Investing in Climate for Growth and Development

Annex A. Modelling framework for the OECD

Overview of the modelling framework used for the OECD analysis

The scenario analysis of the Current Policies and the Enhanced NDCs scenarios is based on a modelling framework, primarily based on the OECD’s ENV-Linkages model (Château, Dellink and Lanzi, 2014[1]). ENV-Linkages is a dynamic; multi-sector, multi-region computable general equilibrium (CGE) model that links greenhouse gas emissions to economic activities. As a general equilibrium model, ENV‑Linkages is the ideal tool to compare different projections of the global economy, namely its current state with two possible future states by 2040, under Current Policies and under Enhanced NDCs. CGE models are however usually weak in evaluating short-term macroeconomic effects, such as the positive effects of investment on economic growth. To capture short- and medium-term investment effects, ENV-Linkages has been calibrated relying on simulations with the National Institute Global Econometric Model (NiGEM) model, developed by the National Institute of Economic and Social Research (NIESR) (Hantzsche, Lopresto and Young, 2018[2]). NiGEM is a structural macro-econometric model of the world economy, used for the OECD Economic Outlooks. NiGEM captures demand, supply, monetary and financial dynamics, reflecting the current macro-economic outlook. NiGEM is also endowed with a climate module, which allows for an analysis of the economic consequences of climate policies, related revenue recycling as well as the effects of energy-related investment.

This report combines a set of modelling input and tools (Figure A A.1. ). Inputs from the IEA Global Energy and Climate model (IEA, 2023[3]) are used to calibrate scenarios in NiGEM and ENV‑Linkages. Specifically, energy-related investment, energy system projections and when available energy policies are used to calibrate the scenarios. The Current policies projection of the economic models is calibrated on the IEA Stated Policies scenario (STEPS) scenario while the Enhanced NDCs scenario rely on the IEA Announced Pledges (APS) scenario. The OECD Long Term Model (LTM) (Guillemette and Turner, 2021[4]) (Guillemette and Château, 2023[5]) provides macro-economic projections to 2100 (including GDP, Investment, Employment, etc.) used to calibrate the ENV-Linkages model. While the LTM usually provides projections for 116 countries, the model was extended for this report to produce projections for 146 countries.

Moreover, the simulation of the Enhanced NDCs scenario with the NiGEM model provides additional inputs to the ENV-Linkages model about the effect of investment on potential and effective GDP, employment, government balance and current accounts. The ENV-Linkages model would otherwise not capture the Keynesian-like dynamics of the effect of investment on potential GDP and corresponding investment‑multiplier effects.

To understand the distributional implications of the Enhanced NDCs scenario produced by the ENV‑Linkages model, focusing on two country case studies (France and Türkiye), a new microsimulation framework mSPIN (O’Donoghue et al., 2025[6])) is used. The microsimulation framework is calibrated on ENV-Linkages scenario projections in terms of employment, industry, wage and other income growth as well as price growth by industrial sector or commodity.

Finally, the report includes a modelling analysis that illustrates how the climate transition can be implemented in a way that is compatible with development goals. This analysis is based on the UNDP’s SDG Push 3.0 framework and the International Futures (IFs) large-scale, long-term, integrated global modelling system (Hughes and Hillebrand, 2015[7]) from the Frederick S. Pardee Center for International Futures. The IF model is used to model an SDG Push 3.0 scenario which comprises additional efforts to meet the SDGs, in addition to the climate action outlined in the Enhanced NDCs scenario.

Figure A A.1. Modelling framework

While not fully integrated in the economic modelling analysis, the emission and temperature pathways underlying the scenarios are used to quantify the economic benefits from reduced climate damages between the modelled scenarios. The methodology for calculating these benefits is also outlined in this Annex.

ENV-Linkages

The OECD’s dynamic computable general equilibrium (CGE) model ENV-Linkages is used as the basis to estimate the effect of climate mitigation policies on economic output. ENV-Linkages is a multi-sectoral, multi-regional model that links economic activities to energy and greenhouse gas emissions. A more comprehensive model description is given in Chateau, Dellink and Lanzi (2014[1]).

The baseline scenario’s macroeconomic calibration is described by Fouré (2020[8]) and its sectoral calibration by Château et al. (2020[9]) while recent baseline results are illustrated in OECD (2024[10]).

The model is based on the Social Accounting Matrices (SAM) contained within the GTAP 11 database (Aguiar et al., 2023[11]). This database describes bilateral trade patterns, production, consumption and intermediate use of commodities and services, including capital, labour and tax revenues and use. The base year of the SAM and of the model is 2017. The short‑term changes to the economy reflect short-term economic changes from international databases: the OECD Economics Department (OECD, 2024[12]) and the International Monetary Fund (2024[13]).

In ENV-Linkages, the link between GHG emissions to economic activity is calculated using the GTAP emissions database (Aguiar et al., 2023[11]). There are several drivers of GHG emissions by activities, covering: CO₂ (fossil) from burning fossil fuels for any economic activity and fugitive emissions; CO₂ (non‑fossil) from industrial process emissions, and land-use, land-use change and forestry sector (LULUCF); CH4 from rice cultivation, livestock production (enteric fermentation and manure management), fugitive methane emissions from coal mining, crude oil extraction, natural gas and services (landfills and water sewage); N2O from crops (nitrogenous fertilizers), livestock (manure management), chemicals (non‑combustion industrial processes) and services (landfills); Fluorinated gases (SF6, PFCs and HFCs) from chemicals industry (foams, adipic acid, solvents), aluminium, magnesium and semiconductor production. Non-CO₂ GHG emissions are converted into CO₂ equivalents using standard Global Warming Potential over 100 years coefficients defined in the IPCC’s 5^th Assessment Report (Myhre et al., 2013[14]).

The ENV-Linkages aggregation is very flexible and tailored for each study. For this report, the world is divided in 12 isolated countries and 13 more aggregated regions. In this report, results are presented for 4 regional groupings: High-income countries, Middle-income countries, Low-income countries and Oil producers. Economic activity within a region in ENV-Linkages is aggregated into 42 economic sectors, but for sake of clarity, the analysis in this chapter will present nine aggregate sectors: three energy sectors (fossil-fuel, fossil fuel-power electricity and non-fossil power) and six end‑use sectors. The aggregation of sectors facilitates the analysis of key parts of the economy that are particularly affected by climate policies, such as emission-intensive industries, energy, transport or agriculture.

Table A A.1. ENV-Linkages regional aggregation

Aggregated region	ENV-Linkages region	GTAP countries
High-Income Countries	Canada	Canada
	Germany	Germany
	Spain	Spain
	France	France
	Italy	Italy
	Poland	Poland
	United States of America	United States of America
	Other OECD EU Countries	Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Latvia, Lithuania, Luxembourg, Netherlands, Portugal, Slovakia, Slovenia, Sweden
	EU non-OECD countries	Cyprus, Malta, Bulgaria, Croatia, Romania
	Other OECD countries	Australia, Chile, Colombia, Costa Rica, Iceland, Israel, Japan, Liechtenstein, Mexico, New Zealand, Norway, South Korea, Switzerland, United Kingdom
Middle-Income Countries	China	China, Hong Kong
	South Africa	South Africa
	Türkiye	Türkiye
	Other Latin America	Rest of North America, Argentina, Plurinational Republic of Bolivia, Brazil, Ecuador, Paraguay, Peru, Uruguay, Venezuela, Rest of South America, Guatemala, Honduras, Nicaragua, Panama, El Salvador, Rest of Central America: Belize, Rest of Caribbean, Haiti, Dominican Republic, Jamaica, Puerto Rico, Trinidad and Tobago
	Other Eurasia	Albania, Belarus, Ukraine, Rest of Eastern Europe: Republic of Moldova, Rest of Europe, Serbia, Kazakhstan, Kyrgyzstan, Tajikistan, Armenia, Azerbaijan, Georgia, Rest of Former Soviet Union: Turkmenistan, Uzbekistan
Low-Income Countries	India	India
	Other East Asia	Rest of Oceania, Mongolia, Chinese Taipei, Rest of East Asia: North Korea and Macao, Cambodia, Indonesia, Lao People's Democratic Republic, Malaysia, Philippines, Singapore, Thailand, Viet Nam, Rest of Southeast Asia: Myanmar and Timor-Leste, Brunei Darussalam, Bangladesh, Nepal, Pakistan, Sri Lanka, Rest of South Asia: Bhutan and Maldives, Afghanistan
	Other Africa	Egypt, Morocco, Tunisia, Rest of North Africa, Algeria, Cameroon, Côte d'Ivoire, Ghana, Nigeria, Senegal, Benin, Burkina Faso, Guinea, Togo, Rest of Western Africa, Mali, Niger, Central African Republic, Chad, Equatorial Guinea, Gabon, Congo, South Central Africa, Democratic Republic of the Congo, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, United Republic of Tanzania, Uganda, Zambia, Zimbabwe, Rwanda, Rest of Eastern Africa, Comoros, Sudan, Botswana, Namibia, Eswatini, Rest of South African Customs Union: Lesotho
Oil Producers	Russia	Russian Federation
	Other middle east countries	Bahrain, Islamic Republic of Iran, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates, Rest of Western Asia, Jordan, Iraq, Lebanon, Syrian Arab Republic, Yemen

Note: ENV-L model countries and regions are grouped into Low-income, Middle-income and High-income country aggregates based on their 2021 gross national income (GNI) per capita adapting the 2021 World Bank Country classifications (World Bank, 2022[15]). Specifically, Low-Income Countries are defined as those with GNI per capita below USD 4,095; Middle-Income Countries between USD 4,095 and USD 12,695; and High-Income Countries above USD 12,695. Countries included in the Oil producers’ group are excluded from any of the three income groups.

Table A A.2. ENV-Linkages sector aggregation

Aggregate Sector	Included ENV-L activities
Agriculture and food	All Crops (cro-a), Livestock (lvs-a), Forestry (frs-a), Fisheries (fsh-a), Food products (fdp-a)
Services	Collective services (osg-a), Electricity transmission and distribution (etd-a), Water supply; sewerage; waste management and remediation activities (wts-a), Business services (osc-a)
Construction	Construction (cns-a)
Energy-intensive and trade-exposed industry (EITE)	Iron and Steel (i_s-a), Paper & Paper Products (ppp-a), Non-metallic minerals (nmm-a), Chemical products (crp-a), Non-ferrous metals (nfm-a)
Other Industry	Textiles (txt-a), Electronics (ele-a), Fabricated metal products (fmp-a), Wood products (fmp-a), Motor vehicles (mvh-a), Electrical equipment (eeq-a), Machinery and equipment n.e.s. (ome-a), Rubber and plastic products (rpp-a), Basic pharmaceuticals (bph-a), Minerals n.e.s. (OMN-a), Textiles (txt-a), Electronics (ele-a)
Transport	Water Transport (wtp-a), Air Transport (atp-a), Transport n.e.s.: Land transport and transport via pipelines (otp-a)
Renewable and nuclear electricity	Hydro power (hyd-a), Wind power(wnd-a), Solar power (sol-a), Other power (xel-a), Nuclear power (nuc-a)
Fossil fuel-based electricity	Coal powered electricity (clp-a), Oil powered electricity (olp-a), Gas powered electricity (gsp-a)
Fossil fuel extraction and distribution	Coal extraction (coa-a), Crude Oil extraction (oil-a), Nat. gas: extraction plus manufacture & distribution (gas-a), Petroleum and coal products (p_c-a)

Production in ENV Linkages is assumed to operate under cost minimisation with perfect markets and constant returns-to-scale technology. Each production sector is composed of one representative firm that combines labour, capital and intermediate inputs (including energy) to supply one commodity. This production decision reflects the firm’s objective to maximise its profit taking as given prices of these inputs. The production technology is specified as nested Constant Elasticity of Substitution (CES) production functions in a branching hierarchy. This structure is replicated for each output, while the parameterisation of the CES functions may differ across sectors. The model adopts a putty/semi-putty technology specification, where substitution possibilities among factors are assumed to be higher with new vintage capital than with old vintage capital. In the short run this ensures inertia in the economic system, with limited possibilities to substitute away from more expensive inputs, but in the longer run this implies a relatively smooth adjustment of quantities to price changes. Capital accumulation is modelled as in the traditional Solow/Swan neo classical growth model, where economic growth is assumed to stem from the combination of labour, capital accumulation and technological progress. GHG emissions in ENV-Linkages are modelled either as linked to the use of the inputs (e.g. fossil fuels, chemicals product, etc.) in the different sectors, or for process emissions related to the amount of sectoral output produced.

Household consumption demand is the result of static maximisation behaviour which is formally implemented as an “Extended Linear Expenditure System”. A representative consumer in each region – who takes prices as given – optimally allocates disposal income among the full set of consumption commodities and savings. Saving is considered as a standard good in the utility function and does not rely on forward looking behaviour by the consumer. The government in each region collects various kinds of taxes to finance government expenditures. Assuming fixed public savings (or deficits), the government budget is balanced through the adjustment of the income tax on consumer income. In each period, investment net-of-economic depreciation is equal to the sum of government savings, consumer savings and net capital flows from abroad.

International trade is based on a set of regional bilateral flows. The model adopts the Armington specification, if domestic and imported products are not perfectly substitutable. Moreover, total imports are also imperfectly substitutable between regions of origin. Allocation of trade between partners then responds to relative prices at the equilibrium.

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

ENV-Linkages is fully homogeneous in prices and only relative prices matter. All prices are expressed relative to the numéraire of the price system that is arbitrarily chosen as the index of OECD manufacturing exports prices. Each region runs a current account balance, which is fixed in terms of the numéraire.

As ENV-Linkages is recursive-dynamic and does not incorporate forward-looking behaviour, price‑induced changes in innovation patterns are not represented in the model. The model does, however, entail technological progress through an annual adjustment of the various productivity parameters, including e.g. autonomous energy efficiency and labour productivity improvements. Furthermore, as production with new capital has a relatively large degree of flexibility in choice of inputs, existing technologies can diffuse to other firms. Thus, within the CGE framework, firms choose the least-cost combination of inputs, given the existing state of technology. The capital vintage structure also ensures that such flexibilities are larger in the long run than in the short run.

Calibration of the Enhanced NDCs scenario on the IEA’s World Energy Outlook

ENV-Linkages is used to compare changes in the global and regional economies under the Current Policies and under Enhanced NDCs scenarios by 2040. The Current Policies scenario reflects policies already in place or legislated, incorporating the existing ambition and implementation gap in current NDCs. Under this scenario, global greenhouse gas (GHG) emissions decline by 7% by 2040 compared to 2022, leading to an estimated 2.45°C of warming by the end of the century. The Enhanced NDCs scenario assumes accelerated climate policies and investments able to achieve emission reductions in line with the “well-below 2°C” goal of the Paris Agreement. Under this scenario, global emissions are reduced by 34% by 2040, putting the world on a pathway consistent with limiting global warming to 1.7°C by the end of the century

The calibration of the two scenarios relies on inputs from the International Energy Agency’s World Energy Outlook (IEA, 2023[3]) projections (respectively STEPS and APS scenarios), which provides information on future policy-induced changes in the energy system.

NiGEM

As described in Hantzsche, Lopresto and Young (2018[2]), NiGEM is a global macroeconomic model, used by policymakers and the private sector for economic forecasting, scenario building and stress testing. It consists of individual country models for the major economies that are linked through trade in goods and services and integrated capital markets.

The individual country models within NiGEM are economy-wide systems of dynamic equations, based around the internationally accepted national accounting framework, with parameters estimated from aggregate time-series data. They all have broadly the same New Keynesian structure in that agents in the model are assumed to have rational expectations, though this can be amended and there are nominal rigidities that slow the process of adjustment to external events. Importantly, the individual country models incorporate well-specified supply-side behaviour that underpins the sustainable growth rate of each economy in the medium term. As far as possible the same theoretical structure has been adopted for each of the individual country models, except where clear institutional or other factors prevent this. As a result, variation in the properties of each country model reflects genuine differences emerging from estimation, rather than different theoretical approaches.

NiGEM is a global model. Almost all countries in the OECD are modelled individually within it. There are also separate models of Argentina, Chile, China, India, Russia, Hong Kong, Taiwan, Brazil, South Africa, Estonia, Latvia, Lithuania, Slovenia, Romania and Bulgaria, while the rest of the world is modelled through regional blocks.

All country models contain the determinants of domestic demand, export and import volumes, prices, current accounts and net assets. In the long run, output is tied down by factor inputs and technical progress interacting through production functions. In the short run, the dynamic properties of the model are consistent with the data and well-determined.

International linkages come from patterns of trade, the influence of trade prices on domestic prices, the impacts of exchange rates and patterns of asset holding and associated income flows. The structure of the trade block ensures overall global consistency of trade volumes by imposing that the growth of import volumes is equal to the growth of export volumes at the global level. Trade volumes and prices are linked by Armington matrices, based on 2016 trade patterns. The volumes of exports and imports of goods and services are determined by foreign or domestic demand, respectively and by competitiveness as measured by relative prices or relative costs.

International Futures (IFs) model

The International Futures (IFs) is a large-scale, long-term, integrated global modelling system (Hughes and Hillebrand, 2015[7]). Its primary purpose is to serve as a strategic tool for exploring near- to long-term futures at country, regional and global levels, encompassing a wide range of interconnected sub‑models. These include population, economy, education, health, energy, agriculture, technology, infrastructure, environment, sociopolitical and international relations. The sub-models are dynamically interconnected, allowing IFs to simulate how changes in one system lead to cascading effects across others (Figure A A.2). The platform represents the interactions of 188 countries and can forecast trends up to the year 2100.

Figure A A.2. Sub-models and interactions represented in the International Futures (Ifs) model

The energy model

The energy model operates as a partial equilibrium system with a physical foundation that is translated into monetary terms for integration with the economic model. It incorporates resources and reserves on the production side, distinguishing between oil, natural gas, coal, hydro, nuclear and a general category of renewable energy that includes wind and solar. The dynamics surrounding fossil resource stocks and their utilisation, as well as the development of renewable energy forms, are critical components. On the demand side, the model is driven by the size of economies and populations, also capturing the ongoing reduction in energy intensities across most countries. On the supply side, production depends not only on resource availability but also on the accumulation of capital stock through investment, which competes with other sectors. Trade in the energy model uses a pooled approach rather than bilateral flows and responds to cost and price differentials across countries, while global prices are typically calculated to balance supply and demand in the market. Most data for the model are sourced from the International Energy Agency (Hughes, 2016[16]).

How does climate change affect development in IFs?

In IFs, climate change is driven by GHG emissions, which trap heat in the atmosphere. This leads to increased global average temperatures and alters precipitation and rainfall patterns (Moyer, Pirzadeh and Stone, 2023[17]). These changes impact economic activity by i) affecting agricultural yields and production patterns, which can influence development through changes in food prices and the cost of food baskets and ii) decreasing production and altering capital stock, which negatively impact development by reducing economic growth and increasing inequality. The model applies a damage function to simulate the decline in production resulting from rising temperatures (Hughes and Hillebrand, 2015[7]).

SDG Push 3.0 scenario parameters

The table below outlines the parametric scenario interventions implemented in IFs for the SDG Push 3.0 scenario. The first column provides a general description of the intervention, the second specifies the parametric adjustments made in IFs and the third identifies the geography or country grouping targeted by the intervention. Unless stated otherwise, all interventions are initiated in 2025.

Table A A.3. SDG Push 3.0 scenario parameters

Description	Parametric intervention	Geography
Digital Disruptions/Innovation
Increased rates of tertiary graduates with science and engineering degrees.	edterscienshradd increased from 0 to 10 over 13 years.	World
Increased government spending on education.	gdsm for Education increased from 1 to 2 over 13 years.	World
Increased government spending on research & development.	gdsm for R&D increased from 1 to 2 over 13 years.	World
Increased government spending on infrastructure.	gdsm for Infrastructure increased from 1 to 2 over 13 years.	World
Increased access to broadband.	ictbroadm increased from 1 to 1.5 over 20 years.	World
Increased access to mobile broadband.	ictbroadmobilm increased from 1 to 1.5 over 20 years.	World
Increased spending on research and development.	randdexpm increased from 1 to 1.2 over 13 years.	World
Increased tertiary graduation rates.	edtergradm increased from 1 to 1.5 over 24 years.	World
Accelerated growth in lower secondary graduation rates.	edseclowrgradgr increased from 0 to 2.5 over one year.	World
Equitable, low carbon pathways
Energy demand falls for World Bank High Income Countries.	endemm decreased from 1 to 0.8 over 15; then 0.8 to 0.6 over the next 20 years; finally, from 0.6 to 0.5 over 30 years.	World Bank High Income Countries
Energy demand gradually increases then gradually falls for Low-middle income countries.	endemm increased from 1 to 1.15 over 33 years; then dcreased from 1.15 to 0.863 over 42 years.	World Bank Low-middle Income Countries
Energy demand falls for World Bank high-middle income countries.	endemm decreased from 1 to 0.88 over 10 years; then to 0.6 over 10 years; then to 0.58 over 10 years; finally, to 0.3 over 40 years.	World Bank High-middle Income Countries
Energy demand to GDP ratio held to simulate energy efficiency gains.	enrgdpgr held at -1.344 for 15 years and then increased at base rate.	World Bank Upper-middle Income Countries
Energy demand to GDP ratio held to simulate energy efficiency gains.	enrgdpgr increased at its normal rate and then held at 1.06 from 2055 to 2061 to 2074.	World Bank Low Income Countries
Energy demand to GDP ratio held to simulate energy efficiency gains.	enrgdpgr increased at its normal rate and then held at -1.1 from 2078 onward.	World Bank Low-middle Income Countries
Energy demand to GDP ratio held to simulate energy efficiency gains.	enrgdpgr held at 1.344 for 30 years and then increased at its base rate after.	World Bank High Income Countries
Decreased capital costs-to-output ratio for renewable energy.	qem for OthRenew decreased from 1 to 0.5 over 30 years.	World
Increase capital costs-to-output ratio for coal.	qem for Coal increased from 1 to 1.25 over 15 years.	World
Energy production of renewables not including hydro or nuclear increased.	enpm (other renewables) increased from 1 to 4.9706 over 75 years.	World Bank High Income Countries
Energy production of renewables not including hydro or nuclear increased.	enpm (other renewables) increased from 1 to 2.3412 over 75 years	World Bank Low-middle Income Countries
Energy production of renewables not including hydro or nuclear increased.	enpm (other renewables) increased from 1 to 5 over 75 years	World Bank Upper-middle Income Countries; and China
Energy production of renewables not including hydro or nuclear increased.	enpm (other renewables) increased from 1 to 3.6275 over 75 years.	World Bank Low Income Countries
Energy production of nuclear increases.	enpm (nuclear) increased from 1 to 2.975 over 75 years.	World Bank Low Income Countries
Energy production of nuclear increases.	enpm (nuclear) increased from 1 to 2.55 over 75 years.	World Bank Upper-middle Income Countries
Energy production of nuclear increases.	enpm (nuclear) increased from 1 to 2.14 over 75 years	World Bank High Income Countries
Energy production of nuclear increases.	enpm (nuclear) increased from 1 to 4.375 over 75 years.	World Bank Low-middle Income Countries
Energy production of hydro increases.	enpm (hydro) increased from 1 to 2 over 25 years.	World Bank Low-middle Income Countries; and World Bank Low Income Countries
Energy production of hydro increases.	enpm (hydro) increased from 1 to 2.065 over 75 years.	World Bank Upper-middle Income Countries
Energy production of hydro increases.	enpm (hydro) increased from 1 to 1.475 over 75 years.	World Bank High Income Countries
Governance
Increased government participation as defined by the Polity project index.	democm increased from 1 to 1.3 over 13 years.	World
Improved government effectiveness.	goveffectm increased from 1 to 1.3 over 13 years.	World
Green Economy
Reduced agricultural losses in all stages of production, transport and consumption.	aglossconsm, aglossprodm and aglosstransm are interpolated from 1 to 0.7 over 30 years.	World
Carbon tax is introduced at USD 200 per ton for OECD countries.	carbtax interpolated from 0 to 200 over 13 years.	OECD countries
Carbon tax is introduced at USD 50 per ton for non-OECD countries.	carbtax interpolated from 0 to 50 over 13 years.	Non-OECD countries
Reduced caloric demand for meat products.	clpcm for Meat interpolated from 1 to 0.8 over 30 years.	World
Increased caloric demand for crops.	clpcm for Crop interpolated from 1 to 1.106 over 13 years.	World
Reduction in residential PM2.5 levels.	envpm2pt5m decreased from 1 to 0.7 over 36 years.	World
Reduction of the annual rate of energy production cost reduction for coal.	etechadv for Coal decreased from 0.004 to 0.002 in one year.	World
Acceleration of the annual rate of energy production cost reduction for renewable energy.	etechadv for OthRenew increased from 0.008 to 0.01 over one year.	World
Reduction in fish catch for countries currently catching more than 2mmt of fish per year.	fishcatchm decreased from 1 to 0.75 over 50 years.	China, Indonesia, Japan, Myanmar, Peru, Russian Federation, Thailand, United States
Increase in forested land area.	forestm increased from 1 to 1.0015 over 1 year.	World
Reduced electricity transmission loss.	infraelectranlossm decreased from 1 to 0.8 over 30 years.	World
Reduced water demand.	waterdemandm decreased from 1 to 0.7 over 33 years.	World
Increased crop yields.	ylm for upper-middle- and high-income economies increased from 1 to 1.2 over 15 years.	High-income economies, Upper-middle-income economies
Increased crop yields.	ylm for lower-middle income economies increased from 1 to 1.5 over 50 years.	Lower-middle-income economies
Increased crop yields.	ylm for low-income economies increased from 1 to 2 over 50 years.	Low-income economies
Increased electricity access.	infraelecaccm increased from 1 to 1.5 over 24 years.	World
Social Protection
Increased availability and use of modern cookstoves.	cookstovesadd for Improved increased from 0 to 500 over 13 years.	World
Increased government spending on health.	gdsm for Health increased from 1 to 2 over 13 years.	World
Increased government welfare transfers to unskilled households.	govhhtrnwelm for 1 Unskilled increased from 1 to 1.5 over 13 years	World
Increased government welfare transfers to unskilled households.	govhhtrnwelm for 1 Unskilled increased from 1 to 2 over 13 years.	Low-income economies
Increased access to improved sanitation.	sanitationm for Improved increased from 1 to 2 over 30 years.	World
Increased access to improved water.	watsafem for Piped increased from 1 to 2 over 30 years.	World
More equitable distribution of calories.	clpccvm decreased from 1 to 0.85 over 15 years.	World
Improved calorie allocation to those most in need.	malelimprecisesw switched from 0 to 1 in one year.	World
Increased contraception use.	contrusm increased from 1 to 1.15 over 25 years.	World

mSPIN microsimulation model

The distributional implications of the Enhanced NDCs scenario are estimated adapting the mSPIN1 microsimulation framework (O’Donoghue et al., 2025[6]; Sologon et al., 2023[18]) to link it to the results of the ENV-Linkages model. The methodology involves the use of aggregate economic projections from the ENV-Linkages model (up to 2035), together with population estimates and projections from the United Nations to calibrate the structure of household survey data using alignment or calibration totals from these sources (Figure A A.3. ). The models are calibrated in terms of employment, industry, wage and other income growth as well as price growth by industrial sector or commodity.

Figure A A.3. Structure of the projection model

Note: UN pop : United Nations population. HH: household.

Source: Authors, based on (O’Donoghue et al., 2025[6]; Sologon et al., 2023[18]).

In terms of micro-data, the 2019 Türkiye Household Budget Survey as well as the 2019 EUROMOD base datasets for 2019 have been used for calibration. A pre-COVID income distribution was used as it was considered more representative for the future than data that overlapped with the COVID Pandemic.

Alignment Parameters from ENV-Linkages

ENV-Linkages is used to create alignment or calibration parameters for the mSPIN microsimulation model for 2035 for different scenarios (Figure A A.4). The principal definitional difference between the models is the categorisation of industrial sectors and commodities. The ENV-Linkages model includes 41 producing sectors, while the microsimulation model considers only eight sectors. On the consumption side, the ENV‑Linkages model includes 34 commodities, while the microsimulation model has 24 sectors. Conversion matrices are thus used to convert the calibration totals from ENV-Linkages to the microsimulation categories. The alignment categories considered are as follows:

• Employment Totals

• Industry Employment Totals

• Average Wage Rate by Industry

• Average price increase by Commodity

Weighted averages are created from the ENV-Linkages numbers to create the microsimulation control totals. Prices (in real terms) and wages are used as control totals.

Figure A A.4. ENV-Linkages alignment parameters

Note: Conv: conversion. CPI: consumer price index.

Source: Authors, based on (O’Donoghue et al., 2025[6]; Sologon et al., 2023[18]).

UN Population estimates and projections

The scale of employment and other calibration totals from ENV-Linkages are based upon population totals from existing UN population estimates and UN population projections (United Nations, Department of Economic and Social Affairs, Population Division, 2024[19]). The projections assume the medium fertility variant. Taking age group by sex and by education shares from the household budget survey and assuming that gains in education levels by younger cohorts are maintained, UN population estimates and projections which are by 5 year age bands for men and women, are further disaggregated into education profiles (Figure A A.5. ). These serve as population weights for the household surveys.

Figure A A.5. UN Population estimates and projections

Note: Pop: population.

Source: Authors, based on (O’Donoghue et al., 2025[6]; Sologon et al., 2023[18]).

Calibration Totals for Microsimulation Model

Figure A A.6 describes how ENV-Linkages control totals are converted into alignment calibration totals for the microsimulation model. A series of tabulations are created for each country considered to produce age group by sex and by education totals for:

• Employment Rates

• Industry Employment Rates.

In the first round, a shift share analysis is undertaken to calculate using the adjusted UN population weights with cohort specific age by sex by education employment and industry shares. A secondary adjustment is then undertaken to make the total employment or industry totals be consistent with the totals from ENV Linkages, while maintaining the cohort specific shares from the micro data. An alternative scenario was produced where later cohorts exit the labour market at the same rate of change relative to their peak employment rate as men. Once employment totals are produced, industry rates conditional on the employment rates for these cohorts are calculated. A second-round adjustment ensures consistency with the ENV-Linkages totals as in the case of employment.

Figure A A.6. Calibration totals for the microsimulation model

Source: Authors, based on (O’Donoghue et al., 2025[6]; Sologon et al., 2023[18]).

Distributional Simulated Projection

In order to run the distributional simulated projection (Figure A A.7), the first step is to estimate a series of equations that describe the labour market within the country, with a series of process that are defined with their simulation order and explanatory variables in the Model Spine. This system of equations is known as an income generation model that describes the income generating process within the country (Sologon et al., 2021[20]; Sologon et al., 2022[21]). Equation estimates and equation error structures are stored. Calibration is done through the alignment method described in (O’Donoghue and Sologon, 2023[22]). Calibrating to the control totals for employment industry on the one hand and the relative wage and other income indexation rates on the other hand, a series of market income distributions is produced, consistent both with the CGE totals and with the underlying micro relationships. Both employment and industry are calibrated, while for other labour market characteristics, the existing relationship structures conditional on employment, industry, sex, age-group and education is maintained. The population is reweighted to account for the population weights created above.

To generate disposable incomes, the tax-benefit system of the relevant country is applied, utilising EUROMOD (Sutherland and Figari, 2013[23]) for France and Poland and the Türkiye Tax Model (Okan Erol, 2022[24]) for Türkiye. To model expenditures, maintaining an existing income and price relationships, a linear expenditure system (LES) demand system is used to generate a new set of expenditure group-specific consumption totals. Finally, the share of renewables is modelled. This relies on a two-stage model which simulates first the total energy demand and secondly the share of domestic fuels, motor fuels and electricity. Decarbonisation is assumed to take place through the decarbonisation of electricity and the shift from fossil fuel based domestic and motor transport related energy. The distributional nature of this adjustment is undertaken by two sets of budget elasticity assumptions separately for domestic fuels and motor fuels, conditional on the energy requirement of the household. These budget elasticities vary from an (unlikely) budget elasticity of less than 1, where the poor have a higher share of uptake, to a variety of budget elasticities of greater than 1, where the rich have a higher share of uptake. With total household energy use being maintained, the subsequent residual energy use is replaced with electricity.

Figure A A.7. Distributional simulated projection

Source: Authors, based on (O’Donoghue et al., 2025[6]; Sologon et al., 2023[18]).

Projected damages and avoided damages under different damage functions

While not fully integrated in the economic modelling toolkit, the scenario analysis presented in Chapter 2 includes estimates of the economic benefits from reduced climate damages between the modelled scenarios. Benefits from reduced climate damages are quantified starting from the emission and temperature pathways underlying the Current Policies and Enhanced NDCs scenarios. These scenarios are on pathways that would imply an average global temperature increase by the end of the century of 2.4°C and 1.7°C, respectively.

For each scenario, the economic costs of climate damages are calculated based on three specific damage functions, aiming to reflect a range of plausible estimates from the literature (Barrage and Nordhaus, 2024[25]; Howard and Sterner, 2017[26]; Kotz, Levermann and Wenz, 2024[27]). While virtually all studies agree that the global long-term damages from climate change will be substantial, there is significant uncertainty surrounding the exact magnitude of these impacts. Although specific channels through which climate change can affect economic and welfare outcomes are empirically well-established for a range of outcomes and across multiple sectors, quantifying the aggregate expected economic impact of climate change on economic output has proven to be challenging and remains an active and heavily debated area of research (O’Neill, 2022[28]; Howard and Sterner, 2017[26]; Tol, 2024[29]).

Uncertainty surrounds each step needed for the calculations of the economic benefits from reduced climate damages. The climate system’s response to rising GHG concentrations, which forms the basis to calculate economic damages, depends on the extent to which rising GHG concentrations translate into temperature increases, sea-level rise or the likelihood and the frequency and severity of highly stochastic extreme weather events (O’Neill, 2022[28]). Similarly, there are uncertainties on how these climate projections translate into economic damages. Climate change affects socioeconomic systems through both gradual and sudden impacts, and these effects are difficult to predict in both timing and location. Moreover, damages will unfold over decades, with cumulative effects that are not immediately observable. Researchers have employed a variety of methodological approaches to estimate the economic damages. These approaches differ in terms of their underlying assumptions, data sources, methods, and model structures, leading to a wide range of estimates in the literature (Box A A.1).

Box A A.1. Estimating the economic costs of climate change

Researchers have employed a variety of methodological approaches to quantify the economic impacts of changes in climatic conditions (“damage function”). Existing studies differ in terms of their underlying assumptions, data sources and in how they incorporate the underlying mechanisms through which climate change affects the economy (Table A A.4).

Table A A.4. Overview of methodologies to estimate economic impacts of climate change

Method/Approach	Description	Drawbacks/Benefits	Literature Examples
Enumeration	Attempts to quantify all possible channels through which damages across sectors can occur often building on previous studies and sums them up to construct an aggregate estimate	+ Transparency and tractability + Can explicitly account for non-market damages and adaptation impacts -Ignores feedback effects between channels and interactions within economic system (Piontek et al., 2021[30]) - Can omit important impact channels thus often underestimates systemic impacts	(Nordhaus, 1994[31]; Barrage and Nordhaus, 2024[25])
Econometric/Statistical	Estimation of relationship between climatic changes and economic output based on observed historical climate and economic data, often using panel data approaches.	+ Empirical approach captures observed relationships - Prone to omitted variable bias, reverse causality, and external validity concerns (Tol, 2024[29]) -Often captures short term weather impacts as opposed to climate change impacts	(Dell, Jones and Olken, 2012[32]; Kotz, Levermann and Wenz, 2024[27])
Structural-economic	Use of CGE models or agent based IAMs to compute aggregate losses based on sector-specific impacts as an input.	+ Accounts for indirect effects by allowing for the reallocation of resources across sectors and regions in response to climate damages. - Calibration relies on empirically estimated channel-specific impacts - Does not capture non-market damages.	(Ciscar et al., 2011[33]; Roson and Mensbrugghe, 2012[34]; Dellink, Lanzi and Chateau, 2019[35])
Expert elicitation	Damage estimates are directly obtained from interdisciplinary experts.	+ Incorporates knowledges across disciplines + Includes estimates about tipping point damages. -Prone to subjectivity (Tol, 2024[29])	(Nordhaus, 1994[36])
Meta analysis	Synthesizes results across previous studies to produce a central estimate of economic damages.	+ Consolidates findings across studies - Limited by quality and consistency of underlying studies	(Howard and Sterner, 2017[26])

Source: Adapted from (NGFS, 2024[37]). Lists of drawbacks and benefits non-exhaustive.

Next to the variety of methodologies, the variability in damage estimates across the literature is driven by differences in modelling assumptions. Since it is unclear how climate damage will evolve at increasing warming levels, the choice of a functional form for damage functions strongly affects the projected economic impact: For example, the relationship can be modelled as linear (assuming damages increase proportionally with temperature), quadratic (assuming accelerating damages as temperatures rise), or with polynomials of a higher order (i.e. attempting to capture tipping points).

Additionally, some studies assume that climate change solely leads to reductions in GDP levels (Kalkuhl and Wenz, 2020[38]), without affecting its endogenous determinants of growth, while others (Dell, Jones and Olken, 2012[32]), argue that climate damages could have a direct effect on growth rates implying much more severe long-term consequences. Challenges also remain in accounting for non-market impacts, such as biodiversity or climate-induced socioeconomic risks (e.g. mortality, migration), future autonomous adaptation measures and extreme weather events. These are difficult to quantify and frequently excluded from damage projections, further compounding uncertainties in the estimates. Consequently, current estimates most likely do not yet provide a full picture of the economic costs of climate change impacts and a single comprehensive estimate remains elusive.

Given the uncertainties, estimates of future global damages from climate change range widely. Overall, the distribution of literature estimates across all methodologies suggests i) higher economic damages and ii) higher uncertainty regarding their exact magnitude as temperature levels rise (O’Neill, 2022[28]). Notably, more recent studies tend to project higher economic damages, reflecting improved methodologies and a growing recognition of the severe consequences of climate change.

Source: (O’Neill, 2022[28]).

Considering the uncertainty in the quantification of economic costs of climate change, this report employs three different damage functions to reflect a range of plausible estimates from the literature, rather than opting for a single one. The 2023 DICE damage function (Barrage and Nordhaus, 2024[25]) represents a low-end estimate. The function is calibrated using an enumerative strategy and is widely used in integrated assessment models. As a middle-ground, the damage function from Howard and Sterner (2017[26]) is used. This function is based on a meta-analysis of previous studies on the global impacts of climate change and is also used in the OECD Long-Term Macroeconomic Model (Guillemette, forthcoming[39]). At the higher end, damage estimates based on Kotz, Levermann and Wenz (2024[27]) are employed. Their approach econometrically estimates the relationship between historical subnational economic growth rates and various climate variables, reflecting recent advancements in climate-econometric modeling. These damage estimates form the basis for the recent NGFS (Network for Greening the Financial System) climate scenarios (NGFS, 2024[37]).

The Howard and Sterner and DICE 2023 damage functions express global climate damages (${wld\_damage}_t$) as the fraction of global output lost in year t relative to a no-warming scenario. This is done using a reduced-form quadratic function of the global surface temperature anomaly ($T_t^S$) in year t compared to pre-industrial levels (1850-1900). For both, the author’s preferred specification from the respective papers is used:

Howard and Sterner : ${wld\_damage}_t=0.010038\bullet {\left(T_t^S\right)}^2$

DICE 2023: ${wld\_damage}_t=0.003467\bullet {\left(T_t^S\right)}^2$

As the modelling in this report only projects emissions until 2040, it is assumed that emission pathways consistent with the Enhanced NDCs and Current Policies scenarios are maintained beyond 2040 and follow the ones of the IEA’s scenarios on which this report’s scenarios were calibrated to (IEA, 2023[3]).2 However, due to limited data availability for the IEA scenarios, temperature trajectories had to be approximated using the 2024 climate scenarios of the Network for Greening the Financial System (NGFS, 2024[37]). Specifically, temperature projections for the Enhanced NDCs scenario are approximated using the median (50^th) percentile of the temperature distribution estimated with the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) climate model NGFS “Below 2°C” pathway. Likewise, projections for the Current Policies scenario are approximated using the NGFS “Fragmented World” pathway. Both approximations are chosen based on the alignment of the end of century temperatures with the projected temperature levels for the corresponding IEA WEO scenario.3

The high damage estimates used in this report are based on the NGFS Phase V climate scenarios, which rely on Kotz, Levermann and Wenz (2024[27]). Since Kotz, Levermann and Wenz (2024[27]) do not provide a reduced form damage function, the NGFS aggregated the original damage estimates to the national level and derived country-specific reduced-form damage function which allows the temperature-damage relationship to be generalised across alternative emission scenarios (NGFS, 2024[37]). For this report damages are used directly from the median (50^th) percentile of the damage distribution of the NGFS Climate Scenarios corresponding to the Enhanced NDCs and Current Policies scenarios.

The disaggregation of global economic damages into region-level estimates for DICE 2023 and Howard and Sterner is aligned with the OECD long term model (Guillemette, forthcoming[39]). The attribution rule is designed to meet two broad objectives: 1) individual-country damages should aggregate up to the global damage function and 2) the cross-country distribution of damages should relate to country-specific characteristics in a way consistent with the scientific literature. Country-specific damage factors are based on a corresponding reduced form estimation from a meta-analysis of Tol (2024[29]), which suggests that most of the cross-country variance in economic damages from climate change can be reduced into two dimensions: i) current climate conditions and ii) national income levels. For the high-end damage estimates based on Kotz, Levermann and Wenz, subnational damage projections based on historical regional climate impacts (1979–2019) were aggregated by the NGFS to the national level using population weights.4 Subnational differences in impacts can be significant, especially in large counties with diverse climates such as the USA; such regional differences could not be considered. Figure 2.8 in the report presents avoided damages by region under the damage function of Howard and Sterner (2017[26]). For completeness, maps of avoided damages between the scenarios by 2100 for the Kotz, Levermann and Wenz (2024[27]) and 2023 DICE (Barrage and Nordhaus, 2024[25]) damage functions are shown below. Additionally, maps illustrating the geographic distribution of estimated climate damages to potential GDP in 2100 under a Current Policies scenario are shown for all three damage functions.

Figure A A.8. Projected economic benefits from avoided damages with Enhanced NDCs in 2100: Kotz, Levermann and Wenz (2024)

Avoided climate damages (% of potential GDP) in 2100, based on damages estimates from Kotz, Levermann and Wenz (2024)

Source: Authors, based on (Kotz, Levermann and Wenz, 2024[27]) and inputs from IEA’s GCEM model and the 2024 NGFS climate scenarios (NGFS, 2024[40]).

Statlink

Figure A A.9. Projected economic benefits from avoided damages with Enhanced NDCs in 2100: DICE 2023

Avoided climate damages (% of potential GDP) in 2100, based on damages estimates from DICE 2023

Source: Authors, based on (Barrage and Nordhaus, 2024[25]) and inputs from IEA’s GCEM model and the 2024 NGFS climate scenarios (NGFS, 2024[40]).

Statlink

Figure A A.10.  Projected damages from climate change with Current Policies in 2100: Howard and Sterner (2017)

Climate damages (% of potential GDP) in 2100, based on damages estimates from Howard and Sterner (2017)

Source: Authors, based on (Howard and Sterner, 2017[26]) and inputs from IEA’s GCEM model and the 2024 NGFS climate scenarios (NGFS, 2024[40]).

Statlink

Figure A A.11.  Projected damages from climate change with Current Policies in 2100: Kotz, Levermann and Wenz (2024)

Climate damages (% of potential GDP) in 2100, based on damages estimates from Kotz, Levermann and Wenz (2024)

Source: Authors, based on (Kotz, Levermann and Wenz, 2024[27]) and inputs from IEA’s GCEM model and the 2024 NGFS climate scenarios (NGFS, 2024[40]).

Statlink

Figure A A.12. Projected damages from climate change with Current Policies in 2100: DICE 2023

Climate damages (% of potential GDP) in 2100, based on damages estimates from DICE 2023

Source: Authors, based on (Barrage and Nordhaus, 2024[25]) and inputs from IEA’s GCEM model and the 2024 NGFS climate scenarios (NGFS, 2024[40]).

Statlink

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Notes

← 1.  The development of the analytical framework continues to be expanded as part of the SPIN (Spatial Economics of Income Distribution Across Borders: Drivers of Spatial Inequalities using Microsimulation) project (2023-2026) funded by the Luxembourg National Research Fund (CORE) (LISER, 2023[42]).

← 2.  The Current Policies scenario is calibrated on IEA’s Stated Policies scenario (STEPS) for and the Enhanced NDCs scenario is calibrated on IEA’s Announced Pledges scenario (APS) (IEA, 2023[3]).

← 3.  Following the IEA (2023[3]) global mean temperature increase would be limited to approximately 1.7°C above pre-industrial levels by 2100 in the APS scenario. In contrast, under STEPS, temperature rise is projected to reach 2.4°C by 2100. These estimates correspond to the median (50^th) percentile of the temperature distributions derived using the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC). Similarly, the median temperature increase projected by the MAGICC climate model for the NGFS “Below 2°C” and “Fragmented World” scenarios ( for which the underlying emission pathways are obtained through the REMIND-MagPIE integrated assessment model) is 1.75°C and 2.45°C, respectively (NGFS, 2024[37]).

← 4.  See the NGFS Phase V technical documentation for more methodological details (NGFS, 2024[43]).