RIVM (National Institute of
Public Health and Environmental Protection)
IMAGE 2.0 consists of three sub-systems of models --
"Energy-Industry", "Terrestrial Environment", and
"Atmosphere-Ocean". The model gives roughly equal weight to each
of these sub-systems. The Energy-Industry models compute the
emissions of greenhouse gases in 13 world regions as a function
of energy consumption and industrial production. End use energy
consumption is computed from various economic/demographic driving
forces. The Terrestrial Environment models simulate grid-scale
changes in global land cover based on climatic and socio-economic
factors, and the flux of CO2 and other greenhouse gases between
the biosphere and atmosphere. The Atmosphere-Ocean models compute
the buildup of greenhouse gases in the atmosphere and the
resulting zonal-average temperature and precipitation patterns.
The time horizon of model calculations extends from 1970 to 2100,
and many terrestrial calculations are performed on a grid of 0.50
latitude by 0.50 longitude; economic-based calculations (relating
to energy, industrial production, and agricultural demand) are
performed for 13 world regions rather than on a global grid.
Climate calculations are performed on a two-dimensional grid of
100 latitudinal bands and nine or more vertical layers in both
the atmosphere and ocean.
The fully linked model has been tested
against data from 1970 to 1990, and after calibration, can
reproduce the following observed trends: regional energy
consumption and energy-related emissions, terrestrial flux of
carbon dioxide and emissions of other greenhouse gases,
concentrations of greenhouse gases in the atmosphere, and
transformation of land cover. The model can also simulate the
observed latitudinal variation of annual average atmospheric
temperatures (averaged over a climatologic period).
An overview of the content and testing of IMAGE 2.0 is given in a companion
paper (Alcamo et al., 1994). The Energy-Industry system is
described in de Vries et al. (1994); the Terrestrial Environment
sub-system in Klein Goldewijk et al. (1994), Kreileman and
Bouwman (1994), Leemans and van den Born (1994), and Zuidema et
al. (1994); and the Atmosphere-Ocean sub-system in Krol and van
der Woerd (1994) and de Haan et al. (1994).
The following scenarios are described in this paper:
(i) Conventional Wisdom
Scenario--This scenario
makes conventional assumptions about future demographic, economic, and
technological driving forces. This is a reference scenario in
that it makes no assumptions about climate-related policies.
Input data for the main driving forces are based partly on the
assumptions of the IS92a scenario of the Intergovernmental Panel
on Climate Change (IPCC, 1992).
(ii) Biofuel Crops Scenario--The Conventional Wisdom and
Biofuel Crops scenarios have identical amounts of biofuel usage
in the global energy system, and only differ in their assumptions
about how/where modern biofuels are grown. In the Conventional
Wisdom scenario, biofuels are assumed to come from sources that
do not require new cropland, whereas the Biofuel Crops scenario
assumes that a substantial amount of biofuels come from new
cropland. [In this paper, the terms biofuels and biomass are used interchangeably and both refer to modern biofuels from crop
residues, energy crops, plantations and similar sources rather
than traditional biofuels such as fuelwood. The IMAGE 2.0 energy
model (de Vries et al., 1994) has separate categories for modern
biomass, fuelwood and renewables (excluding modern biomass and
fuelwood) such as hydroelectric, solar and wind power.]
(iii) No Biofuels Scenario--In this other variation of the
Conventional Wisdom scenario, the sensitivity of the global
climate system to modern biofuel use is investigated. For this
scenario biofuels are removed from the Conventional Wisdom
scenario and are replaced by oil.
(iv) Ocean Realignment--This "surprise" scenario investigates
the consequences on the global society-biosphere-climate system
of a major change of the ocean's circulation pattern.
P.O Box 1
3720 BA Bilthoven, The Netherlands
Abstract. This paper presents scenarios computed with IMAGE 2.0,
an integrated model of the global environment and climate change.
Results are presented for selected aspects of the
society-biosphere-climate system including primary energy
consumption, emissions of various greenhouse gases, atmospheric
concentrations of gases, temperature, precipitation, land cover
and other indicators. Included are a "Conventional Wisdom"
scenario, and three variations of this scenario: (i) the
Conventional Wisdom scenario is a reference case which is partly
based on the input assumptions of the IPCC's IS92a scenario; (ii)
the "Biofuel Crops" scenario assumes that most biofuels will be
derived from new cropland; (iii) the "No Biofuels" scenario
examines the sensitivity of the system to the use of biofuels;
and (iv) the "Ocean Realignment" scenario investigates the effect
of a large-scale change in ocean circulation on the biosphere and
climate. Results of the biofuel scenarios illustrate the
importance of examining the impact of biofuels on the full range
of greenhouse gases, rather than only CO2. These scenarios also
indicate possible side effects of the land requirements for
energy crops. The Ocean Realignment scenario shows that an
unexpected, low probability event can both enhance the build-up
of greenhouse gases, and at the same time cause a temporary
cooling of surface air temperatures in the Northern Hemisphere.
However, warming of the atmosphere is only delayed, not avoided.
Keywords: climate change, global change,
integrated assessment, integrated models, scenario analysis, carbon cycle,
biofuels
1. Introduction
Although climate-related research usually centers on one aspect
or spatial scale of the climate change issue, in reality climate
issues involve interrelated elements of the society, biosphere,
and the climate system. This wide range of issues are reflected,
for example, in the reports of IPCC's first assessment (IPCC,
1990a,b,c). The purpose of this paper is to present scenarios
which capture some of the scope and detail of these interrelated
issues. These scenarios are computed by the IMAGE 2.0 model, an
integrated model of the global environment and climate change.
TABLE 1
Overview of Scenarios.
name of global global biofuels
scenario population economic included ?
growth
Conventional 11.5 B by 2100 1990-2025: 2.9 % a YES
Wisdom 2025-2100: 2.3 % a
Biofuel Crops As Conventional As Conventional YES
Wisdom Wisdom
No Biofuels As Conventional As Conventional NO
Wisdom Wisdom
Ocean As Conventional As Conventional YES
Realignment Wisdom Wisdom
(cont,)
biofuels changed
require new ocean
cropland? circulation
Conventional NO NO
Wisdom
Biofuel Crops YES NO
No Biofuels n.a. NO
Ocean Realignment NO YES
Note: only global totals given in this table; see Tables 3-6 for selected regional values.
Table 1 compares the key assumptions of the four scenarios, and
Table 2 summarizes the input data needed to produce these
scenarios. The world regions included in IMAGE 2.0 are listed in
Table 3. The input data of the scenarios are reviewed in section
2 of this paper, and scenario results are reported in section 3.
We note that scenarios ii, iii, and iv can also be viewed as
sensitivity studies (although not sensitivity analysis in the
conventional use of the term) of the Conventional Wisdom
scenario.
TABLE 2
Main scenario-dependent variables in IMAGE 2.0
Category of Variables
Input
Socio-economic Population (total and urban)
GNP
Energy-related Value added of industrial output
Value added of commercial services
Private consumption
Number of passenger vehicles
Fuel mix
Fuel prices
Efficiency of primary energy conversion
Autonomous efficiency improvements
Emission factors
Agriculture-related Food trade, export/import
N fertlizer use
Technology-related crop yield increase
Animal production coefficient
Ratio of concentrate: roughage for livestock
Fraction of animal feed provided by a particular
crop
Related to Atmosphere- Ocean circulation pattern
Ocean
TABLE 3
Regional Population Assumptions for Conventional Wisdom Scenario. Source:
IPCC (1992), Scenario: "IS92a". Units: Millions.
Region year
1900 2000 2025 2050 2100
Canada 27 28 29 28 27
USA 250 270 302 298 295
Latin America 448 534 715 824 877
Africa 642 844 1540 2208 2875
OECD Europe 378 393 407 395 388
Eastern Europe 123 131 143 149 148
CIS 289 306 335 350 347
Middle East 203 272 508 730 937
India + S. Asia 1171 1412 1970 2375 2644
China + C.P. Asia 1248 1431 1756 1896 1963
East Asia 371 447 624 752 837
Oceania 23 24 25 24 24
Japan 124 131 136 132 130
World 5297 6223 8490 10161 11492
The scenario of urban
vs. rural population in each region (Table 4) is based on
extrapolating the urbanization trend between 1970 and 1990 in
each region up to a maximum of 85% urbanization. The future
linear increase is consistent with UN estimates for Africa and
Asia up to year 2025 (WRI, 1990); the assumed maximum 85%
urbanization is the UN estimate for Latin America in 2025 and
corresponds to the current percentage of urbanization in some
northern European countries, where a maximum may have been
reached.
TABLE 4
Regional Urban Population Assumptions for Conventional Wisdom Scenario.
Source: 1990 data -- WRI (1990)
Units: Percent of Total Population
Region year
1990 2025 2100
Canada 77 80 85
USA 75 78 84
Latin America 71 85 85
Africa 34 54 85
OECD Europe 71 85 85
Eastern Europe 71 85 85
CIS 66 80 85
Middle East 57 85 85
India + S. Asia 26 39 66
China + C.P. Asia 33 63 85
East Asia 36 61 85
Oceania 80 81 83
Japan 77 85 85
TABLE 5
Regional Economic Growth Assumptions for Conventional
Wisdom Scenario. Source: "IS92a" scenario (IPCC, 1992).
Units: GNP Annual Percentage Growth
Region year
1990-2025 2025-2100
Canada 2.06 1.31
USA 2.09 1.25
Latin America 1.85 2.20
Africa 1.57 2.39
OECD Europe 2.06 1.31
Eastern Europe 1.87 1.18
CIS 1.87 1.18
Middle East 1.36 1.98
India + S. Asia 2.97 2.84
China + C.P. Asia 4.23 3.07
East Asia 2.97 2.84
Oceania 2.71 1.28
Japan 2.71 1.28
To compute future end use consumption of energy, assumptions are required about future levels of "activity" in each end use sector. The measures of activity are: value-added of industrial output (industry sector), value-added of services (commercial sector), private consumption (residential sector), number of passenger vehicles (transport sector), and GNP ("other" sector). These data are summarized in Table 6.
TABLE 6
Assumed activity levels for energy end-use sectors
Value added industrial output ($ cap a )
Region 1970 1990 2025 2050 2100
Canada 3141 4282 8746 12114 23240
USA 3635 4629 9555 13020 24172
Latin America 535 619 1222 2099 6199
Africa 335 268 491 848 2531
OECD Europe 3025 4189 8556 11851 22736
Eastern Europe 646 1122 2058 2637 4430
CIS 1850 2560 4210 5640 10123
Middle East 1524 1299 2357 3523 7873
India + S. Asia 45 82 353 688 2619
China + C.P. Asia 93 375 1249 2413 9008
East Asia 159 490 1519 2819 9713
Oceania 2768 3470 8853 12164 22965
Japan 3032 6330 16147 22186 41887
Assumed activity levels for energy end-use sectors
Value added commercial services ($ cap a )
Region 1970 1990 2025 2050 2100
Canada 3686 7114 14529 20124 38607
USA 6338 9210 19011 25903 48091
Latin America 723 915 1796 3190 10070
Africa 241 295 520 1036 4112
OECD Europe 4251 6791 13869 19210 36854
Eastern Europe 861 1138 2509 3537 7033
CIS 617 1341 6094 8164 14653
Middle East 641 1094 2221 3981 12789
India + S. Asia 65 111 388 862 4254
China + C.P. Asia 38 131 1062 2547 14633
East Asia 207 513 1451 3214 15779
Oceania 4571 6370 16251 22329 42155
Japan 3895 7409 18900 25969 49029
Assumed activity levels for energy end-use sectors
Private consumption ($ cap a )
Region 1970 1990 2025 2050 2100
Canada 5024 6969 14234 19715 37824
USA 6528 9341 19281 26272 48774
Latin America 1022 1211 2244 3718 10202
Africa 455 456 770 1367 4305
OECD Europe 4440 6882 14056 19469 37350
Eastern Europe 1077 1305 2732 3761 7126
CIS 2503 4388 6928 9282 16661
Middle East 1038 1396 2362 4165 12958
India + S. Asia 175 241 672 1249 4310
China + C.P. Asia 137 323 1496 3213 14826
East Asia 321 658 1944 3924 15987
Oceania 4645 6083 15516 21320 40251
Japan 4259 7860 20050 27549 52011
Assumed activity levels for energy end-use sectors
Number of passenger vehicles (vehicles per 1000 cap)
Region 1970 1990 2025 2050 2100
Canada 327 472 600 605 615
USA 450 566 600 604 611
Latin America 30 73 132 135 140
Africa 12 15 43 55 61
OECD Europe 197 375 420 420 420
Eastern Europe 34 145 215 222 237
CIS 18 59 215 222 237
Middle East 12 41 75 75 75
India + S. Asia 1 3 16 36 59
China + C.P. Asia 0 3 15 35 58
East Asia 5 16 43 56 61
Oceania 307 413 485 485 485
Japan 102 254 315 315 315
Industrial Output and Services. For OECD regions, it is assumed
that the value-added of industrial output and services remains at
their current fraction of GNP. Therefore, as GNP in a region
increases according to the Conventional Wisdom scenario, the
value-added of industrial output and commercial services in this
region proportionately increases. As for non-OECD regions, they
are assumed to follow the historical pattern of structural change
of OECD economies, i.e., as GNP rises, industrial output
initially increases, then peaks and declines; meanwhile the
decline of industrial output is paralleled by an increase in
commercial services (Maddison, 1991). In this scenario, non-OECD
regions are assumed to follow this pattern. The fraction of GNP
devoted to industrial output increases, peaks, and then declines
while the fraction of GNP devoted to commercial services
increases when the industrial output fraction decreases.
Private Consumption. Private consumption in OECD regions remains fixed at its current fraction of GNP. This means that private consumption increases proportionately to GNP. By comparison, in developing regions this fraction is not fixed, but is assumed to increase to the current average fraction in OECD countries, as GNP in the developing region approaches the current average GNP of OECD countries.
Passenger Vehicles. Studies of historical trends in transportation have shown that the number of vehicles in a society are proportionately related to wealth, but are also constrained by the availability of roads, the density of populations, and other country-specific factors (Grübler and Nakicenovic, 1991). As a result it is probably not wise to assume that there is a universal relationship between income and vehicles per person, nor that there is a typical time period by which each region will reach the saturation number of vehicles. For our estimates we use technological diffusion data from different countries which indirectly take into account constraints to number of vehicles (Grübler and Nakicenovic, 1991). We use these data to estimate the saturation value of vehicles per capita for each region; we further assume that saturation will be reached in year 2100. For the year 2025, we use vehicle estimates from the U.S. EPA (1990) for different regions, and interpolate for years in-between. An exception is made for the four world regions currently having very low levels of vehicle usage (Africa, India plus South Asia, China plus Centrally Planned Asia, and East Asia). For these regions we assume that the current global average (61 vehicles/ 1000 cap) will be reached in year 2100. For intermediate years, we assume that the increase in vehicles in these four regions will follow a typical "S curve" trajectory, as proposed by Grübler and Nakicenovic (1991).
Fuel Mix and Prices. The trend of greenhouse gas emissions from each region's energy economy is closely related to the amount and mix of fuels consumed. The IMAGE 2.0 model endogenously computes the amount of energy consumed in each of five end-use sectors (industry, commercial, residential, transport, and "other") of each region, based on the activity levels just described. However, the fuel mix in each sector is prescribed (although version 2.1 of IMAGE will endogenously compute the fuel mix in each region.) For the Conventional Wisdom scenario, the fuel mix for each sector (i.e. the fraction of total end use energy consumption delivered by each energy carrier) has been estimated from results of the model used to generate the IS92a scenario (IPCC, 1992; Pepper et al., 1992).
The computation of end use energy consumption in IMAGE 2.0 also depends on a scenario of future fuel prices which are used to determine the level of energy conservation. Future trends in prices of coal, gas and oil are the same for each region and are taken from the Edmonds-Reilly model (Edmonds and Reilly, 1985). For coal, the price index (scaled to 1975) is 1.55 in 2050 and 2.37 in 2100. For gas, the index is 4.10 in 2050 and 7.71 in 2100, and for oil 2.46 in 2050 and 2.38 in 2100. Prices of fuelwood are held constant. Prices of biomass are held constant until 2025 and are then assumed to be 10% higher than current prices in 2050, and 20% higher in 2100.
Energy Conversion Efficiency. Emissions of greenhouse gases also depend on the energy used to convert primary to secondary energy, which in turn depends on the assumed efficiency of electricity and heat generation. For the Conventional Wisdom scenario, it was assumed that efficiency of converting coal, gas, and oil to electricity increases linearly with time from its 1990 value (which varies from region to region) to the value of 0.50 in 2100 in OECD regions, Eastern Europe, CIS and Middle East; and to 0.45 in 2100 in other regions. Other assumed conversion efficiencies are presented in de Vries et al. (1994).
Autonomous Efficiency Improvements. Another important variable affecting end use consumption of electricity and heat are so-called "autonomous" factors that lead to improvements in end use energy efficiency. By definition, these are improvements that are not directly related to increases in fuel prices. For electricity, autonomous improvements of the energy intensity are assumed to arise from technological development rather than from higher fuel prices. For energy in the form of heat, we assume that technologies for delivering heat become cheaper, making price-driven energy conservation more attractive. The assumed rate of improvement is region-specific, ranging from 0 to 2% a-1 for heat and 0 to 5.5% a-1 for electricity (de Vries et al., 1994 ). The higher rates of improvement are assigned to developing regions under the assumption that they can realize large gains in their currently inefficient energy systems.
Emission Factors. In order to compute future emissions of greenhouse gases, it is also necessary to assign future emission factors to these gases. For the Conventional Wisdom scenario, it is assumed that emission controls lead to decreases of emission factors of NOx in all sectors, CH4 in fuel production, and CO and VOC in transport. Emission factors for N2O in transport are assumed to increase as a side effect of catalyst-type emission controls on vehicles. All other emission factors are held constant. More information about these assumptions is available in de Vries et al. (1994).
Food Trade. Future agricultural demand will strongly affect land cover patterns and these will affect the flux of CO2 and other greenhouse gases from the terrestrial environment. Agricultural demand, of course, depends on the global trade of agricultural commodities. However, since IMAGE 2.0 does not compute world food trade (this is planned for version 2.1), a very simple approach is taken. We assume current exports of food products from the developed world increase by 50% from 1990 to 2050 and level off afterwards. Net exports from developing regions double their 1990 level by 2100. Export of animal products stay constant at their 1990 level, while sugar export is assumed to be zero. The allocation of crop exports to importing regions is weighted according to the crop consumption of importing regions.
Crop Yield. Future land cover patterns will greatly depend on the need for agricultural land, and this in turn will depend on the potential yields of crops. There are two aspects to these yields -- The first is the potential yield resulting from local climate and unmanaged soil conditions; this is computed by the Terrestrial Vegetation submodel of IMAGE 2.0 (see Leemans and van den Born, 1994) and is not a scenario variable. The second is the influence of fertilizer and other technological inputs (tractors, management know-how) on yield. These variables must be prescribed for each scenario. The yield increase due to nitrogen fertilizer is based on a representive yield response curve for cereals from Addiscot (1991). Future nitrogen fertilizer use is also a scenario variable and is derived from the IS92a scenario of the IPCC. The effect of other technological inputs (tractors, management know-how) on yield after 1990 is based on trends in each region between 1970 and 1990 (see Zuidema et al., 1994).
Animal Productivity and Other Variables. The future trend of animal productivity (ratio of non-productive animals versus productive animals, production of meat and dairy products per cow) can influence future land requirements in developing regions because improved productivity can lead to smaller grassland requirements per unit animal product. This scenario assumes that animal efficiencies in developing regions will linearly approach the current (1990) efficiencies in OECD Europe as incomes of these regions approach the current income of OECD Europe. Two other scenario variables also affect future land requirements for animals: the assumed composition of feed (roughage or concentrate) influences the amount of range land required, while the type of crop used to provide feed determines how feed will compete with human requirements for crops. Both of these variables were fixed at their 1990 values. Quantitative information about these assumptions is available in Zuidema et al. (1994).
TABLE 7
Summary of scenario results. These are global average or total results unless otherwise specified.
YEAR+ Carbon Cycle Methane
SCENARIO (Pg C a ) emissions
CO2-emissions net biosphere ocean (Tg CH a )
from energy flux* flux*
/industry
1990 6.1 -1.2 -1.6 492
2050
Conv. Wisdom 15.2 -7.2 -3.0 688
Biofuel Crops 15.2 -6.0 -3.1 692
No Biofuels 17.0 -7.5 -3.2 677
Ocean Realign. 15.2 -4.5 -3.1 686
2100
Conv. Wisdom 24.0 -8.2 -4.2 778
Biofuel Crops 24.0 -6.7 -4.5 793
No Biofuels 29.2 -8.9 -4.8 746
Ocean Realign. 24.0 -6.7 -4.1 778
Summary of scenario results. These are global average or total results unless otherwise specified.
YEAR+ Atmospheric Change of agri- Change of
SCENARIO Concentrations cultural area forest area
CO CH trop.O (10 km ) (10 km )
(ppm) (ppm)
1990 358 1.7 - 26.7 47.2
2050 ** ** **
Conv. Wisdom 522 2.5 +11.6% +9% -26%
Biofuel Crops 534 2.6 +12.6% +30% -32%
No Biofuels 539 2.4 + 9.0% +9% -26%
Ocean Realign. 563 2.6 +12.7% +12% -27%
2100
Conv. Wisdom 777 2.3 +10.0% +14% -27%
Biofuel Crops 821 2.4 +12.0% +65% -31%
No Biofuels 857 1.7 + 0.2% +15% -27%
Ocean Realign. 863 2.4 +11.7% +18% -28%
Summary of scenario results. These are global average or total results unless otherwise specified.
YEAR+
SCENARIO
Northern Southern
Hemisphere Hemisphere
1990 14.2 13.0
2050 *** ***
Conv. Wisdom +1.4 +1.0
Biofuel Crops +1.5 +1.0
No Biofuels +1.4 +1.0
Ocean Realign. +0.0 +1.1
2100
Conv. Wisdom +2.4 +1.8
Biofuel Crops +2.7 +2.0
No Biofuels +2.4 +1.9
Ocean Realign. +1.2 +2.0
By contrast to OECD Europe, large increases in population and income in Africa lead to tremendous increases in consumption of all fuels in the second half of next century (Figure 2). Moreover, the increase in end use energy consumption for each unit increase in economic activity remains relatively high in the next century because Africa has a high energy intensity relative to its level of economic activity at the start of the simulation (1970).
Since we will be analysing different biofuel-related scenarios later, we note here that modern biofuels (excluding fuelwood, dung, and other "traditional biofuels") account for 14.4 EJ a-1 of Africa's primary energy consumption in 2050 and 57.7 EJ a-1 in 2100. For OECD Europe, these figures are 4.1 EJ a-1 in 2050 and 3.3 EJ a-1 in 2100, and for the world, 74.1 EJ a-1 in 2050 and 208.0 EJ a-1 in 2100. By comparison, the "Renewables-Intensive Global Energy Scenario" of Johansson et al. (1993b) includes 206 EJ a-1 of world biomass consumption in year 2050.
The trend in European emissions is also quite different from Africa's trend (Figures 3 and 4). Emissions of CO2 from the energy system in OECD Europe decline due to the combined effect of slowly increasing energy consumption and a shift to low or non-CO2 fuels ( A HREF="image-2.0.gifs/2-fig3.gif">Figure 3). Currently, the main source of CO2 in OECD Europe is power generation, followed by the sectors of industry and transport (Figure 3). According to this scenario, future power generation will account for an even greater share of total emissions from energy in OECD Europe.
After an initial increase, emissions of O3 precursors (CO, NOx, and VOC) and N2O decrease because the consumption of end use energy decreases in the transport sector, one of the main sources of these emissions. The emissions of O3 precursors also decrease because of assumed air pollution controls in various energy sectors. Emissions of CH4 are reduced because of a shift from fossil fuels to nuclear power in OECD Europe (according to this scenario) and increased efficiencies.
Emissions of CO2 and other gases from Africa spiral upwards following increased energy consumption and industrial activity (Figure 4). By 2030, Africa's CO2 emissions surpass OECD Europe's emissions. For this scenario, the main source of energy-related CO2 emissions in Africa is power generation (as in OECD Europe), but the second most important source is the residential sector. Future emissions of CH4 mainly stem from losses in the gas distribution system, while large increases in N2O arise from increasing industrial activity. Most of the increase in NOx emissions comes from power generation, while increases in VOC emissions can be attributed to increased industrial production for which no emission controls are assumed. The increase in CO emissions in the second half of the next century stems from energy consumed by industry.
The trend of CO2 emissions from other developed regions (U.S., Canada, Eastern Europe, CIS, Oceania, and Japan) resembles OECD Europe's trend (although trends in the CIS are somewhat anomalous in showing a strong increase up to 2025) while regions in the developing world are closer to Africa's trends. The pattern of global CO2 emissions shows that increasing emissions from developing regions prevail over the stabilization of emissions in OECD regions (Figure 5). As a result, global CO2 emissions increase from 6.1 Pg C a-1 in 1990 to 24.0 Pg C a-1 in 2100. Emissions of other energy-related greenhouse gases show similar increases over the simulation period.
Estimates of emissions of CO2 from the energy/industry system for this scenario fall within the range of the minimum (IS92c) and maximum (IS92e) scenarios of the IPCC(1992). This is not too surprising since some of the inputs of the intermediate scenario, IS92a, are also used as inputs to the Conventional Wisdom scenario. Emissions of the Conventional Wisdom scenario fall in the upper range of the IPCC scenarios because we compute a somewhat higher total primary energy consumption (1815 EJ a-1 in 2100) than the IS92a scenario (1453 EJ a-1 in 2100), and because different emission factors are assumed.
The leveling off of demand for animal products leads to stable numbers of most types of livestock after 2025. This leads to a stabilization of the amount of feed required for these animals, which together with a decrease in human consumption of cereals and other crops, leads to a leveling off or decline of total crop demands (Figure 6b). Less area is needed in Europe to grow crops as the total demand for crops levels off and crop yields increase per hectare because of more favorable future climate, increased fertilizer use, and technological crop improvements (Figures 7, and compare Figures 8a and b). As a consequence, some agricultural land reverts to its climate-potential land cover. In the case of Europe this is mostly deciduous forests (Figure 9a).
By contrast, increased income in Africa leads to an increase in per capita consumption of most agricultural commodities (Figure 6a). The larger per capita meat demand and increase in population leads to a large increase in the number of animals. Rising per capita food consumption is multiplied by increased population so that total crop demand in Africa rises steeply between 1990 to 2100 (Figure 6b). To satisfy the demand for crops and meat products, the model computes that extensive new areas will be needed for agriculture and grassland (Figure 7b, and compare Figures 8a and b), even though fertilizers and other inputs are assumed to enhance yield per hectare. The amount of agricultural land increases from 325 to 980 Mha between 1990 and 2100 (Figure 7). The expansion of agricultural land and grassland (Figure 9b) is mainly at the expense of savanna and tropical forested areas (Figure 8a). By the year 2060 the demand for grassland cannot be met since all savanna and forest have been cleared; subsequently animal densities increase on the available grasslands. We note that this scenario assumes that most food demand in Africa is met by growing crops within the region, rather than by importing food from Europe and other regions with excess agricultural land. Since this scenario assumes that per capita income increases substantially in Africa, it can be argued that Africans may grow less of their own food in the future, and import more.
Globally, the amount of agricultural land expands to the end of next century, when it begins to level off (Figure 7c and Table 7). Not only Africa experiences a huge expansion of grassland and agricultural land, but also Asia, and the Middle East (Figure 8). In Asia this leads to extensive deforestation. At the same time, the trends discussed above for Europe also apply to North America and all of the CIS; for example, forests replace abandoned agricultural land in Siberia and on the East Coast of USA and Canada (Figure 8).
Methane is emitted mostly from land-related sources (e.g. wetlands and animals). Since agricultural activity expands in the next century, CH4 emissions continue to increase (Figure 10). Emissions of N2O are also chiefly land-related (from natural soils in particular) and increase because of moisture and temperature feedbacks to soil.
Land-related emissions of ozone precursors (NOx, CO, and VOC) stem from seasonal savanna burning, biomass burning following deforestation, and agricultural waste burning. Emissions from savanna and biomass burning will decrease steadily because of the declining rate of deforestation rates and dwindling extent of savanna lands. By comparison, the main sources of NOx and VOC emissions are related to energy and industry rather than land use, and these sources continue to rise throughout the next century, especially because of expanded economic activity in developing regions (Figure 10). The net result is that total NOx and VOC emissions continue to increase. The situation is similar for CO, except that land-related emissions make up a much larger part of total emissions. Consequently, the decrease in land-related emissions outweighs the increase in energy/industry emissions, and total emissions decline after 2025 (Figure 10). More information about land-related emissions for the Conventional Wisdom scenario is given by Kreileman and Bouwman (1994).
As noted above, the source of CO2 from the world's energy/industrial system increases from 6.1 in 1990 to 24.0 Pg C a-1 between 1990 and 2100. The sum of these fluxes in 2100 result in a net build-up of 11.6 Pg C a-1 in the atmosphere.
The net effect of the changes in greenhouse gas concentrations is a substantial increase in surface temperature in both the Southern and Northern Hemispheres (Figure 14a). Model results show the zonal pattern of temperature change that is typical of more complicated general circulation models, namely a lower temperature increase in the tropics because of extensive heat flux from this region, with a substantially higher increase in temperate regions (Figure 14a). Around the equator, surface temperatures between 1970 and 2100 increase about 1.50C, whereas in the middle northern latitudes the increase is around 3 to 50C (Figure 14a). Temperature changes in the Southern Hemisphere are smaller than in the Northern Hemisphere because of the modifying effects of the South's larger surface area of ocean (Figures 14a and 15).
Since calculations of society, biosphere, and climate are coupled in IMAGE 2.0, increases in surface temperature affect potential crop productivity, productivity of existing vegetation, and the rates of emissions of different greenhouse gases (e.g. N2O from soils). These factors profoundly affect atmospheric levels of greenhouse gases, which in turn affect surface temperatures and other aspects of climate, which again feed back to potential crop productivity, productivity of vegetation, and so on, until the loop is closed in the society-biosphere-climate system for each model time step.
In developing regions, large increases in population and GNP also increases energy consumption and industrial activity, leading to increased emissions. The demand for food also greatly increases, and results in expanding agricultural and grassland areas, depleting forests and savanna in Africa and Asia, and increasing flux of CO2 between the biosphere and atmosphere. The net global effect of these trends is a rapidly increasing atmospheric level of most greenhouse gases, and significant increase in surface temperatures.
It is emphasized that land requirements for biofuels are likely to be overestimated because we assume ad hoc that a large percentage of biofuels must come from energy crops on new cropland. Some studies contend that large quantities of biofuels can be provided on marginal lands outside of prime cropland (see, for example, Swisher, 1993 and Woods and Hall, 1993). In addition, we only take into account three energy crops, whereas there are many other crops that might be better suited to a particular climate and soil and consequently have higher local yields. Moreover, we do not consider the costs of growing the assumed energy crops, which in reality should lead to efficient use of land.
All other assumptions in this scenario are the same as in the Conventional Wisdom scenario.
IMAGE 2.0 takes into account the growing characteristics of the energy crops and estimates their change in potential productivity as climate changes. As an example, in this scenario the potential productivity of elephant grass increases between the years 1970 and 2100 especially in Canada and Russia due to changes in temperature and precipitation (Figure 16). Changes in productivity, together with the change in demand for these biofuels, leads to the allocation of additional agricultural land for these crops. The reader is referred to Leemans and van den Born (1994) and Zuidema et al. (1994) for descriptions of the methodology for calculations of potential crop productivity and land cover changes.
As to changes in land cover, we again focus on Europe and Africa as examples. Figure 9a depicts the new land cover types that appear between 1990 and 2050 according to the Conventional Wisdom scenario. As noted previously, large new areas of grassland and agricultural land are needed in Africa to satisfy increased food demand, whereas forested areas reappear in Europe because of stabilizing food demand and increased crop yield. Figure 9b shows the additional agricultural areas required in year 2050 for energy crops according to the Biofuels Crops scenario (over and above the new agricultural areas shown in Figure 9a).
In year 2050, 14% more agricultural area is required for biofuel crops in Africa and 71% more for OECD Europe as compared to the Conventional Wisdom scenario (Figures 7a and b). In OECD Europe this leads to deforestation instead of the forestation computed in the Conventional Wisdom scenario. The relatively small increase of agricultural land in Africa for year 2050 can be explained by the relatively small absolute amount of modern biofuels used in the first half of the 21st century. The use of biofuels in Africa becomes more substantial in the second half of the century, and this is reflected in the land cover simulation which indicates large new areas of agricultural land necessary for energy crops (Figure 7b, and compare 9b and 9c). By comparison, the use of biofuels declines in the second half of the century in Europe, and the yield per hectare of energy crops increases because of technology. Consequently, less area is required for biofuels in 2100 than in 2050 (Figure 7a, and compare 9b and 9c).
Not only will Africa require substantial new agricultural areas for biofuels in this scenario, but globally a 20% increase is required for 2050 and 45% for 2100 (Figure 7c, Table 7). Since agriculture replaces forests and other land cover types capable of assimilating more carbon, there is a substantial reduction in the carbon assimilated by the biosphere (Figures 11c and 12a). What follows is an increase in atmospheric CO2 in year 2100 from 777 ppm in the Conventional Wisdom scenario to 821 ppm in this scenario (Figure 13a). This results in a slight increase in temperature for the Northern and Southern Hemispheres, as compared to the Conventional Wisdom scenario (Figure 15, Table 7). Synopsis of Results of Biofuel Crops Scenario
Summing up, following the assumptions of this scenario, the need for biofuels may take up large amounts of new agricultural land in the world. A consequence of expansion of agricultural land is a reduction of the CO2 assimilated by the biosphere, a small increase in atmospheric CO2 as compared to the Conventional Wisdom scenario, and somewhat larger global warming. However, we reiterate that the energy cropland requirements assumed in this scenario may be exagerated since it may be possible to provide a much larger fraction of biofuels from agricultural wastes, plantations on marginal land, and other non-cropland sources (see, for example, Woods and Hall, 1993; Johansson et al. (1993a and b). Moreover, the requirements for land would not have been as large, nor the reduction in C uptake by the biosphere so great, if energy crops/trees had been selected that were better suited to local climate and soil. Perhaps this scenario provides a useful estimate of the upper range of land requirements of biofuels.
All other assumptions are the same as the Conventional Wisdom scenario.
Methane emissions and atmospheric concentrations, on the other hand, decrease relative to the Conventional Wisdom scenario because unit emissions of CH4 from biofuels are higher than from oil (Figure 13). This is a crucial result and depends on the implicit assumption of how biofuels are burned. If they are gasified, for example, most of the CH4 would be utilized rather than emitted to the atmosphere. However, scenario assumptions imply that it is combusted without gasification. Lower emissions of CH4 lead to lower concentrations of this substance in the atmosphere. This also applies to CO and NOx, two other important precursors of tropospheric ozone. The lower concentrations of O3 precursors leads to lower concentrations of tropospheric ozone.
The net effect of these changes on radiative forcing are important. The increase in CO2 concentration tends to increase radiative forcing, while the decrease in CH4 and tropospheric O3 tends to decrease it. The net effect is a very small difference in the change of surface temperature between this and the Conventional Wisdom scenario (Figure 15 and Table 7).
Synopsis of Results of No Biofuels Scenario
Summing up, emissions from biofuels result in lower atmospheric levels of CO2, but higher levels of CH4 and O3. The net result is a small difference in climate change between scenarios with and without biofuels. These results point out the importance of taking into account all emissions as well as the composition of the atmosphere. However, as noted above, these conclusions also depend on assumptions about biomass utilization. These results also raise interesting questions -- How sensitive are scenario results to the assumed mix of fuels that are used instead of biofuels? How does the effect of biofuels on tropospheric ozone depend on the background atmospheric concentration of ozone precursors? What influence does the timing of introduction of biofuels have on the rate of climate change?
We emphasize that this scenario is meant to illustrate a low probability, "surprise" occurrence. Indeed, the rapidness of the changes in ocean circulation found by Mikolajewicz et al. are likely to be due to the nature of their modeling exercise, namely, that they omitted the effect of ocean feedbacks on the atmospheric energy balance. The IPCC notes that such rapid changes in ocean circulation are not computed by models that take ocean feedbacks into account (IPCC, 1992).
Other assumptions are the same as in the Conventional Wisdom scenario.
Cooling in the Northern Hemisphere has an important influence on the global build-up of greenhouse gases. For example, carbon uptake is especially reduced in northern boreal forests because of their extensive area, the cooling they are exposed to, and the assumed relationship between net primary productivity and temperature (Figure 11d). (See Klein Goldewijk et al., 1994 for a description of this relationship.) This effect is particularly pronounced in CIS where C uptake in year 2050 decreases from 2.6 Pg C a-1 to 1.0 Pg C a-1 between the Conventional Wisdom and Ocean Realignment scenarios (Figure 12b). The global biospheric uptake of the two scenarios differs by about 2.7 Pg C a-1 in year 2050 (Figure 12a). With reduced C uptake, atmospheric CO2 reaches 90 ppm higher than the Conventional Wisdom scenario in year 2100 (Figure 13a).
Another effect of the cooler temperatures in this scenario is a lower mixing ratio of water vapor in the atmosphere. This results in lower production of hydroxyl radical, which is the main atmospheric sink of CH4. Consequently, CH4 concentrations are higher in this scenario than in the Conventional Wisdom scenario (Figure 13b). Although higher levels of greenhouse gases increase radiative forcing, this is compensated by the reduced transport of heat from the tropics. Nevertheless, the trend of declining surface temperatures in the Northern Hemisphere is reversed after 2035 because of the increase in radiative forcing (Figures 14b and 15a). However, by the end of the century the temperature gain in the middle latitudes is only 1.5 0C as compared to 3 to 50C in the Conventional Wisdom scenario (Figures 14a, b).
Cooler temperatures also reduce potential crop productivity which leads to larger land requirements for the same amount of agricultural demand. This is especially important in the northern temperate regions such as the CIS where the area of agricultural land in year 2100 increases from 137 Mha in the Conventional Wisdom scenario to 164 Mha in this scenario. In Eastern Europe, agricultural area in 2100 increases from 61 to 70 Mha, and in OECD Europe from 111 to 146 Mha (Figure 7). The larger area of agricultural land comes partly at the expense of forest land; in year 2100 there is 60 Mha less global forest area in the Ocean Realignment scenario than in the Conventional Wisdom scenario (see Table 7).
Synopsis of the Ocean Realignment Scenario
A change in the circulation of the ocean can result in a temporary cooling rather than warming of the Northern Hemisphere. This cooling would reduce uptake of carbon in the northern boreal forests and other areas, and leads to a greater build-up of CO2 in the atmosphere than in the Conventional Wisdom scenario. The build-up of CO2 and other gases will eventually reverse the cooling trend, although temperatures will remain substantially cooler in the Northern Hemisphere as compared to the Conventional Wisdom scenario up to 2100 and beyond. One outcome of the cooler temperatures is the need for more land to produce the same amount of food in the North (assuming no change in trade patterns), and subsequently a lower rate of forestation of abandoned land. We repeat, however, that this is a low probability scenario and is most useful in illustrating the large differences between the Conventional Wisdom scenario, and an unexpected "surprise" scenario. These differences underscore the need to test the robustness of climate policies against different kinds of uncertainties and "surprises" (Clark, 1986).
As a general comment, because of the omissions of the model, it is best to view the scenarios in this paper as a type of sensitivity study that can provide insight into couplings and linkages in the society-biosphere-climate system. With this in mind, we review some of the scenarios' main conclusions:
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