Snapshot of - AIM-Hub

Archive of AIM-Hub, version: V2.2

Reference card - AIM-Hub

The reference card is a clearly defined description of model features. The numerous options have been organized into a limited amount of default and model specific (non default) options. In addition some features are described by a short clarifying text.

Legend:

• not implemented
• implemented
• implemented (not default option)

About

Name and version

AIM-Hub V2.2

Model link

https://www-iam.nies.go.jp/aim/about_us/index.html

Institution

National Institute for Environmental Studies (NIES), Japan, https://www.nies.go.jp/index-e.html., Kyoto-University (Kyoto-University), Japan, https://www.kyoto-u.ac.jp/en.

Documentation

AIM-Hub documentation consists of a referencecard and detailed model documentation

Process state

published

Model scope and methods

Model documentation: Model scope and methods - AIM-Hub

Model type

• Integrated assessment model
• Energy system model
• CGE
• CBA-integrated assessment model

Geographical scope

• Global
• Regional

Objective

To understand, economy, energy and land-use interaction particularly related to climate policy

Solution concept

• Partial equilibrium (price elastic demand)
• Partial equilibrium (fixed demand)
• General equilibrium (closed economy)

Solution horizon

• Recursive dynamic (myopic)
• Intertemporal optimization (foresight)

Solution method

• Simulation
• Optimization

Temporal dimension

Base year:2005, time steps:Annual, horizon: 2100

Note: 2005- 2015 is used for calibration and parameters are adjusted to be close to the IEA energy balance table

Spatial dimension

Number of regions:17

1. Japan
2. China
3. India
4. Rest of Asia
5. Rest of Europe
6. Former Soviet Union
7. Turkey
8. Canada
9. United States
10. Brazil
11. Rest of South America
12. Middle East
13. North Africa
14. Rest of Africa
15. Rest of East and South East Asia
16. EU
17. New Zealand and Australia

Time discounting type

• Discount rate exogenous
• Discount rate endogenous

Policies

• Emission tax
• Emission pricing
• Cap and trade
• Fuel taxes
• Fuel subsidies
• Feed-in-tariff
• Portfolio standard
• Capacity targets
• Emission standards
• Energy efficiency standards
• Agricultural producer subsidies
• Agricultural consumer subsidies
• Land protection
• Pricing carbon stocks

Socio-economic drivers

Model documentation: Socio-economic drivers - AIM-Hub

Population

• Yes (exogenous)
• Yes (endogenous)

Population age structure

• Yes (exogenous)
• Yes (endogenous)

Education level

• Yes (exogenous)
• Yes (endogenous)

Urbanization rate

• Yes (exogenous)
• Yes (endogenous)

GDP

• Yes (exogenous)
• Yes (endogenous)

Income distribution

• Yes (exogenous)
• Yes (endogenous)

Employment rate

• Yes (exogenous)
• Yes (endogenous)

Labor productivity

• Yes (exogenous)
• Yes (endogenous)

Total factor productivity

• Yes (exogenous)
• Yes (endogenous)

Autonomous energy efficiency improvements

• Yes (exogenous)
• Yes (endogenous)

Macro-economy

Model documentation: Macro-economy - AIM-Hub

Economic sector

Industry

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Energy

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Transportation

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Residential and commercial

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Agriculture

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Forestry

• Yes (physical)
• Yes (economic)
• Yes (physical & economic)

Other economic sector

• Services - Yes (Economic)

Macro-economy

Trade

• Coal
• Oil
• Gas
• Uranium
• Electricity
• Bioenergy crops
• Food crops
• Capital
• Emissions permits
• Non-energy goods

Cost measures

• GDP loss
• Welfare loss
• Consumption loss
• Area under MAC
• Energy system cost mark-up
• Equivalent Variation

Categorization by group

• Income
• Urban - rural
• Technology adoption
• Age
• Gender
• Education level
• Household size

Institutional and political factors

• Early retirement of capital allowed
• Interest rates differentiated by country/region
• Regional risk factors included
• Technology costs differentiated by country/region
• Technological change differentiated by country/region
• Behavioural change differentiated by country/region
• Constraints on cross country financial transfers

Resource use

Coal

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Conventional Oil

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Unconventional Oil

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Conventional Gas

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Unconventional Gas

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Uranium

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Bioenergy

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Water

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Raw Materials

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Land

• Yes (fixed)
• Yes (supply curve)
• Yes (process model)

Other resource use

Note: Bioenergy linked with AIM/PLUM

Technological change

Energy conversion technologies

• No technological change
• Exogenous technological change
• Endogenous technological change

Energy End-use

• No technological change
• Exogenous technological change
• Endogenous technological change

Material Use

• No technological change
• Exogenous technological change
• Endogenous technological change

Agriculture (tc)

• No technological change
• Exogenous technological change
• Endogenous technological change

Energy

Model documentation: Energy - AIM-Hub

Energy technology substitution

Energy technology choice

• No discrete technology choices
• Logit choice model
• Production function
• Linear choice (lowest cost)
• Lowest cost with adjustment penalties

Energy technology substitutability

• Mostly high substitutability
• Mostly low substitutability
• Mixed high and low substitutability
• Mostly high substitutability in some sectors and mostly low substitutability in other sectors

Energy technology deployment

• Expansion and decline constraints
• System integration constraints

Energy

Electricity technologies

• Coal w/o CCS
• Coal w/ CCS
• Gas w/o CCS
• Gas w/ CCS
• Oil w/o CCS
• Oil w/ CCS
• Bioenergy w/o CCS
• Bioenergy w/ CCS
• Geothermal power
• Nuclear power
• Solar power
• Solar power-central PV
• Solar power-distributed PV
• Solar power-CSP
• Wind power
• Wind power-onshore
• Wind power-offshore
• Hydroelectric power
• Ocean power

Hydrogen production

• Coal to hydrogen w/o CCS
• Coal to hydrogen w/ CCS
• Natural gas to hydrogen w/o CCS
• Natural gas to hydrogen w/ CCS
• Oil to hydrogen w/o CCS
• Oil to hydrogen w/ CCS
• Biomass to hydrogen w/o CCS
• Biomass to hydrogen w/ CCS
• Nuclear thermochemical hydrogen
• Solar thermochemical hydrogen
• Electrolysis

Refined liquids

• Coal to liquids w/o CCS
• Coal to liquids w/ CCS
• Gas to liquids w/o CCS
• Gas to liquids w/ CCS
• Bioliquids w/o CCS
• Bioliquids w/ CCS
• Oil refining

Refined gases

• Coal to gas w/o CCS
• Coal to gas w/ CCS
• Oil to gas w/o CCS
• Oil to gas w/ CCS
• Biomass to gas w/o CCS
• Biomass to gas w/ CCS

Heat generation

• Coal heat
• Natural gas heat
• Oil heat
• Biomass heat
• Geothermal heat
• Solarthermal heat
• CHP (coupled heat and power)

Grid Infra Structure

Electricity

• Yes (aggregate)
• Yes (spatially explicit)

Gas

• Yes (aggregate)
• Yes (spatially explicit)

Heat

• Yes (aggregate)
• Yes (spatially explicit)

CO2

• Yes (aggregate)
• Yes (spatially explicit)

Hydrogen

• Yes (aggregate)
• Yes (spatially explicit)

Energy end-use technologies

Passenger transportation

• Passenger trains
• Buses
• Light Duty Vehicles (LDVs)
• Electric LDVs
• Hydrogen LDVs
• Hybrid LDVs
• Gasoline LDVs
• Diesel LDVs
• Passenger aircrafts

Freight transportation

• Freight trains
• Heavy duty vehicles
• Freight aircrafts
• Freight ships

Industry

• Steel production
• Aluminium production
• Cement production
• Petrochemical production
• Paper production
• Plastics production
• Pulp production

Residential and commercial

• Space heating
• Space cooling
• Cooking
• Refrigeration
• Washing
• Lighting

Land-use

Model documentation: Land-use - AIM-Hub

Land cover

• Cropland
• Cropland irrigated
• Cropland food crops
• Cropland feed crops
• Cropland energy crops
• Forest
• Managed forest
• Natural forest
• Pasture
• Shrubland
• Built-up area

Agriculture and forestry demands

• Agriculture food
• Agriculture food crops
• Agriculture food livestock
• Agriculture feed
• Agriculture feed crops
• Agriculture feed livestock
• Agriculture non-food
• Agriculture non-food crops
• Agriculture non-food livestock
• Agriculture bioenergy
• Agriculture residues
• Forest industrial roundwood
• Forest fuelwood
• Forest residues

Agricultural commodities

• Wheat
• Rice
• Other coarse grains
• Oilseeds
• Sugar crops
• Ruminant meat
• Non-ruminant meat and eggs
• Dairy products

Emission, climate and impacts

Model documentation: Emissions - AIM-Hub, Climate - AIM-Hub, Non-climate sustainability dimension - AIM-Hub

Greenhouse gases

• CO2 fossil fuels
• CO2 cement
• CO2 land use
• CH4 energy
• CH4 land use
• CH4 other
• N2O energy
• N2O land use
• N2O other
• CFCs
• HFCs
• SF6
• PFCs

Pollutants

• CO energy
• CO land use
• CO other
• NOx energy
• NOx land use
• NOx other
• VOC energy
• VOC land use
• VOC other
• SO2 energy
• SO2 land use
• SO2 other
• BC energy
• BC land use
• BC other
• OC energy
• OC land use
• OC other
• NH3 energy
• NH3 land use
• NH3 other

Climate indicators

• Concentration: CO2
• Concentration: CH4
• Concentration: N2O
• Concentration: Kyoto gases
• Radiative forcing: CO2
• Radiative forcing: CH4
• Radiative forcing: N2O
• Radiative forcing: F-gases
• Radiative forcing: Kyoto gases
• Radiative forcing: aerosols
• Radiative forcing: land albedo
• Radiative forcing: AN3A
• Radiative forcing: total
• Temperature change
• Sea level rise
• Ocean acidification

Carbon dioxide removal

• Bioenergy with CCS
• Reforestation
• Afforestation
• Soil carbon enhancement
• Direct air capture
• Enhanced weathering

Climate change impacts

• Agriculture
• Energy supply
• Energy demand
• Economic output
• Built capital
• Inequality

Co-Linkages

• Energy security: Fossil fuel imports & exports (region)
• Energy access: Household energy consumption
• Air pollution & health: Source-based aerosol emissions
• Air pollution & health: Health impacts of air Pollution
• Food access
• Water availability
• Biodiversity

Model Documentation - AIM-Hub

The AIM is an integrated assessment model framework which consists of 4 models. The core of the model is AIM/CGE. The whole economic activities are represented in the AIM/CGE, but energy, agriculture and land use are relatively disaggregated than other sectors. AIM/PLUM deals with spatial explicit land use downscaling and also provides bioenergy supply curve. AIM/Dynamic provides global total emissions constraints for AIM/CGE incorporating MAC (Marginal Abatement Cost Curve) and non-CO2 information from AIM/CGE. The climate outcomes are made by MAGICC. This wiki page mostly explains about AIM/CGE.

1) Model scope and methods - AIM-Hub

The Asia-Pacific Integrated Modeling/Computable General Equilibrium (AIM/CGE) model was developed to analyze the climate change mitigation and its impact. To meet this objective, the energy system is disaggregated into energy supply and demand sides. Agricultural sectors are also disaggregated for appropriate land-use treatment. The model is designed to have the flexibility to be used at a global and individual country scale.

1.1) Model concept, solver and details - AIM-Hub

The AIM/CGE model was developed to analyze the future climate change mitigation and its impact on economic conditions. AIM/CGE is classified as a computable general equilibrium model, which covers all economic goods while considering production factor interactions. The trade of goods and services is also considered.

To meet this objective, the energy system is disaggregated into energy supply and demand sides. Agricultural sectors are also disaggregated for appropriate land-use treatment. The model is designed to have the flexibility for use at global and individual country scales.

The model is implemented for GAMS/MCP (Mixed Complementarity Problem), and PATH is used as a solver. The model is a dynamic recursive model using a 1-year time step. The simplified climate component is connected through soft links using MAGICC6. The CGE model itself has no feedback from the climate component.

1.3) Temporal dimension - AIM-Hub

In terms of temporal scale, the base year of AIM/CGE is 2005. AIM/CGE can be run for the 2005--2100 period. For some applications, the model is run up to 2050. The time step of the model solution is one year.

1.4) Spatial dimension - AIM-Hub

The geographical resolution of this system is 17 socio-economic regions. The regional classification is shown below.

Code	Description	Code	Description
JPN	Japan	TUR	Turkey
CHN	China	CAN	Canada
IND	India	USA	United States
XSE	Southeast Asia	BRA	Brazil
XSA	Rest of Asia	XLM	Rest of South America
XOC	Oceania	XME	Middle East
XE25	EU 25	XNF	North Africa
XER	Rest of Europe	XAF	Rest of Africa
CIS	Former Soviet Union

Error creating thumbnail: File missing

Map of AIM/CGE regional classification.

1.5) Policy - AIM-Hub

AIM/CGE can assess several types of policy. Key areas where policy responses can be introduced in to the model are:

• Climate policy
  • Mitigation (e.g. carbon tax and recycle, emissions trading)
  • Adaptation (e.g. food consumption aid or subsidy)
• Energy policy (e.g. air pollution, energy taxes)
• Land use and agriculture policy
• Other policies (e.g. income tax change, subsidy change and so on )

  2) Socio-economic drivers - AIM-Hub

Socio-economic drivers are typically informed by a scenario narrative that in qualitative terms describes the overall logic behind the scenarios. In the case of AIM/CGE, the Shared Socio-economic Pathways (SSPs, see O’Neill et al., 2014 1) provide this overall scenario logic based on which the main socio-economic drivers, population and GDP, have been quantified. The subsections of this chapter describe how these quantitative drivers are used in AIM/CGE.

2.1) Population - AIM-Hub

Demography
Future demographic change is one of the key drivers to change the demand for goods in the future, including energy and food. The production side is also affected by demographic changes through labor participation. Population and labor forces are exogenous parameters in AIM/CGE. Currently, the Shared Socioeconomic Pathways (SSPs) population data made available by the International Institute for Applied Systems Analysis (IIASA) is used as the reference demographic assumption which is originally represented at the country level SSP database. Usual model exercise, AIM/CGE uses SSP2 scenario.

2.2) Economic activity - AIM-Hub

Macro-economy
The future macro-economic assumption also causes changes in the supply and demand of goods. The macroeconomic assumption is also an exogenous assumption. There are two ways of treatment of macroeconomic assumption differentiating between baseline and mitigation scenarios. In baseline scenario, GDP is assumed as exogenous. Instead, TFP is assumed as endogenous. Usually, the change in GDP is used for the macroeconomic assumption for future scenario simulation. However, the actual outcome from the model is not exactly the same as the assumptions. Therefore, the GDP assumption is used to calculate the total factor productivity (TFP), and this is a totally exogenous parameter of the model. In mitigation scenario, we use the TFP values which is calculated in baseline scenarios.

3) Macro-economy - AIM-Hub

AIM/CGE represent whole economic activity. The production activities are represented as production functions which is mostly formulated by multi-nested CES function. The household expenditure is based on Stone-Geary utility function which derives LES consumption function (https://en.wikipedia.org/wiki/Stone%E2%80%93Geary_utility_function). The consumption, production and trade of goods and services are determined by market prices. Capital and labor allocation is also determined by wages and return of capital. Hence, Macroeconomy is a results of those activities.

The industrial classification is shown below

Agricultural sectors	Energy supply sectors	Other production sectors
Rice	Oil mining	Mineral mining and other quarrying
Wheat	Gas mining	Food products
Other grains	Coal mining	Textiles, apparel, and leather
Oil seed crops	Petroleum refinery	Wood products
Sugar crops	Coal transformation	Paper, paper products, and pulp
Other crops	Biomass transformation (1st generation)	Chemical, plastic, and rubber products
Ruminant livestock	Biomass transformation (2nd generation with energy crop)	Iron and steel
Raw milk	Biomass transformation (2nd generation with residue)	Nonferrous products
Other livestock and fishery	Gas manufacture distribution	Other manufacturing
Forestry	Coal-fired power	Construction
	Oil-fired power	Transport and communications
	Gas-fired power	Other service sectors
	Nuclear power	CCS services
	Hydroelectric power
	Geothermal power
	Photovoltaic power
	Wind power (onshore)
	Wind power (offshore)
	Waste biomass power
	Other renewable energy power generation
	Advanced biomass--- power generation

4) Energy - AIM-Hub

AIM/CGE is a computable general equilibrium model which deals with detailed sectoral representation in energy sectors. The energy demand is determined by production function in industrial activities and consumption function in the household sector. The industrial activities have substitution between energy and value-added and household consumption is formulated by LES (Linear Expenditure System) function. The fossil resource cost is associated with cumulative resource extraction. The energy transformation sectors are represented by multi power generation sectors and refineries for oil and biomass.

AIM/CGE covers all greenhouse gas (GHG)-emitting sectors, including energy, industrial processes as well as agriculture and forestry. The emissions of the full basket of greenhouse gases including CO2, CH4, N2O and F-gases (CF4, C2F6, HFC125, HFC134a, HFC143a, HFC227ea, HFC245ca and SF6) as well as other radiatively active gases, such as NOx, volatile organic compounds (VOCs), CO, SO2, and BC/OC is represented in the model. AIM/CGE is used in conjunction with MAGICC (Model for Greenhouse gas Induced Climate Change) (cf. Section Climate of AIM-Hub) for calculating atmospheric concentrations, radiative forcing, and annual-mean global surface air temperature increase.

4.1) Energy resource endowments - AIM-Hub

The energy resource endowments are explained for each subsections.

4.2) Energy conversion - AIM-Hub

AIM/CGE includes petroleum refinery, coal transformation, biomass transformation, town gas, and power generation sectors. Energy conversion occurs in production sectors. The share of energy consumed by a particular source of power generation is determined by a logit function which is often used for selection of several alternatives (https://en.wikipedia.org/wiki/Logit). Biomass transformation is also included in biofuels. Almost all sectors can install carbon capture and storage (CCS) as one of their CO2 emission-reduction measures, with the exception of town gas.
There are sectors which provide CCS service to corresponding sectors (coal power generation) and they install at certain carbon price level. The input of all energy conversion sectors is formulated as a Leontief-type production function (fixed input-output coefficient) to deal appropriately with the energy balance condition or energy conversion factor.

4.2.3) Gaseous fuels - AIM-Hub

AIM/CGE deals with town gas.

4.3) Energy end-use - AIM-Hub

Energy end-use is formulated differently in each sector, and therefore, the explanation of those sectors are in each section Transport, Residential and commercial sectors, and Industry.

4.3.1) Transport - AIM-Hub

The transport sector in the IAM usually includes industrial activities that provide transport services and household (i.e., own-use) car driving. The former is formulated as part of the industrial activity in AIM/CGE, and the latter is considered part of the consumption of household goods.

4.3.2) Residential and commercial sectors - AIM-Hub

In terms of the commercial sector, energy demand is determined as for the industrial sector.

For the residential sector, there are two options. One is the use of LES functions which Stone-Geary utility function is the basis (https://en.wikipedia.org/wiki/Stone%E2%80%93Geary_utility_function). The parameters that determine expenditure preference are recursively updated according to the given income elasticity. The other option enables the consideration of bottom-up energy technological information and the energy demand explicitly determined by detailed energy technologies. The default treatment is LES.

4.3.3) Industrial sector - AIM-Hub

The model has two options for determining energy demands from the industrial sector. One is the use of traditional functions such as the CES function for production sectors. The other option enables the consideration of bottom-up energy technological information and the energy demand explicitly determined by detailed energy technologies. Usually, for relatively long-term analysis (such as 2100), the Constant Elasticity Substitution (CES) function is used. The nested structure and elasticity values are shown in fiugre 1.

Error creating thumbnail: File missing

Figure 1: Industrial structure for non-energy industry sectors

5) Land-use - AIM-Hub

A function is used whereby land is an input for the production of crops and livestock products, and landowners change its use in accordance with the prices of goods produced on cropland, pastureland, and in forests. The model has a land nesting strategy, which is similar to the treatment in Sands and Edmonds (2005) and Wise and Calvin (2011). Land is categorized as one of nine ecological zones, and there is a land market for each zone. Allocation of land by sector is formulated as a multinominal logit function to reflect the differences in substitutability across land categories with land rent. Multinominal logit function allows us multi level nesting structure in logit selection. The function assumes that the landowner of each region and Agro-Ecological Zone (AEZ) decides on the land distribution among the possible options, with the land rent dependent on the production of each land type (i.e., crops, livestock, and wood products). We deal with all land excluding desert, rock, ice, tundra, and built-up land. The original social accounting matrix data has 18 AEZ classifications but the model deals with aggregated 9 classification.

The figure shows the nesting diagram for land using AEZ classification. We consider all land excluding desert, rock, ice, tundra, and built-up land. There are 18 AEZ classifications. At the top is all land, which is divided into two main nodes: forestry land and non-forest land. The forestry-land node contains two competing uses: primary forest (unmanaged forest) and secondary forest (managed forest). The non-forested land can be divided into grassland and cropland. The grassland can be further divided into primary grassland (unmanaged pasture) and grazing grassland (managed pasture that feeds marketed livestock); the latter is further divided into livestock types (1 to n). The cropland could be divided further into cropland for each crop type (1 to n) and fallow land. The nesting strategy is based on the assumption that the land regions are small enough that all competing options are equally substitutable. This assumption implies that it is as easy to switch from forest to wheat as it is to switch from corn to wheat. However, this conversion would not happen unless wheat was more profitable than forest or corn. The function assumes that the landowner of each region and AEZ subregion decides on the land distribution among the possible options depending on the land rent obtained from production with each land use (i.e., crops, livestock, and wood products). To calibrate the function for both the managed and unmanaged land in the base year, we took the mean base-year land rent of the managed land to be that of the unmanaged land because data for the unmanaged land were lacking. The carbon stock on forest land was evaluated by the price in the case of the climate mitigation scenario. The land rent of forest areas includes both the revenue from wood products and the price of the carbon stock.

5.1) Agriculture - AIM-Hub

There are three cereal sectors, three other aggregated crop sectors, and two aggregated livestock sectors in AIM/CGE. Producers are assumed to maximize profits subject to the availability of appropriate technology (production functions) and the price of inputs. The first-order conditions for profit maximization essentially define the factor demands and output supply behavior of producers. The production structure is the same as for other industrial sectors except for the treatment of land input. The land input is assumed by multiplying output by a coefficient. However, in some cases, this fixed coefficient approach makes it difficult to solve the program if the land constraint is substantially critical. Therefore, the term related to output price elasticity is assumed. If price elasticity is very small (e.g., 0.05) and the model results can be interpreted, the land input is treated almost as a Leontief-type input.

6) Emissions - AIM-Hub

In the sub-sections of this chapter, the GHG and non-GHG emissions included in AIM/CGE are presented.

6.1) GHGs - AIM-Hub

AIM/CGE simulates emissions from long-lived GHGs (CO2, CH4, N2O, F-gases, Montreal Protocol gases, HFCs). CO2 emissions from fuel combustion are calculated based on energy sources with fixed coefficient. CO2 resulting from land-use changes is endogenously calculated as a consequence of the land use (taking difference of land use from previous year). Other CO2 emissions, CH4, and N2O emissions are basically associated with each sector's activity level. Emissions of the other gases are calculated based on constant income elasticity.
Reduction in energy-related emissions is associated with detailed technological options, which provide both improved energy efficiency and a carbon factor reduction. The CO2 resulting from land-use changes is reduced under the scenario including the pricing of carbon stock. Other reduction measures for the non-CO2 emissions use models based on the marginal abatement cost (MAC) curve with an exponential function.

6.2) Pollutants and non-GHG forcing agents - AIM-Hub

Air pollution implications are derived with the help of the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model which allows for the development of cost-effective emission control strategies to meet environmental objectives on climate, human health and ecosystem impacts until 2030. These impacts are considered in a multi-pollutant context, quantifying the contributions of sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), non-methane volatile organic compounds (VOC), and primary emissions of particulate matter (PM), including fine and coarse PM as well as carbonaceous particles (BC, OC). The results of such scenarios are used as input to global IAM frameworks to characterize air pollution trajectories associated with various long-term energy developments.

7) Climate - AIM-Hub

Climate in AIM/CGE is modeled by the MAGICC6 model. The MAGICC model is a simple climate model that has been calibrated based on historical data and data from more complex climate models. It can therefore represent individual climate models that were used in CMIP3 and C4MIP. The main inputs into the MAGICC model are emissions of greenhouse gases and air pollutants that are the outcomes of the CGE simulations. These are fed into MAGICC. The main outputs are the changes in global mean temperature and radiative forcing levels. For more information about the model, see www.magicc.org.

7.1) Modelling of climate indicators - AIM-Hub

Climate in AIM/CGE is modeled by the MAGICC6 model. The MAGICC model is a simple climate model that has been calibrated based on historical data and data from more complex climate models. It can therefore represent individual climate models that were used in CMIP3 and C4MIP. The main inputs into the MAGICC model are emissions of greenhouse gases and air pollutants that are the outcomes of the CGE simulations. These are fed into MAGICC. The main outputs are the changes in global mean temperature and radiative forcing levels.

8) Non-climate sustainability dimension - AIM-Hub

Food security dimension is treated with risk of hunger, its DALY and VSL.

10) References - AIM-Hub

1. Hasegawa T, Fujimori S, Shin Y, Tanaka A, Takahashi K, Masui T. Consequence of Climate Mitigation on the Risk of Hunger. Environmental science & technology 2015. (http://pubs.acs.org/doi/abs/10.1021/es5051748?journalCode=esthag)
2. Hasegawa T, Fujimori S, Masui T, Matsuoka Y. Introducing detailed land-based mitigation measures into a computable general equilibrium model. Journal of Cleaner Production, (http://www.sciencedirect.com/science/article/pii/S0959652615003406).
3. Hasegawa T., Fujimori S., Takahashi K. and Masui T., (2015) Scenarios for the Risk of Hunger in the 21st Century using Shared Socioeconomic Pathways, Environmental Research Letters, 10(1), 014010. (http://iopscience.iop.org/1748-9326/10/1/014010)
4. Fujimori S., Masui T., Matsuoka Y. (2015) Gains from Emissions Trading Under Multiple Stabilization Targets and Technological Constraints. Energy Economics, 48, 306-315.?(http://www.sciencedirect.com/science/article/pii/S0140988314003272).
5. Fujimori S., Kainuma M., Masui T., Hasegawa T., Dai H. (2014) The Effectiveness of Energy Service Demand Reduction: a Scenario Analysis of Global Climate Change Mitigation. Energy Policy, 75, 379-391, doi: 10.1016/j.enpol.2014.09.015. (http://www.sciencedirect.com/science/article/pii/S0301421514005060)
6. Fujimori S., Hasegawa T., Masui T. and Takahashi K., Land use representation in a global CGE model for long-term simulation: CET vs. logit functions, Food Security, 6(5), 685-699,doi:10.1007/s12571-014-0375-z. (http://link.springer.com/article/10.1007%2Fs12571-014-0375-z)
7. Fujimori S., Masui T., Matsuoka Y. (2014) Development of a global computable general equilibrium model coupled with detailed energy end-use technology. Applied Energy, 128 (1), 296-306, (http://www.sciencedirect.com/science/article/pii/S0306261914004371)
8. Ishida H., Kobayashi S., Kanae S., Hasegawa T., Fujimori S., Shin Y., Takahashi K., Masui T., Tanaka A., Honda Y. (2014) Global-scale projection and its sensitivity analysis of the health burden attributable to childhood undernutrition under the latest scenario framework for climate change research. Environmental Research Letters, 9 (6), 064014-064022. (http://iopscience.iop.org/1748-9326/9/6/064014)
9. Hasegawa T., Fujimori S., Shin Y., Takahashi K., Masui T., Tanaka A. (2014) Climate change impact and adaptation assessment on food consumption utilizing a new scenario framework. Environmental Science and Technology, 48, 438-445. (http://pubs.acs.org/doi/abs/10.1021/es4034149)
   
10. S. Fujimori, T. Masui, Y. Matsuoka, 2012, AIM/CGE [basic] manual. Discussion paper series, 2012-01, Center for Social and Environmental Systems Research, National Institute EnvironemntalStudies ([http://www.nies.go.jp/social/dp/pdf/2012-01.pdf