Graphs depicting economic downturns overlaid on a stormy ocean representing economic shocks.

Is Your Portfolio Pandemic-Proof? Understanding COVID-Type Economic Shocks and Climate Change

Soraya Malik in Climate & Environment August 2025 • 4 min read.

"A Deep Dive into the Stochastic DICE Model and Its Implications for Economic and Climate Resilience"

The COVID-19 pandemic delivered a stark reminder of the vulnerability of the global economy. Its impact dwarfed the 2008 financial crisis, sending ripples through markets and prompting urgent questions about long-term economic and climate stability. As we navigate an uncertain future, understanding and preparing for similar ‘COVID-type’ events is crucial for both policymakers and investors.

Integrated assessment models (IAMs) like the widely-used DICE (Dynamic Integrated Climate-Economy) model, help us understand the intricate relationship between economic activity and climate change. Traditionally, DICE has treated economic and climate variables as evolving predictably. However, the pandemic highlighted the need to incorporate sudden, unexpected shocks into these models.

This article unpacks a research paper that extends the classical DICE model to include stochastic shocks – random, disruptive events mirroring the economic impact of a pandemic. By understanding this enhanced model, we can gain insights into how such events affect temperature, carbon concentration, and economic resilience, paving the way for more informed decision-making.

The Stochastic DICE Model: Adapting to Economic Earthquakes

Graphs depicting economic downturns overlaid on a stormy ocean representing economic shocks.

The original DICE model provides a framework for modeling economic and climate systems jointly. It assumes that key variables like carbon concentration, temperature, and economic capital evolve predictably over time, influenced by factors like carbon emission mitigation and consumption. A recent study introduces a critical addition: a discrete stochastic shock variable. This variable simulates the shift between normal and stressed economic conditions, mirroring the abrupt impact of events like the COVID-19 pandemic.

Imagine the global economy as a ship sailing a steady course. The stochastic shock is like a rogue wave – it temporarily reduces the world's gross output, leading to decreased net output and carbon emissions. The study models these shocks as occurring randomly, on average, once every 100 years, and lasting for five years. The model then solves for optimal strategies under these conditions, offering a more realistic view of economic and climate interactions.

State Variables: Six variables related to carbon concentration, temperature, and economic capital.
Controls: Carbon emission mitigation rate and consumption.
Stochastic Shock: A discrete variable representing events like pandemics.
Scenarios: Models solved under various assumptions about shock frequency, duration, and impact.

Several scenarios were tested within the study. For example, researchers looked into a conservative case where the world's gross output fully recovers after each event. They also modeled what would happen with conservative shocks that have an annual gross drop over a 5-year period. All these results paint a nuanced picture of how different types of economic shocks interact with long-term climate and economic trends.

Key Takeaways: Preparing for an Uncertain Future

The study's findings offer several important insights. First, if the world's gross output fully recovers after a pandemic-like event, the long-term impact on temperature and carbon concentration may be minimal, even with a conservative 10% drop in annual gross output. However, persistent shocks, where a 5% output drop affects subsequent periods, can lead to noticeable, albeit small, long-term temperature drops (around 0.1°C). Perhaps most interestingly, the study suggests that if we apply policies designed for a predictable world to one with stochastic shocks, we may find that more ambitious mitigation targets become feasible at a lower cost.

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Everything You Need To Know

What is the Stochastic DICE model, and how does it differ from the original DICE model?

The Stochastic DICE model is an extension of the original DICE (Dynamic Integrated Climate-Economy) model. The original DICE model provides a framework for modeling economic and climate systems, assuming that key variables evolve predictably. The Stochastic DICE model enhances this by incorporating stochastic shocks, which are random, disruptive events. This addition allows the model to simulate the impact of events like the COVID-19 pandemic, making it a more realistic tool for understanding economic and climate interactions.

How do stochastic shocks, as implemented in the Stochastic DICE model, affect carbon concentration and temperature?

The research indicates that the impact of stochastic shocks on temperature and carbon concentration depends on the nature of the shocks. If the global output fully recovers after a shock, the long-term impact on temperature and carbon concentration may be minimal. However, persistent shocks, where an output drop affects subsequent periods, can lead to noticeable, albeit small, long-term temperature drops. The Stochastic DICE model helps to quantify these relationships, offering insights into the climate consequences of economic disruptions.

What are the key components or variables within the Stochastic DICE model?

The Stochastic DICE model comprises several key components. It includes six state variables related to carbon concentration, temperature, and economic capital. The model also has control variables, such as carbon emission mitigation rate and consumption. A crucial element is the stochastic shock variable, which represents events like pandemics. Different scenarios are created by varying assumptions about shock frequency, duration, and impact to analyze their effects.

What are the implications of applying policies designed for a predictable world within a Stochastic DICE model framework?

The research suggests an interesting implication. If policies designed for a predictable world are applied to a world with stochastic shocks, more ambitious mitigation targets may become feasible at a lower cost. This highlights the importance of considering the possibility of unexpected events in economic and climate policy-making. This offers a new perspective on how the design of climate policies can be optimized within a more realistic framework.

In the context of the Stochastic DICE model, what does the term 'stochastic shock' represent, and what is its relevance?

In the Stochastic DICE model, a 'stochastic shock' is a discrete variable representing random, disruptive events, such as pandemics or economic crises. It mirrors the abrupt impact of events like the COVID-19 pandemic, simulating a shift between normal and stressed economic conditions. This component is crucial because it allows the model to account for unpredictable events and their consequences. By including stochastic shocks, the model provides a more realistic view of economic and climate interactions, offering insights into the effects of disruptive events on long-term temperature, carbon concentration, and economic resilience.