Reference card model matrix

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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 Institution
AIM-CGE

AIM-CGE

BLUES

BLUES 1.0

COFFEE
DNE21+

DNE21+

GEM-E3

GEM-E3

IMACLIM

IMACLIM- R I.0

IMAGE

IMAGE framework 3.0

IPETS

IPETS 2.0

MESSAGE-GLOBIOM

MESSAGE-GLOBIOM 1.0

POLES

POLES ADVANCE (other versions are in use in other applications)

REMIND

REMIND 1.5

TIAM-UCL

TIAM-UCL

WITCH

WITCH

Model scope and methods

Objective Concept Solution method Anticipation Temporal dimension Spatial dimension Policy implementation
AIM-CGE

AIM/CGE is developed to analyse the climate mitigation and impact. The energy system is disaggregated to meet this objective in both of energy supply and demand sides. Agricultural sectors have also been disaggregated for the appropriate land use treatment. The model is designed to be flexible in its use for global analysis.

General Equilibrium with technology explicit modules in power sectors

Solving a mixed complementarity problem

Myopic

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

Number of regions:17

  1. Japan
  2. China
  3. India
  4. Southeast Asia
  5. Rest of Asia
  6. Oceania
  7. EU25
  8. Rest of Europe
  9. Former Soviet Union
  10. Turkey
  11. Canada
  12. United States
  13. Brazil
  14. Rest of South America
  15. Middle East
  16. North Africa
  17. Rest of Africa

Climate policy such as emissions target, Emission permits trading and so on Energy taxes and subsidies

BLUES

BLUES is a model of Brazilian land use and energy system. The objective of the model is to explore long-term dynamics and impacts resulting from the interaction of energy, emissions and land use constraints under different policy scenarios.

BLUES is a cost-optimization model aiming to represent interactions between human activities and the environment. Such activities include agriculture and industrial activity; energy services demand for mobility, heat and lighting; and environmental protection.

Perfect Foresight Cost Minimization

Used in conjunction with the global COFFEE model which determines boundary conditions such as global energy costs and emissions budgets. this allows for globally consistent representation of traded commodities in the national context. Currently under development is a CGE model that will provide global macroeconomic consistency.

Base year:2010, time steps:5 years, horizon: 2050

Number of regions:6

  1. Brasil, North, Northeast, Midwest, Southeast, South

Climate policy

Energy policies (System expansion, shares, intermittent source constraints)

Land use policies (food and bio-energy)

COFFEE

Base year:, time steps:, horizon:

Number of regions:

DNE21+

Base year:, time steps:, horizon:

Number of regions:

GEM-E3

The model puts emphasis on:

i) The analysis of market instruments for energy-related environmental policy, such as taxes, subsidies, regulations, emission permits etc., at a degree of detail that is sufficient for national, sectoral and World-wide policy evaluation.

ii) The assessment of distributional consequences of programmes and policies, including social equity, employment and cohesion for less developed regions.

General equilibrium

The model is formulated as a simultaneous system of equations with an equal number of variables. The system is solved for each year following a time-forward path. The model uses the GAMS software and is written as a mixed non-linear complementarity problem solved by using the PATH algorithm using the standard solver options.

Myopic

Base year:2011, time steps:Five year time steps, horizon: 2050

Number of regions:38

  1. Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Germany, Denmark, Spain, Estonia, Finland, France, United Kingdom, Greece, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Malta, Netherlands, Poland, Portugal, Slovakia, Slovenia, Sweden, Romania, USA, Japan, Canada, Brazil, China, India, Oceania, Russian federation, Rest of Annex I, Rest of the World

Taxes, Permits trading, Subsidies, Energy efficiency standards, CO2 standards, Emission reduction targets, Trade agreements.

IMACLIM

Imaclim-R is intended to study the interactions between energy systems and the economy, to assess the feasibility of low carbon development strategies and the transition pathway towards low carbon future.

Hybrid: general equilibrium with technology explicit modules. Recursive dynamics: each year the equilibrium is solved (system of non-linear equations), in between two years parameters to the equilibrium evolve according to specified functions.

Imaclim-R is implemented in Scilab, and uses the fonction fsolve from a shared C++ library to solve the static equilibrium system of non-linear equations.

Recursive dynamics: each year the equilibrium is solved (system of non-linear equations), in between two years parameters to the equilibrium evolve according to specified functions.

Base year:2001, time steps:Annual, horizon: 2050 or 2100

Number of regions:12

  1. USA
  2. Canada
  3. Europe
  4. China
  5. India
  6. Brazil
  7. Middle East
  8. Africa
  9. Commonwealth of Independant States
  10. OECD Pacific
  11. Rest of Asia
  12. Rest of Latin Amercia

Baseline do not include explicit climate policies.

Climate/energy policies can be implemented in a number of ways, depending on the policy. A number of general or specific policy choices can be modelled including:

Emissions or energy taxes, permit trading, specific technology subsidies, regulations, technology and/or resource constraints

IMAGE

IMAGE is an ecological-environmental model framework that simulates the environmental consequences of human activities worldwide. The objective of the IMAGE model is to explore the long- term dynamics and impacts of global changes that result. More specifically, the model aims

  1. to analyse interactions between human development and the natural environment to gain better insight into the processes of global environmental change;
  2. to identify response strategies to global environmental change based on assessment of options and
  3. to indicate key inter-linkages and associated levels of uncertainty in processes of global environmental change.

The IMAGE framework can best be described as a geographically explicit assessment, integrated assessment simulation model, focusing a detailed representation of relevant processes with respect to human use of energy, land and water in relation to relevant environmental processes.

Recursive dynamic solution method

Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations.

Base year:1970, time steps:1-5 year time step, horizon: 2100

Number of regions:26

  1. Canada
  2. USA
  3. Mexico
  4. Rest of Central America
  5. Brazil
  6. Rest of South America
  7. Northern Africa
  8. Western Africa
  9. Eastern Africa
  10. South Africa
  11. Western Europe
  12. Central Europe
  13. Turkey
  14. Ukraine +
  15. Asian-Stan
  16. Russia +
  17. Middle East
  18. India +
  19. Korea
  20. China +
  21. Southeastern Asia
  22. Indonesia +
  23. Japan
  24. Oceania
  25. Rest of South Asia
  26. Rest of Southern Africa

Key areas where policy responses can be introduced in the model are:

  • Climate policy
  • Energy policies (air pollution, access and energy security)
  • Land use policies (food)
  • Specific policies to project biodiversity
  • Measures to reduce the imbalance of the nitrogen cycle

IPETS

The iPETS model is developed to analyze greenhouse gas mitigation and climate change impacts with a special emphasis on the implications of demographic heterogeneity.

Computable General Equilibrium

The economic problem is formulated as a three-level nested problem. The solution of these three sub-problems yield the dynamic capital path (investment/consumption trade-off in each simulation year), and factor and output prices which clear all factor and goods markets.

Forward looking

Base year:2004, time steps:annual, horizon: 2100

Number of regions:9

  1. China
  2. EU27+
  3. India
  4. Latin America
  5. Other Developing Countries
  6. Other Industrialized Countries
  7. sub-Saharan Africa
  8. Transition Countries
  9. USA

Climate policy through (global or regional) carbon tax or emission target (annual level or temporal budget)

MESSAGE-GLOBIOM

MESSAGE-GLOBIOM is an integrated assessment framework designed to assess the transformation of the energy and land systems vis-a-vis the challenges of climate change and other sustainability issues. It consists of the energy model MESSAGE, the land use model GLOBIOM, the air pollution and GHG model GAINS, the aggregated macro-economic model MACRO and the simple climate model MAGICC.

Hybrid model (energy engineering and land use partial equilibrium models soft-linked to macro-economic general equilibrium model)

Hybrid model (linear program optimization for the energy systems and land-use modules, non-linear program optimization for the macro-economic module)

Myopic/Perfect Foresight (MESSAGE can be run both with perfect foresight and myopically, while GLOBIOM runs myopically)

Base year:2010, time steps:1990, 1995, 2000, 2005, 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100, 2110, horizon: 1990-2110

Number of regions:11+1

  1. AFR (Sub-Saharan Africa)
  2. CPA (Centrally Planned Asia & China)
  3. EEU (Eastern Europe)
  4. FSU (Former Soviet Union)
  5. LAM (Latin America and the Carribean)
  6. MEA (Middle East and North Africa)
  7. NAM (North America)
  8. PAO (Pacific OECD)
  9. PAS (Other Pacific Asia)
  10. SAS (South Asia)
  11. WEU (Western Europe)
  12. GLB (international shipping)

GHG and energy taxes; GHG emission cap and permits trading; energy taxes and subsidies; micro-financing (for energy access analysis); regulation: generation capacity, production and share targets

POLES

POLES was originally developed to assess energy markets, combining a detailed description of energy demand, transformation and primary supply for all energy vectors.

It provides full energy balances on a yearly basis using frequent data updates to as to deliver robust forecasts for both short and long-term horizons. It has quickly been used, in the late 90s, to assess energy-related CO2 mitigation policies.

Over time other GHG emissions have been included (energy and industry non-CO2 from the early 2000s), and linkages with agricultural and land use models have been progressively implemented.

Partial equilibrium

Recursive simulation

Myopic

Base year:1990-2015 (data up to current time -1/-2), time steps:Yearly, horizon: 2050-2100

Number of regions:57

- Energy taxes per sector and fuel, carbon pricing

- Feed-in tariffs, green certificates, low interest rates, investment subsidies

- Fuel efficiency standards in vehicles and buildings, white certificates

REMIND

REMIND is a global multi-regional model incorporating the economy,

the climate system and a detailed representation of the energy sector. REMIND allows for a sophisticated analysis of technology options and

policy proposals for climate mitigation. It accounts for economic and energy investments in the model regions, and interregional trade in goods, energy carriers and emissions allowances.

Hybrid Hybrid model that couples an economic growth model with a detailed energy system model and a simple climate model.

Inter-temporal optimization that maximizes cumulated discounted global welfare: Ramsey-type growth model with Negishi approach to regional welfare aggregation.

Perfect Foresight

Base year:2005, time steps:flexible time steps but the default is 5-year time steps until 2050 and 10-year time steps until 2100; the period from 2100-2150 is calculated to avoid distortions due to end effects, but typically we only use the time span 2005-2100 for model applications, horizon: 2005-2150

Number of regions:11

  1. AFR - Sub-Saharan Africa
  2. CHN - China
  3. EUR - European Union
  4. JPN - Japan
  5. IND - India
  6. LAM - Latin America
  7. MEA - Middle East, North Africa, and Central Asia
  8. OAS - other Asian countries (mainly South-East Asia)
  9. RUS - Russia
  10. ROW - rest of the World (Australia, Canada, New Zealand, Norway, South Africa)
  11. USA - United States of America

Pareto-optimal achievement of policy targets on temperature, radiative forcing, GHG concentration, cumulative carbon budgets, or CO2 emissions over time under full when- and where-flexibility. Implementation of permit allocation rules among regions. Possibility of pre-specified carbon tax pathway. Fossil fuel subsidies and taxes.

TIAM-UCL

TIAM-UCL (TIMES Integrated Assessment Model) uses the TIMES modelling platform, which

is a successor of the MARKAL platform. The markal/times modelling concept was originally intended to analyse energy systems at a regional or global level and has evolved to also describe greenhouse gas emissions. Scenario based simulations maximize the total discounted sum of consumer and supplier

surplus over the model horizon, while taking into account the constraints (e.g. energy demand to be fulfilled, availability of energy resources etc).

Energy Systems partial equilibrium

Linear optimisation

Perfect Foresight (Stochastic and myopic runs are also possible)

Base year:2005, time steps:5 years up to 2070 and 10 years beyond, horizon: 95 years (2005-2100)

Number of regions:16

  1. Africa
  2. Australia
  3. Canada
  4. Central and South America
  5. China
  6. Eastern Europe
  7. Former Soviet Union
  8. India
  9. Japan
  10. Mexico
  11. Middle East
  12. Other Developing Asia
  13. South Korea
  14. United Kingdom
  15. United States of Amercia
  16. Western Europe

Policies can be implemented in a number of ways, depending on the type of policy.

A number of general or specific policy choices can be modelled including:

Emissions taxes, permit trading, specific technology subsidies, technology and/or resource constraints.

WITCH

WITCH evaluates the impacts of climate policies on global and regional economic systems and provides information on the optimal responses of these economies to climate change. The model considers the positive externalities from leaning-by-doing and learning-by-researching in the technological change.

Hybrid: Economic optimal growth model, including a bottom-up energy sector and a simple climate model, embedded in a `game theory` framework.

Regional growth models solved by non-linear optimization and game theoretic setup solved by tatonnement algorithm (cooperative solution: Negishi welfare aggregation, non-cooperative solution: Nash equilibrium)

Perfect foresight

Base year:2005, time steps:5, horizon: 2150

Number of regions:14

  1. cajaz: Canada, Japan, New Zeland
  2. china: China, including Taiwan
  3. easia: South East Asia
  4. india: India
  5. kosau: South Korea, South Africa, Australia
  6. laca: Latin America, Mexico and Caribbean
  7. indo: Indonesia
  8. mena: Middle East and North Africa
  9. neweuro: EU new countries + Switzerland + Norway
  10. oldeuro: EU old countries (EU-15)
  11. sasia: South Asia
  12. ssa: Sub Saharan Africa
  13. te: Non-EU Eastern European countries, including Russia
  14. usa: United States of America

Quantitative climate targets (temperature, radiative forcing, concentration), carbon budgets, emissions profiles as optimization constraints.

Carbon taxes. Allocation and trading of emission permits, banking and borrowing.

Subsidies, taxes and penalty on energies sources.

Socio economic drivers

Exogenous drivers Endogenous drivers Development
AIM-CGE
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • GDP
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
BLUES
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • Learning-by-doing
  • Population
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
COFFEE
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
DNE21+
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
GEM-E3
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • Active population growth
  • Learning-by-doing
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
IMACLIM
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • Population
  • Active Population
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
IMAGE
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • Energy demand
  • Renewable price
  • Fossil fuel prices
  • Carbon prices
  • Technology progress
  • Energy intensity
  • Preferences
  • Learning by doing
  • Agricultural demand
  • Population
  • Value added
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
IPETS
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
MESSAGE-GLOBIOM
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • Population
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
POLES
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • Population
  • Value added
  • Mobility needs
  • Fossil fuel prices
  • Buildings surfaces
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
REMIND
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
TIAM-UCL
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • Population
  • GDP per household
  • Learning by doing
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate
WITCH
  • Exogenous GDP
  • Total Factor Productivity
  • Labour Productivity
  • Capital Technical progress
  • Energy Technical progress
  • Materials Technical progress
  • GDP per capita
  • GDP per capita
  • Income distribution in a region
  • Urbanisation rate
  • Education level
  • Labour participation rate

Macro economy

Economic sectors Cost measures Trade
AIM-CGE
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
BLUES
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • Households
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
  • Bioenergy products
  • Diesel
  • Gasoline
  • Agriculture
COFFEE
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
DNE21+
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
GEM-E3
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • other
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
  • Agriculture
  • Ferrous and non ferrous metals
  • Chemical Products
  • Other energy intensive
  • Electric Goods
  • Transport equipment
  • Other Equipment Goods
  • Consumer Goods Industries
IMACLIM
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • Construction
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
  • Refined Liquid Fuels
IMAGE
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
  • Bioenergy products
  • Livestock products
IPETS
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
MESSAGE-GLOBIOM
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
POLES
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
  • Liquid biofuels
REMIND
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
TIAM-UCL
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods
  • Diesel
  • LNG
  • Gasoline
  • Heavy fuel oil
  • Natural gas liquids
  • Naphtha
WITCH
  • Agriculture
  • Industry
  • Energy
  • Transport
  • Services
  • other
  • GDP loss
  • Welfare loss
  • Consumption loss
  • Area under MAC
  • Energy system costs
  • Coal
  • Oil
  • Gas
  • Uranium
  • Electricity
  • Bioenergy crops
  • Food crops
  • Capital
  • Emissions permits
  • Non-energy goods

Energy

Resource use Cost measures Electricity technologies Conversion technologies Grid and infrastructure Energy technology substitution Energy service sectors
AIM-CGE
  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
BLUES
  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
  • Agriculture
COFFEE
  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
DNE21+
  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
GEM-E3

The GEM-E3 model endogenously computes energy consumption, depending on energy prices, realised energy efficiency expenditures and autonomous energy efficiency improvements. Each agent decides how much energy it will consume in order to optimise its behaviour (i.e. to maximise profits for firms and utility for households) subject to technological constraints (i.e. a production function).

At a sectoral level, energy consumption is derived from profit maximization under a nested CES (Constant Elasticity of Substitution) specification. Energy enters the production function together with other production factors (capital, labour, materials). Substitution of energy and the rest of the production factors is imperfect (energy is considered an essential input to the production process) and it is induced by changes in the relative prices of each input.

Residential energy consumption is derived from the utility maximization problem of households. Households allocate their income between different consumption categories and savings to maximize their utility subject to their budget constraint. Consumption is split between durable (i.e. vehicles, electric appliances) and non-durable goods. For durable goods, stock accumulation depends on new purchases and scrapping. Durable goods consume (non-durable) goods and services, including energy products. The latter are endogenously determined depending on the stock of durable goods and on relative energy prices.

  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • Hydropower
  • CCS
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
IMACLIM

Price response (via elasticities), and non-price drivers (infrastructure and urban forms conditioning location choices, different asymptotes on industrial goods consumption saturation levels with income rise, speed of personal vehicle ownership rate increase, speed of residential area increase).

  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
  • Agriculture
IMAGE

In the energy model, substitution among technologies is described in the model using the multinomial logit formulation. The multinomial logit model implies that the market share of a certain technology or fuel type depends on costs relative to competing technologies. The option with the lowest costs gets the largest market share, but in most cases not the full market. We interpret the latter as a representation of heterogeneity in the form of specific market niches for every technology or fuel.

  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CSP
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
IPETS
  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • non-fossil
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Refined fuels
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
MESSAGE-GLOBIOM
  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CSP
  • Geothermal
  • Hydropower
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
POLES

Activity drivers depend on income per capita and energy prices via elasticities.

Energy demand depends on activity drivers, energy prices and technology costs.

Primary energy supply depends on remaining resources, production cost and price effects.

  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • Hydropower
  • Geothermal
  • Solar CSP
  • Ocean
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
REMIND

Price response through CES production function. No explicit modeling of behavioural change. Baseline energy demands are calibrated in such a way that the energy demand patterns in different regions slowly converge when displayed as per capita energy demand over per capita GDP"

  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • Solar CSP
  • Hydropower
  • Geothermal
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Heat plants
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
TIAM-UCL

Elastic demand mode available (includes exogenous elasticity of each energy demand with respect to their own price) Technology and region specific hurdle rates.

  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • Geothermal
  • Hydropower
  • Solar CSP
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial
  • Agriculture
WITCH
  • Coal
  • Oil
  • Gas
  • Uranium
  • Biomass
  • Coal
  • Gas
  • Oil
  • Nuclear
  • Biomass
  • Wind
  • Solar PV
  • CSS
  • CHP
  • Heat pumps
  • Hydrogen
  • Fuel to gas
  • Fuel to liquid
  • Electricity
  • Gas
  • Heat
  • CO2
  • H2
  • Discrete technology choices
  • Expansion and decline constraints
  • System integration constraints
  • Transportation
  • Industry
  • Residential and commercial

Land-use

Land-use
AIM-CGE
  • Abandoned land
  • Cropland
  • Forest
  • Grassland
  • Extensive Pastures
BLUES
  • Cropland
  • Extensive Pastures
  • Forest
  • Grassland
  • Intensive Pastures
  • Protected land
  • pasture
  • Integrated Systems
  • Double Cropping
  • Savannas
COFFEE
DNE21+
GEM-E3
  • GEM-E3 considers land as a separate production factor
IMACLIM
  • Cropland
  • Forest
  • Extensive Pastures
  • Intensive Pastures
  • Inacessible Pastures
  • Urban Areas
  • Unproductive Land
IMAGE
  • Forest
  • Cropland
  • Grassland
  • Abandoned land
  • Protected land
IPETS
  • Cropland
  • Forest
  • pasture
MESSAGE-GLOBIOM
POLES
  • Cropland
  • Forest
  • Grassland
  • Urban Areas
  • Desert
REMIND
TIAM-UCL
WITCH
  • Cropland
  • Forest

Other resources

Other resources
AIM-CGE
  • Water
  • Metals
  • Cement
BLUES
  • Water
  • Metals
  • Cement
  • Chemicals
COFFEE
  • Water
  • Metals
  • Cement
DNE21+
  • Water
  • Metals
  • Cement
GEM-E3
  • Water
  • Metals
  • Cement
IMACLIM
  • Water
  • Metals
  • Cement
IMAGE
  • Water
  • Metals
  • Cement
IPETS
  • Water
  • Metals
  • Cement
MESSAGE-GLOBIOM
  • Water
  • Metals
  • Cement
POLES
  • Water
  • Metals
  • Cement
REMIND
  • Water
  • Metals
  • Cement
TIAM-UCL
  • Water
  • Metals
  • Cement
WITCH
  • Water
  • Metals
  • Cement

Emissions and climate

Green house gasses
AIM-CGE
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
BLUES
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
COFFEE
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
DNE21+
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
GEM-E3
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
IMACLIM
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
IMAGE
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • VOC
  • NH3
  • CO
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
IPETS
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
MESSAGE-GLOBIOM
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO
  • NH3
  • VOC
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
POLES
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • PFCs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
REMIND
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO
  • VOC
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
TIAM-UCL
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent
WITCH
  • CO2
  • CH4
  • N2O
  • HFCs
  • CFCs
  • SFs
  • NOx
  • SOx
  • BC
  • OC
  • Ozone
  • CO2e concentration (ppm)
  • Radiative Forcing (Wm2 )
  • Temperature change (C°)
  • Climate damages $ or equivalent