James A. Rising

The social cost of carbon (SCC) represents the economic cost incurred by society for each additional ton of carbon dioxide (CO2) emitted into the atmosphere. This metric is essential for shaping effective climate change policies. The higher the SCC, the more justification there is for actions aimed at reducing emissions, such as transitioning to renewable energy sources and implementing stricter regulations.

However, estimating the SCC is not straightforward. It requires a complex integration of climate models and economic impact assessments, compounded by uncertainties that span across time and geography. Many studies have attempted to measure the SCC, but the results have been varied and sometimes contradictory.

In our recent paper, “Synthesis of Evidence Yields High Social Cost of Carbon Due to Structural Model Variation and Uncertainties,” we embarked on a comprehensive review of literature over the past two decades to shed light on these variations.

Key Findings

1. Wide Variability in Estimates: We synthesized 1,823 SCC estimates from 147 different studies and found a wide range of values. The estimated costs were significantly right-skewed, indicating that while some studies report lower figures, there are others with much higher estimates that warrant attention.
2. Beyond Damage Functions and Discount Rates: Traditional assessments of SCC often focus on specific aspects like damage functions (how much economic damage results from levels of warming) and discount rates (the value placed on future costs and benefits). Our research emphasizes that structural decisions within models—such as how persistent damages are handled and how the Earth’s systems are represented—are equally important in determining the SCC.
3. Expert Insights Reveal Undervaluation: We conducted a survey of authors from the studies we reviewed to gather expert opinions. The consensus was striking: experts believe that existing SCC estimates are too low. They pointed to several factors, including the undersampling of model structures, incomplete characterization of damages, and high discount rates as contributors to this undervaluation.
4. The New “Best-Estimate” SCC: To address these gaps in the literature, we employed a random forest model to refine our understanding of the SCC. This innovative approach allowed us to deduce a new synthetic distribution of SCC estimates that is more in line with expert evaluations. Our analysis yielded a mean SCC of $283 per ton of CO2 for the year 2020, with a range between $32 and $874. This is notably higher than official estimates—such as those from the U.S. Environmental Protection Agency (EPA) released in 2023.

We call our result a “Synthetic SCC” because we are generating it by meta-analyzing results from the literature, combined with expert opinions. The figure below shows (top) how our estimate compares to various official SCC estimates (it’s higher than all but the few with very low discount rates), and (bottom) how structural choices and uncertainty combine to take us from a “traditional” (DICE model) estimate to our synthetic SCC.

Methodological Innovations

Our paper also introduces several methodological advancements in the field of climate econometrics:

• We performed a meta-analysis that accounts for different model features, leading to more reliable estimates of the SCC.
• By translating expert opinions from Likert scale surveys into quantitative metrics, we created a more nuanced understanding of how various factors influence the SCC.
• Our random forest algorithm, where the leaves represent whole distributions rather than specific points, offers a robust way to incorporate uncertainty into our estimates.

Moving Forward

These results matter for policy. With an elevated SCC estimate, policymakers may find renewed justification for aggressive climate action. It emphasizes the need for investment in renewable technologies, carbon pricing mechanisms, and robust international climate agreements.

As we seek to navigate the challenges posed by climate change, having accurate and comprehensive estimates of the SCC will be key. Our research shows the importance of considering a diverse range of structural elements in modeling. Only by doing so can we ensure that policies aim at the maximum benefit to society and safeguard our planet for future generations.

For more details, you can access the full paper here: https://www.pnas.org/doi/10.1073/pnas.2410733121

I’m excited to share my latest study with Ph.D. student Caitlin Wilson, highlighting the significant economic impact of Delaware’s coastal regions.

Here are some key insights:

• The coastal economy has experienced unprecedented growth—5x faster than Delaware’s overall economy since 2011!
• The coastal economy supports a staggering 104,540 jobs across Delaware, which includes 30,510 jobs located outside of coastal areas.
• The state benefits from approximately $820 million in tax revenue derived from coastal activities.
• For every $100 in annual revenue generated in the coastal region, an additional $59 is earned throughout Delaware, totaling about $8.3 billion annually.

These findings underscore an essential truth: when we manage coastal risks, like flooding, more effectively, the entire state benefits. Many coastal challenges are best tackled at the state or regional level, and our recommendations aim to foster resilience and sustainable growth.

There’s a methodological contribution here too. Much work that does economic analyses makes some serious errors:

First, it will equating the economic contribution of a region or industry with its total revenue. But that double-counts anything where some value was first purchased elsewhere. The real benefit is called “value added”, and we make sure to show that throughout the report.

Second, it uses simple “multipliers” to find out the total contribution, when these are actually meant to describe changes in a sector or region. You can’t take the total economic output of a region and multiply it by an output multiplier, because that will include benefits that feed back into the region– and the region itself is no bigger than its observed to be. We use proper industry contribution analysis and multi-region IO modeling to handle this.

Third (the real contribution), is that it assumes that the every dollar has an equal contribution, down to the first one spent in the industry or region. But when we look at the Coastal DE economy 11 years ago, we can see that it had very different multiplier effects. The structure of the economy is always changing, and each dollar has a different marginal impact, and we can estimate these changing marginal impacts by using historical data.

For a deeper dive into our findings, check out the publication here: https://www.deseagrant.org/hazards#seaside-statewide.

One of the most advanced models of the impacts of climate change on GDP is Kotz et al. (2022), because it includes not only variables to distinguish the effect of temperature shocks from long-term effects (at least, in theory), but also five additional variables that get at extremes and variability.

The original paper found statistically significant effects across many of those variables, but it did not actually look at projections of the model under future climate change.

Our new study, published in Nature Climate Change, takes this step and looks closely at what matters. We look at how effects vary across time, countries, and variables. We also have a pretty big headline number: at 3°C, the world could be 10% poorer.

Maybe the most interesting result is that non-temperature predictors contribute almost nothing to global damages. That could mean that we– as the research community– are still looking for the right temperatures, but it certainly suggests that temperature is hugely important. In fact, including these variability and extremes variables actually increases the estimate of the role of temperature effects.

You can read the complete study here: https://rdcu.be/dNwDT

In the evolving landscape of climate econometrics, researchers often face a maze of methodological choices and data challenges. Understanding how social, economic, and biophysical systems respond to weather requires meticulous analysis and careful integration of diverse datasets. It is within this context that our latest publication, “A practical guide to climate econometrics: Navigating key decision points in weather and climate data analysis,” offers a crucial resource.

Now published in the Journal of Open Source Education, this guide aims to demystify the nuances of climate econometric practices. The tutorial is packed with practical insights and reusable code snippets in multiple programming languages, making it accessible for both early-career researchers and seasoned professionals.

Link to tutorial: climateestimate.net

The tutorial walks you through essential steps in climate econometric analysis, including:

1. Data collection and preprocessing: Navigate through weather and climate data attributes, NetCDF formats, supported programming languages, and common data limitations.
2. Model specification: Develop robust reduced-form models considering weather variables, spatial-temporal processes, and weighting schemes.
3. GIS integration: Effectively use geographic information systems and shapefiles.
4. Research organization: Implement best practices for version control, data management, and reproducibility.

Since its inception, our tutorial has been utilized by Ph.D. and Master’s students and featured in several international workshops. Moving forward, we hope to expand the tutorial with additional modules focusing on advanced analysis techniques, policy relevance, and dynamic data visualization.

The threat of climate change to agricultural production is significant, but for perennial crops in developing countries, the risks have new complications. In my latest study, “Confounding Adaptation in Perennial Climate Damages: A Unified Statistical Approach for Brazilian Coffee,” I uncover the alarming, yet previously unestimated risks affecting the Brazilian coffee industry.

Misreported perennial yields and crop management decisions can significantly impact our understanding of these risks. The study reveals that extreme temperatures do reduce yields – but they also shrink the reported harvest area due to plant death and farmers’ selective harvesting. In other words, the actual damage from extreme temperatures on coffee production is twice as severe as effects recorded on reported yields.

In the study, I’ve developed a framework that merges perennial supply and statistical yield literature to distinguish the effects of management decisions. The process unearthed a direct impact of temperatures on biophysical yields and a plant death effect. Together they support a detailed understanding of how prices, weather, and farmer adaptations interact in the realm of perennial crops – particularly crucial in developing economies.

Dive in by reading the full paper here.

This semester, I taught an amazing new version of a class on “Coupling Human to Natural Systems”. The goal of the class is to understand how social systems and environmental systems impact one another and co-evolve. To do so, the class provides a broad introduction to system dynamics, and the students get to develop their own group projects.

The resulting projects were excellent, with an analysis of real-world issues by developing sophisticated coupled models. Below are a summaries of a couple of the group projects.

Offshore Wind and North Atlantic Right Whales: Modeling an Uncertain Interaction

Authors: Lorren Ruscetta, Emma Korein & Laura Taylor

Abstract: Amidst the ambitious US goal to deploy 30 gigawatts of offshore wind energy by 2030, concerns about the environmental impact on marine life, particularly the North Atlantic right whale (NARW), a vulnerable species under the Endangered Species Act, have become prevalent. This study focuses on the proposed Ocean Wind 1 project off the coast of New Jersey and its potential impacts on NARW populations, balancing the deployment of 98 planned turbines with stakeholder support dynamics. Utilizing an integrated model that considers factors such as noise pollution, vessel collisions, artificial reef benefits, and stakeholder support, the research explores the influence of offshore wind infrastructure on NARW populations and the extent of the project’s realization.
The model outcomes indicate that while NARW populations may not face significant decline due to the wind project, stakeholder support is sensitive to whale mortalities associated with turbine construction. Simulations predict that despite the initial aim, only 36 out of 98 turbines would be constructed due to stakeholder opposition following 1.6 NARW deaths. Sensitivity analyses further reveal the robustness of NARW populations under various scenarios but consistently suggest fewer turbines than anticipated will be erected, highlighting stakeholder perception as a crucial factor in determining the project’s scale. Consequently, the model provides policy insights and underscores the need for careful consideration of both marine conservation and stakeholder concerns in executing offshore wind projects.

Keywords: Offshore wind energy, North Atlantic right whale, stakeholder support, marine conservation, environmental impact modeling.

Impact of Ecotourism on Florida Keys Coral Reefs

Authors: Christina Marchak, Travis Pluck & Yuleny Gomez Rodriguez

Abstract: Coral reefs, such as those in the Florida Keys, offer critical ecosystem services and serve as a hub for tourism and ecotourism. However, they face threats from climate-related factors like rising sea surface temperatures (SST) leading to coral bleaching, and from socioeconomic activities. This paper explores the dynamics between ecotourism, SST, and coral reef health. A dynamic coupled system model is developed to analyze the impacts of human activities (ecotourism and normal tourism) on the health and cover of coral reefs, using the representative coral species Acropora palmata. The model integrates variables like tourism revenue, bleaching rate due to SST, and coral recovery rate. The study found that ecotourism and normal tourism, affected by SST, influence coral health and cover. Higher SST was linked to increased bleaching rates and reduced coral cover. Conversely, appropriate levels of ecotourism funding can mitigate these effects through conservation efforts such as coral seeding, creating a positive feedback loop for coral cover. The model revealed thresholds of coral decline that affect tourism and showed both declines and stabilization scenarios in the presence of varying levels of ecotourism intervention. The research provides critical insights for policymakers and stakeholders to consider the balance between tourism and coral conservation in the Florida Keys. It suggests that directing resources from ecotourism revenue towards restoration and conservation strategies may help sustain these vital ecosystems.

Keywords: Coral reefs, ecotourism, sea surface temperature, bleaching, conservation, tourism revenue, dynamic coupled system model.

Climate change will affect all aspects of our lives and economies. As summers get hotter and storms stronger, it will undermine our ability to grow food, to have secure homes, and produce sustainable incomes. At the same time, stopping climate change will also have consequences for society. Our ability to compare the costs and benefits of climate action is crucial to making sound global decisions.

Earlier this month, I led a team to complete a comprehensive economic assessment of climate risks for the United Kingdom. The UK is a leader in shifting its economy toward Net Zero, and has years of experience understanding the costs of shifting to green energy. But it has not had a corresponding cost number for the impacts it can expect. Particularly since the UK cannot direct the actions of the rest of the emitting world, this numbers are important to know.

The big challenge in producing an estimate like this corralling results from many other studies into a consistent framework. Across the studies that have tried to do this before (for the US and the EU), we were able to produce perhaps the most comprehensive assessment of all, with impacts on everything from cows to coasts, from biodiversity to productivity.

To jump to the conclusion, we find that the costs of unmitigated climate change (“current policies”) reach 7.4% of the UK’s GDP. And this is for one of the most un-vulnerable countries in the world. On the other hand, the costs for going to Net Zero are actually negative, once you account for the health co-benefits and the investment boost.

There is a lot more work to do to understand who is at risk and what they can do about it. But there is a lot to dig into in these results already. Our data is all available (link below), and I invite you to start digging!

Agriculture is going to be one of the sectors most disrupted by climate change. Our ability to produce food relies on a stable climate. Crops and management practices are carefully catered to local climate conditions, including the timing and intensity of rainfall, the length of growing seasons, and the complex biology of soil. Climate change is going to disrupt all of this, demanding new practices, new varietals, and for many regions, new sources of water or arable land.

Will farmers be able to adapt? Or are the details of effective farming too complicated, so that it will take decades to find the right new practices and seeds, by which time the climate will have changed again? This is a fundamental question for the future of food security globally and the livelihoods of millions of farmers.

One piece of evidence comes from following the harmful effect of high temperatures on crops in the United States. Temperatures over 29 °C damage corn (maize), but this effect can be attenuated by irrigation. As a result, the damaging effect of high temperatures is observed to be much less in the extensively-irrigated US West than in the East. It has also been observed that the impact of high temperatures has slowly declined over the course of the historical yield record, from 1950 onward (Burke & Emerick, 2013). This is climate adaptation occurring as we watch!

So how quickly have farmers reduced the impact of high temperatures? Unfortunately, very slowly. Reducing the damages by 10% takes about 40 years. We modeled this effect across the next century, and whereas climate change would reduce yields by almost 60% by the end of the century (under business-as-usual), adaptation results in yields only being reduced by about 50%.

Clearly adaptation in the past cannot be a blueprint for adaptation in the future.

One of the most common stories about agriculture in the US is that it will just move north. If it’s too hot here, it will be just right in Canada. That will be bad for US farmers, but certainly not an existential threat to our ability to feed people in the future. But can agriculture find as fertile grounds north?

To answer this, I collaborated with Naresh Devineni, a hydrologist and Bayesian modeler. We developed a new model of the sensitivity of crops in the US to climate change, and a way to project crop switching into the future. The paper was recently published in Nature Communications.

The results suggest caution. Can crop-switching reduce the impacts of climate change? Yes, but 50% of the losses from climate change cannot be adapted away. Will many farmers have to change what they grow? A ton: To get the benefits we describe, over 50% of farmers will have to change what they grow.

Why can’t crop-switching remove all of the impacts of climate change? Almost everywhere, the value of the land for planting any of the crops we model will fall. In fact, in our model about 5% of current agricultural land will be left fallow by 2070, because any crop will cost more than it would generate in profit.

NPR Marketplace recently put these results in the broader context of risks from climate change. Listen to the piece to learn more:

The Climate Impact Lab just got a great write-up for our work on the risk of mortality under climate change in Bloomberg Green. There are a bunch of excellent dynamic visualizations that dig into the data.

There are two big messages here. The first is that poor people are going to get hammered by climate change, with some areas experiencing deathrates from the additional heat that are greater than the combined global rates for heart disease, stroke, all forms of cancer, all forms of infectious disease death, and all forms of death from injury.

The other is that we can use this information to start to estimate the total cost of climate change to society at large, because it gives us a lower-bound. Just the effect of additional mortality costs society about $22 per ton of CO2. That’s already more than the total social cost used by the Trump administration and half way to the total cost used by the Obama administration.

Take a look at the summary write-up of the research behind the Bloomberg article, and look forward to the reports that we are going to produce on the effects of climate change on labor productivity, agriculture, energy demand, and coastal impacts.

There is a rising tide of people who want to get involved in climate econometrics, dissipating against the shallows of unfinished research ideas, and spinning like weather vanes trying in vain to understand weather. Well, no longer! I would like to introduce climateestimate.net!

ClimateEstimate.net is an introduction to the secrets of using climate data, generating weather panels, choosing specifications, and getting results. Think of it like a practical complement to Solomon Hsiang’s SHCIT list:
http://www.g-feed.com/2018/11/the-shcit-list.html

The tutorial is still new, and we would love your feedback and suggestions. And you are welcome to get involved and help us extend the tutorial (there’s an ocean still to cover!).