Emissions Scenarios - IPCC

344 Six Modeling Approaches
Table IV-7: Illustration of basic energy conversion cost
coefficients in MARIA used for calculating the SRES B2
Scenario.
COAL OIL GAS BIO
IND 6.00 2.50 3.25 4.15
TRN 8.58 3.43 4.56 5.02
PUB 6.00 2.50 3.25 4.15
ELC 51.00 12.20 13.70 15.76
IND, TRN, PUB, and ELC denote industry, transportation, public and other
services, and electric power generation sectors. The values in the first three
rows (non-electricity) are millions per MJ. Those in the last row ai'e millions
per kWh.
employed to assess the intemational equilibrium prices of
tradable goods under the budget constraints (Negishi, 1972).
Illustrative intemational energy trade prices for scenario B2 are
summarized in Chapter 4 and are not repeated here.
The Global Warming Subsystem in MARIA is based on
Wigley's five-time constant model for the emissionconcentration
mechanism. A two-level thennal reservoir model
is also employed following the DICE model (Wigley, 1994;
Nordhaus, 1994). Only global carbon emissions are cun-ently
treated in this model component.
MARIA'S Food and Land Use module serves to assess the
potential contributions of biomass. A simplified food demand
and land-use subsystem was included. Nutrition, calorie, and
protein demand is a function of per capita income. Either directiy
or via meat, crop and pasture supply these demands. Forests are
a source of biomass and wood products, but also their function as
a carbon sink is evaluated. The relationships among the abovementioned
subsystems are shown in Figure IV-5.
Since MARIA is designed for macro-level evaluation of
various options consistently, detailed information, such as
gridded SO^ emissions, industrial structure change, and
urbanization issues, is not generated. However, MARIA can
provide long-term profiles of fuel mix changes and possible
trade premiums under various scenarios.
More detailed information can be obtained by referring to the
following web site: http://shun-sea.ia.noda.sut.ac.jp/indexj.html.
IV.6.
The Mini Climate Assessment Model
The Mini Climate Assessment Model (MiniCAM) is a small
rapidly running Integrated Assessment Model that estimates
global GHG emissions with the ERB model (Edmonds et al.,
1994, 1996a) and the agriculture, forestry and land-use model
(Edmonds et al., 1996b). MiniCAM uses the Wigley and Raper
MAGICC (Wigley and Raper, 1993) model to estimate climate
changes, the Hulme et al. (1995) SCENGEN tool to estimate
regional climate changes, and the Manne et al. (1995) damage
functions to examine the impacts of climate change. MiniCAM,
developed by the Global Change Group at Pacific Northwest
Laboratory, undergoes regular enhancements. Recent changes
include the addition of an agriculture land-use module and the
capability to estimate emissions of all the Kyoto gases.
At present the model consists of 11 regions (USA, Canada,
Western Europe, Japan, Australia, Eastern Europe and the
Former Soviet Union, Centrally Planned Asia, the Mid-East,
Africa, Latin America, and South and East Asia) that provide
complete world coverage (see Table IV-1), A 14-region version
is nearing completion.
MiniCAM uses a straightforward population times labor
productivity process to estimate aggregate labor productivity
levels. The resultant estimate of GNP is corrected for the
impact of changes in energy prices using GNP/energy
elasticity. For the scenario exercise, an extended economic
activity level process was developed to allow a clearer
understanding of the potential impacts of the new population
scenarios. First, a detailed age breakdown was included so
working age populations could be computed. Second, a labor
force participation rate was added to estimate the labor force,
and third an external process was created to estimate the longterm
evolution of the rate of labor productivity increase.
ERB is a partial equilibrium model that uses prices to balance
energy supply and demand for the seven major primary energy
categories (coal, oil, gas, nuclear, hydro, solar, and biomass) in
the eleven regions in the model.
The energy demand module initially estimates demands for
three categories of energy services (residential/commercial,
industrial, and transportation) as a function of price and
income. Energy services are provided by four secondary fuels
(solids, liquids, gases, and electricity). Demand for the
secondary fuels depends upon their relative costs and the
Table IV-8: Parameter adjustments to meet the key driving forces interpretation of the SRES scenario storylines.
Storylines Potential economic Autonomous Potential Energy cost
growth rates energy efficiency cropland coefficients of coal
Al High Middle High 260% of gas
Bl Middle High High 250% of gas
B2 Low Low Low 185% of gas