Climate Transition Scenarios

Transition scenarios constitute a central component of Scientific Portfolio climate analytics, offering projections of how the global economy may evolve under varying climate policy and socioeconomic pathways. These scenarios enable the assessment of potential financial risks and opportunities arising from the transition to a low-carbon economy, and provide reference pathways for measuring the climate alignment of portfolio emissions. The platform currently integrates two principal scenario frameworks: those developed by the Network for Greening the Financial System (NGFS) and the International Energy Agency (IEA).

Relevance of transition scenarios for investors

Transition scenarios provide projections up to 2050, detailing variables such as greenhouse gas emissions, carbon pricing, energy demand, sectoral activity levels, and the deployment of mitigation technologies. For investors, these projections serve two main purposes:​

• Financial Risk Assessment: By applying these scenarios, investors can stress-test their portfolios to estimate potential financial losses if the energy transition follows a specific pathway. The Conditional Transition Loss (CTL) metric, for instance, quantifies potential declines in equity valuation due to climate transition. It decomposes the effects of carbon pricing and demand shifts on firm-level cash flows, utilizing a discounted cash flow model informed by scenario-derived variables. This forward-looking measure complements traditional, backward-looking models.​

• Portfolio Alignment: Normative transition scenarios can serve as benchmarks for assessing the alignment of portfolio emissions with desired climate outcomes. The alignment module evaluates the divergence between a portfolio’s projected emissions and a reference scenario trajectory derived from a transition scenario. This methodology incorporates ethical considerations, including allocation principles and emission scopes, acknowledging the inherently normative nature of alignment metrics.

Construction of transition scenarios

Transition scenarios are developed using Integrated Assessment Models (IAMs), which integrate macroeconomic, technological, and energy system models with climate modules. Two main classes of IAMs are distinguished:​

• Cost-Benefit IAMs: These models, such as the Dynamic Integrated Climate-Economy (DICE) model, aim to identify optimal pathways by minimizing the economic cost of climate action under assumed damage functions.​

• Process-Based IAMs: Employed in NGFS and IEA scenarios, these models simulate sector-specific transformations under predefined climate targets (e.g., 1.5°C or 2°C), subject to biophysical and technological constraints, without assuming a globally optimal solution.

Models Utilized in NGFS Scenarios

The NGFS employs a suite of models to develop its climate scenarios:

• REMIND-MAgPIE: Developed by the Potsdam Institute for Climate Impact Research, this model combines an energy-economy model (REMIND) with a land-use model (MAgPIE) to assess interactions between energy production, land use, and climate policies.​

• GCAM (Global Change Analysis Model): Created by the University of Maryland, GCAM is an integrated model that simulates linkages between energy, water, land, climate, and economic systems to explore implications of different climate policies.​

• MESSAGEix-GLOBIOM: Developed by the International Institute for Applied Systems Analysis (IIASA), this framework combines the an energy system model (MESSAGEix) with a land-use model (GLOBIOM) to analyze how energy and land-use policies can achieve climate targets.​

Models Utilized in IEA's World Energy Outlook (WEO)

The IEA employs the Global Energy and Climate Model (GEC Model) to generate its annual World Energy Outlook scenarios. This integrated framework combines elements of the former World Energy Model (WEM) and Energy Technology Perspectives (ETP) model. The GEC Model is particularly suited for evaluating energy system transformations, offering granular insights into fuel supply, electricity generation, end-use technologies, and investment needs across regions and sectors.

Step-by-step overview of transition scenario generation

Despite differences in their underlying architecture, most process-based models follow a common set of steps to generate transition scenarios.

Step 1: selection of socioeconomic and climate forcing narratives

The construction of a transition scenario begins with the definition of a narrative. Since …, the Intergovernmental Panel on Climate Change (IPCC) has developed standardized narratives to to provide a consistent set of inputs across different modelling teams. These reference narratives are organised around two dimensions : Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs).

Shared Socioeconomic Pathways (SSPs): The SSPs describe alternative trajectories of global development, capturing long-term trends in population, economic growth, technological change, and urbanisation. These narratives are not normative but descriptive: for example, SSP2—often referred to as the “Middle of the Road” pathway—assumes a continuation of historical development trends without significant shifts in global cooperation or inequality.

Representative Concentration Pathways (RCPs): To complement the socioeconomic dimension, RCPs describe alternative trajectories of radiative forcing—a measure of the energy imbalance in the Earth’s climate system caused by greenhouse gas concentrations—by the year 2100. Each RCP corresponds to a specific level of radiative forcing, expressed in watts per square metre (W/m²), and serves as a proxy for global warming outcomes. For instance, RCP1.9 corresponds to a peak radiative forcing of approximately 1.9 W/m², and is consistent with limiting global temperature rise to around 1.5°C above pre-industrial levels. This RCP implies a strict cumulative carbon budget over the 21st century; the lower the radiative forcing, the smaller the total quantity of greenhouse gases that can be emitted.

When a modeller selects a specific SSP-RCP combination—for example, SSP2 with RCP1.9—they define both the socioeconomic trajectory (moderate economic growth and institutional development) and the climate ambition (a stringent mitigation pathway compatible with 1.5°C). This pairing sets the boundary conditions for the scenario, determining both the demand-side pressures on the energy system and the emissions constraint that must be respected. In practice, this is operationalised within models such as MESSAGEix by imposing a global greenhouse gas emissions cap that ensures compliance with the RCP’s cumulative emissions target. For instance, to remain below RCP1.9, the model may be constrained to limit global CO₂ emissions to less than 420 GtCO₂ over the century. The scenario then explores the least-cost combination of energy and land-use system transformations required to satisfy both the economic demand structure defined by SSP2 and the climate constraint defined by RCP1.9.

Linking socioeconomic narratives to climate outcomes: The SSP–RCP Framework

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Source: Meinshausen et al. (2020): The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geoscientific Model Development 13: 3571–3605

Step 2: Baseline Calibration

Once the SSP-RCP pairing is selected, the transition scenario is operationalised through the initialization of the integrated assessment model. In the case of MESSAGEix, this involves setting up a dynamic intertemporal optimisation over a long-term horizon, typically from 2020 to 2100. The model begins by constructing a baseline scenario that reflects a “current policies” world, i.e. one in which no additional climate mitigation measures are introduced beyond those already implemented or legislated.

This baseline is calibrated using inputs derived from the chosen SSP including projections for socioeconomic variables such as population growth, GDP evolution, and global productivity, which determine energy service demands and the structure of economic activity. In parallel, the model incorporates technological and resource availability constraints, such as fossil fuel reserves, renewable energy potentials, and assumptions on technology learning rates or deployment limits. Furthermore, it embeds the initial configuration of the global energy system, including the installed capacities of different energy sources. This baseline serves not only as a counterfactual but also as a reference point for measuring the incremental costs, emissions reductions, and systemic changes required to meet a given climate target.

Step 3: Optimization under carbon constraint

With the baseline established and a climate constraint defined, the MESSAGEix model proceeds to optimize the evolution of the global energy and land-use systems over the modelling horizon. The objective is to minimize total system costs—including energy production, land-use transitions, and emissions mitigation—while satisfying both final energy demand and the emissions constraint. The model endogenously determines a wide array of decision variables, including the energy supply mix, infrastructure investments, emissions abatement strategies, technology deployment rates, and land-use allocation.

Carbon price emerge endogenously as the shadow cost of staying within the emissions cap. Importantly, MESSAGEix is soft-linked to the GLOBIOM model, which provides land-use feedbacks by simulating agricultural production, forestry, and bioenergy supply in response to changing biomass demand and land-based mitigation policies (e.g., afforestation). The land-use emissions and biomass potentials generated by GLOBIOM are then re-integrated into MESSAGEix, enabling a consistent treatment of land-energy interactions across sectors. This iterative coupling ensures that both the energy and land-use systems evolve coherently under the imposed climate constraint.

The final output of the modelling exercise is a comprehensive set of long-term projections detailing the evolution of key systems under the selected transition pathway. These include the global and regional energy mix disaggregated by carrier and sector, total and gas-specific greenhouse gas emissions, carbon price trajectories reflecting the marginal cost of abatement, and technology deployment pathways such as carbon capture and storage (CCS), hydrogen, nuclear, and solar.

Comparison of main transition scenario outputs

While both the IEA and NGFS scenarios are designed to inform stakeholders about plausible decarbonisation pathways, they differ substantially in terms of modelling structure and output granularity. The IEA scenarios are grounded in detailed energy system analysis. They provide high-resolution projections of energy demand, fuel mix evolution, technology deployment, investment flows, and emissions disaggregated by sector and fuel type.

In contrast, the NGFS scenarios are developed using global integrated assessment models (IAMs) that adopt a broader macroeconomic framework to link climate policy with long-term dynamics in energy systems, land use, economic output, and emissions.

This section reviews key variations among selected IEA and NGFS scenarios, focusing sequentially on (i) projected trajectories of greenhouse gas (GHG) emissions, (ii) energy production and transformation, and (iii) carbon price.

The comparative analysis centers on three reference scenarios that are central in both risk and impact functionalities of Scientific Portfolio:

• Current Policies: Assumes no additional climate policies beyond those already implemented; consistent with a global temperature rise of approximately 3°C by 2100.

• Delayed Transition: No new climate action is undertaken until 2030, followed by an accelerated policy effort; results in a likely temperature increase of around 2°C.

• Net Zero 2050: Envisions the early deployment of stringent climate policies and technological innovation, achieving global net-zero CO₂ emissions by mid-century; consistent with limiting warming to 1.5°C.

Greenhous gas emissions

Greenhouse gas (GHG) emissions trajectories vary substantially across climate transition scenarios.

While the Net Zero 2050 scenario is designed to align with global climate objectives by achieving net-zero carbon dioxide (CO₂) emissions by mid-century, this label can be misleading if interpreted as implying that all emissions sources drop to zero in 2050.

In practice, total GHG emissions—encompassing not only CO₂ but also methane (CH₄), nitrous oxide (N₂O), and various fluorinated gases—do not reach zero in 2050 under any scenario, including Net Zero 2050. This persistence reflects the technical and economic challenges of fully eliminating non-CO₂ emissions across sectors such as agriculture, land use, and industry.

Furthermore, even CO₂ emissions from energy do not fall precisely to zero by 2050. This is due to residual emissions from hard-to-abate subsectors such as aviation, shipping, and industrial heat. These residuals are expected to be offset through carbon removals (e.g., bioenergy with carbon capture and storage or land-based sinks), resulting in net-zero CO₂ emissions at the global level. It is also important to distinguish CO₂ emissions from energy use—primarily driven by fossil fuel combustion—from emissions arising in the agriculture, forestry, and other land use (AFOLU) sector. While energy-related emissions are the dominant source of anthropogenic CO₂ for listed companies, the AFOLU sector contributes both emissions (e.g., from deforestation and livestock) and removals (e.g., from reforestation), and thus plays a dual role in the global net emissions balance.

The following graphics present projected global GHG emissions and CO₂ emissions from energy by scenario, highlighting both the scale of required reductions and the timing differences across transition pathways.

Energy production and transformation

To complement the analysis of emissions trajectories, this section examines the evolution of key technological and energy system variables that underpin different transition pathways. For each scenario, we track the relative change—expressed as a growth factor normalized to 1 in 2020—in three strategic variables: fossil fuel production, low-carbon electricity generation, and alternative transportation.

These variables are particularly relevant for the calculation of the Conditional Transition Loss (CTL), as they determine the trajectory of revenue streams associated with specific technologies across scenarios. The CTL framework evaluates the sensitivity of firm-level financial performance to structural shifts in the real economy induced by the low-carbon transition. In this context, the growth factor of each technology—fossil fuels, low-carbon electricity, and alternative transportation—acts as a proxy for the evolution of market demand and, by extension, firm revenues linked to these sectors.

For instance, consider a firm with 30% of its revenue derived from electric vehicles. In the CTL methodology, this revenue share is projected to grow in proportion to the scenario-specific trajectory of the alternative transportation variable. Under a scenario characterized by strong policy support and technological diffusion (e.g., Net Zero 2050), the growth factor for alternative transportation rises significantly, implying higher projected revenue for the firm's electric vehicle segment. Conversely, revenue linked to fossil fuel technologies would contract in line with the corresponding decline in the fossil fuel production growth factor.

Carbon price

Another key variable in transition scenario analysis is the shadow carbon price, which plays a central role in the NGFS modelling framework. Within integrated assessment models (IAMs), the shadow carbon price represents the implicit marginal cost of abating one additional tonne of CO₂, conditional on meeting the scenario’s climate objective, i.e. its carbon budget, in the most cost-effective manner. It is important to note that this value does not reflect future climate damages, nor does it represent an explicit carbon tax or traded price. Instead, it emerges endogenously from the model as the economic signal required to steer the global economy along a least-cost mitigation pathway, given assumptions about technology availability and sectoral dynamics.

In practical terms, the shadow carbon price serves as an indicator of mitigation stringency: higher shadow prices imply more constrained technological and economic conditions, and thus more expensive emissions reductions. In the NGFS scenarios, the shadow price increases gradually under Net Zero 2050, reflecting early, orderly, and sustained climate action. In the Delayed Transition scenario, the price remains low until 2030, then rises sharply to reflect the higher marginal cost of rapid decarbonisation in a compressed time frame. The Current Policies scenario, which assumes no additional measures, features a negligible or absent shadow price, consistent with an emissions trajectory exceeding Paris-aligned limits.

In the context of the Conditional Transition Loss (CTL) metric, the shadow carbon price is used to approximate potential future regulatory costs. Specifically, firm-level emissions are multiplied by the scenario-specific carbon price to estimate the impact of regulatory transition risk on operational costs. This approach captures how exposure to carbon-intensive activities may translate into financial vulnerabilities, not through direct forecasting of policy, but through alignment with the marginal cost structure implied by each transition scenario.

Heterogeneity across models

While scenario narratives offer structured pathways, substantial heterogeneity remains in the variables produced by different integrated assessment models. This variation reflects underlying differences in model architecture, sectoral resolution, behavioural assumptions, and representations of technological change.

The figure below illustrates this heterogeneity in the case of fossil fuel production, showing the normalized growth factor (2020 = 1) by model under a common Net Zero 2050 scenario. While all models exhibit a decline in fossil fuel production under a 1.5°C-aligned pathway, the pace and depth of this decline vary markedly. For instance, REMIND often projects faster reductions due to more rapid electrification and early carbon pricing, whereas GCAM may depict a more gradual transition owing to continued fossil fuel use with carbon capture and storage (CCS). Such divergences underscore the importance of considering model uncertainty when interpreting scenario results, especially in the context of forward-looking financial risk assessments.