Blueprint to Foundations: A New Generation of Climate Scenarios

This editorial by Frédéric Ducoulombier, Director of EDHEC-Risk Climate, was originally published in the December newsletter of the Institute. To subscribe to this complimentary newsletter, please contact: [email protected].

Introduction: A Call to Action

Two years ago, EDHEC-Risk Climate Impact Institute was established with a clear priority: to advance the integration of climate considerations into financial decision-making. Our mission was to equip investors, financial managers, and policymakers with the tools to navigate the evolving economic landscape shaped by climate change and climate action. This included modelling interactions between climate change and policies, assessing their economic and financial impacts, and developing better tools for climate-aware investment and risk management.

Eighteen months ago, in a Financial Times editorial, we challenged the financial industry to rethink its approach: existing climate scenarios rigidly link socio-economic narratives to warming outcomes, overlook critical uncertainties—such as variability in climate responses and dynamic policy and economic feedbacks—and remain silent on the relative likelihood of different pathways. Furthermore, global approaches obscure regional and sectoral disparities, limiting their practical value for decision-makers.

A year ago, our first position paper highlighted concerns with how the institutional investment industry was using climate scenarios and modelling tools. It noted that models originally designed for optimisation could not be repurposed for scenario analysis without recalibration. The paper also criticised the overly precise and surprisingly tame damage estimates produced by practitioners, which undermined trust in the analyses and fostered a false sense of security about climate risks. It underscored the urgent need to recalibrate damage functions, integrate uncertainty, and adopt probabilistic approaches to scenario analysis.

The Starting Point: Modernising IAMs

Our journey began with a focus on revisiting Integrated Assessment Models (IAMs), tools that integrate insights from climate science, economics, and energy systems to assess the impacts and costs of climate change and climate policies. Optimisation-based IAMs, such as the Dynamic Integrated Climate-Economy (DICE) model, aim to balance the costs of mitigation and climate damages, while process-based IAMs simulate detailed physical and economic systems based on given emissions trajectories.

Our initial efforts centred on enhancing the seminal DICE model to bring it up to date with the latest climate science and make it applicable to financial economics. Early versions of the DICE model faced significant criticism for relying on highly aggregated damage functions—mathematical representations that estimate economic costs caused by climate change based on variables like global temperature. These functions often smoothed over regional disparities and underestimated the severity of climate impacts. Furthermore, the deterministic nature of the original DICE model excluded uncertainties inherent in climate and economic systems, and its simplified utility function failed to reflect the nuanced ways individuals and markets value uncertain future outcomes. Perhaps most controversially, the model's results suggested that a temperature increase of 3°C or more by the end of the century could be economically optimal, a conclusion that appeared counterintuitive.

To address these limitations, we undertook a series of significant updates to bring the model up to the latest standards in climate science and financial economics:

1. Aligning with State-of-the-Art Climate Science:

We recalibrated the damage function to reflect the latest understanding of climate physics, including non-linear impacts, tipping points, and the potential for irreversible damages. These updates allow the modified model to produce more realistic assessments of the economic consequences of higher warming scenarios.

2. Incorporating Uncertainty:

Moving from a deterministic to a probabilistic framework, we updated the model to account for multiple sources of uncertainty, such as the response of temperature to greenhouse gas concentration, economic growth, abatement costs, and economic damages. This allows the upgraded model to explore a range of plausible outcomes and pathways rather than presenting a single fixed trajectory.

3. Improving the Utility Function:

The standard utility function was replaced with one that separates people's attitudes toward risk (risk aversion) from how they value future outcomes (intertemporal substitution). This adjustment, supported by theoretical and computational advances, better aligns the extended model with empirical observations and financial theory

These updates had a profound impact on the model’s policy guidance and applicability. Using the updated framework, we find that the Paris Agreement targets are not just aspirational but also economically optimal. However, achieving these targets requires extraordinary abatement efforts, demanding unprecedented peacetime levels of investment in mitigation technologies and strategies.

Crucially, the revised model is now also fit for financial economics. It recovers key stylised facts about financial markets, such as the relationship between risk and return, and enables the assessment of how climate risks affect asset prices. By bridging climate science and finance, it provides a robust foundation for understanding and managing climate risks in economic and financial systems.

The results of the updated model reveal a stark dual challenge for investors: inaction leads to high damages from physical risks, while the ambition required to meet climate targets entails significant transition costs. This reality is difficult to reconcile with claims that portfolios are largely insulated from climate change and climate action. In our first academic publication, Asleep at the Wheel, we warned against market complacency and highlighted the risk of significant price adjustments when recognition of climate risks finally occurs.

The Inflection Point: Repurposing IAMs for a New Climate Scenario Framework

As we advanced our work on modernising optimisation-based IAMs, it became clear that the tool we were building could offer more than just better policy recommendations grounded in the latest climate science. It could also be repurposed to address pressing limitations in existing climate scenario frameworks and serve as an engine for modular, probabilistic scenarios tailored to the needs of investors and policymakers.

The Backbone of Mainstream Climate Scenarios

Most of the climate scenarios used in business and finance are linked to the Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP) framework, a cornerstone of the Intergovernmental Panel on Climate Change’s (IPCC) scenario analysis, particularly in its assessments of impacts, adaptation, and mitigation

• SSPs: Narratives describing global socio-economic trajectories, such as population growth, urbanisation, technological advancement, and economic inequality. For example, SSP2—the "middle-of-the-road" pathway—assumes a continuation of historical trends, balancing moderate challenges to mitigation and adaptation.
• RCPs: Emissions trajectories delivering different levels of warming by 2100. These are identified by levels of radiative forcing (e.g., RCP2.6, RCP4.5, RCP8.5), which reflect the intensity of greenhouse gas concentrations.

Pairing SSPs with RCPs forms the backbone of most climate scenarios. A notable example is provided by the widely used scenarios developed by the Network for Greening the Financial System (NGFS), which pair SSP2 with various RCPs to explore policy and economic impacts under different warming outcomes.

Limitations of Existing Climate Scenario Frameworks

While foundational, the approach suffers from limitations that significantly restrict its relevance for effective decision-making:

1. Lack of Probabilities: The pairing of SSPs with RCPs relies on IAMs to determine a set of policy and technological adjustments—such as carbon pricing, renewable energy deployment, or land-use changes—that align socio-economic trajectories with emissions targets. However, quantitative feasibility need not be synonymous with socio-economic plausibility. For instance, achieving a stringent warming target (like RCP2.6) under a middle-of-the-road socio-economic pathway (i.e. SSP2) could require significant financial intervention, [1] representing a substantial shift in economic priorities that could face political resistance or social backlash, particularly in regions heavily reliant on fossil fuels. While this pairing illustrates the challenges inherent in aligning these frameworks, even more ambitious combinations could reveal greater strain, underscoring the importance of assigning probabilities to scenarios to better inform decision-making. Yet, these scenarios are often presented without probabilities, creating the misleading perception that all futures are equally plausible and deserving of equal attention. This lack of prioritisation hinders the ability of public policy and corporate decision-makers to focus on the most actionable and realistic pathways. And without probabilities, there can be no asset pricing or meaningful financial risk assessment.

2. Incomplete Coverage: The rigid coupling of socio-economic pathways and emissions trajectories limits flexibility and narrows exploration of alternative possibilities.[2] This constraint is further compounded by the ad-hoc selection of scenarios, which cannot guarantee that the chosen pairings collectively span the full range of plausible futures. For example, NGFS scenarios anchored on SSP2 offer consistency but risk overlooking critical pathways, such as those involving more severe mitigation failures or divergent socio-economic developments. This lack of comprehensive coverage reduces the utility of these frameworks for financial decision-making, potentially leaving investors unprepared for key risks and blind to emerging opportunities.

Repurposing IAMs as Scenario Engines

To address the limitations of existing climate scenario frameworks, we adapted our enhanced DICE-like IAM to serve as a dynamic scenario engine. The evolution of the model was underpinned by two key advancements:

1. Decoupling and Modular Flexibility

Our model’s modular architecture decouples socio-economic pathways, emissions trajectories, and temperature outcomes. This decoupling enables a more flexible exploration of climate, economic, and policy pathways, providing decision-makers with tools that better reflect real-world complexities. By separating these components, the model supports a broader range of scenario analyses that are not constrained by predefined linkages or rigid assumptions.

2. Integration of Multiple Dimensions of Uncertainty

A critical advancement of the model is its ability to incorporate several layers of uncertainty, reflecting the realities of climate risks:

• Uncertainty in Economic Growth: We adapted a well-established method (the long-run-risk approach by Bansal and Yaron) to account for the uncertainty in economic growth, which plays a major role in driving emissions.
• Uncertainty in Emissions: By removing predefined linkages to socio-economic pathways, the model explores varied global cooperation scenarios, delayed emissions reductions, and abrupt transitions, incorporating policy and technological tipping points.
• Uncertainty in Climate Sensitivity: There is considerable uncertainty about how temperatures will respond to greenhouse gas concentrations—a relationship influenced by factors like cloud feedbacks and ocean heat uptake (collectively known as Equilibrium Climate Sensitivity). Our model captures this uncertainty by incorporating probabilistic distributions for temperature outcomes, allowing for a more realistic representation of both moderate and extreme warming scenarios.

The Breakthroughs: From Scenario Generation to Probabilistic Asset Pricing and Scenarios

Building on our enhanced IAM framework, we demonstrated how these tools could transform climate-aware financial decision-making. This effort culminated in two key innovations: linking climate pathways to asset pricing and assigning probabilities to these pathways. These groundbreaking methodologies enhanced our ability to quantify the economic and financial risks associated with varying levels of climate change and policy action, delivering a robust framework for climate-aware asset pricing.

Linking Climate Scenarios to Asset Pricing

In our July 2024 white paper How Climate Risk Affects Global Equity Valuation, we demonstrated how climate scenarios could be used to inform equity valuation. By relating abatement trajectories to transition costs and physical damages, we developed a method that quantifies the financial impacts of varying levels of climate action. By linking discount rates to climate-driven economic states, we delivered a framework to price assets under climate risk and action, conditional on abatement schedules.

While the primary value of this work lies in its methodological advancement, its application has also yielded practical insights that have attracted considerable attention. For example:

• Material financial risks from inaction: The model suggests that if abatement efforts remain limited, global equity valuations could decline by up to 40%, driven primarily by significant physical damages resulting from climate change;
• Benefits of robust mitigation: Strong abatement policies aligned with the Paris Agreement may limit valuation losses to as low as 5–10%, underscoring the financial benefits of ambitious climate action.

Of course, these results depend on parameter choices that, by nature, are open to critique. However, the key contribution of this work lies in its rigorous probabilistic framework, which provides a robust foundation for bridging climate modeling and financial decision-making.

Assigning Probabilities to Climate Pathways

Recognising the limitations of relying solely on conditional analyses, our forthcoming white paper, presented as a feature contribution in this newsletter, addresses the challenge of deriving unconditional probabilities. Using a combination of empirical data on mitigation trends and insights from economists, we developed a framework to assign relative likelihoods to different emissions trajectories.

By way of illustration, the analysis estimates:

• A very low likelihood of limiting end-of-century temperature increases to 1.5°C, reflecting the persistent disconnect between abatement policies and economists’ recommendations.
• A median 2100-temperature anomaly of approximately 2.5°C, indicating that achieving the 2.0°C target is unlikely under current trends.
• A significant probability (20–40%, depending on modeling choices) of exceeding 3.0°C, which would push us into uncharted territory with heightened risks of tipping points and severe physical damages.

The framework addresses a critical gap in traditional climate scenario analyses by introducing probabilities where none previously existed. It offers two alternative methods for attaching unconditional probabilities to abatement pathways. The first is a structured approach that tempers economists’ Social Cost of Carbon (SCC) distributions with observed data on carbon permit prices, ensuring that probabilities reflect political realities. The second is a least committal approach, which derives probabilities directly from observed mitigation trends. Importantly, these methods produce coherent results, providing confidence in the approximate probabilities derived and strengthening the framework’s value for decision-making by public and private stakeholders.

While the scenario engine does not require narratives like SSPs or RCPs, it is flexible enough to integrate with these frameworks. This allows users to assess the plausibility of the scenarios that investors and regulators have already been using. In this way, we provide tools that not only align with the complexities of climate risks but also adapt to contexts that have gained traction in the industry.

For investors, this framework translates directly into the realm of asset pricing and risk management. By integrating probabilistic climate scenarios into valuation models, it equips asset managers to assess the financial implications of different pathways. The framework helps identify where significant risks or opportunities may arise under varying levels of climate action, enabling informed portfolio decisions that account for both likelihoods and impacts.

This framework lays a strong foundation for future refinements and continued contributions to the evolving landscape of climate-aware decision-making.

The Focal Point: Addressing Regional and Sectoral Disparities

Early IAMs like DICE, while groundbreaking in their ability to quantify climate change damages in economic terms, faced significant limitations in their treatment of regional and sectoral disparities. By relying on global aggregate damage functions, these models often masked localised vulnerabilities and failed to capture the disproportionate risks faced by certain sectors and regions under extreme warming scenarios. These shortcomings underscored the need for models capable of delivering more granular insights into climate impacts.

In response, we have significantly advanced our geospatial capabilities, enabling the modelling of climate variables forward at high resolutions and linking them to economic variables. These innovations allow us to provide actionable insights at regional and sectoral levels, addressing the gaps left by traditional IAMs.

Our high-resolution analysis spans 3,672 subnational regions, an unprecedented level of granularity in climate modelling. This detailed approach uncovers critical heterogeneities in climate exposure and economic sensitivity, significantly improving the accuracy of global damage projections compared to traditional models. It highlights the distinct vulnerabilities of regions with varying levels of adaptive capacity and exposure to extreme weather. By integrating extreme weather variability and refining damage functions, our models offer a more nuanced and realistic assessment of climate-driven economic risks.

Key findings, as discussed in this newsletter’s interview, include:

• Increased Damage Estimates: Greater spatial resolution exposes sharper disparities, leading to global damage projections that exceed those of traditional IAMs.
• Regional and Sectoral Insights: Linking high-resolution climate variables to economic outcomes clarifies geographic and sectoral vulnerabilities.
• Threshold Effects and Nonlinearities: The models capture abrupt transitions and disproportionate impacts beyond critical climate thresholds, addressing risks often overlooked by aggregate approaches.

These advancements address long-standing critiques of IAMs, bridging the gap between climate science and economic decision-making. Granular regional and sectoral insights enable targeted adaptation and mitigation strategies. For financial intermediaries and end investors, the framework supports informed risk management and decision-making, contributing to a climate-resilient economy. Beyond enhancing our understanding of current vulnerabilities, these insights also lay the foundation for expanding practical applications in the years ahead.

The Next Horizon: Building Practical Applications on a Solid Foundation

The progress made over the past two years has created a robust platform for advancing climate finance. The tools and frameworks developed—modular, probabilistic, and granular—represent practical solutions for bridging the gap between climate science and economic decision-making. From probabilistic scenarios to high-resolution geospatial insights, the work has the potential to empower stakeholders with actionable methodologies for managing climate-related risks.

Looking ahead, the focus will shift to deepening geospatial research, refining our understanding of regional and sectoral vulnerabilities, and improving our damage functions from the bottom-up. At the same time, supporting industry adoption will be key—working closely with end-investors, financial intermediaries, corporates, and policymakers to operationalise probabilised climate scenarios and high-resolution insights within investment and risk management practices, long-term planning, and policymaking.

With the groundwork laid and the roadmap clear, the next steps hold immense potential to shape the future of climate finance. These advancements are not just milestones but foundations for future action, empowering stakeholders to better manage risks, allocate resources, and foster a sustainable and resilient global economy.

Footnotes

[1] In the article "Dealing with Climate Change: Asset Pricing Implications of Monetary and Fiscal Choices", Professor Riccardo Rebonato discusses the financial challenges associated with achieving stringent climate targets, such as those represented by the RCP2.6 scenario. The article notes that meeting these targets requires substantial investments in decarbonising the economy, with estimates ranging from $1.6 trillion to $3.8 trillion annually between 2020 and 2050 for energy system transformations alone. The article also notes that such significant financial interventions may necessitate increased public funding, whether through higher taxation or increased.

[2] For a detailed analysis of the strengths and limitations of the RCP/SSP framework in this context, see Dherminder Kainth's article: "Assessing the RCP / SSP Framework for Financial Decision Making".