Please note that the data and information posted under the "Open Process" are for historical information only. They were preliminary information released in 1998-1999 as part of the SRES open process and for use in analysis to be contained in the IPCC Third Assessment Report (TAR). For final data (version 1.1) please go back to home page and follow the link.

A1 Marker Scenario

T. Morita, Y. Matsuoka, K. Jiang, T. Masui, K. Takahashi, M. Kainuma, R. Pandey

 

1. Brief of A1 storyline and scenario family

Four important scenario variants have been identified for the A1 scenario family that differ in the directions assumed to be taken by technological change. The key differences lie with the energy resources that might become economically accessible in the future, and the technologies that might become available to convert these into the final goods and services demanded by the consumers. Four pathways are considered:

A1B: "Balanced" progress across all resources and technologies from energy supply to end use (A1 marker scenario, presented on the SRES web site);

A1C: "Clean coal" technologies that are generally environmentally friendly with the exception of GHG emissions;

A1G: An "oil and gas" rich future with a swift transition from conventional resources to abundant unconventional resources including methane clathrates; and

A1T: A "non-fossil" future with rapid development of solar and nuclear technologies on the supply side and mini-turbines and fuel cells used in energy end use applications.

The divergence between the four scenario variants in terms of resource availability and the direction of technological change result in a wide range of GHG emissions from about 5 Gigatons of Carbon (GtC) (A1T "non-fossil technologies") to more than 35 GtC (A1C "clean coal" and A1G "oil and gas") by 2100 compared to 13 GtC in the A1B ("balanced") marker scenario.

 

2. Implementation with the AIM Model

This is a brief summary of the quantification of A1 marker scenario by the AIM model. More detailed information can be obtained by referring to the World Wide Web site (http://www-cger.nies.go.jp/ipcc/aim/), which also provides detailed information about the AIM model, other AIM-based quantification for the A2, B1 and B2 scenario s, as well as the detailed A1 simulation results.

The family of A1 scenarios (A1B, A1C, A1G, and A1T) was based on a century of expanded economic prosperity with the emergence of global governance. Regional economic growth rates used in the A1 quantification are from the IIASA scenarios, which were estimated for the SRES process using the highest group of historical levels for each development stage. World GDP is assumed to be between $529 and $537 trillion by 2100 (for all storylines in A1 family), with particularly high growth rates in non-OECD countries. Trade levels are high, accompanied by low costs. Annex I Per Capita GDP reaches over $30,000 in 2030 and over $100,000 in 2100; while Non Annex I Per Capita GDP grows to $7,000 in 2030, $14,000 in 2040 and over $65,000 in 2100. Non-OECD GDP growth rates rise to a peak of over 8 percent between 2010-2030, and then decline. Non-Annex I GDP is equal to Annex-I GDP in 2030. World population growth is assumed to be relatively small, using the IIASA low projections (9 billion in 2050, 7 billion in 2100) with an accompanying rapid increase in urbanization.

These assumptions for economic growth and population growth have significant implications for energy consumption, land use changes, industrial production and pollution control. For example, GDP and population determine the level of energy service demand, food demand, cement and zinc demand and the timing of pollution control initiatives. The potential GDP growth and population assumptions are the same in all four A1 scenarios. However, different assumptions can cause the trend lines to diverge and generate very different estimate of GHG emissions even within the A1 scenario family. This requires that the four A1 scenarios be quantified.

Table 1. Annual growth rate for GDP and population (in percent).

GDP

1990-2050

2050-2100

1990-2100

OECD

2.02

1.62

1.84

EFSU

4.15

2.05

3.19

ASIAP

6.41

2.42

4.58

ROW

5.65

2.35

4.14

World

3.67

2.16

2.98

POPULATION

1990-2050

2050-2100

1990-2100

OECD

0.38

0.05

0.23

EFSU

0.04

-0.44

-0.18

ASIAP

0.69

-0.76

0.03

ROW

1.54

-0.18

0.75

World

0.84

-0.42

0.27

The supply of energy - oil, gas and biomass - in the A1 scenario family is assumed to be very high because high economic growth would result in extreme pressures on these resources. The A1 quantification assumes: the availability of unconventional oil and gas; large areas of land for biomass farms; and, a high use of renewable energy resources. A1 also assumes significant innovations in energy supply technologies, which improve energy efficiency and reduce the cost of energy supply. In particular, A1B assumes drastic reductions in power generation costs from the use of solar, wind and other modern renewable energies.

Energy resources are taken to be plentiful by assuming large reserves of unconventional resources and high levels of improvement in the efficiency of energy exploitation technologies, energy conversion technologies and transport technologies. The grades of energy resources used in the model differ on the basis of exploitation cost. When combined with the level of improvement in exploitation technology efficiency (expressed as the rate of improvement in the marginal costs of producing energy), the graded energy resource exploitation cost decides the primary energy production cost (price). For A1B, large amounts of unconventional oil and natural gas were introduced. Table 2 provides the total energy resources assumed in the model.

Table 2. Energy resource, EJ

CONV. Oil

CONV. Gas

COAL

UNCON. OIL

UNCON. GAS

OECD

1271

2186

56808

12709

73062

EFSU

1606

2679

62439

451

36628

ASIAP

912

657

20385

556

9379

ROW

7325

3449

3368

4403

45026

World

11114

8971

143000

18119

164095

Improvements in energy efficiency on the demand side are assumed to be relatively low in the A1B scenario because low energy prices provide very little incentive to improve end-use-energy efficiencies and high income levels will encourage people to pursue comfortable and convenient life styles (especially in the household, service and transport sectors). This will result in the consumption of much more energy. Efficient technologies are not fully introduced into the end-use side; dematerialization processes in the industrial sector are not well promoted; lifestyles become energy intensive; and, private motor vehicles are used more in developing countries as per capita GDP increases. As a result, the rate of energy efficiency improvement in Annex I countries is around 1.16 percent per year, and in Non-Annex I countries 1.44 percent over the next 100 years (Table 3). Thus, final energy use for A1B is much higher than other scenarios (A2, B1, B2) while the difference in final energy use per capita between Annex 1 countries and non-Annex 1 countries in 2100 is relatively small.

Table 3: Final energy intensity world and regions (1990, 2020, 2050, 2100) in MJ/$ and improvement rate (in percent) 1990-2100 (A1B, A1C, A1G)

Region

1990

2020

2050

2100

1990-2100

OECD

7.4

5.8

4.0

2.7

0.93

EFSU

51.5

17.5

6.8

3.3

2.47

ASIAP

21.8

11.1

5.3

3.2

1.74

ROW

13.7

12.6

7.1

3.9

1.13

World

11.3

8.8

5.5

3.3

1.11

The final energy use mix is determined by the relative cost of energy service among energy types. The energy service cost relies on the device cost in its end use, energy use efficiency, and the primary energy cost. The primary energy cost is determined by exploitation cost and transport cost. Electricity and gas dominate the final use because of their low energy price under the A1B story line which assumes a reduction in the price for power generated using modern renewable energy resources (solar, wind, geothermal and ocean energy) and a large amount of unconventional natural gas.

Table 4 summarizes the estimated GHG and related gas emissions from energy use. The huge energy demand in A1B does not produce very high CO2 emissions. Global accumulated CO2 emissions for A1B is around 1500 GtC from 1990 to 2100, which is similar to the IPCC IS92a scenario. On the other hand, the A1 scenario family has a wide range, in which A1C could be the highest emitted scenario for CO2 and other gases, and A1T could be one of the lowest emitted scenarios.

Table 4. World GHG and related gas emission from energy use (A1B)

GasesUnit

1990

2020

2050

2100

CO2

GtC

5.96

12.14

16.03

13.12

CH4

Mt CH4

91.34

133.39

109.82

95.22

N2O

Mt N2O-N

0.16

0.30

0.34

0.24

NOx

Mt N

21.68

38.49

42.49

35.47

CO

Mt CO

262.56

742.95

1426.27

2033.44

SO2

Mt SO2

115.60

177.20

112.00

36.80

The quantification of A1 family scenarios is also based on other assumptions related to land use changes. The A1 scenarios assume a higher productivity increase in biomass and crop land - 1.5 percent in comparison with 0.5 percent and 1.0 percent in the A2 and B2 scenarios. A shift in diet is also seen among the scenarios. Meat consumption in most developing countries reaches the western level by 2050 in the A1 scenarios, while in other scenarios it reaches the same level at the end of next century.

In the AIM model, land use changes at the beginning of next century are mainly determined by previous trends, while after that time, they are primarily determined by the relative expected rent on a unit of land in each land use type. The latter mechanism is reproduced as an equilibrium process in international agricultural markets. This model is linked to the energy module, and the biomass energy demand calculated in the energy model is reflected in the land use changes. In the A1B scenario, the rapid increase in the demand for biomass energy would raise the expected rent of biomass farm land, and this causes a drastic increase in the area farm land after 2020.

Reduction in forest area is predicted until 2020 because of high population growth in developing countries. However, demand for forest resources would increase in line with economic growth, and the expected rent of forestland would increase after 2020. This would stop deforestation and increase the area of tree-covered land in the latter half of the next century. On the other hand, crop land and pasture land would be under strong pressure to expand in A1 scenario because of the rapid change in diet. The high productivity increases would reduce this pressure in the latter half of the next century.

The above processes were reflected in the land use simulation. Forest area would decrease in the beginning of next century, and would recover in the latter half. The land for biomass energy production would increase rapidly from 2020. Based on these simulations of land use changes, CO2 and other GHG gases were estimated. CH4, N2O, NOx, CO and SO2 emissions were also quantified based on those simulations of land use changes.

In addition to the process of energy use and land use change, the AIM model has two more modules to reproduce the specific processes, those are: (1) industrial processes including cement, steal, capper, nitric acid and adipic acid industries to estimate CO2, N2O, NOx and SO2 emissions, and (2) waste management processes including landfill and sewage to estimate CH4 emissions. Table 4 shows the summarized quantification of GHG and related gas emissions from land use change, industrial processes, and waste management processes.

Table 4. World GHG and related gas emissions from non-energy activities (A1B).

Gases

Unit

1990

2020

2050

2100

CO2

GtC

1.01

1.46

-0.29

-0.61

CH4

Mt CH4

250.41

406.06

509.38

306.02

N2O

Mt N2O-N

3.88

5.07

5.18

3.20

NOx

Mt N

5.88

8.40

4.75

2.79

CO

Mt CO

489.13

585.00

360.78

234.63

SO2

Mt SO2

22.57

26.33

15.56

11.98

Most of the GHGs and related gases emission trajectories have reverse-U curves in A1B scenario. Energy related global CO2 emissions peak around the middle of the next century, CO2 emissions from land use changes peak at 2020, CH4 emissions peak at 2030, and SO2 emissions at 2020.

In conclusion, it can be said that the A1B and other A1 scenario family members are based on high economic growth extrapolated from the past, which produces high results for the energy system. Such scenarios may be possible; thus it is useful to examine such scenarios to better understand the range of future divergence over the next dynamic century.