Economic resilience in the face of global crises.

Can the Economy Weather the Storm? Understanding COVID-Type Events and Climate Change

Mira Elwood in Business & Economy December 2025 • 4 min read.

"A Deep Dive into Stochastic DICE Models and Economic Resilience."

The global economy faces unprecedented challenges from both pandemics, like COVID-19, and the escalating crisis of climate change. Understanding how these events impact our economic and environmental systems is crucial for policymakers and individuals alike. Integrated assessment models, such as the Dynamic Integrated Climate-Economy (DICE) model, offer a framework for analyzing these complex interactions.

The classical DICE model provides a deterministic view, where economic and climate variables evolve predictably over time. However, real-world events introduce uncertainty and shocks that can significantly alter these trajectories. By extending the DICE model to incorporate stochastic elements, researchers can better simulate the impacts of unpredictable events and explore optimal policy responses.

This article delves into a study that uses a stochastic DICE model to assess the economic and climatic consequences of COVID-19-type events. By examining different scenarios and policy interventions, we gain insights into the resilience of our systems and the potential for effective mitigation strategies.

The Stochastic DICE Model: A Framework for Understanding Economic Shocks

Economic resilience in the face of global crises.

The classical DICE model is a widely-accepted tool for jointly modeling economic and climate systems. In its original form, all model state variables evolve over time in a deterministic manner. The model includes state variables related to carbon concentration, temperature, and economic capital, influenced by controls like carbon emission mitigation rate and consumption.

To better reflect real-world uncertainties, the DICE model can be extended by adding a discrete stochastic shock variable. This variable models the economy in stressed and normal regimes, mimicking jump processes caused by events like the COVID-19 pandemic. These shocks can reduce world gross output, leading to reductions in both net output and carbon emissions.

State Variables: Carbon concentration, temperature, and economic capital.
Controls: Carbon emission mitigation rate and consumption.
Stochastic Shock Variable: Models stressed and normal economic regimes.
Jump Process: Represents sudden economic downturns caused by events like pandemics.

By solving the extended model under various scenarios, policymakers can assess the effectiveness of different interventions. The key is to understand how these stochastic shocks influence long-term temperature changes, carbon concentrations, and overall economic stability.

Implications for Policy and Future Research

The insights gained from stochastic DICE models have significant implications for policy decisions. Understanding the potential impacts of economic shocks, whether from pandemics or other unforeseen events, allows for more robust and adaptive strategies. While the DICE model serves as a reference point for climate-economy modeling, it is essential to acknowledge its limitations and continue refining these tools to better capture the complexities of the real world. Further research should focus on incorporating more granular data, exploring a wider range of scenarios, and developing more sophisticated methods for quantifying uncertainty. By doing so, we can better equip ourselves to navigate the challenges of the 21st century and build a more sustainable and resilient future.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1007/s10018-021-00332-8,

Title: Impact Of Covid-19 Type Events On The Economy And Climate Under The Stochastic Dice Model

Subject: econ.gn q-fin.ec

Authors: Pavel V. Shevchenko, Daisuke Murakami, Tomoko Matsui, Tor A. Myrvoll

Published: 01-11-2021

Everything You Need To Know

What is the main purpose of using the Stochastic DICE model in economic analysis?

The main purpose of using the Stochastic DICE model is to assess the economic and climatic consequences of unpredictable events, such as COVID-19-type pandemics. By incorporating stochastic elements, the model simulates the impacts of these events and helps in exploring optimal policy responses to enhance economic resilience and develop effective mitigation strategies. This framework extends the classical DICE model by including a discrete stochastic shock variable to mimic jump processes caused by events like the COVID-19 pandemic. This allows policymakers to understand how these shocks influence long-term temperature changes, carbon concentrations, and overall economic stability, leading to more robust and adaptive strategies.

How does the Stochastic DICE model differ from the classical DICE model?

The Stochastic DICE model differs from the classical DICE model primarily in its ability to account for uncertainty. The classical DICE model provides a deterministic view, where economic and climate variables evolve predictably. However, the Stochastic DICE model introduces a 'discrete stochastic shock variable' to represent unpredictable events, such as pandemics. This allows the model to simulate 'stressed and normal' economic regimes, mimicking jump processes like those seen during the COVID-19 pandemic. The inclusion of this variable enables a more realistic assessment of the impact of unforeseen events on the economy, carbon concentrations, temperature, and enables the exploration of optimal policy responses.

What are the key state variables and controls within the Stochastic DICE model?

The Stochastic DICE model uses several key state variables and controls to analyze economic and climate interactions. The state variables include 'carbon concentration', 'temperature', and 'economic capital'. These variables represent the core components of the climate-economy system. The model also includes 'controls' such as 'carbon emission mitigation rate' and 'consumption'. These controls are policy levers that can be adjusted to influence the model's outcomes. By understanding these variables and controls, policymakers can assess the effectiveness of different interventions and develop strategies to address climate change and economic shocks.

How do 'Stochastic Shock Variables' and 'Jump Processes' function within the model, and what are their implications?

Within the Stochastic DICE model, the 'stochastic shock variable' is a key feature that represents the impact of unpredictable events, such as pandemics. This variable models the economy in 'stressed and normal' regimes, mimicking 'jump processes' caused by events like the COVID-19 pandemic. These 'jump processes' can lead to sudden economic downturns, reducing world gross output and consequently impacting both net output and carbon emissions. The inclusion of these elements allows the model to better simulate real-world uncertainties and assess the potential impacts of unforeseen events. The implications include understanding the influence of these shocks on long-term temperature changes, carbon concentrations, and overall economic stability, which informs the development of more robust and adaptive policy strategies.

What are the main implications of using the Stochastic DICE model for policymakers and future research?

The main implications of using the Stochastic DICE model for policymakers lie in its ability to inform robust and adaptive policy decisions. By understanding the potential impacts of economic shocks, whether from pandemics or other unforeseen events, policymakers can develop strategies that enhance economic resilience and ensure overall stability. Further research should focus on incorporating more granular data, exploring a wider range of scenarios, and developing more sophisticated methods for quantifying uncertainty. These advancements will enable a more comprehensive understanding of complex interactions and better equip society to navigate the challenges of the 21st century, fostering a more sustainable and resilient future. The DICE model serves as a reference point for climate-economy modeling, it is essential to acknowledge its limitations and continue refining these tools to better capture the complexities of the real world.