Gen AI in Business - Global Trends Report 2024.pdf
Energy policy 20120725
1. Alternative Projection of World Energy Consumption
Compared to the 2010 International Energy Outlook
June 2012
15
2. Abstract
A projection of future energy consumption is a vital input to many analyses of
economic, energy, and environmental policies. We provide a benchmark projection which can
be used to evaluate any other projection. Specifically, we base our projection of future energy
consumption on its historical trend, which can be identified by an experience model. We
compare our projection with forecasts by the U.S. Energy Information Administration (EIA)
for eight countries - the U.S., China, India, Brazil, Japan, South Korea, Canada, and Mexico.
We find that the EIA’s projections are lower than ours in the case of China, the U.S., India,
Japan, and Mexico. This indicates that for these five countries, the EIA uses assumptions
which cannot be rationalized by historical data.
Keywords: Energy Consumption; Experience Model
2
3. 1. Introduction
A projection of future energy consumption is a vital input to many analyses of
economic, energy, and environmental policies (Craig, Gadgil and Koomey(2002);
Bhattacharyya and Timilsina(2009)). For example, the decision on future energy investment
requires an outlook on future energy consumption. Thus, it is very important to forecast
future energy consumption as accurately as possible.
In this paper, we provide a benchmark projection which can be used to evaluate any
other projection. Without any information on the future industrial structure and level of
energy efficiency in each industry for any given country, we may start from the assumption
that the future energy consumption of that country will follow the historical trend observed in
the past. In such a case, we have to examine whether there is a structural break in the past so
that only historical data after the structural break should be used to forecast future energy
consumption. For this purpose, we use an experience curve model which has been applied to
energy-supply and energy-demand technologies to project future energy consumption based
on past trends.
To show how to use our benchmark projection to evaluate any other forecast, we
compare our prediction with that of the EIA for eight countries – namely, the U.S., China,
India, Brazil, Japan, South Korea, Canada, and Mexico. The EIA forecasts future energy
consumption annually and recently published its International Energy Outlook(IEO), which
provided outlooks on energy consumption through 2035 (U.S. Energy Information
Administration(2010)).1
The rest of the paper is organized as follows. In Section 2, we provide a brief overview
of the IEO 2010 report and review literature on the experience curve model. In Section 3, we
1
We use the EIA’s projection just because it is one of the most widely used projections on future energy
consumption. Alternatively, we may use the projection on future energy consumption by the International
Energy Agency (IEA).
3
4. explain the data and methodology we use. In Section 4, we provide our own forecasts on the
energy intensity and consumption for our sample of eight countries and compare our own
forecasts with the IEO’s predictions. Lastly, in Section 5, we discuss our findings and provide
conclusions.
2. Background Information
2.1. Overview of the IEO 2010 Report
The IEO 2010 report provides forecasts of primary energy consumption for the world
and sixteen regions or countries for the years 2015, 2020, 2025, 2030 and 2035. The sixteen
regions or countries include seven OECD regions or countries (U.S., Canada, Mexico, OECD
Europe, Japan, Australia/New Zealand, and South Korea) and nine non-OECD regions or
countries (Russia, other non-OECD nations in Europe and Eurasia, China, India, other non-
OECD states in Asia, Middle East, Africa, Brazil, and other countries in Central and South
America).2
The forecasts are made by the EIA’s World Energy Projection System Plus (WEPS+)
system.3 The WEPS+ system consists of a Macroeconomic Model, Demand Models
(Residential, Commercial, Industrial, and Transportation Models), Supply Models
(Petroleum, Natural Gas, Coal, and Refinery Models), a Main Model, Transformation Models
(Electricity and District Heat Models), and a Greenhouse Gases Model. Figure 1 shows the
sequential procedure of the WEPS+ model. The WEPS+ model is viewed as one of the most
comprehensive and detailed models that can generate a long-term projection of world energy
consumption.
In the WEPS+ model, the forecast of energy consumption is primarily based on
2
U.S. Energy Information Administration (2010)
3
Ibid
4
5. projections of the two key determinants of energy consumption: (i) energy intensity, which is
defined as energy consumed per dollar of GDP (Gross Domestic Product), and (ii) GDP. The
energy consumption for a country is forecasted as the multiplication of the forecasts of its
energy intensity and GDP. The U.S. EIA’s 2010 forecasts on world energy consumption are
summarized in Table 1.
Figure 1. The World Energy Projection System Plus (WEPS+) Model Sequence
Start
Preprocessor
Main
N ot Converged
Converg ed
Greenhouse
Macroeconomic
Gases
Postprocessor
(Reports)
Demand Models Finish Supply Models
Refinery
Residential (Part 2)
Coal
Commercial
Natural Gas
Industrial
Petroleum
Transportation
Refinery
(Part 1)
Transformation Models
Electricity District
Generation Heating
Source: U.S. Energy Information Administration (2011)
Table 1. Summary of the U.S. Energy Information Administration’s 2010 Forecasts on World
5
6. Energy Consumption
Forecast on Annual Growth Rate Forecast
2007
(from 2007 to 2035) for 2035
GDP (US dollar) 3.2% $63.1 Trillion $153.7 Trillion
Energy Intensity
-1.7% 7,800 4,800
(Btu4 per dollar)
Energy Consumption
1.4% 495.2 738.7
(Quadrillion Btu)
Source: U.S. Energy Information Administration (2010)
2.2. A Brief History of the Experience Curve Model: Classical vs. Kinked Experience
Models
Beginning with a study of the man-hour required for manufacturing a Boeing aircraft by
Wright (1936), an experience curve model has been widely applied to various industrial
sectors (Day (1977); Day and Montgomery (1983); Dutton and Thomas (1984); Liberman
(1984); Stern and Deimler (2006)). Recently, the model has been applied to new technology
areas such as alternative energy, climate control and health care (Kahouli-Brahmi (2008);
Chambers and Johnston (2000); Ethan, Clara, and Chassin (2002); Grantcharov, et al. (2003);
Hopper, Jamison, and Lewis (2007); Horowitz and Salzhauer (2006); Nemet (2006); Weiss,
et al. (2010 A, 2010 B); Yeh et al. (2005)). In a recent review article on the application of the
experience curve, Weiss, et al. (2010 B) have identified 124 cases of applications in the
manufacturing industry as well as 132 and 75 cases of specific applications to energy-supply
and energy-demand technologies, respectively.
In an experience curve model, a relationship between (i) a performance measure such as
unit price, unit cost, fatality rate, or other physical efficiency metric declines and (ii)
cumulative product volume or experience is examined. In a classical experience model, the
relationship between the two variables is assumed to be linear when both variables take a
4
Btu is the acronym of British thermal unit. British thermal unit is a unit of energy equal to about 1,055 joules
(Source: http://en.wikipedia.org/wiki/British_thermal_unit).
6
7. logarithmic form. Thus, in the classical experience model, a given percentage change in the
cumulative volume or experience will result in a proportional improvement of the
performance measure.
Whereas the experience slope is assumed to be constant in the classical experience
curve model, the Boston Consulting Group (1968) observed that the experience slope differs
across stages of a product life cycle. Thus, the group introduced a kinked (piece-wise linear)
experience model where the experience slope may change across stages. In addition, some
energy models have used an experience model where less steep experience slopes are used
for more mature stages (McDonald and Schrattenholzer (2001); Grubler, Nakicenovic, and
Victor (1999)). Recently, Van Sark (2008) has shown that the experience slope become
steeper in the later stages of photovaltic, ethanol production and wind technologies. Chang
and Lee (2010) and Chang, Lee, and Jung (2011) have also found a kinked experience pattern
for road fatalities rates as well as survival rates in organ transplants.
In this paper, we will identify a historical trend in energy intensity explained in Section
2.1 by classical and kinked experience curve models and provide alternative forecasts of
energy consumption based on historical trend.
3. Data and Methodology
3.1. Data
In this paper, we provide an alternative projection of primary energy consumption for 8
countries - China, the U.S., India, Japan, Brazil, Canada, South Korea, and Mexico. For the
alternative projection, we use historical trends in energy intensity identified by classical and
kinked experience curve models. Thus, we collect data on annual energy intensity of primary
7
8. energy consumption for the period from 1980 to 2007 from the EIA.5
The IEO report provides projections for 10 nations which include Russia and
Australia/New Zealand in addition to our sample of eight countries. However, we have not
included Russia because the data on it is only available starting in 1992. Also, we have not
included Australia/New Zealand because we cannot separate the IEO’s forecast for the two
nations by country. In addition, we cannot make a prediction of only energy consumption for
the world because data on the world’s energy intensity is only available beginning in 1991.
3.2. Methodology
As the IEO’s forecasts on the energy consumption, we forecast future energy
consumption by the product of the energy intensity and GDP for each year. For GDP, we use
the same GDP forecasted by the IEO. However, we make our own projection on the energy
intensity through an experience curve model. By multiplying the IEO’s GDP forecast by our
own projected energy intensity, we produce alternate estimates of energy consumption for
each year.
As suggested in International Energy Agency (2000), both internal structural change in
technology as well as external structural change in the market may lead to the occurrence of a
kinked pattern in energy intensity. Thus, in our paper, we use two types of experience models,
classical and kinked. In our experience models, the dependent variable is the energy intensity
in year t and the independent variable is the cumulative volume of energy consumption from
1980 to year t.6 Note that the cumulative energy consumption is computed from 1980 because
5
The EIA only provides data on energy intensity for the eight countries starting in 1980.
6
Alternatively, we may use a time-services analysis of the energy intensity, where the independent variable is
the variable of year instead of cumulative volume of energy consumption. However, the key concept of
experience model is that parties learn from cumulative experiences of how to perform tasks more efficiently.
Thus, we chose the cumulative energy consumption, not the variable of year, as an independent variable.
8
9. the data is only available from 1980.7
Our classical experience equation on the energy intensity is
y(xt) = a*xtb (2)
where t = 1980, 1981, 1982, ∙∙∙∙∙∙∙∙, 2007
xt = cumulative volume of energy consumption from year 1980 through year t
y(xt) = energy intensity in year t
a, b = parameters for equation (2)
In logarithmic form, the classical experience equation is expressed as follows:
log y(xt) = log a + b log xt (2)’
The progressive ratio (PR) for cumulative doubling of energy consumption is computed
by the equation PR = 2m and the learning rate (LR) is defined as LR = 1 – PR.8
The kinked experience equations on the energy intensity are
y(xt) = a1*xtb1 (3)
where t = 1980, 1981, 1982, ∙∙∙∙∙∙∙, k-1
a1, b1 = parameters for equation (3), and
y(xt) = a2*xtb2 (4)
where t = k, k+1, ∙∙∙∙∙∙∙∙, 2007
a2, b2 = parameters for equation (4).
In logarithmic form, the kinked experience equation for the first period would be
log y(xt) = log a1 + b1 log xt (3)’
7
When we start from 1980 due to the limited data availability, the learning rate thus estimated may be
somewhat lower compared to the learning rate derived when a complete set of historical data are available.
Thus, the learning rate derived in this paper should not be regarded as the true measure of technology learning in
energy consumption covering the entire historical time period. We thank an anonymous referee for pointing out
this issue.
8
Van Sark (2008)
9
10. and the kinked experience equation for the second period would be
log y(xt) = log a2 + b2 log xt (4)’.
We can combine the two kinked experience equations in logarithmic form, (3)’ and (4)’,
using a dummy variable which takes the value of one if the year belongs to the second period
and zero otherwise:
log y(xt) = log a1 + (log a2 - log a1)*P + b1 log xt+ (b2 - b1) log xt *P (5)
where P = 0 if t = 1980, 1981, 1982, ∙∙∙∙∙∙∙, k-1,
P = 1 if t = k, k+1, ∙∙∙∙∙∙∙∙, 2007.
In the kinked experience model, k is the year when a kink in the pattern of energy
intensity occurred. We consider all the possible years for the kinked year and compute the R2
or the coefficient of determination, which denotes the goodness of fit of an equation, of the
kinked experience equation (5) for each candidate year. Then, we choose the year with the
largest R2 as the kinked year. Thus, the kinked year may vary by country.
Then, for the equation (5) with the largest R2, we test whether the difference between b1
and b2 is statistically significant or not. If the difference between b1 and b2 is not statistically
significant, we can conclude that the relationship between the energy intensity and the
cumulative energy consumption is not different between the first and second periods. Thus,
the classical experience curve model should be used for this case in order to predict future
energy intensity. However, if the difference between b1 and b2 is statistically significant, we
can conclude that the relationship between the energy intensity and the cumulative energy
consumption is different between the first and second periods. Thus, the kinked experience
curve model should be used for this case. Especially, the relationship between the energy
intensity and the cumulative energy consumption for the second period is used in order to
predict future energy intensity.
10
11. For the prediction of future energy intensity with the experience model, we need to know
the future cumulative energy consumption. In order to estimate the future cumulative energy
consumption up to 2035 for each of our sample countries, we use the actual energy
consumption for 2007 and the IEO(2010)'s projection of the energy consumption for the
years of 2015, 2020, 2025, 2030, and 2035. We assume that the energy consumption for the
period between two adjacent years would grow at the constant geometric rate of growth. In
this way, we can estimate the annual energy consumption for a country up to the year 2035
and add up the annual energy consumption up to a certain year in order to compute the
cumulative energy consumption for the year. In order to compute the standard error and thus
the confidence interval of our forecast on the energy intensity, we follow the procedure
suggested by Wooldridge (2008).
Lastly, for our projection of a country’s energy consumption for the years 2015, 2020,
2025, 2030, and 2035, our forecast of the energy intensity for each year is multiplied by the
IEO's forecast of GDP for the year for the country.9
4. Results
9
The unit of GDPs for the years 2015, 2020, 2025, 2030, and 2035 is the 2005 U.S. dollar. Thus, those GDPs
are comparable to one another across the years.
11
12. 4.1. Classical vs. Kinked Experience Models of Energy Intensity
We have applied both the classical and kinked experience models to our sample of eight
countries (Appendix 1). For the kinked experience model, we have identified 2002, 1997,
1995, 1994, 1998, 1998, 1997, and 1989 as the kinked for China, the U.S., India, Japan,
Brazil, Canada, South Korea, and Mexico, respectively. Then, given the kinked year for each
country, we compute b1 and b2 for each of eight countries (Figure 2) and find that the
difference between b1 and b2 in the equation (5) is significant at the one percent level for the
U.S., India, Brazil, Canada, South Korea, and Mexico. Thus, we conclude that the
relationship between the energy intensity and the cumulative energy consumption for the
second period denoted in equation (4) should be used for the prediction of future energy
intensity for the U.S., India, Brazil, Canada, South Korea, and Mexico. On the other hand, the
difference is not significant at the five percent level for China and Japan. Therefore, the
classical experience model should be used for China and Japan in order to forecast future
energy intensity.
Figure 2. First and Second Slopes of Kinked Experience Model for Eight Countries
12
13. 0.2
0.13
0.11
0.1
0.05 0.04 0.05
0
-0.04
-0.1 -0.07 -0.07
-0.11 -0.12
-0.2
-0.19
-0.26
-0.3 -0.28
-0.32
-0.34
-0.4
-0.40
-0.5
China U.S. India Japan Brazil Canada South Korea Mexico
b1 b2
4.2. Our Projection vs. IEO Projection on Energy Consumption
We project the energy consumption for a country for the years of 2015, 2020, 2025,
2030, and 2035 by the multiplication of our forecast on the energy intensity for each year and
the IEO's forecast on GDP for the year for the country. We also base our projection on the
energy consumption using the 95 percent and 99 percent confidence intervals on the energy
intensity. Lastly, we check whether the EIA’s projection belongs to the 95 percent and 99
percent confidence intervals or not (Appendix 2).
We compare the EIA’s and our projections (Figure 3) and find that our projection on
the energy consumption is significantly higher than the EIA’s projection, at least at the five
percent level, for China, the U.S., India, Japan, and Mexico. For Canada, our projection on
the energy consumption is higher than the EIA’s forecast, but the difference between our
prediction and the EIA’s outlook is not significant at the five percent level. For Brazil, our
13
14. projection on the energy consumption is lower than the EIA’s, and the difference is only
significant, at least at the five percent level, for the years of 2015 and 2020. For South Korea,
our projection of energy consumption is lower than the EIA’s for the years 2015 and 2020,
but the difference is significant at the five percent level only for the 2015. And our energy
consumption projection is higher than the EIA’s for 2025, 2030 and 2035, but the difference
is not significant at the five percent level.
Figure 3. The U.S. Energy Information Administration (2010)’s and Our Projections on
Energy Consumption for Eight Countries (Year of 2035)
250
(Quadrillion Btu)
218.9
200
181.9
150 143.4
114.5
100
51.0
50 37.6
22.2 26.6 24.320.9
18.2 19.2 14.9 15.4 13.516.9
0
China U.S. India Japan Brazil Canada South Korea Mexico
EIA's Projection Our Projection
14
15. 5. Discussion and Conclusion
In the previous section, we show that the IEO’s projections on the energy consumption
significantly differ from ours in the case of China, the U.S., India, Japan, and Mexico. For the
case of Brazil, Canada, and South Korea, the IEO’s projections do not significantly differ
from ours for all the years of 2015, 2020, 2025, 2030, and 2035.
Without any information on the future industrial structure and level of energy efficiency
in each industry for a country, we may start from the assumption that the future energy
consumption for the country will follow the historical trend observed in the past. This is
exactly how we have made our projections on the future energy intensity and consumption:
the projections are based on their historical trend which can be identified by the experience
model. Since our projections are based on historical data without any further assumptions on
the future industrial structure and level of energy efficiency in each industry, we believe that
our projections can provide natural benchmark projections for the evaluation of any other
outlook. If the other forecast’s prediction model differs from ours, the projection should
provide a rationale for why it uses assumptions which cannot be predicted by historical data.
Our results indicate that for China, the U.S., India, Japan, and Mexico, the IEO uses
assumptions about the future industrial structure or the level of energy efficiency for each
industry, which cannot be predicted by historical data. Since a projection of future energy
consumption is a vital input to many analyses of economic, energy, and environmental
policies, it is very important to examine whether such divergence from historical trends can
be rationalized.
Lastly, we acknowledge that forecasting future energy consumption is fraught with
difficulties. There are inevitably unforeseen events at the aggregate level, such as energy
price shocks or economic recession, as well as many structural changes at the disaggregate
15
16. level that can throw off forecasts. In other words, the energy intensity slope can change over
time, as our kinked analysis has shown. It is possible that another “kink” and higher rates of
energy intensity reduction may take place in the future.
In conclusion, it is not obvious that future energy intensity reductions will be the same
as those in the past. Thus, all energy consumption forecasts are subject to a high degree of
uncertainty. These include our own.
16
17. Acknowledgements
The authors are extremely grateful for the detailed and constructive comments from two
anonymous referees. We also appreciate competent editorial help from Jenifer K. Chang.
Finally, we are grateful to the KDI School of Public Policy and Management for providing
financial support.
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20. U.S. Energy Information Agency, 2010, International Energy Outlook 2010.
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20
21. Appendix 1. Classical and Kinked Experience Equations of Energy Intensity for Eight Countries
Classical Experience Equation (2)’ Kinked Experience Equation (4)’ and (5)
Kinked Model
Country Adjusted Adjusted
log a b PR(=2 )b
Year log a1 b1 log a2 b2 b2 - b1 PR2(=2b2) Selection
R2 R2
-0.34 -0.34 0.13 0.47
China 4.75 0.88 0.79 2002 4.74 1.61 0.88 1.09 Classical
(0.02)** (0.03)** (0.20) (0.38)
-0.15 -0.11 -0.40 -0.30
U.S. 3.38 0.88 0.90 1997 3.08 5.20 0.99 0.76 Kinked
(0.01)** (0.01)** (0.02)** (0.02)**
-0.01 0.05 -0.28 -0.34
India 2.03 -0.04 1.00 1995 1.85 3.48 0.89 0.82 Kinked
(0.01) (0.01)** (0.02)** (0.02)**
-0.03 -0.07 -0.07 0.00
Japan 1.97 0.42 0.98 1994 2.12 2.19 0.78 0.95 Classical
(0.01)** (0.01)** (0.03)* (0.03)
0.10 0.11 -0.26 -0.36
Brazil 1.29 0.74 1.07 1998 1.28 3.08 0.84 0.84 Kinked
(0.01)** (0.01)** (0.08)* (0.08)**
-0.10 -0.04 -0.32 -0.27
Canada 3.10 0.68 0.94 1998 2.88 4.27 0.97 0.80 Kinked
(0.01)** (0.01)** (0.05)** (0.04)**
South 0.04 0.04 -0.19 -0.23
2.07 0.29 1.02 1997 2.07 3.13 0.50 0.88 Kinked
Korea (0.01)** (0.01)* (0.02)** (0.07)**
-0.01 0.05 -0.12 -0.17
Mexico 1.78 0.03 0.99 1989 1.61 2.27 0.82 0.92 Kinked
(0.01) (0.01)** (0.01)** (0.02)**
Note: (1) PR is the progressive rate for the classical experience equation and PR2 is the progressive rate for the second period of the kinked experience equation.
(2) The numbers in the parentheses are the standard errors of the slope coefficients.
(3) ** and * denote the statistical significance of 1% and 5%, respectively.
15
22. Appendix 2. Comparison Between the U.S. Energy Information Administration (2010)’s and Our Projections on Energy Consumption
U.S. Energy Information Administration Our Projection
(2010)’s Projection
Year GDP Energy Energy Intensity Energy Difference in
(Billion 2005 Consumption (Thousand Btu per Consumption 95% Confidence 99% Confidence Energy
dollars) (Quadrillion Btu) 2005 dollar of (Quadrillion Btu) Interval Interval Consumption (%)
GDP)
(A) (B) (C) (D=A*C) (D-B)/B
China
2015 12,732 101.4 9.3 118.5 106.7~131.6 102.8~136.6 16.9 **
2020 17,353 121.4 8.5 146.7 130.5~165.1 125.2~172.0 20.8 **
2025 22,446 142.4 7.8 174.1 153.0~198.1 146.2~207.3 22.3 **
2030 27,596 162.7 7.2 197.9 172.1~227.6 163.8~239.1 21.6 **
2035 32,755 181.9 6.7 218.9 188.4~254.2 178.8~267.9 20.3 *
U.S.
2015 15,022 101.6 7.0 104.5 102.3~106.7 101.4~107.7 2.9 *
2020 17,427 105.0 6.6 114.3 111.3~117.3 110.1~118.7 8.9 **
2025 19,851 108.3 6.2 123.5 119.7~127.4 118.1~129.1 14.0 **
2030 22,475 111.2 5.9 133.2 128.6~138.1 126.6~140.2 19.8 **
2035 25,278 114.5 5.7 143.4 137.8~149.2 135.4~151.8 25.2 **
India
2015 4,847 24.3 5.7 27.6 26.1~29.0 25.6~29.7 13.6 **
2020 6,342 28.2 5.3 33.5 31.4~35.8 30.6~36.8 18.8 **
2025 7,833 31.1 5.0 38.9 35.9~42.0 34.8~43.4 25.1 **
2030 9,529 34.1 4.7 44.7 39.7~47.4 38.3~49.2 31.1 **
2035 11,454 37.6 4.5 51.0 46.2~56.3 44.3~58.6 35.6 **
Japan
2015 4,258 21.1 5.8 24.6 24.1~25.2 23.9~25.5 16.6 **
2020 4,437 21.9 5.8 25.6 24.9~26.2 24.7~26.5 16.9 **
2025 4,520 22.1 5.7 25.9 25.2~26.7 25.0~26.9 17.2 **
2030 4,601 22.1 5.7 26.3 25.6~27.1 25.3~27.3 19.0 **
2035 4,665 22.2 5.7 26.6 25.8~27.4 25.5~27.7 19.8 **
Note: ** and * denote the statistical significance of 1% and 5%, respectively.
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