Incorporating Climate Change and Climate Policy into Macroeconomic Modeling: Proceedings of a Workshop (2024)
Chapter: 8 Applying Insights
8
Applying Insights
The final session of the workshop synthesized insights from discussions across the 2-day workshop. Three panelists engaged in a discussion to explore possible next steps to enhance the incorporation of climate into macroeconomic modeling.
MACROECONOMIC MODELING CAVEATS AND CONSIDERATIONS
Emi Nakamura, University of California, Berkeley, reminded participants that federal macroeconomic models are primarily used to forecast growth, deficits, fiscal policy scenarios, etc. (see Chapter 2). They are a parsimonious representation of the economy. To a large extent, these models rely on climate science as inputs rather than modeling climate impacts directly. Nakamura emphasized the importance of model parameters, as simple models are only as good as their inputs. She remarked that macroeconomic forecasting is challenging due to limited data on economic changes, making it difficult to gauge the magnitude of potential impacts. Nakamura encouraged a realistic approach to adding channels to models, noting that historically, more complicated models often yield less accurate forecasts.
Then, Nakamura highlighted key limitations or (partly) missing factors in macroeconomic models. She said that to some extent economists are aware of these limitations but may not emphasize them as much as they should. They include
- Distinction between gross domestic product (GDP) and welfare;
- Aggregate vs. sectoral or regional outcomes;
- Trade and spillover effects;
- Short term vs. long term where the economic and climate communities have different definitions;
- Two-way feedback vs. one-way causal chain for climate effects. In simple models, there is no two-way feedback;
- Uncertainty.
The Macroeconomic Advisers U.S. (MAUS) model is a one-sector model with a one-way causal chain from climate into macroeconomic outcomes where climate enters through productivity. Nakamura offered a few practical questions that might be asked by people trying to advance the macroeconomic models.
- If the MAUS model and other simplified models are used as an approximation, what would be the best parameters to use?
- Is there a gold standard that economists can use from the climate science literature for what parameters should be used, and at what horizons and in which cases?
- Are there particular horizons that macroeconomic modelers should focus on when they are contemplating climate effects?
- Are there realistic scenarios that should be considered?
- Is productivity the primary channel through which one would want to consider climate effects?
RESEARCH DIMENSIONS
James Rising, University of Delaware, helped organize a workshop with the Royal Society in London in March 2023 that was called New Horizons for Increasing the Understanding of Economic Consequences of Climate Change. He highlighted the similarities between the Royal Society workshop and this one and suggested important research dimensions for incorporating climate feedbacks into macroeconomic models. First, he emphasized the need to understand transition costs and damages better, particularly the hidden costs of achieving massive emission reductions and the mechanisms to incentivize people to adopt them.
In terms of damage estimation, multiple models, such as Marshall Burke’s work, provide various estimates of GDP damages, with differing assumptions on factors such as temperature, precipitation, differential vulnerability, and adaptation. These assumptions lead to significant variations in the estimated damage levels, making it challenging for economists to determine the correct magnitude of damage to incorporate into macroeconomic models. Furthermore, there is uncertainty regarding the most important channels for climate impacts on the macroeconomy.
Rising emphasized the interconnected nature of the U.S. economy, highlighting the global impact of macroeconomic damages with potentially large-scale spillovers and discontinuities in the economic system structure. He argued that both local and international impacts interact, with international impacts playing an important role. For example, a UK panel proposed that considering spillovers and potential large-scale disruptions could increase the general scale of damages from 1.3 percent to 5.5 percent. Rising expected that the United States could face even greater losses in 2050. Moreover, he acknowledged the significance of inequality, heterogeneity, and temporal variability.
In summary, Rising emphasized the significance of considering transition cost and damage estimate inputs at the appropriate scale. He pointed out the lack of models to comprehensively capture the feedback loops needed to understand how these damages and transitionary costs will interact, or ones that properly capture nonstationarity and disruptions. He highlighted the poor coverage of investment under uncertainty, including deep uncertainty, or the role of heterogeneity and variability, which are going to have significant macroeconomic consequences.
Rising pointed out that GDP may be inadequate, even as a metric for the macroeconomic discussions that models need to inform. Echoing Stock’s opening keynote, GDP is a flow based on various underlying stocks (e.g., natural stocks and wealth stocks). He suggested that future models should consider inclusive wealth and the natural stock features that influence GDP.
Rising said that an important area of research is considering both nonmarket and market impacts on indicators such as GDP and tax receipts. He remarked that while macroeconomists typically focus on the market impacts of climate changes, the scale of factors such as nonmarket impacts, mortality risk, and labor disutility are going to have significant market consequences.
Relevant to this workshop, Rising shared a few key takeaways from the Royal Society workshop that are research dimensions at the intersection of climate and macroeconomics. They include
- The importance of interdisciplinarity;
- Adaptation and the role of changing developments at the industry and sector level;
- Inequality and justice;
- A future that is not going to look the same as the past;
- Extreme events and tipping points;
- Migration, displacement, and climate-induced conflict.
DEEP UNCERTAINTIES
Chris Varvares, Co-Head of US Economics at S&P Global Market Intelligence and a former principal of Macroeconomic Advisers, stressed the necessity for collaboration. He highlighted the difficulty of projecting economic indicators accurately, especially in the face of natural and economic system interactions. He suggested that a portfolio of specialized models created and operated by subject-matter experts and the integration of their outcomes will be necessary. With that being said, Varvares listed several deep uncertainty issues that arose during the workshop.
Will the cost of fossil fuels rise or fall over time as we approach net zero? Varvares noted disagreement on this issue. For example, some experts suggest that investments in fossil fuels will decline faster than the demand for fuels, keeping oil prices from falling.
Will the equilibrium risk-free interest rate rise or fall? And by how much? He noted that higher risky rates due to increased uncertainty and risk premia related to transition policies could put downward pressure on risk-free rates. On the other hand, large investments in energy transitions, including public climate-resilient infrastructure, and faster capital stock depreciation due to climate damages may require higher gross investment to maintain desired capital stocks, leading to upward pressure on interest rates. If subsidies for such investments are debt financed, the decline in government savings could reinforce the upward pressure on rates. Moreover, the private savings rate depends on the risk-free rate, suggesting personal saving could decline relative to the baseline rather than increase.
Varvares emphasized that investment equals savings, which may mean a reliance on foreign savings to finance increased investment. However, foreign savings are unlikely to increase since all economies will be facing the same needs for capital, crowding out investment of nongreen, nonsubsidized capital, meaning a potentially weaker economic output because the path of capital stock will be lower.
Are there sufficient reserves and will there be sufficient supplies of critical minerals to achieve the wholesale remaking of our energy economy? Varvares expressed the uncertainty around critical mineral prices in the face of increased demand or geopolitical frictions that may limit supply. For example, a significant increase in global copper demand may face supply challenges, leading to potential price increases that disrupt the transition. This situation could result in price volatility and hinder economic growth.
What’s a reasonable path for the economywide energy intensity parameter or the energy efficiency parameter? Varvares thought that this is a huge factor.
What incentives are needed to push energy efficiency along the required path? Varvares thinks that the United States falls short of the necessary goals. He argued that, without a significant increase in energy or carbon prices, it is unlikely that energy efficiency will improve as needed. Current U.S. policies focus on regulatory mandates, demand subsidies, production, investment subsidies, among others. Varvares suggested that a carbon tax is a crucial incentive that is currently missing from the agenda. He also suggested investigating whether European carbon border adjustment mechanisms might indirectly raise carbon prices in the United States through tariffs on U.S. exports to the European market.
Will the magnitude of stranded assets whose value will quickly fall to near zero be a material hit to household sector wealth, and will that have implications for consumption? Varvares acknowledged that people tend to think about fossil fuel–related assets, but he said stranded assets extend to whether sea level rise will lead people to abandon parts of cities as well.
How will we finance the massive investments needed globally? Even if advanced economies can facilitate a reallocation of investments from fossil fuel to green investments and possibly increase capital flows to manage this task, Varvares questioned how the Global South will manage to do the same.
How will labor markets respond if there are frictions? Like the aftermath of the Great Recession and the COVID-19 pandemic, industrial geographical occupational mismatch arising from the decline in fossil fuel usage will lead to a decline in labor market matching efficiency and presumably a temporary rise in the natural rate of unemployment. This will adversely impact potential GDP as people dropping out of the labor force are not effectively able to be reemployed. Varvares said that policies to facilitate the transition for employees departing from fossil fuel–related industries could be useful.
Climate-induced migration. Lastly, Varvares said that immigration will be a big question, echoing much of the previous workshop discussions.
DISCUSSION
During the open discussion, Robert Kopp asked the panelists to offer guidance to their federal colleagues. Nakamura suggested that the National Science Foundation and the National Oceanic and Atmospheric Administration find connections between complex modeling and simple macroeconomic frameworks to develop a common language for effective communication and collaboration. Rising suggested that the Office of Management and Budget and the Congressional Budget Office (CBO) develop a tiered plan for improvements, with a focus on incorporating climate damages and emissions into their framework, and noted the potential of advanced technologies such as language-based artificial intelligence models (e.g., ChatGPT) for a holistic understanding of the world.
This discussion also touched on using climate models to understand the economic impacts of climate change. Rising suggested that biophysical models may be more suitable. Varvares emphasized the importance of simplifying economic models and highlighted two key entry points for climate: estimating how temperature influences total factor productivity and considering the impact on physical capital stock from the increasing severity and frequency of climate events. This affects the cost of capital and the necessary gross investment for the private sector to achieve their desired capital stock. Varvares said that other important factors difficult to forecast are the influence on immigration and its impacts on the labor force, working age population, and agricultural prices.
Nakamura compared the challenge for macroeconomists to regularization in machine learning, as both involve limited historical data. She highlighted the importance of simplicity in machine learning models to avoid overfitting, even if the model is not perfect, which parallels the situation in economics. Nakamura said that it is a fundamental challenge for macroeconomics and is well captured in machine learning ideas, but macroeconomic data is not the typical use case for those methods.
One participant asked about the accuracy and reliability of macroeconomic and forecast models, specifically in predicting various variables. He asked the panelists about the strengths and weaknesses of macroeconomic forecasting in terms of predictability and scale. Nakamura noted the difficulty in predicting longer term interest rates, even without factoring in climate change. She said that more complex models have often not outperformed CBO’s more simplistic models. James Stock emphasized that comparing across domains is challenging because massive shocks to economies are almost impossible to predict. Nonetheless, there are areas where economists can have more confidence (e.g., conditional forecasts).
From a climate science perspective, Kopp noted that climate scientists may deem a situation “low confidence” and would not give a best estimate but could potentially give a range. However, practical economics (e.g., Troika, CBO) focus on a central scenario, implying a distribution centered on that central scenario as opposed to a range of options. Kopp asked the panelists how they handle alternative forms of uncertainty communication and whether focusing on the central scenario is appropriate. Varvares responded by explaining that they use various forecasts, including baseline, optimistic, and pessimistic sce-
narios based on different assumptions. They have also built additional scenarios and probability assessment tools, like Monte Carlo simulations, to assess uncertainty around the baseline forecast for clients.
CONCLUDING THOUGHTS
In summary, Nakamura, Rising, and Varvares discussed many pathways to improve macroeconomic modeling. Most notably, Nakamura emphasized the importance of model parameters in macroeconomic forecasting, as well as using a realistic approach to adding channels to models, acknowledging the limitations of more complicated models. Rising highlighted a few areas for research, including developing a better understanding of the energy transition’s costs and damages, estimating damages with varying assumptions, and addressing uncertainties related to the channels through which climate impacts the macroeconomy.
Varvares stressed the importance for collaboration among subject-matter experts using a portfolio of specialized models. He outlined deep uncertainties where he thinks research opportunities lie in addressing them and understanding their implications for economic indicators. These deep uncertainties include questions about fossil fuel costs, equilibrium risk-free interest rates, critical mineral supplies, energy intensity parameters, and incentives for energy efficiency. Moreover, he raised questions about how the Global South will manage the transition, highlighting the importance of research on financing mechanisms and reallocation of investments from fossil fuels to green energy on a global scale.
In addition to this final panel, invited speakers and workshop participants discussed many areas for future research and possible opportunities to improve macroeconomic modeling across the entire 2-day workshop. Some workshop participants emphasized the difficulty but importance of more accurately capturing historical data, especially for extreme events, and supply-side factors of macroeconomic modeling, such as migration. Another participant highlighted the absence of commodity prices in models, which may lead to unaccounted spillover effects. Several workshop participants noted the importance of improving the granularity of models regarding regional, sectoral, and distributional perspectives and the opportunity for better data collection and the development of specialized models or approaches to capture these aspects. Additionally, some participants acknowledged the limitation of U.S.-specific models that do not account for global factors, suggesting potential areas for research.
As highlighted by Rising, some workshop participants noted the limitations of existing models in capturing feedback loops and human responses to climate change. A few participants stressed the importance of addressing uncertainty and computational challenges, as well as improving communication to policymakers. Some suggested more flexible models or a collaborative multimodel approach can provide a comprehensive understanding of climate impacts and their macroeconomic implications. One participant suggested a network approach may help address feedbacks, while another participant proposed incorporating real-time rates into energy modeling frameworks. Moreover, some
participants suggested better coordination across government agencies in the context of real-world policy implementation and the use of macroeconomic models to inform decisions.
Rising noted that this workshop contributes to a global effort to update macroeconomic models and highlighted the robust ongoing work in this field. Indeed, many workshop participants noted the exciting opportunities for future research and improved cross-disciplinary, cross-agency collaboration. As noted by workshop participants, this workshop included productive conversations that may serve to push the work forward.
