Snapshot of - ENV-Linkages

Archive of ENV-Linkages, version: 4

Reference card - ENV-Linkages

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

ENV-Linkages 4

Model link

https://www.oecd.org/env/indicators-modelling-outlooks/flyer%20ENV-Linkages%20model%20-%20version%2025%20Sept%202013.pdf

Institution

Organisation for Economic Co-operation and Development (OECD), France, https://www.oecd.org/.

Documentation

ENV-Linkages documentation consists of a referencecard and detailed model documentation

Process state

in preparation

Model scope and methods

Model documentation: Model scope and methods - ENV-Linkages

Model type

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

Geographical scope

• Global
• Regional

Objective

The modelling work based on ENV-Linkages aims to assist governments in identifying least-cost policies or policy mixes on a range of environmental issues, including mitigation of climate change, phasing out fossil fuel subsidies and other green growth policies, such as environmental tax reform, policies to promote the transition to a circular economy, including linking to material and resources.

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

Note: ENV-Linkages model is written in the General Algebraic Modeling System (GAMS) modelling language. GAMS is particularly useful for numerical modelling of linear, nonlinear and mixed integer optimization systems.

Anticipation

The ENV-Linkages model is a recursive dynamic neo-classical general equilibrium model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise.

Temporal dimension

Base year:2014, time steps:Annual, horizon: 2050 or 2060

Spatial dimension

Number of regions:26

1. China
2. Japan
3. South Korea
4. India
5. Canada
6. United States
7. Mexico
8. Brazil
9. Russia
10. OECD Oceania (Australia and New Zealand)
11. Caspian countries
12. Chile and Colombia
13. Other Latin America
14. France, Germany and Italy
15. Other European Union
16. Bulgaria, Croatia, Cyprus, Malta, Romania
17. Indonesia
18. Middle East
19. North Africa
20. Other Africa
21. Other Southeast Asia
22. Other Asia
23. EFTA, Israel,Turkey
24. Other Europe
25. United Kingdom
26. South Africa

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
• Carbon pricing
• Fossil fuel support removal (FFSR)
• Regulations in the power sector to enforce a switch away from fossil fuels
• Regulations to stimulate investments to decarbonise building and transport emissions
• Policies to stimulate firms’ energy efficiency improvement
• Subsidies to reduce and decarbonise energy consumption by households

Socio-economic drivers

Model documentation: Socio-economic drivers - ENV-Linkages

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 - ENV-Linkages

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)

Macro-economy

Trade

• Coal
• Oil
• Gas
• Uranium
• Electricity
• Bioenergy crops
• Food crops
• Capital
• Emissions permits
• Non-energy goods
• Energy goods
• All other major traded economic activities

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)

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 - ENV-Linkages

Behaviour

Energy demands (projected by using elasticities of demands to GDP), for all kind of fuels demands, is controlled by calibration of the Autonomous Energy Efficiency Improvements (AEEIs) in energy use, by sector and type of fuel. Value-added is shown as being composed of a labour input, along with a composite capital-energy bundle. The energy bundle is of particular interest for analysis of climate change issues. Energy is a composite of fossil fuels and electricity. In turn, fossil fuel is a composite of coal and a bundle of the “other fossil fuels”. At the lowest nest, the composite “other fossil fuels” commodity consists of crude oil, refined oil products and natural gas. The value of the substitution elasticities are based on existing literature and calibrated to imply a higher degree of substitution among the other fuels than with electricity and coal. According to the vintage-structure of technologies, the fuel mix in energy production is more flexible when associated with new capital. For old capital vintage production technology the substitution possibilities between fuels are very limited.

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

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 - ENV-Linkages

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 - ENV-Linkages, Climate - ENV-Linkages, Non-climate sustainability dimension - ENV-Linkages

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

1) Model scope and methods - ENV-Linkages

The OECD ENV-Linkages Computable General Equilibrium (CGE) model is an economic model that describes how economic activities are inter-linked across several macroeconomic sectors and regions. It links economic activity to environmental pressure, specifically to emissions of greenhouse gases (GHGs). The links between economic activities and emissions are projected for several decades into the future, and thus shed light on the impacts of environmental policies for the medium- and long-term future.

The advantages of multi-sectoral, multi-regional (recursive-) dynamic GE models, like ENV-Linkages, involve their global dimension, their overall consistency, and the fact that they are based on rigorous microeconomic foundations. Each of the regions is underpinned by an economic input-output table (usually sourced from national statistical agencies). These tables quantify economic flows across the different economic agents, including purchases of intermediate products and primary factors in all industries and the associated production outputs, as well as sources of income for households and governments and the associated consumption expenditures.

1.1) Model concept, solver and details - ENV-Linkages

ENV-Linkages is fully homogeneous in prices and only relative prices matter. All prices are expressed relatively to the numéraire of the price system that is arbitrarily chosen as the export price index of high-income manufactured exports.

Market goods equilibria imply that, on the one side, the total production of any good or service is equal to the demand addressed to domestic producers plus exports; and, on the other side, that total demand is allocated, according to the Armington principle, between the demands (both final and intermediary) addressed to domestic producers and to import demand (see below).

All production in ENV-Linkages is assumed to operate under cost minimisation with an assumption of perfect markets and technologies that exhibit constant returns to scale.

Household consumption demand is the result of static maximization behaviour which is formally implemented as an “Extended Linear Expenditure System” (ELES).

In the standard macro closure:

-       household savings is determined by the ELES demand system,

-       government savings are fixed (in real terms) with the direct household tax schedule endogenous,

-       foreign savings are fixed (with the real exchange rate adjusting to ex ante changes in the trade balance).

-       Investment is savings determined.

The process of calibrating the ENV-Linkages model is broken down into three stages. First, a number of parameters are calibrated, given some elasticity values, to represent the data for historical year 2014 as an initial economic equilibrium. This process is referred to as the static calibration. Second, the 2004 equilibrium is updated to a reference year for the model baseline (currently 2019) by simulating the model dynamically to match historical trends over the period 2014-2019; static calibration is performed again for the reference year with price re-normalisation in order to express all variables in constant 2019 real USD. This step is important, as it ensures that the basis for the simulations is a relatively recent year. Third, the baseline projection for the model horizon 2019-2050 is based on conditional convergence assumptions about labour productivity and other socio-economic drivers (demographic trends, future trends in energy prices and energy efficiency improvements.

The baseline projection is then obtained by running the model dynamically over the period 2015-2050, keeping these key variables exogenous but letting the model parameters adjust endogenously. Thus, the model parameters are calibrated using the structural relations of the model (production functions, household preferences, etc.) to mimic the evolution of the key variables over time.

Dynamics involves three elements. Labor supply (by skill level) grows at an exogenously determined rate. The aggregate capital supply evolves according to the standard stock/flow motion equation, i.e. the capital stock at the beginning of each period is equal to the previous period's capital stock, less depreciation, plus the previous period's level of investment. The third element is technological change. The standard version of the model assumes labor augmenting technical changes - calibrated to given assumptions about GDP growth and inter-sectoral productivity differences. In policy simulations, technology is typically assumed to be fixed at the calibrated levels.

Reference scenario and counterfactual simulations

The Baseline scenario is carefully calibrated to offer a credible projection of economic activity by 2050 without ambitious climate action but including the impact of existing and stated policies. The Baseline calibration includes technological progress through various productivity parameters (e.g. autonomous energy efficiency improvements and labour productivity improvements). Particular attention is given to the calibration of the energy sector, including power generation and energy demand.

The baseline projection is then obtained by running the model dynamically over the period 2014-2050, keeping these key variables exogenous but letting the model parameters adjust endogenously. Thus, the model parameters are calibrated using the structural relations of the model (production functions, household preferences, etc.) to mimic the evolution of the key variables over time.

Several types of environmental policies can be simulated with ENV-Linkages. It should be emphasised that when the policy simulations are performed on this calibrated baseline, the model parameters are exogenously fixed, while the model variables are fully endogenous. In policy simulations, technology is typically assumed to be fixed at the calibrated levels. In most dynamic baseline or reference simulations, the growth rate of real per capita GDP will be exogenous and an economy-wide variable, for example a uniform labor productivity shifter, is endogenous and will serve as the instrument to target GDP growth. In policy or alternative simulations, the economy-wide factor would normally be exogenous and the growth rate of GDP would be endogenous.

Solution algorithm

ENV-Linkages model is written in the General Algebraic Modeling System (GAMS) modelling language. GAM is particularly useful for numerical modelling of linear, nonlinear and mixed integer optimization systems. The software has a number of solvers that can be used for a particular problem and, in many cases, switching between solvers is straightforward. In the past this has proved useful since problems that don’t solve with one solution algorithm may solve with another. For economic problems, GAMS can be particularly useful since it allows problems to be written as mixed complementarity – which specifies inequalities that the solution must meet. This facilitates the solutions to problems involving budgets constraints or homogeneous products being produced by multiple sectors.

History of ENV-Linkages model

The ENV-Linkages model is the successor to the OECD GREEN model, which was initially developed by the OECD Economics Department (Burniaux, et al. 1992) and is now hosted at the OECD Environment Directorate. GREEN was originally used for studying climate change mitigation policies (see Burniaux, 2000). It was developed into the Linkages model (Van der Mensbrugghe, 2005), and subsequently became the JOBS/Polestar modelling platform that was used to help underpin the first OECD Environmental Outlook (OECD, 2001). The JOBS model, though initially designed to assess labor market policies, incorporated various features of both the GREEN and RUNS models. As environmental issues continued to be a high priority at the OECD, the JOBS model was incorporated in the work program of the OECD Environment Directorate and it helped frame discussion and projections for the OECD Environmental Outlook. Over the last 20 years, the model has continued to evolve and is currently known as the ENV-Linkages model (see Chateau, Dellink, and Lanzi (2014) for a recent description of the model).

1.3) Temporal dimension - ENV-Linkages

The ENV-Linkages model has a simple recursive-dynamic structure as agents are assumed to be myopic and to base their decisions on static expectations concerning prices and quantities. The current version of the ENV-Linkages is calibrated to 2014 (base year data). The model runs up to 2050 with a 1 year time step.

1.4) Spatial dimension - ENV-Linkages

In this paper, the GTAP database has been aggregated to 26 regions and 37 sectors . Regions have been chosen to match the ones used by the World Energy Outlook 2021. Economic activities isolate energy activities, energy intensive industries, transport and equipment sectors, while the rest of the economy is aggregated in broad sectors.

Table 1: Countries/regions represented in ENV-Linkages model

1	China	14	France Germany Italy
2	Japan	15	Other European Union
3	South Korea	16	Bulgaria Croatia Cyprus Malta Romania
4	India	17	Indonesia
5	Canada	18	Middle East
6	United States	19	North Africa
7	Mexico	20	Other Africa
8	Brazil	21	Other Southeast Asia
9	Russia	22	Other Asia
10	OECD Oceania (Australia and New Zealand)	23	EFTA Israel Turkey
11	Caspian	24	Other Europe
12	Chile and Colombia	25	United Kingdom
13	Other Latin America	26	South Africa

1.5) Policy - ENV-Linkages

In most dynamic baseline or reference simulations, the growth rate of real per capita GDP will be exogenous and an economy-wide variable, for example a uniform labor productivity shifter, is endogenous and will serve as the instrument to target GDP growth. In policy or alternative simulations, the economy-wide factor would normally be exogenous and the growth rate of GDP would be endogenous.

2) Socio-economic drivers - ENV-Linkages

Dynamics involves three elements. Labor supply (by skill level) grows at an exogenously determined rate. The aggregate capital supply evolves according to the standard stock/flow motion equation, i.e. the capital stock at the beginning of each period is equal to the previous period's capital stock, less depreciation, plus the previous period's level of investment. The third element is technological change. The standard version of the model assumes labor augmenting technical change - calibrated to given assumptions about GDP growth and inter-sectoral productivity differences. In policy simulations, technology is typically assumed to be fixed at the calibrated levels.

Population - ENV-Linkages

Population growth by cohort is assumed to be exogenous and is typically taken from a population scenario such as the UN or from those developed by the IAMC community known as the shared socio-economic pathways, or SSPs.

Economic activity - ENV-Linkages

ENV-Linkages Computable General Equilibrium (CGE) model is an economic model that describes how economic activities are inter-linked across several macroeconomic sectors and regions. It links economic activity to environmental pressure, specifically to emissions of greenhouse gases (GHGs).

4) Energy - ENV-Linkages

The final set of nests in production concern the energy bundle. It will be decomposed into demand for the energy commodities. The energy bundle is first decomposed into electric and non-electric bundles. The latter is then decomposed into a coal bundle and a non-coal bundle (or the oil & gas bundle). The oil & gas bundle is then split into a gas bundle and an oil bundle. The four-remaining bundle- electric, coal, oil and gas- represent a combination of existing or future energy sources.

4.2.1) Electricity - ENV-Linkages

One of the features of the model is that it integrates the new GTAP power database that disaggregates GTAP's electricity sector ('ely') into 11 different power sources plus electricity transmission and distribution. Though the database has both the supply and demand side for all 11 power sources, the aggregation facility permits the aggregation of electricity demand into a single commodity and the 'make' matrix specification combines the output from the different power activities into a single electricity commodity. The bundling of electricity uses a nested CES structure instead of a single nest. The top nest combines aggregate power supply with distribution and transmission services to form aggregate domestic electric supply. The power nest combines a number of different (and user-determined) power bundles. Subsequently, each of these power bundles are formed by the different power activities that are segmented into the different power bundles (under user-mapping). For example, the power bundles may be composed of coal-, oil- and gas-generation, nuclear, and all other. Using the GTAP power database, base load coal could be mapped to the coal power bundle, base and peak load oil could be mapped to the oil power bundle, base and peak load gas could be mapped to the gas power bundle, base load nuclear would be mapped to the nuclear power bundle, and all other power activities (wind, solar, hydro and other) could be mapped to the other power bundle. The strategy for future technologies would be to bundle them in power bundles. For example, coal capture and storage could be in the coal power bundle, and advanced nuclear could be incorporated in the nuclear power bundle.

4.3) Energy end-use - ENV-Linkages

AEEIs in energy use have been dynamically calibrated based on elasticities for each kind of energy demand to GDP for 2007-2030, as projected in the IEA World Energy Outlook (2009, 2012). These elasticities are assumed to be constant after 2030, governing the long-term development of the AEEIs.

4.4) Energy demand - ENV-Linkages

The consumer demand system is thus implemented as a nested structure that starts with disposable income. A top nest allocates disposable income between savings and consumer commodities. Each commodity is decomposed into demand for the various product commodities, using a series of nested CES functions. The top CES nest decomposes demand for a commodity into a non-energy aggregate bundle and an energy bundle. The former is decomposed into demand for non-energy Armington goods with a single nest (and added across all consumer goods). The latter is decomposed using the same energy nesting as in production with energy demand similarly aggregated across all consumer goods to generate total energy demand by carrier by households.

4.5) Technological change in energy - ENV-Linkages

In policy simulations, technology is typically assumed to be fixed at the calibrated levels.

5) Land-use - ENV-Linkages

The supply side of the land market has two components. The first component provides the aggregate supply of land. The second step allocates aggregate land across different activities allowing for a nested CET structure, the possibility of perfect mobility, and the use of the adjusted CET that preserves land additivity.

The aggregate land supply curve is allowed to have four shape - constant elasticity, a logistic curve with an upward asymptote, a generalized hyperbola also with an upward asymptote, and perfectly horizontal.

6) Emissions - ENV-Linkages

The model incorporates several greenhouse gas (GHG) emissions. The standard database includes carbon emissions, methane (CH4), nitrous oxides (N2O) and an aggregate emissions bundle of fluoridated gases. The input data for carbon is in millions of metric tons of CO2 and the data for the other GHGs are both in physical units (metric tons) as well as in CO2-equivalent. The model allows for conversion between CO2e and Ce, i.e. carbon equivalent, depending on the needs of the user.

Emissions are generated by three sources: 1) direct consumption of a commodity; 2) factor-based emissions (e.g. capital, i.e. herds, in the livestock sectors); and 3) output or processed based emissions (e.g. methane from landfills). Carbon emissions in the current model version are only generated by the combustion of fossil fuels-coal, oil (crude and refined) and natural gas. Emissions of other GHGs can be a combination of all three sources of emissions. Emissions are based on the consumption level multiplied by the rate of emission per unit of consumption.

7) Climate - ENV-Linkages

A recent paper focused on six key policies to decarbonise the economy, which were chosen because (i) they reach all key sources of CO2 emissions,10 and (ii) they contain some of the most widely used instruments available to governments.

The instruments considered are:

• Carbon pricing

• Fossil fuel support removal (FFSR)
• Regulations in the power sector to enforce a switch away from fossil fuels
• Regulations to stimulate investments to decarbonise building and transport emissions
• Policies to stimulate firms’ energy efficiency improvement
• Subsidies to reduce and decarbonise energy consumption by households.

Instruments other than carbon pricing and fossil fuel support removal are calibrated using preliminary scenarios, because calibrating all the required endogenous variables at the same time using multiple MCP is too complex in a large scale model like ENV-Linkages for the solver to find easily a solution.

Therefore, a total of 16 scenarios are implemented to calibrate the full set of instruments:

1. For each of the four activities group (Power, Transport services, Other services, Households), a scenario calibrating energy efficiency or energy mix.

2. For each of the four activities group, and for each instrument type (regulation or subsidy), a scenario calibrating input efficiency or subsidies rate, on top of the same assumptions as step 1.

3. Four scenarios gathering the information from steps 1 and 2: regulation for households and regulation for firms, regulation for households and subsidies for firms, subsidies for households and regulation for firms, and finally regulation for households and regulation for firms.

The calibration steps 1 and 2 use information for the World Energy Outlook. In the end, only the 3rd step is used the results for the combination of policy instruments other than carbon pricing and fossil fuel support removal.

8) Non-climate sustainability dimension - ENV-Linkages

The model has been developed to assess the economic consequences of air pollution up until 2050, as used in previous OECD work (OECD, 2021[1]; OECD, 2016[9]). The modelling framework is based on a stepwise approach, which uses different modelling tools to link projections of (1) sectoral economic activities to (2) emissions of air pollutants, (3) concentrations of fine particulate matter and ground-level ozone, and finally to (4) the biophysical and (5) economic impacts of outdoor air pollution . These steps are repeated for each sectoral policy scenario.

Concentration-response functions, based on the Global Burden of Disease studies, provide projections of the health impacts of outdoor air pollution by country (e.g. numbers of deaths, cases of illnesses, work days lost), based on the country-average concentrations of PM2.5 and O3 obtained with the TM5- FASST model.

8.2) Water - ENV-Linkages

The market for water is somewhat similar to the land market. An aggregate supply curve is provided, and aggregate supply is then allocated to different uses using a nested CET structure. However, unlike land, water is in direct use in only the irrigated crop sectors. Demand for water in other sectors is specified using aggregate demand functions. The aggregate water supply curve is allowed to have four shapes - constant elasticity, a logistic curve with an upward asymptote, a generalized hyperbola also with an upward asymptote, and perfectly horizontal.