Title: A critical review of the IEA’s oil demand forecast for China. Authors: Willem P Nel Christopher J Cooper Institute for Energy Studies, University of Johannesburg Abstract: China has a rapidly growing economy with a rapidly increasing demand for oil. The International Energy Agency investigated possible future oil demand scenarios for China in the 2006 World Energy Outlook. The debate on whether oil supplies will be constrained in the near future, because of limited new discoveries, raises the concern that the oil industry may not be able to produce sufficient oil to meet this demand. This paper examines the historical relationship between economic growth and oil consumption in a number of countries. Logistic curve characteristics are observed in the relationship between per capita economic activity and oil consumption. This research has determined that the minimum statistical (lowerbound) annual oil consumption for developed countries is 11 barrels per capita. Despite the increase reported in total energy efficiency, no developed country has been able to reduce oil consumption below this lower limit. Indeed, the IEA projections to 2030 for the OECD countries show no reduction in oil demand on a per capita basis. If this lower limit is applied to China, it is clear that the IEA projections for China are under-estimating the growth in demand for oil.

This research has determined that this under-estimation could be as high as 10 million barrels per day by 2025. If proponents of Peak Oil such as Laherrère, Campbell and Deffeyes are correct about the predicted peak in oil production before 2020 then the implications of this reassessment of China’s oil demand will have profound implications for mankind. Keywords: China, Oil, Forecast

1. Introduction Concerns regarding the depletion of oil resources have sparked fierce debate amongst scientists, economists, politicians and business executives in recent years. The oil depletion debate has a history that started with a paper presented by M King Hubbert at an American Petroleum Institute meeting in 1956 (Hubbert, 1956). Hubbert used graphical methods to construct a logistic curve for oil production in the lower 48 US states. Based on his most optimistic estimation of conventional oil reserves, Hubbert’s analysis suggested a peak in oil production to occur in the early 1970s. Hubbert’s assessment turned out to be valid when oil production in the lower 48 states did indeed peak in 1970 (BP, 2006). Many attempts have since been made to analyse peaking of global oil production. The phenomenon of a global peak in the production of conventional oil is termed “Peak Oil”1. Hubbert’s 1977 analysis, based on estimated ultimately recoverable resources, showed that Peak Oil would occur in 1996 (Bentley, 2002).

Given global economics, technological advances and geopolitical dynamics, it is not surprising that Hubbert’s assessment of Peak Oil was inaccurate. The revaluation of OPEC oil reserves in the mid-1980s was part of the geopolitical dynamics. Based on updated information on reserves, demand, technology and so on, some Peak Oil proponents are predicting a production peak as early as 2008 (Campbell, 2005:6). The global concern over Peak Oil is evident in the attention it receives from government institutions (Wood et.al., 2004; Hall, 2005; Hirsch,

1

Peak Oil does not signify the end of oil production, but a peak in production at some point when a significant portion of the recoverable reserves has been produced as is suggested by standard logistics curve approaches. Hubbert and others suggest that a peak in production occurs when approximately half the producible resources have been produced.

2005; Minestère De L’Économie Des Finances Et De L’Industrie, 2005; Siewert, 2006), financial institutions (Auer, 2004) and Oil Executives (Ghanem, 2006). According to Ghanem (2006), despite disagreements regarding the exact date, it is generally agreed that Peak Oil will occur within the next decade. The Peak Oil debate is important in the context of global economic growth and human development. Some economists suggest that energy consumption became decoupled from economic growth, as measured by GDP, towards the end of the 20th century. Cleveland et. al. (2000) consider energy quality to be a contributing factor in the decoupling of energy consumption and GDP growth. Most of the apparent decoupling effect can be explained in terms of the differences in energy quality in the aggregation of energy. Cleveland et al (2000: 309) also reported that energy consumption causes GDP growth in a multivariable Granger causality test if energy quality is taken into consideration. The term ‘energy quality’ refers to the specific attributes of an energy source such as energy density, cleanliness, capacity to do useful work, suitability for storage and conversion amongst others. Kaufmann (1994) calculated the marginal products of various energy sources and demonstrated that the results compared positively with several other studies demonstrating that petroleum is the highest quality fossil fuel, producing up to 3.45 times more GDP per thermal equivalent unit when compared to coal. The causal relationship between energy consumption and GDP growth is further demonstrated by considering useful work as a factor of production (Ayres and Warr, 2005; Ayres et. al. 2007). Using this approach, Ayres et. al. (2007: 638) reproduced GDP growth in the USA between 1900 and 2000 to a high degree of accuracy, but with a notable residual from 1980 onwards. According to Ayres and Warr (2005: 198), the “substitution of information for energy” may be a

possible explanation for the post-1980 residuals. Other researchers (Brown et. al., 2000: 3.16; Chen, 1994) have also explored the consequences of a causal relationship between developments in information technology and GDP growth. Energy projection models like the NEMS used by the Energy Information Administration (EIA, 2005: 44) and the WEM used by the International Energy Agency (IEA, 2004: 532 - 533) use exogenous input assumptions for the price and availability of energy commodities. These models, however, neglect the role of energy as a factor of production, as has been proposed by the studies referred to above (Cleveland et. al., 2000; Ayres and Warr, 2005; Ayres et. al. 2007). The causal relationship between energy consumption and GDP growth is evident in the 2006 IEA report in which China and India have the greatest GDP growth and energy demand growth (IEA, 2006a:59,86). Despite increasing concerns over oil supply by Peak Oil study groups and forums, the International Energy Agency (IEA) projects balanced oil markets to 2030 (IEA, 2006a). Concerns raised by the IEA over investment in the oil and gas industry (IEA, 2006a:315; IEA, 2007:1) suggest that the oil supply outlook in the reference scenario should be considered optimistic. Assuming that the 2006 IEA oil supply outlook is plausible, an enquiry into oil market balances needs to focus on the question of oil demand. This paper provides an analytical review of the IEA’s projected demand forecast for China against historical trends.

2. Global Trends in Oil Consumption The transport sector’s share of total oil consumption has increased steadily to 57.7% in 2004 (IEA, 2006b:33). Oil is the fuel of choice in the transport industry because of its high energy density and transportability. Oil is also important for its various non-energy uses such as chemical feedstock, asphalt, lubricants and solvents. The non-energy use of oil contributed to almost 17% of the global final oil demand in 2004 (IEA, 2006b:33). The relationship between oil consumption and economic growth is essential to an understanding of future trends in demand if existing patterns continue into the future. Logistic curves have been widely applied to model per capita energy consumption patterns (Ausubel, 1988; Seifritz and Hodgkin, 1991; Mohamed and Bodger, 2005) as well as to vehicle ownership patterns (Dargay and Gately, 1999). A Gompertz relationship, expressed in equation 1, is used in this paper to model logistic relationships.

β(GDP/Capita)

Consumption/Capita = γeαe

(1)

Global oil consumption trends were evaluated for 2004 by assessing the distribution of country statistics of oil consumption per capita in comparison to GDP per capita. Numerical values and sources of information are listed in Table 1 while Figure 1 provides a graphical presentation of this data. The graph in Figure 1 shows considerable scatter in the data. An assessment of outlying values identified geopolitical and socioeconomic factors that resulted in lower or higher oil intensity as measured by consumption in barrels per capita. Examples of these factors include:

•

Oil exporting countries have a tendency to exhibit high oil consumption per capita.

•

Total Primary Energy Supply (TPES) in island nations with a strong tourism focus and small populations is generally strongly dominated by oil. Energy quality factors such as transportability for medium quantity import purposes and relative cleanliness could be important factors in making oil more attractive than other fuel sources for these countries.

•

Some Eastern European (former USSR) countries have a history of poor energy efficiency, but were rapidly restructured from 1991 onwards to align with European Union standards (Ürge-Vorsatz et. al., 2006). However, some of the positive effects of the communist era which have been retained have resulted in the reduced dependency on oil in some of these countries.

•

Most politically or economically unstable counties have low levels of per capita oil consumption.

•

Some countries have known factors that lead to low crude oil intensity. One of these is South Africa, where a large percentage of liquid fuels are manufactured synthetically from coal and gas.

The data in Figure 1 exhibits logistic behaviour if the outlying points (based on the criteria above) are excluded. A numerical iteration procedure was used to do a least squares fit of a Gompertz curve on the data points considered in the assessment (solid diamonds in Figure 1). The labels used in Figure 1 are listed in Table 1 for the countries considered. Parameters for the least squares Gompertz curve are listed in Table 2.

Oil Consumption per Capita (Barrels)

35

USA, Canada Netherlands, Belgium 30 OPEC Island Nations Oil Dominated TPES 25 Political Instability Known Factors 20 Eastern Europe 15

10 10.759 5

0 0

5

10

15

20

25

30

35

GDP per Capita (2000$ PPP x 1000) Least Squares Gompertz

Figure 1: Oil consumption per capita vs. GDP per capita for 2004 based on PPP in 2000$ for various countries

For the purposes of discussion, the plateau region of the logistic curve in Figure 1 is referred to as the saturation zone. The UK, which has an oil intensity of approximately 11 barrels per capita, has the lowest oil consumption in the saturation zone.

40

Table 1: Country data for 2004 used in Figure 1. Country

Label Population GDP/Cap Oil/Cap [Million] [2000$ PPP] [barrels/y] [x 1000]

Country

Label Population GDP/Cap Oil/Cap [Million] [2000$ PPP] [barrels/y] [x 1000]

Albania

3.135

5.405

2.934

Kuwait*

2.754

16.038

35.233

Algeria*

32.364

6.833

2.711

Kyrgyzstan

5.103

1.978

0.715

Angola

14.973

2.463

1.170

Laos

5.792

1.969

0.189

0.081

11.100

17.123

Latvia

2.319

11.396

7.398

Antigua and Barbuda Argentina*

37.431

12.940

3.846

Lebanon

3.608

6.601

10.825

Armenia

3.244

3.943

4.613

Lesotho

2.322

2.083

0.220

Australia*

20.143

29.859

15.516

Liberia

3.241

0.989

0.394

Austria*

8.176

32.209

12.719

Libya

5.671

11.090

15.254

Azerbaijan*

8.347

3.810

4.038

3.446

12.856

5.772

Bahamas, The

0.321

19.171

30.701

Madagascar

17.362

0.858

0.305

Bahrain

0.720

20.037

13.688

Malawi

12.608

0.581

0.159

149.208

1.890

0.195

Malaysia*

25.467

10.552

7.060

Barbados

0.273

16.825

14.707

Belarus*

9.800

6.988

5.705

Bangladesh*

Belgium & Luxembourg*

Lithuania*

Maldives Mali

0.325

7.637

8.086

12.198

1.082

0.129 17.691

10.402

30.142

27.543

Malta

0.392

19.100

Belize

0.273

8.055

4.011

Mauritania

2.758

2.401

3.203

Benin

7.235

1.135

0.706

Mauritius

1.227

12.259

6.396

Bhutan

0.750

3.629

0.565

Mexico*

103.785

9.788

6.674

Bolivia

9.227

2.614

1.859

Moldova

3.607

2.184

1.417

Bosnia and Herzegovina

3.889

5.631

2.159

Mongolia

2.557

2.175

1.602

Botswana

1.590

10.674

2.640

Morocco

29.839

4.395

2.079

19.129

1.279

0.219

2.008

7.099

3.272

Brazil* Bulgaria* Burkina Faso Burundi

186.771

8.917

4.104

Mozambique

7.723

9.223

5.131

Namibia

12.805

1.240

0.234

Nepal

23.589

1.761

0.179

7.344

0.701

0.154

Netherlands*

16.276

29.957

22.497

Cambodia

14.099

2.256

0.097

New Zealand*

4.062

23.826

13.485

Cameroon

17.356

2.361

0.505

Nicaragua

5.774

3.636

1.593

Canada*

31.932

32.798

25.699

12.182

0.842

0.165

Cape Verde

0.467

5.996

0.899

Nigeria

Central African Republic

4.023

1.098

0.220

Norway*

Niger

Chad

8.815

1.434

0.060

Oman

Chile*

16.382

12.737

5.303

Pakistan*

China*

142.655

1.144

0.742

4.597

40.177

16.673

2.379

16.162

9.206

149.650

2.456

0.794

1307.560

7.198

1.951

Panama

3.169

6.986

9.099

Colombia*

45.325

6.978

1.796

Paraguay

5.702

4.707

1.728

Comoros

0.588

1.829

0.447

Peru*

27.547

5.611

2.009

Congo, Republic of the

3.256

1.258

0.673

Philippines*

82.663

4.674

1.485

Costa Rica

4.248

10.040

3.781

Poland*

38.182

12.293

4.400

10.509

18.782

11.167

0.756

30.566

40.720

21.790

8.132

3.848

Côte d'Ivoire

17.872

1.484

0.470

Portugal*

Croatia

4.439

11.626

7.647

Qatar*

Cyprus

0.826

19.897

23.420

10.207

17.220

7.255

Russia*

142.700

11.041

7.043 0.229

Czech Republic*

Romania*

Denmark*

5.427

36.074

11.501

Rwanda

8.590

1.329

Djibouti

0.716

2.168

6.066

Samoa

0.181

6.087

2.017

Dominica

0.072

6.184

4.563

Saudi Arabia*

22.674

14.281

29.060 0.994

Dominican Republic

8.407

7.221

5.514

Senegal

11.386

1.661

Ecuador*

13.027

4.158

4.023

Serbia

8.332

4.543

3.724

Egypt*

69.330

4.098

2.985

Seychelles

0.081

12.104

25.235

El Salvador

6.757

4.398

2.323

Sierra Leone

5.312

0.840

0.454

Equatorial Guinea

1.106

16.536

0.403

Slovakia*

5.405

14.904

4.578

Eritrea

4.522

0.850

0.428

Slovenia

1.998

20.537

9.682

Estonia

1.351

14.926

16.210

Solomon Islands

0.468

1.819

0.998

Ethiopia

71.037

0.769

0.149

South Africa*

46.461

11.476

4.110

0.847

6.200

4.309

Spain*

41.127

25.014

14.137

Fiji

Table 1 (Concluded): Country data used in Figure 1 for 2004.

Country

Label Population GDP/Cap Oil/Cap [Million] [2000$ PPP] [barrels/y] [x 1000]

Finland* France*

Country

Label Population GDP/Cap Oil/Cap [Million] [2000$ PPP] [barrels/y] [x 1000]

5.219 62.702

29.952 29.187

15.659 11.417

Sri Lanka Sudan

19.773 32.700

4.70 2.340

1.514 0.737

Gabon

1.330

6.902

3.568

Suriname

0.503

5.363

8.127

Gambia, The

1.471

1.914

0.496

Swaziland

1.087

5.029

1.175

Georgia

4.315

3.258

1.100

Sweden*

9.003

28.551

12.921

Germany*

82.501

29.581

11.652

7.261

31.583

12.946

Ghana

20.368

2.498

0.788

Switzerland* Syrian Arab Republic

19.149

3.976

4.384

Greece*

11.098

22.392

14.114

Taiwan*

22.689

26.241

14.161

Grenada Guatemala

0.104

8.007

6.317

Tajikistan

6.305

1.261

1.621

14.076

4.266

1.906

Tanzania

36.700

0.674

0.229

Guinea

9.555

2.116

0.369

Thailand*

65.082

7.890

5.119

Guinea-Bissau

1.540

0.723

0.593

Togo

5.423

1.635

0.942

Guyana

0.754

4.634

5.422

0.102

7.569

3.221

Haiti

8.181

1.752

0.518

Tonga Trinidad and Tobago

1.287

13.087

9.643

Honduras

7.048

2.887

1.916

Tunisia

10.006

7.770

3.247

Hong Kong SAR*

6.971

33.479

14.923

Turkey*

71.605

7.950

3.312

Hungary*

10.117

15.838

4.920

4.945

7.321

7.629

Iceland*

0.294

33.072

24.270

Uganda

27.821

1.433

0.143

1081.500

3.058

0.868

46.581

7.816

2.230

219.195

4.459

1.945

Ukraine* United Arab Emirates*

4.348

27.799

29.840

68.357

7.488

8.408

United Kingdom*

59.834

29.303

10.759

293.816

39.377

25.752

3.399

9.285

4.091

25.904

1.833

2.179

India* Indonesia* Iran, Islamic Republic of*

Turkmenistan*

Ireland

4.044

38.547

16.463

United States*

Israel

6.894

24.357

13.210

Uruguay

Italy*

57.888

28.106

11.812

Uzbekistan*

Jamaica

2.648

4.263

9.935

127.737

30.615

15.316

Kazakhstan*

15.090

8.318

5.038

Kenya

32.808

1.372

0.612

Japan*

Kiribati

0.092

2.339

0.793

Korea*

48.082

19.430

17.329

716.020

32.179

18.613

G7*

Sourced Data:

Vanuatu

0.212

3.249

1.067

Venezuela*

25.910

5.709

7.401

Vietnam Yemen, Republic of

82.000

2.784

1.024

24.912

0.736

1.245

Zambia

11.323

0.885

0.419

Zimbabwe

11.732

2.737

0.700

Population and GDP data from IMF (2007)

Oil consumption data from the CIA World Factbook (2007) except countries with asterisk.

* Oil consumption data from BP statistical review (BP, 2006). The BP statistical review data for 2004 was used where available because it ensures consistency with time series trends available from the same source.

G7 countries are Canada, France, Germany, Italy, Japan, USA and UK

Table 2: Least square fit parameters for equation 1.

γ

α

β

14.50547

-3.59622

-0.14812

The BP Statistical Review of World Energy (BP, 2006) lists oil consumption data for a number of countries from 1965 to 2005. Time series trends for typical countries with periods of persistent growth were superimposed on the 2004 country statistics shown in Figure 1, as indicated in Figure 2. Each point on the time series curves represents one year generally increasing in time towards the right-hand side of the curve. OPEC and the oil intensive island nations as well as countries with political or economic instability were omitted. The two lower Gompertz curves in Figure 2 indicated with dashed lines are graphically adjusted lower-bound estimations for data excluding and including the low oil intensity Eastern European countries. Both of these curves are based on a saturation level of annual oil consumption of 11 barrels per capita (parameter γ in equation 1). The value of 11 is derived from the lower bound value for the UK in the saturation zone as shown in Figure 1.

20

18

16

Oil/Capita [Barrels per year]

14

12

10

8

6

4

2

0 0

5

10

15

20

25

30

GDP per Capita (2000$ PPP x 1000) Country Statistics (2004)

China

Hong Kong

G7 Countries

Portugal

Taiwan

Thailand

Chile

Indonesia

Korea

Least Squares Gompertz

Eastern Europe

Figure 2: Oil consumption per capita vs. GDP per capita based on PPP in 2000$ for various countries and time series trends for selected countries

Major advances in energy efficiency improvements, resulting in substantial decreases in energy intensity (WEC, 2001:10), have not resulted in decreases in oil consumption per capita in the developed world. Figure 2 shows that the saturation level of approximately 18.5 barrels per capita has been maintained over the last decade in the G7 nations.

35

This analyses of countries’ oil-use and of the relationship between oil consumption and economic activity provides the background for an analysis of possible future oil demand patterns for China. 3. Oil Demand Projections for China China’s rapidly expanding transport sector is responsible for most of the country’s growth in oil demand (Wang, 2004: 23). The automobile industry is one of the development targets for the Central Commission of the Communist Party of China (Wang, 2004: 7). In addition, Wang (2004) lists several factors that cause high expectations with regard to China’s transport sector and related oil demand growth, including: •

Government development goals for infrastructure include expressway expansions of 35 000 km/year (Wang, 2004: 8).

•

GDP per capita has reached the “threshold value” of $1000 for the uptake of private vehicles in many Chinese cities with a concomitant rapid expansion of car sales. Car sales in the first quarter of 2003 increased by more than 100% over the same period in 2002 (Wang, 2004: 27). The Economist (2007) reports that Beijing experienced new vehicle sales of 1000 units per day in 2005.

•

The energy intensity of highway transport increased by 37% during the 1990s resulting in an overall increase of 6% in energy intensity for the transport sector (Wang, 2004: 34).

Several study groups have made projections for the growth of China’s oil demand. The World Energy Outlook 2006, published by the International Energy Agency (IEA, 2006) contains recent projections of China’s population, oil

demand and GDP growth. These IEA projections for China will be evaluated against the global oil consumption trends shown in Figures 1 and 2. China’s population is expected to grow at 0.6% per annum to 2030 (IEA, 2006a:56) while GDP growth is predicted to average 7.3% from 2004 to 2015 and 4.3% from 2015 to 2030 (IEA, 2006a:59). China’s rapidly growing economy will raise GDP per capita. The IEA predicts that China’s oil demand would increase from 6.5 million barrels per day (Mbl/day) in 2004 to 8.4 Mbl/day in 2010, 10 Mbl/day in 2015 and 15 Mbl/day in 2030. The World Energy Outlook 2006 also includes projections of GDP growth, population growth and oil consumption for OECD countries (IEA, 2006a). Tables 3 and 4 give a list of assumptions with regard to China and the OECD countries as used by the IEA for the reference case scenario. Table 3: Population and real GDP growth assumptions Period

OECD

China

Population Growth

2004 - 2015

0.5

0.6

Population Growth

2015 - 2030

0.3

0.3

Real GDP Growth

2004 - 2015

2.6

7.3

Real GDP Growth

2015 - 2030

1.9

4.3

Table 4: Oil consumption in billion barrels per year (for the reference case) Year 2004 2010 2015 2030

OECD 17.34 18.18 19.13 20.11

China 2.37 3.07 3.65 5.58

Time series projections were calculated using the IEA (2006a) assumptions with results shown in Figure 3, where each point on the graph represents one year. When the OECD curve in Figure 3 is compared with trends in Figure 2, it is evident that the projected OECD trend follows established logistic behaviour i.e. no fundamental breakthroughs are foreseen that would allow for a reduction in per capita consumption of oil while increasing GDP per capita. Since the OECD consumption is already in the saturation zone (see Figure 1), the OECD projection represents a continuation of historical patterns into the future.

18

Oil Consumption per Capita (Barrels)

16 14 12

2004

10 8 6 4 2 0 0

5

10

15

20

25

30

35

40

GDP per Capita (2000$ PPP) China Historical

China Projection

OECD Projection

Least Squares Gompertz

Figure 3: IEA (2006a) projections of oil consumption and GDP growth (see Figure 2 for the basis of the Least Squares Gompertz curve used here)

The growth in demand for China was evaluated by superimposing the projections to 2030 on the graphs in Figure 2. The result is shown in Figure 4. A Lower-Bound Gompertz curve was constructed by numerical manipulation of the parameters in equation 1 to yield the curve shown in Figures 2 and 4. A

saturation level (parameter γ) of 11 barrels per capita was assumed as explained earlier. The Lower-Bound Gompertz curve represents an historical lower boundary: in other words, no country has ever consumed oil at a rate below this curve for a given GDP per capita except in isolated cases such as Equatorial Guinea, where special circumstances prevail. The parameters for the Lower-Bound Gompertz curve are listed in Table 5. Table 5: Parameters for curves in Figure 3.

γ

α

β

Lower-Bound Gompertz

11.00000

-7.45093

-0.14000

Least Squares China

11.00000

-2.96979

-0.07801

Description

An alternative to the Lower-Bound Gompertz curve is a least squares fit through China’s historical data with a lower bound saturation level (11 barrels per capita was used as before). Parameters for the Least Squares China curve are listed in Table 5. This curve approaches the saturation level far more gradually when compared to historical examples of nations that experienced rapid growth as is shown by the time-series examples in Figure 2. Historical data and projections to 2030 for China are listed in Table 6.

20 18

Oil/Capita [Barrels per year]

16 14 12 10 8 6 4 2 0 0

5

10

15

20

25

30

35

GDP per Capita (2000$ PPP x 1000) Country Statistics (2005)

China Projections to 2030 (IEA)

China Historical

Eastern Europe

Korea

Thailand

Least Squares Gompertz

Lower Bound Gompertz

Least Squares China

G7 Historical

OECD Projections to 2030 (IEA)

Figure 4: Graphical presentation projected demand growth for China compared to historical data for other countries and candidate logistics curves.

Figure 4 shows that the IEA projection for oil demand in China diverges significantly from the historical trends for other countries and is unlikely to occur in a scenario where the fundamental market conditions are static (as the projections made for the OECD countries indicate). A notable decrease in oil intensity for OECD countries would have indicated changing market conditions and energy efficiency improvements, possibly as a result of scarcity or supply constraints. However, it is not evident from the projections for OECD shown in Figure 4 that major technological breakthroughs or socio-economic restructuring are foreseen that would fundamentally change oil consumption patterns for this group of countries.

40

China has many unique attributes as a country including: •

The largest population in the world.

•

Communist political ideology.

•

A changing economic regime with centrally planned components.

•

Foreign exchange controls.

•

The largest current account surplus in the world.

Some of these factors may influence China’s oil consumption patterns in relation to its GDP growth. Although the extent of such influences are not clear, a least squares Gompertz fit through China’s historical data (labelled Least Squares China curve in Figure 4) is suggested as a plausible deviation from lower bound trends for the purposes of further assessment. However, it is uncertain whether the closely coupled relationship between GDP growth and oil consumption would allow for such a large deviation from historical trends.

10

8 Oil/Capita [Barrels per year]

14 Mb/day 11 Mb/day

6 8 Mb/day 5 Mb/day

4 2 Mb/day

2

2010

2015

2020

2025

2030

0 0

5

10

15

20

25

GDP per Capita (2000$ PPP x 1000) Country Statistics (2005)

China Projections to 2030 (IEA)

Least Squares Gompertz

Least Squares China

China Historical

Figure 5: Possible underestimation of China’s oil demand projection. (Mb/day = million barrels per day)

Figure 5 demonstrates the margins by which China’s oil demand would be underestimated using the Least Squares China curve. Numerical values for historical data as well as projections for China are listed in Table 6. The difference in oil consumption between the projected data (IEA, 2006) and the Least Squares China curve through historical data is 2 million barrels per day by 2010, increasing to 14 million barrels per day by 2030. Table 6: Historical and projected data for China from 1980 to 2030. Historical

Population

Oil Consumption**

Year

[Million]

[million barrels/day]

1980 1981 1982 1983 1984

987.05 1000.72 1016.54 1030.08 1043.57

1.694 1.616 1.601 1.642 1.700

GDP/Cap Actual

Oil/Cap Actual

Least Squares China

Least Squares China

[2000$ PPP] [million [barrels/year] [barrels/year] (x 1000) barrels/day] 0.451 0.512 0.583 0.664 0.783

0.627 0.589 0.575 0.582 0.594

0.614 0.623 0.633 0.645 0.662

1.661 1.708 1.763 1.820 1.894

Underestimation

[million barrels/day] 0.033 -0.092 -0.162 -0.177 -0.194

1985 1058.51 1986 1075.07 1987 1093.00 1988 1110.26 1989 1127.04 1990 1143.33 1991 1158.23 1992 1171.71 1993 1185.17 1994 1198.50 1995 1211.21 1996 1223.89 1997 1236.26 1998 1247.61 1999 1257.86 2000 1267.43 2001 1276.27 2002 1284.53 2003 1292.27 2004 1299.88 2005 1307.56 2006 1314.10 Projections Population IEA Forecast Year

[Million]

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2025 2030

1323.30 1331.24 1339.23 1347.26 1355.34 1363.48 1371.66 1379.89 1388.17 1392.33 1396.51 1400.70 1404.90 1409.11 1430.38 1451.96

1.825 1.941 2.062 2.211 2.340 2.323 2.524 2.740 3.051 3.116 3.395 3.702 4.179 4.228 4.477 4.772 4.872 5.288 5.803 6.772 6.988 7.274* Oil Consumption IEA Forecast [million barrels per day] 7.547 7.830 8.123 8.428 8.727 9.037 9.358 9.690 10.034 10.309 10.591 10.882 11.180 11.486 13.148 15.051

0.903 0.629 0.680 1.972 -0.148 0.988 0.659 0.693 2.041 -0.100 1.115 0.689 0.712 2.134 -0.071 1.263 0.727 0.736 2.238 -0.027 1.344 0.758 0.749 2.312 0.029 1.428 0.741 0.762 2.387 -0.065 1.594 0.795 0.789 2.504 0.020 1.840 0.854 0.831 2.666 0.074 2.113 0.940 0.878 2.850 0.201 2.413 0.949 0.932 3.059 0.057 2.702 1.023 0.985 3.270 0.124 2.998 1.104 1.042 3.495 0.207 3.298 1.234 1.102 3.731 0.448 3.562 1.237 1.156 3.950 0.278 3.840 1.299 1.214 4.184 0.293 4.221 1.374 1.296 4.502 0.270 4.649 1.393 1.392 4.869 0.003 5.127 1.503 1.504 5.293 -0.005 5.720 1.639 1.648 5.836 -0.033 6.426 1.902 1.828 6.510 0.262 7.198 1.951 2.034 7.287 -0.299 7.678 2.018 2.166 7.800 -0.526 GDP/Cap Oil/Cap Least Least Underestimation IEA IEA Squares Squares Forecast Forecast China China [2000$ PPP x [million [million [barrels/year] [barrels/year] 1000] barrels/day] barrels/day] 8.189 2.082 2.311 8.379 -0.833 8.735 2.147 2.469 9.006 -1.177 9.316 2.214 2.642 9.693 -1.569 9.937 2.283 2.829 10.443 -2.015 10.599 2.350 3.033 11.263 -2.536 11.304 2.419 3.254 12.155 -3.118 12.057 2.490 3.492 13.124 -3.766 12.860 2.563 3.749 14.173 -4.483 13.717 2.638 4.024 15.304 -5.270 14.264 2.703 4.200 16.021 -5.712 14.833 2.768 4.383 16.768 -6.176 15.424 2.836 4.572 17.544 -6.662 16.039 2.905 4.767 18.349 -7.169 16.679 2.975 4.969 19.183 -7.696 20.281 3.355 6.055 23.730 -10.582 24.661 3.784 7.217 28.708 -13.656

Source Data: * (EIA, 2007); ** (BP, 2007); All other from (IMF, 2007)

Note: The column labelled “underestimation” lists the difference between the Oil Consumption (including the IEA (2006a) projections beyond 2006) and the Least Squares China curve (also shown in Figure 5).

4. Conclusions and Implications The rapidly growing Chinese economy places high demands on oil supply, which is complicated by concerns about Peak Oil and resource scarcity. While some researchers consider the notion that energy consumption could decouple from economic growth, others have demonstrated that the perceived decoupling disappears if useful work is introduced as a factor of production. This implies that the apparent decoupling is caused by incremental energy efficiency improvements. Earlier attempts to explain the decoupling focused on energy quality and noted that the decoupling is much weaker if energy aggregation is quality-adjusted. Researchers have demonstrated that quality-adjusted energy is a Granger cause for GDP growth in a causality test. Crude oil is an important energy source because it has been demonstrated in several studies that this is the highest quality primary energy source of all fossil fuels. Confirmation of the causality between energy consumption and GDP growth also serves to confirm the basis of historical energy consumption trends. Significant divergence from historical trends in the form of lower energy intensities would require fundamental structural changes in market conditions or technological breakthroughs that increase the useful work performed by energy supplied to the economy. Quantitative increases in primary energy consumption would serve the same purpose. The application of logistic curves to the lower bound global historical data of GDP per capita in comparison to oil consumption per capita emphasizes the fact that the IEA forecasts for China diverge significantly from historical trends. Although potential contributing factors associated with the unique attributes of China could contribute to such divergence, the IEA projections are unprecedented in history.

Measured

against

lower-bound

trends,

the

magnitude

of

potential

underestimation is large. Table 7 shows the potential market imbalance based on the assessment of China only. Table 7: Supply deficit based on assessment of Chinese demand projections Projections Year 2007 2010 2015 2020 2025 2030

Supply deficit [million barrels/day] -0.833 -2.015 -5.270 -7.696 -10.582 -13.656

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