however, especially for areas in Africa and the
Middle East due to their strong dependence on hydrocar-
bon usage and exports.
Interestingly, the inverted ”U” pattern of the EKC held
for standard deviations of carbon intensities of GDP per
level of HDI. This suggests that developing countries are
more heterogeneous among themselves in what concerns
their carbon intensities. Their heterogeneity is particularly
Middle East
Non-OECD Americas
Non-OECD Europe and
OECD Asia Oceania
European Union - 27
OECD Americas
OECD Europe
Calculated learning rate
reduced emmission
increased emmission
intensities (19712010)
Figure 6. Calculated learning rates of reductions in emissions intensities. Considering macro-regions in OECD statistics
between 1971 and 2010.
< 0.45 0.45-0.50 0.50-0.55 0.55-0.60 0.60-0.65 0.65-0.70 0.70-0.75 0.75-0.80 0.80-0.85 0.85-0.90 0.90-0.95 0.95-1.00
Heterogeneity in carbon intensities of
(standard deviation of CO2/GDP figures)
HDI levels
Average standard deviation
of emmissions per dollar
Figure 7. Standard deviation of carbon intensities of economies, as measured by kg CO
according to Figure
1. Source: [9].
clear when compared to least and highly developed coun-
tries, since for those the statistics converge more visibly. A
similar finding was presented by Steinberger (2002, [20]).
Although it cannot be said that countries grow more
polluting during intermediary development stages, they do
indeed become more heterogeneous in their emission in-
tensity during such stages. A lack of direct observation
of the EKC can be seen as a positive sign, since it sug-
gests there is no unavoidable rule of carbon-intensive de-
velopment paths for all countries. Instead, the curious re-
sults found for learning rates point to a plurality of decar-
bonization paths for the developing world. The identifica-
tion of successful examples of low carbon development is
extremely important, to providing a functional bottom-up
approach for more effective international climate change
The limits of the parameters chosen in this work must
be recognized. Carbon intensities are very aggregated in-
dicators of underlying factors such as fuel shares, energy
intensity and economic structure (e.g. the share of service
sectors, agriculture and manufacture within economies).
Suggestions for further research include an analysis ad-
justed for economic structure and specific sectors of coun-
tries. In particular, studies examining what are the most
important factors for national low-carbon development, as
well as the transferability of those factors to developing
countries. China could be an interesting case for in-depth
examination, due to the fact that it has experienced strong
industrialization and yet became the country with largest
reductions in carbon intensities between 1971 and 2010
(Figure 6). Future studies could also attempt to disentan-
gle the effects of policy-based low carbon paths from other
phenomena, such as carbon leakage via regulatory com-
petition in international markets.
Another suggestion for future studies would encom-
pass the idea of mutual dependencies emerging from
global trade patterns, since countries might find it more dif-
ficult to lower carbon intensities if their economies are spe-
cialized in emission-intensive manufacture for exports. The
dispersion of carbon intensities and learning rates among
developing countries could also suggest mutual depen-
dency among developing countries due to increased south-
south interactions. The verification of the latter is another
fertile ground for investigations.
References and Notes
[1] Silveira S. Bioenergy - Realizing the Potential. Oxford,
UK: Elsevier; 2005.
[2] UN Energy. Looking to the Future [Press Release].
New York, NY, USA: United Nations; 2010.
[3] Kaya Y, Yokobori K. Environment, Energy and Econ-
omy; Strategies for Sustainability. United Nations Uni-
versity Press; 1997.
[4] Slow Progress in Bonn Confirms Fragility of Cli-
mate Talks. Bridges Weekly Trade News Digest.
2010;14(13). Available from:
[5] UN Climate Change Conference in Warsaw keeps
governments on a track towards 2015 climate agree-
ment. Warsaw, Poland: United Nations Framework
Convention on Climate Change; 2013.
[6] The Green Economy: Trade and Sustainable De-
velopment Implications. Geneva, Switzerland:
United Nations Conference on Trade and Devel-
opment; 8–11 November 2011. Report No. UNC-
[7] Ferioli F, van der Zwaan B. Learning in times of
Change: A Dynamic Explanation for Technological
Progress. Energy Policy. 2009;43(11):4002–4008.
[8] Rivera-Tinoco R, Schoots K, van der Zwaan B. Learn-
ing curves for solid oxide fuel cells. Energy Conver-
sion and Management. 2012;57:86–96.
[9] CO
emissions from fuel combustion. Highlights.
Paris, France: International Energy Agency; 2012.
Available from:
[10] Goldsmith RW. The Comparative Study of Economic
Growth and Structure. In: Kusnetz S, editor. On Com-
parative Study of Economic Structure and Growth of
Nations. Cambridge, MA, USA: The National Bureau
of Economic Research; 1959. p. 162–176.
[11] Dinda S. Environmental Kuznets Curve Hypothesis: A
Survey. Ecological Economics. 2004;49(4):431–455.
[12] Grossman GM, Krueger AB. Environmental Impacts
of a North American Free Trade Agreement. National
Bureau of Economic Research; 1991. Working Paper
No. 3914.
[13] Suri V, Chapman D. Economic growth, trade and
energy: implications for the environmental Kuznets
curve. Ecological Economics. 1998;25(2):196.
[14] Stern DI. The Rise and Fall of the Envi-
ronmental Kuznets Curve. World Development.
[15] Arrow K, Bolin B, Costanza R, Dasgupta P, Folke C,
Holling CS, et al. Economic growth, carrying capacity,
and the environment. Science. 1995;268(5210):520–
[16] Stern DI, Common MS, Barbier EB. Economic growth
and environmental degradation: The environmental
Kuznets curve and sustainable development. World
Development. 1996;24(7):1151–1160.
[17] Brajer V, Mead RW, Xiao F. Health benefits of tunnel-
ing through the Chinese environmental Kuznets curve
(EKC). Ecological Economics. 2008;66(4):674–686.
[18] Tierney J. Use Energy, Get Rich and Save the Planet.
The New York Times. 20 April 2009.
[19] Costa L, Rybski D, Kropp JP. A Human Develop-
ment Framework for CO
Reductions. PLoS ONE.
[20] Steinberger JK, Roberts JT, Peters GP, Baiocchi G.
Pathways of human development and carbon emis-
sions embodied in trade. Nature Climate Change.
[21] MDG Gap task force 2013. The Global Partnership for
Development: The Challenge we face. New York, NY,
USA: United Nations Department of Economic and
Social Affairs; 19 September 2013.
[22] BRICs is a grouping acronym which represents Brazil,
Russia, India and China.
[23] Hamilton C, Turton H. Determinants of emis-
sions growth in OECD countries. Energy Policy.
[24] Mu
noz P, Steininger KW. Austria’s CO
ity and the carbon content of its international trade.
Ecological Economics. 2010;69(10):2003–2019.
[25] Serrano M, Dietzenbacher E. Responsibility and trade
emission balances: An evaluation of approaches.
Ecological Economics. 2010;69(11):2224–2232.
[26] Rockstrom J, Steffen W, Noone K, Persson A, Chapin
FS, Lambin EF, et al. A safe operating space for hu-
manity. Nature. 2009;461(7263):472–475.
[27] CO
emissions from fuel combustion. Highlights.
Paris, France: International Energy Agency; 2010.
Available from:
[28] The broader dimension of human impact on the cli-
mate system is illustrated by the Kaya relationship,
which relates total emissions levels with population,
GDP per capita, energy intensity of economy, and
carbon intensity of energy. Available from: http://www.
[29] Teplitz CJ. The learning curve deskbook: A reference
guide to theory, calculations, and applications. New
York, NY, USA: Quorum Books; 1991.
[30] Harmon C, Schrattenholzer L. Experience curves of
photovoltaic technology. Laxenburg, Austria: Interna-
tional Institute for Applied Systems Analysis (IIASA);
30 March 2000. Report No. IR-00-014. Available
[31] McDonald A, Schrattenholzer L. Learning Curves
and Technology Assessment. International Jour-
nal of Technology Management. 2002;23(7-8):718–
745. Available from: http://inderscience.metapress.
[32] Ferioli F, Schoots K, van der Zwaan B. Use and lim-
itations of learning curves for energy technology pol-
icy: A component-learning hypothesis. Energy Policy.
[33] Wene CO. Experience Curves for Energy Technol-
ogy Policy. Paris, France: Organization for Economic
Cooperation and Development / International Energy
Agency; 2000.
[34] Goldemberg J, Teixeira Coelho S, Nastari PM, Lucon
O. Ethanol learning curve—the Brazilian experience.
Biomass Bioengeneering. 2004;26(3):301–304.
[35] Sagar A, van der Zwaan B. Technological In-
novation in the Energy Sector: R&D, Deploy-
ment, and Learning-by-Doing. Energy Policy.
[36] GDP information on US dollars using 2000 as
the base year. Available from:
[37] OECD Americas, OECD Asia Oceania, OECD Eu-
rope, Non-OECD Europe and Eurasia, Africa, Asia,
China, Non-OECD Americas, Middle East, European
Union - 27 and the World as an average.
[38] Peters GP, Hertwich EG. CO
Embodied in Inter-
national Trade with Implications for Global Climate
Policy. Environmental Sciences and Technology.
[39] Since exchange rates are important in carbon-
intensity analysis, this paper uses values ajusted for
Purchase Power Parity (PPP) in order to improve
the analytical consistency between economic output
and carbon intensities. Hewitt Y, Li Y. The effect of
trade between China and the UK on national and
global carbon dioxide emissions. Energy Policy. 2008;
[40] Abramowitz M. Catching Up, Forging Ahead, and
Falling behind. The Journal of Economic History.
[41] Lindmark M. An EKC-pattern in historical perspective:
Carbon dioxide emissions, technology, fuel prices and
growth in Sweden 1870-1997. Ecological Economics.
[42] Burke PJ. Climbing the Electricity Ladder Gener-
ates Carbon Kuznets Curve Downturns. The Aus-
tralian Journal of Agricultural and Resource Eco-
nomics. 2012;56(2):260–279.