IV.5. The Multiregional Approach for Resource and Industry Allocation Model
The Multiregional Approach for Resource and Industry Allocation Model (MARIA)
is a compact integrated assessment model to assess the interrelationships among
the economy, energy, resources, land use, and global climate change (Mori and
Takahashi, 1999; Mori 2000). The origin of the model is the Dynamic Integrated
Model of Climate and the Economy (DICE) model, developed by Nordhaus (1994).
Involving energy flows and dividing the world into regions, MARIA has been developed
to assess technology and policy options to address global warming. Like Global
2100 developed by Manne and Richels (1992), MARIA is currently an intertemporal
non-linear optimization model that deals with international trading among eight
regions - NAM (USA and Canada), Japan, Other OECD countries, China, ASEAN countries
(Indonesia, Malaysia, Philippines, Singapore, Republic of Korea, Thailand),
SAS (India, Bangladesh, Pakistan, Sri-Lanka), EEFSU (Eastern Europe and the
Former Soviet Union), and ALM (Africa and Latin America). It also encompasses
energy flows and simplified food production and land use changes to show the
potential contribution of biomass.
Economic activities are represented by a constant elasticity of substitution
(CES) production function with capital stock, labor, electricity, and non-electric
energy set for the above eight world regions. Future GDP growth is projected
by considering potential GDP growth rates (the product of two exogenous assumptions
- population and potential per capita GDP growth) as well as endogenously determined
energy costs and prices. The energy module in MARIA involves three fossil primary
energy resources (i.e., coal, natural gas, and oil), biomass, nuclear power,
and renewable energy technologies (e.g., hydropower, solar, wind, and geothermal).
Energy demand consists of industry, transportation, and other public uses. Nuclear
fuel recycling technologies are simply but explicitly formulated. Carbon sequestration
technologies are also taken into account. Typically, MARIA basically generates
resource extraction profiles in which gas is mainly used in the first half of
the 21st century, and subsequently carbon-free sources (e.g., solar, nuclear,
and biomass) and coal assume the main roles in the second half of the 21st century.
Energy costs in the model consist of energy production and utilization costs.
Market prices are determined endogenously on the basis of model-calculated shadow
prices. Among various parameters, the extraction costs of fossil fuel resources
and energy conversion cost coefficients contribute substantially to determining
the model's energy mix and emissions. The latest model version, MARIA-8, applied
Rogner's estimates on fossil resource availability (Rogner, 1997). For the sake
of simplicity, the fossil resource and reserve categories are aggregated into
two classes, assuming a quadratic production function to interpolate the relationships
between resource occurrences and extraction costs. Corresponding model parameters
are summarized in Table IV-6. The cost coefficients of energy conversion technologies
are basically extracted from the GLOBAL 2100 model (Manne and Richels, 1992).
The basic values used in the case of the B2 scenario are illustrated in Table
IV-7, and important model parameters deployed for MARIA's other SRES scenario
quantifications are shown in Table IV-8. Other energy-related
cost parameters correspond to renewable energy sources, methanol and ethanol
processes, nuclear fuel recycle, carbon sequestration, etc. They are described
in more detail in Mori and Takahashi (1999).
International trade prices are generated by the Lagrange multipliers of the
corresponding constraints as a feature of optimization models. The Negishi weight
technique was employed to assess the international equilibrium prices of tradable
goods under the budget constraints (Negishi, 1972). Illustrative international
energy trade prices for scenario B2 are summarized in Chapter
4 and are not repeated here.
|Table IV-7: Illustration
of basic energy conversion cost coefficients in MARIA used for calculating
the SRES B2 Scenario.
|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 are millions per kWh.
The Global Warming Subsystem in MARIA is based on Wigley's five-time constant
model for the emission-concentration mechanism. A two-level thermal reservoir
model is also employed following the DICE model (Wigley, 1994; Nordhaus, 1994).
Only global carbon emissions are currently 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 directly
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 above-mentioned subsystems are shown in Figure
Since MARIA is designed for macro-level evaluation of various options consistently,
detailed information, such as gridded SO2 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
|Table IV-8: Parameter adjustments
to meet the key driving forces interpretation of the SRES scenario storylines.
||Potential economic growth rates
||Autonomous energy efficiency
||Energy cost coefficients of coal
|260% of gas
250% of gas
185% of gas