Since 1993, the IEA has provided medium- to long-term energy projections using a continually evolving set of detailed, world-leading modelling tools. First, the World Energy Model (WEM) – a large-scale simulation model designed to replicate how energy markets function – was developed. A decade later, the Energy Technology Perspectives (ETP) model – a technology-rich bottom-up model – was developed, for use in parallel to the WEM.

In 2021, the IEA adopted for the first time a new hybrid modelling approach relying on the strengths of both models to develop the world’s first comprehensive study of how to transition to an energy system at net zero CO₂ emissions by 2050 and this analysis was updated in 2023. This new integrated modelling framework, the IEA’s Global Energy and Climate Model (GEC Model), is now the principal tool used to generate detailed sector-by-sector and region-by-region long-term scenarios across IEA publications.

The GEC Model brings together the modelling capabilities of the WEM and ETP models. The result is a large-scale bottom-up partial-optimisation modelling framework allowing for a unique set of analytical capacities in energy markets, technology trends, policy strategies and investments across the energy sector that would be critical to achieve climate goals. The IEA’s GEC Model covers 29 regions that can be aggregated to world-level results, and covers all sectors across the energy system with dedicated bottom-up modelling for:

• Final energy demand, covering industry, transport, buildings, agriculture and other non-energy use. This is driven by detailed modelling of energy service and material demand.
• Energy transformation, including electricity generation and heat production, refineries, the production of biofuels, hydrogen and hydrogen-derived fuels and other energy-related processes, as well as related transmission and distribution systems, storage and trade.
• Energy supply, including fossil fuels exploration, extraction and trade and the availability of renewable energy resources.

Inputs to the model include: historical technology stock, cost and performance; energy statistics and balance data; policies and regulations; and socio-economic drivers.

Outputs from the model include: projected technology stock, cost, and performance; energy flows by fuel; investment needs and costs; materials and critical minerals demand; carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) emissions.

Prices, which are both inputs and outputs of the model, include: fuel, end-user and CO₂ prices.

The key sectoral and topic-specific model developments undertaken this year include the following:

• The number of regions included for the 2023 demand modelling increased from 26 to 29, adding country-specific regions for Chile, Colombia, Costa Rica and Argentina for this cycle’s Latin America Energy Outlook.
• The Net Zero Emissions by 2050 Scenario has undergone a significant update in 2023, retaining key design principles while taking into account major changes that have occurred since 2021.
• For buildings, higher technology disaggregation in space heating, water heating and space cooling has been incorporated, particularly for heat pumps. Heating and cooling degree days were updated in line with the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report.
• For industry, granularity has been increased in the iron and steel and aluminium subsectors. Improved stock modelling now allows for better tracking of scrap metal and plastic waste resources and better representation of secondary production dynamics.
• For transport, aviation modelling has been refined and additional details on methanol for shipping have been included. A comprehensive update of the rail modelling was also undertaken.
• In the power sector, new hourly load curve and hourly dispatch models have been developed. This improves capacity to assess the impact of weather-induced variability on power system operations and long-term flexibility needs in systems with rising shares of variable renewables and temperature-sensitive end uses.
• In energy supply, a detailed geospatial analysis of the electrification potential of upstream oil and gas operations was conducted and revised methods to estimate net income and investment from oil and gas operations have been implemented.
• For hydrogen, more granularity has been developed on global methanol trading and the regional production cost curves for hydrogen production from renewable electricity.
• For critical minerals, the model integrates new activity data and new data on battery chemistry developments, with demand projections published in the interactive Critical Minerals Data Explorer.
• For emissions, updated satellite-data, measurement studies, governance indicators and mine-level data have been added to estimate methane emissions from fossil fuel supply.
• For employment, the scope has been expanded to include employment in nuclear fuel supply and detail has been added on critical minerals.
• Government energy spending data has been enhanced, notably with regards to the timeline for disbursement of government funding for clean energy investment support and energy affordability for consumers.

The GEC Model is a bottom-up partial-optimisation model covering energy demand, energy transformation and energy supply. The model uses a partial equilibrium approach, integrating prices sensitivities. It shows the transformation of primary energy along energy supply chains to meet energy service demand, the final energy consumed by the end-user. The various supply, transformation and demand modules of the model are dynamically soft-linked: consumption of electricity, hydrogen and hydrogen-related fuels, biofuels, oil products, coal and natural gas in the end use sector model drives the transformation and supply modules, which in turn feed energy prices back to the demand module in an iterative process. In addition, energy system CO₂, CH₄ and N₂O emissions are assessed. The model contains a number of additional analysis features evaluating further system implications such as investments, critical minerals, employment, temperature outcomes, land-use, and air pollution.

The main exogenous drivers of the scenarios are economic growth, demographics and technological developments. Energy service demand drivers, such as steel demand in industry or number of appliances within households, are estimated econometrically based on historical data and on the socioeconomic drivers. Interactions between energy service demand drivers are also accounted for, such as the influence of the number of vehicles sales on materials demand. This service demand is met by existing and new technologies. All sector modules base their projections on the existing stock of energy infrastructure (e.g. the number of vehicles in transport, production capacity in industry, floor space area in buildings), through detailed stock-accounting frameworks. To assess how the service demand is met in the various scenarios, the model includes a wide range of fuels and technologies (existing and additions). This includes careful accounting of the current energy performance of different technologies and processes, and potential to improve efficiency.  

The sectoral and cross-sectoral energy and emission balances are calculated based on the final energy end uses – the service demand – by determining first the final energy demand needed to serve it, then the required transformations to convert primary energy into the required fuels, and finally the primary energy needs. This is based on a partial equilibrium approach using for some elements a partial optimisation model, within which specific costs play an important role in determining the share of fuels and technologies to satisfy the energy service demand. In different parts of the model, Logit and Weibull functions are used to determine the share of technologies based upon their specific costs. This includes investment costs, operating and maintenance costs, fuel costs and in some cases costs for emitting CO₂. In certain sectors, such as hydrogen production, specially designed and linked optimisation modules are used. 

While the model aims to identify an economical way for society to reach the desired scenario outcomes, the results do not necessarily reflect the least-cost way of doing so. This is because an unconstrained least-cost approach may fail to take account of all the issues that need to be considered in practice, such as political or individual preferences, feasible ramp-up rates, capital constraints and public acceptance. Instead, the analysis pursues a portfolio of fuels and technologies within a framework of cost minimisation, considering technical, economical and regulatory constraints. This approach, tailored to each sector and incorporating extensive expert consultation, enables the model to reflect as accurately as possible the realities of different sectors. It also offers a hedge against the real risks associated with the pathways: if one technology or fuel fails to fulfil its expected potential, it can more easily be compensated by another if its share in the overall energy mix is low.  

All fuels and technologies included in the model are either already commercially available or at a relatively advanced stage of development, so that they have at least reached a prototype size from which enough information about expected performance and costs at scale can be derived. Costs for new clean fuels and technologies are expected to fall over time and informed in many cases by learning curve approaches, helping to make a net zero future economically feasible. 

Besides this main feedback loop between supply and demand, there are also linkages between the transformation and supply modules. Further linkages between energy sectors are captured in the model, e.g. material flows or biogenic or atmospheric CO₂ via direct air capture for synthetic fuel production. Primary energy needs and availability interact with the supply module. Complete energy balances are compiled at a regional level and the CO₂ emissions of each region are then calculated using derived CO₂ factors, taking into account reductions from CO₂ removal technologies. 

The GEC Model is implemented in the simulation software Vensim, but makes use of a wider range of software tools, including TIMES. 

The GEC Model is a highly data-intensive model covering the whole global energy system. Much of the data on historical energy supply, transformation and demand, as well as energy prices, is obtained from the IEA’s own energy and economic databases. Additional data from a wide range of often sector-specific external sources is also used, in particular to establish the historical size and performance of energy-consuming stocks.

The model is recalibrated annually to the latest available data. The formal base year for this year’s projections is 2021, as this is the most recent year for which a full energy balance by country is available. However, we have used more recent data wherever available, including 2022 and 2023 estimates for energy production and demand. Estimates for the year 2022 are based on the IEA’s CO₂ Emissions in 2022 report, in which data was derived from a number of sources including the latest monthly data submissions to the IEA Energy Data Centre, other statistical releases from national administrations and recent market data from the IEA Market Report Series that cover coal, oil, natural gas, renewables and electricity. Investment estimates include the year 2022, based on the IEA’s World Energy Investment 2023 report. Technology data used in different sectors cover 2022 and estimates for 2023, such as the data in Tracking Clean Energy Progress 2023, the Global Hydrogen Review 2023 and the Global Electric Vehicle Outlook 2023.

For a summary of selected key data inputs – including macro drivers such as population, economic developments and prices, as well as techno-economic inputs such as fossil fuel resources and technology costs – please see the GEC Model key input dataset.

The GEC Model covers the energy developments in the full global energy system up to 2050, with the capacity to extend beyond 2050 for some regions. Simulations are carried out on an annual basis, with hourly modelling for the power sector. The current version of the model provides results for 29 regions of the world, of which 16 are individual countries. Several supply components of the model have further regional disaggregation: the oil and gas supply model has 113 regions and the coal supply model 32 regions.

The IEA’s GEC Model offers unparalleled scope and detail about the energy system. Its essential purpose is evaluating energy supply and demand, as well as the environmental impacts of energy use and the impacts of policy and technology developments on the energy system. Through long-term scenario analysis, the model enables analysis of possible futures related to the following main areas:

• Global and regional energy trends: Assessment of energy demand, supply availability and constraints, international trade and energy balances by sector and by fuel.
• Environmental impact of energy use: CO₂ emissions from fuel combustion are derived from the projections of energy consumption. CO₂ process emissions are based on the production of industrial materials and CH₄ and N₂O emissions are assessed for final energy demand as well as for energy transformation. Methane from oil and gas operations are assessed through bottom-up estimates and direct emissions measurements (see the IEA’s Global Methane Tracker 2023). Local air pollutants are also estimated linking the GEC Model with the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model of the International Institute for Applied Systems Analysis (IIASA) and the temperature outcomes of modelled scenarios are assessed using the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC).
• Policy and technology developments: the impact of policy actions and technological developments on energy demand, supply, trade, investments and emissions can be investigated by comparing between scenarios.

Additionally, the GEC Model has multiple detailed features that either underlie or build from the analysis of broader energy trends. These include: 

• Technologies: Detailed techno-economic characterisation of over 800 clean energy technologies, including those still under development (either at prototype or demonstration stage) for different applications in heavy industries, long-distance transport and carbon dioxide removal technologies.
• People-centred transitions: Detailed modelling of behavioural changes, energy sector employment, equity outcomes and energy affordability, among other implications for citizens.
• Critical minerals: Comprehensive analysis of projected demand and supply of critical minerals needed for the energy sector’s transition.
• Infrastructure: Detailed modelling and analysis on energy infrastructure development needs and strategies including electricity systems, fossil fuels, hydrogen-related fuels distribution and CO₂ transport options.
• Variable renewables potential: Detailed geospatial analysis of variable renewables potentials across the globe and modelling of their impact of exploiting those for hydrogen production. 
• Modern energy access: Comprehensive modelling of the implications and opportunities to provide energy access to all communities. This includes access to electricity and clean cooking facilities and an evaluation of additional energy demand, investments and related greenhouse gas emissions. 
• Material efficiency: Granular modelling of strategies along supply chains to make the use of materials including steel, cement, aluminium, plastics and fertilisers more efficient.
• Investments: Detailed modelling of overall energy sector and clean energy investments by sub-sector and technology areas and comprehensive analysis on effective financing strategies. This includes investment requirements in fuel supply chains to satisfy projected energy demand and for demand-side technologies and measures (e.g. energy efficiency, electrification). Government spending is also tracked.
• Decomposition: Detailed mathematical framework to systematically analyse the specific contribution of different strategies to emissions or energy savings between scenarios and over time. 
  

The development of the GEC Model benefits from expert review within the IEA and beyond and the IEA works closely with colleagues in the global modelling community. For example, the IEA participates in and regularly hosts the International Energy Workshop and regularly interacts with the Integrated Assessment Modelling Consortium. The initial Net Zero Emissions by 2050 Scenario in 2021 was informed by discussions with modelling teams from across the world, including from China, the European Union, Japan, the United Kingdom, the United States and the IPCC.

The IEA also has a long-standing history of working with researchers and modellers around the world as part of its Technology Collaboration Programmes (TCP) network. The TCPs support the work of independent, international groups of experts that enable governments and industries from around the world to lead programmes and projects on a wide range of energy technologies and related issues. The Energy Technology Systems Analysis Programme (ETSAP) TCP, established in 1977, is among the longest running TCPs. The ETSAP TCP supports policy makers in improving the evidence base underpinning energy and environmental policy decisions through energy systems modelling tools including the TIMES modelling platform and brings together a unique network of nearly 200 energy modelling teams from approximately 70 countries.

IEA’s GEC Model also interacts closely with other internationally recognised models: 

• The IEA uses the MAGICC Model, developed and maintained by ClimateResource and often used by the IPCC for key publications, to inform its analysis of the impact of different greenhouse gases budgets on the average global temperature rise.
• IEA modelling results are coupled with the GAINS model developed and maintained by IIASA. This allows for detailed analysis on the impact on air pollution of different IEA scenarios.
• IEA results are coupled with the Global Biosphere Management Model (GLOBIOM) developed and maintained by IIASA to complement the IEA’s analysis on bioenergy supplies and effective use strategies.
• The Aviation Integrated Model (AIM) developed by University College London forms the basis for our modelling of the aviation sector.
• IEA modelling results have been linked to the Global Integrated Monetary and Fiscal (GIMF) model of the International Monetary Fund (IMF) to assess the impacts of changes in investment spending on global GDP.
• The Open Source Spatial Electrification Tool (OnSSET), a GIS-based optimisation tool developed as a result of a collaboration among several organisations, is used to inform the IEA’s energy access modelling.